Datasets:

saier
/

unarXive_imrad_clf

_id stringlengths 36 36	text stringlengths 200 328k	label stringclasses 5 values
7b7f37ca-e70e-416d-927f-2fa3db62e3d5	Food recommendation has become an essential method to help users adopt healthy dietary habits [1]}. The task of computationally providing food and diet recommendations is challenging, as thousands of food items/ingredients have to be collected, combined in innovative ways, and reasoned over [2]}. Furthermore, there are...	i
0f83b85b-e18d-4055-b5aa-9bf7096c42cb	We introduce and discuss the FEO that extends the Explanation Ontology [1]} and the FoodKG (a food knowledge graph that uses a variety of food sources) [2]} to model explanations in the food domain, a connection that is currently lacking in the current literature [3]}. Our ontology can be classified under the post-hoc ...	i
61d8f60a-917b-4807-87c2-5b7e5c07f0ae	Prior work has shown that users seek answers and reasoning for nutrition and food questions they might have [1]}, [2]}, [3]}, [4]}, [5]}, [6]}. However, users are increasingly concerned with the evidence and reasoning that lead to those claims. Applying logic, reasoning, and querying on food and culinary arts have cap...	w
13508314-3af5-446f-b353-7d1105b1ce8e	[1]} describe a system based on logical reasoning that supports monitoring the users' behaviors and persuades them to follow healthy lifestyles, including recommending suitable food items, with natural language explanations[1]}. Their system performs reasoning to understand whether the users follow an unhealthy behavio...	w
1587047b-085e-41aa-8e80-6481f716f583	We employ a task-based evaluation [1]} for our ontology using three main competency questions, each aimed at addressing a different explanation type that we attempted to extract from our model in FEO, as detailed in the following section. We have used competency questions as our method of evaluation as they are the acc...	m
f4dc6ca4-ecfd-46cc-aa29-eda892367f86	As our model endeavors to provide explanations and context to users that get lost in black box AI models, we chose to evaluate the FEO by its ability to provide responses to a subset of important explanation types. In tbl:explanationTable, we have included a list of previously identified explanation types and the corre...	m
62489ef1-231c-48cb-991b-8eb8488137e0	We support our choice in the selection of a subset of explanation types from Table REF for our evaluation via competency questions with observations from recent advances in the machine learning community, where we noticed that there is a focus on methods that generate contrastive and counterfactual explanations [1]}. ...	m
da1f6953-f422-4460-8d0b-a6c422d9755a	We undertook this process against the recommendations generated by the Health Coach Application, which uses machine learning techniques to assess users' dietary needs and provide recommendations [1]}.	m
74f18154-445c-46c4-ac77-820d2a63878d	In this paper, we have discussed an ontology modeling for food and diet recommendation explanations, which aims to model and then be used to generate explanations specific to the context of the users and setting. FEO is a domain-specific ontology where the domain concepts are abstracted up in a manner so that they can ...	d
b38859e2-3072-4811-b009-8f13df445a9f	Photolithography is one of the most important processes for semiconductor manufacturing . An exemplar photolithography system is shown in Fig. REF . A laser beam that goes through the integrated circuit patterns on the reticle is projected on the wafer so that the patterns are printed onto the wafer. During this proce...	i
42a82b45-2d55-43c8-857a-6e6ae00d0db7	With the technological development of the semiconductor industry, manufacturers demand more precise performance from the wafer stage. To achieve the goal, researchers have developed many control strategies and applied them on the wafer stage, including iterative learning control (ILC) [1]}, [2]}, [3]}, [4]}, sliding mo...	i
a30dbe41-0845-4204-b74b-18f570de6865	To further improve the performance of SMC, fractional-order calculus is introduced to improve the state dynamics in the sliding surface and is combined with the STA [1]}, [2]}. Although there are some theoretical research and successful precedents for the application of fractional-order super-twisting algorithm (FOSTA)...	i
a4a45dd6-43db-4077-bdbf-07be5ada5db6	Aiming to improve the control performance (i.e., high precision and robust performance) in the presence of large model uncertainties and disturbances, a novel adaptive neural network and fractional-order super-twisting algorithm (ANN-FSA) is proposed in this paper. Firstly, we use the radial basis function (RBF) neural...	i
fc40adda-f282-413d-b8bc-29f0affdf1e1	The remainder of this paper is organized as follows: Section provides the model of the wafer stage. Section presents the proposed controller, and the stability analysis of the controller. Section displays the experimental setup and the experimental results with the proposed controller. Finally, Section presents con...	i
9ab84482-14ea-46f1-90f7-0358109893dc	The overall structure of our experimental system is depicted in Fig. REF . The control algorithm is programmed in the LabView environment on the host computer. The host computer is connected with the remote controller (PXI 7831, from National Instruments) via Ethernet, so that the control algorithm can be deployed in t...	m
e652aa8a-b9f6-4b10-915a-3e8fc5bcaea5	We implement the traditional PID controller, the SMC, the advanced FOSTA, and the proposed ANN-FSA to the wafer stage testbed to investigate the effectiveness of the proposed controller. The reference trajectory is shown in Fig. REF . The scan length is set as \(0.04\) m, and the scan velocity is set as \(0.032\) m/s...	r
4af855ba-1917-4364-b0d1-ab02667d4fd3	Moreover, to study the robustness of these controllers, with all the parameters maintained the same, an extra external sinusoidal disturbance is generated and applied to the system. The amplitude and frequency of the disturbance signal are set as \(0.03\) m (rather large compared with referce signal) and 1 Hz, respect...	r
56d37cd8-83fe-4a3e-9a2a-2d35e7a964f4	In Fig. REF , we note that all the four controllers have large tracking errors when the scanning velocity changes. The peak error of SMC is the largest among the four controllers, which is at around 50 \(\mu \) m. Tracking error via the proposed ANN-FSA is the smallest, about 35 \(\mu \) m. We also note that the errors...	r
445f6fda-7cc7-4613-b911-4257b1b73aa9	In this paper, an adaptive neural-network and fractional-order super-twisting algorithm was proposed and applied to a precision motion system. In this way, not only the dynamics of the states on the sliding surface was improved via the super-twisting algorithm, but also unknown model uncertainties and disturbances of t...	d
d04d3ac9-d3db-4a35-9cc5-52568934052a	The number field sieve is the most efficient method known for solving the integer factorization problem and the discrete logarithm problem in a finite field, in the most general case. However there are many different variants of the number field sieve, depending on the context. Recently, the Tower Number Field Sieve (T...	i
b888a7a7-6fd5-4e32-89df-a763ea276ff9	We briefly discuss the asymptotics of number field sieve-type algorithms. We define the following function: \(L_{p^n}(\alpha ,c) = \exp ((c+o(1))(\log {p^n})^\alpha (\log {\log {p^n}})^{1-\alpha }).\)	i
e3f7862f-da28-4b54-99a4-474b51e0d15c	This function describes the asymptotic complexity of a subexponential function in \(\log {p^n}\) , which is used to asses the complexity of the number field sieve for computing discrete logs in \(\mathbb {F}_{p^n}\) . For a given \(p^n\) , there are two important boundaries, respectively for \(\alpha = 1/3\) and \(\al...	i
c1e2cd57-d423-4fbf-838a-94857b91ae4e	The structure of this paper is as follows: In Section 2, we give an overview of computing discrete logs using ExTNFS. In section 3, sieving in a 4d box (orthotope) is described, and we give implementation details. In section 4, we describe the descent step in detail. In section 5 we give details of the record computati...	i
a5705097-c41c-4e30-ad7a-8b60fe2905b4	We implemented the key components of the Extended Tower Number Field Sieve and together with linear algebra components of CADO-NFS demonstrated a total discrete log break in a finite field \(\mathbb {F}_{p^4}\) of size 512 bits, a new record. This provides another data point in the evaluation of security of systems de...	d
2ccd93b0-7f49-401c-b8ee-6cf316cb9ade	Neural methods for generating entity embeddings have become the dominant approach to representing entities, with embeddings learned through methods such as pretraining, task-based training, and encoding knowledge graphs [1]}, [2]}, [3]}. These embeddings can be compared extrinsically by performance on a downstream task...	i
49515884-2caf-4584-b156-05c7eede690a	Another way to compare these embeddings is intrinsically using probing tasks [1]}, [2]}, which have been used to examine entity embeddings for information such as an entity's type, relation to other entities, and factual information [3]}, [4]}, [5]}, [6]}. These prior examinations have often examined only a few methods...	i
9f0a29f7-d76d-4a93-a126-b10c958003c1	We address these gaps by comparing a wide range of entity embedding methods for semantic information using both probing tasks as well as downstream task performance. We propose a set of probing tasks derived simply from Wikipedia and DBPedia, which can be applied to any method that produces a single embedding per entit...	i
58f8acc2-f63f-43df-919a-0f82dafd1491	We aim to provide a clear comparison of the strengths and weaknesses of various entity embedding methods and the information they encode to guide future work. Our probing task datasets, embeddings, and code are available online.https://github.com/AJRunge523/entitylens	i
b01f524d-cd9b-49c2-b55f-d6030c20b885	Many of our embedding methods have been evaluated on EL tasks in prior work, either in a separate model or as full EL models themselves. However, direct comparison of the impact of of the embeddings on EL performance is confounded by differences in the architectures which leverage the embeddings, as well as difficult t...	m
776298b8-cc2e-4cb1-9315-d6455ccebb64	We test the embeddings using three EL models on two standard EL datasets, the AIDA-CoNLL 2003 dataset [1]} and the TAC-KBP 2010 dataset [2]}. Two of our EL models are the CNN and RNN EL models used to generate our task-learned embeddings. Our third is a transformer model based on the RELIC model of [3]} that encodes a ...	m
42f87d63-7602-4205-b341-1a2cfb5bd6d5	In this work, we propose a new set of probing tasks for evaluating entity embeddings which can be applied to any method that creates one embedding per entity. Using these tasks, we find that entity type information is one of the strongest signals present in all but one of the embedding models, followed by coarse inform...	d
94138ed2-c856-4c13-919d-aafbc8d72777	Overall, we find that while BERT-based entity embeddings perform well on many of these tasks, their high performance can often be attributed to strong entity type information encoding. More specialized models such as Wikipedia2Vec are better able to detect and identify relationships, while the embeddings of [1]} better...	d
cea9e13d-9656-4fc0-b6f0-7e137bcc04cd	Our work provides insight into the information encoded by static entity embeddings, but entities can change over time, sometimes quite significantly. One future line of work we would like to pursue using our tests is to investigate how changes in entities over time can be reflected in the embeddings, and how those chan...	d
55d4f39c-c6c2-4370-b6ec-f2881f0a142e	Bipolar disorder (BD) is a mental health condition that causes extreme mood swings like emotional highs (mania, hypomania), lows (depression), mixed episodes where depression and manic symptoms occur together. The diagnosis of bipolar disorder requires lengthy observations on the patient. Otherwise, it can be mistaken ...	i
90748098-c9f9-463c-88e7-7602a0e028d0	In bipolar disorder, the clinical appearance of the patients changes based on the moods they are in. The changes are seen in both their sound and visual appearance, as well as the energy level changes. In the manic episode, the speech of the patient becomes louder, rushed, or pressured. The patient can be very cheerful...	i
be8152d4-ad99-4786-8cce-08a683992dfa	Today, the diagnosis of mental health disorders rely on questionnaires done by psychiatrists and reports from patients and their caregivers. Psychiatrists perform some tests to collect information about the patient's cognitive, neurophysiological, and emotional situations [1]}. But these reports are subjective, and the...	i
5f241116-9423-4597-bd70-e3959432727e	One of the tools used to rate the severity of the manic episodes of a patient is the Young Mania Rating Scale (YMRS). During the interviews, psychiatrists observe the patient's symptoms and give ratings to them. The 11 items in YMRS assess the elevated mood, increased motor activity-energy, sexual interest, sleep, irri...	i
25a9b2d4-7baf-4d5c-b789-10443582124c	Recent advancements in technologies like social media, smartphones, wearable devices, and improvements in recording techniques like better cameras, neuroimaging techniques, microphones enable us to gather good quality data from people during their everyday lives. This creates an opportunity to create tools to monitor t...	i
9e3e27f1-7c93-4de6-9384-b8717ec2f287	In recent years, there are many works on diagnosing psychiatric disorders like Alzheimer's disease, anxiety, attention deficit hyperactivity disorder, autism spectrum disorder, depression, obsessive-compulsive disorder, bipolar disorder [1]} using machine learning (ML) techniques. The datasets used for the detection of...	i
cae9c630-e4ac-4bc4-85a2-214978176e0c	Assessment of mental health disorders using machine learning methods has been an active research area. Many researchers are working on recognizing mental health disorders varying from depression, Alzheimer’s disease, anxiety to bipolar disorder. The interdisciplinary research between psychiatrists and computer scientis...	w
3f3d47a9-0011-484d-bccc-7d32c4593500	In this chapter, we introduce the features used in audio, textual, and visual modalities, preprocessing, feature selection methods applied to the dataset. After that, we explain the ELM algorithm used as a classification method, cross-validation technique used to evaluate the results, and modality fusion methods applie...	m
ae647d7b-e1fd-42fe-ad88-53a0686f9d60	During this thesis, we worked on the classification of bipolar disorder episodes (mania, hypomania, depression) using the BD dataset that contains video recordings of the bipolar disorder patients while they are interviewed by their psychiatrists. During the interviews, the patients perform seven different tasks. The t...	d
e799b2ad-acf7-41c0-a755-187eb7579bb1	We showed that multimodality improves the generalizability of the classification of bipolar disorder. The information coming from acoustic, textual, and visual modalities complement each other and improve the performance of the unimodal systems. The results suggests that using all three modalities together gives the be...	d
e12eb7c5-29f1-4029-9585-f893f486341a	As a classification algorithm, we use fusion of weighted and unweighted ELMs. ELM was a good fit for this problem, since it is a 2-level neural and prone to overfitting. The data imbalance creates a need for a weighted model, however weighted ELM mostly favor the minority class. So using the fusion of weighted and unwe...	d
644700f7-5da7-4590-bad1-8ed5728efe1b	The best performing model is achieved using eGEMAPS10, LIWC, and FAU features using the fusion of weighted and unweighted kernel ELMS, and fused using majority voting as a late fusion process. We achieve 64.8% test set UAR on this configuration, which is the best result achieved on the BD dataset as can be seen in Figu...	d
258da887-57b6-4668-a9f7-691395eb7e86	eGEMAPS is a commonly used minimalistic acoustic feature set. So we used it for the audio classification, and in the fusion experiments. Besides, we summarized eGEMAPS LLDs with the 10 functionals presented in [1]}. We achieved a better performance using eGEMAPS10 feature set, which shows that eGEMAPS LLDs can give bet...	d
579163b4-b6bf-4010-b0b2-cc5eb1ae82d4	These results are still not high enough to use in a real-world application as a decision system. One of the main difficulties was the small size of the BD corpus. There are 25, 38, and 41 clips in the dataset for the remission, hypomania, and mania classes respectively, which is not enough to generalize with a high cer...	d
0fbed13e-eef2-4764-9f77-131976893f47	Besides the clip level evaluation, we look for the effect of the tasks separately, and by grouping the same emotion eliciting tasks during the classification. Since some tasks are not performed in every clip, the number of clips per task are different. To be able to compare the results among the task groups and the ent...	d
5fa8e7bc-f488-4c83-bdba-5e584336fe3a	Our final best performing model contains information from three different modalities, and each modality is represented using feature vectors with various sizes, which causes poor explainability of the model. It is especially important to create explainable models in medical domain. As a further study, the explainabilit...	d
e349de23-f7a4-4f0a-b4ca-1d2787a3ca63	Deep learning has emerged as a powerful tool for many industrial and scientific applications. However, deep learning requires large centralized datasets, whose collection can be intrusive, for training. The finalized model can either be deployed on a server or edge devices. Federated learning circumvents this problem b...	i
ce0478f1-e907-4432-a48d-af90aa633f35	When data is independently and identically distributed (IID), federated learning algorithms converge rapidly. FedAvg takes as a few as 18 communication rounds to reach 99% accuracy for 100 device federated MNIST [1]}. When the client devices are statistically heterogeneous, learning a single global model becomes very d...	i
eb5bb278-8016-4107-9bf3-f8f8acb4db17	Techniques for faster federated learning on non-IID data range from the simple to the complex. On the simple end, Momentum Federated Learning averages the momenta of different devices into a global momentum which is distributed at the start of each round [1]}. This enables clients to use momentum gradient descent as th...	i
aab22439-f975-4110-a62e-82b21f734c88	To deal with the communication and scalability challenges introduced by above methods, efforts has been made to reduce the amount of rounds required for server-client communication [1]}. FedPAQ has made an initial effort [2]} to periodically average and quantize the client models before making the server update. Then, ...	i
fa8703b9-1117-475f-ae8d-fb939bbabdb8	In this paper, we take a different approach, using server averaging to accelerate convergence. We justify the technique using heuristic arguments and experimentally show that it reaches a given test accuracy faster than FedAvg. Additionally, we propose decay epochs for reducing client computation while maintaining non-...	i
afbc9576-f872-454f-a6b2-9e602eb5fd11	The history of stochastic gradient methods dates back to 1951, and is usually mentioned as Robbins-Monro process [1]}. One technique that has historically been used to improve SGD convergence is iterate averaging [2]}, [3]}, [4]}, also often referred to as Polyak-Rupert averaging. Recently, the stability of an averagin...	w
d42322ba-5a02-48d3-a75a-c3df36c7fc52	Federated learning techniques heavily rely on above mentioned averaging schemes. FedAvg is the most popular aggregation method that averages parameters of local models element-wise. There exists two major branches for improving FedAvg [1]}. One is lead by FedProx [2]} that applies a proximal term to the local lost func...	w
3af8883d-25e6-4ddf-87d4-02c16284a622	Safa et. al. explore iterate averaging in the context of block-cyclic SGD [1]}. Most federated learning algorithms assume that clients are chosen uniformly. In practice, devices conduct local training only when idle, with devices falling into blocks according to their timezone. More formally, we want to minimize \(\ope...	w
59c00e2d-549f-499e-ad1c-358e07e5f086	while sampling \(n\) points from \(\mathcal {D}_1, \dots , \mathcal {D}_m\) in order for \(K\) cycles. In this block-cyclic setting, SGD is worse by a factor of \(\sqrt{mn/K}\) . However, learning personalized models for each block using Averaged SGD [1]}—taking the average of all SGD iterate as the final model para...	w
71a05e05-e4d3-457c-9904-d06d7a14eb68	Stochastic Weight Averaging (SWA) applies Averaged SGD to deep learning [1]}. Izmailov et. al. note that SGD generally converges to points near the boundary of a wide flat region and that optima width has been conjectured to correlate with generalization. The average of the SGD iterates then lies at the the center of t...	w
3436b808-2afd-400b-b6bd-e1bd450df09e	This paper improves upon an existing federated learning algorithm by performing periodic server-side averaging. The proposed adaptation of FedAvg has three major benefits: (1) it uses iterate averaging for accelerated convergence, (2) it learns a better generalizing optima than SGD, (3) the effectiveness of FL is incre...	d
3bc8f89c-0854-4158-813c-809894baa58a	In the future, we wish to extend the server averaging to both various neural network types (i.e. attention, LSTM, etc.) and layer-wise building blocks (i.e. batch normalization layers, etc.). In addition, we wish to investigate the performance of epoch decay paired with state-of-the-art update methods such as match ave...	d
3a6811f9-90ac-47c5-9b37-07f95925330c	Contour is one of the most important object descriptors, along with texture and color. The boundary of an object in an image is encoded in contour description, which is useful in various applications, such as image retrieval [1]}, [2]}, [3]}, recognition [4]}, [5]}, [6]}, and segmentation [7]}, [8]}, [9]}, [10]}, [11]}...	i
6782fdc5-35b5-4b87-8d30-ef3704395e2a	Early contour descriptors were developed mainly for image retrieval [1]}, [2]}, [3]}, [4]}. An object contour can be simply represented based on the area, circularity, and/or eccentricity of the object [5]}. For more precise description, there are several approaches, including shape signature [6]}, [7]}, [8]}, structur...	i
29a190e8-5669-4900-951c-759d211e625e	Recently, contour descriptors have been incorporated into deep-learning-based object detection, tracking, and segmentation systems. In [1]}, bounding boxes are replaced by polygons to enclose objects more tightly. In [2]}, ellipse fitting is done to produce a rotated box of a target object to be tracked. For instance s...	i
258c9349-2eae-453e-aeba-bcc3e101a13d	In this paper, we propose novel contour descriptors, called eigencontours, based on low-rank approximation. First, we construct a contour matrix containing all object boundaries in a training set. Second, we decompose the contour matrix into eigencontours, based on the best rank-\(M\) approximation of singular value d...	i
ab1985af-9e02-4e82-8632-0ee559db99f2	We propose the notion of eigencontours — data-driven contour descriptors based on SVD — to represent object boundaries as faithfully as possible with a limited number of coefficients. The proposed algorithm can represent object boundaries more effectively and more efficiently than the existing contour descriptors. T...	i
2ac7b224-3e3b-4914-bf7a-07ee5c450339	The goal of contour description is to represent the boundary of an object in an image compactly and faithfully. Simple contour descriptors are based on the area, circularity, and/or eccentricity of an object [1]}, and basic geometric shapes, such as rectangles and ellipses, can be also used. However, these simple descr...	w
20ac1d8a-251c-477a-9798-6480b8a96975	Recently, attempts have been made to improve the performances of deep-learning-based vision systems. In [1]}, a bounding box for object detection is replaced by an octagon to enclose an object more tightly via polygonal approximation. In [2]}, a rotated box for a target object is determined based on ellipse fitting, in...	w
5a8c8f29-1f54-4289-a6a3-1c9880e45646	The proposed algorithm aims to represent an object boundary as faithfully as possible by employing as few coefficients as possible. To this end, we develop eigencontours based on the best low-rank approximation property of SVD.	w
6f1d02ce-f89b-4930-a418-867fa8daf441	Dimension of eigencontour space (\(M\) ): Table REF lists the AUC-\(\cal {F}\) performances of the proposed algorithm on the SBD validation dataset according to the dimension, \(M\) , of the eigencontour space. At \(M=10\) , the proposed algorithm yields poor scores, since object boundaries are too simplified and not...	m
1ebd7eb0-42ed-44ec-a646-4dba1cd36855	Categorical eigencontour space: The proposed eigencontours are data-driven descriptors, which depend on the distribution of object contours in a dataset. Thus, different eigencontours are obtained for different data. Let us consider two options for constructing eigencontour spaces: categorial construction and universal...	m
389c1547-abc7-4ef9-a5d2-781bb9c944c4	For the two options, \(\cal {F}\) score curves are presented according to the dimension \(M\) in the supplemental document. Table REF compares the area under curve performances of the \(\cal {F}\) curves up to \(M=18\) . The categorial construction provides better performances than the universal construction, becau...	m
7cd6bac9-81a1-482a-9c69-605195030d7f	Limitations: The proposed eigencontours represent typical contour patterns in a dataset. Thus, if object contour patterns differ among datasets, the eigencontours for a dataset may be effective for that particular dataset only. To assess the dependency of eigencontours on a dataset, we conduct cross-validation tests be...	m
d5854744-09f5-4bf0-bb33-ee4ed71bd3b7	We proposed novel contour descriptors, called eigencontours, based on low-rank approximation. First, we constructed a contour matrix containing all contours in a training set. Second, we approximated the contour matrix, by performing the best rank-\(M\) approximation. Third, we represent an object boundary by a linear...	d
ff7c1911-a348-434c-ae84-e2924bb12d28	Scientists increasingly use machine learning (ML) in their daily work. This development is not limited to natural sciences like ecology or neuroscience , but also extends to social sciences such as psychology and archaeology .	i
7151e7c7-ce93-46b8-97cc-2571a79df978	In particular, when building predictive models for problems with complex data structures, ML outcompetes classical statistical models in both performance and convenience. Impressive recent examples of successful prediction models in science include the automated particle tracking at CERN , or DeepMind's AlphaFold, ...	i
004cc382-3ba7-4af2-b4ff-26ba95978428	What hinders scientists from using ML models to gain real-world insights is the model complexity and the unclear connection between the model and the described phenomenon — the so-called opacity problem , . Interpretable machine learning (IML, also called XAI for eXplainable artificial intelligence) aims to solve t...	i
f1f5210a-6d48-4dfa-8b17-32b06daf335f	Nevertheless, scientists increasingly use IML techniques in their research.e.g. for predicting personality traits from smartphone usage , forecasting crop yield , , or analyzing seasonal precipitation forecasts Although researchers are aware that their IML analysis remains just a model description, it is often imp...	i
7d25e83e-cbfb-47e4-8d54-426ebdd8b9b1	In recent years, digital libraries have moved towards open science and open access with several large scholarly datasets being constructed. Most popular datasets include millions of papers, authors, venues, and other information. Their large size and heterogeneous contents make it very challenging to effectively manage...	i
be8647fa-47ab-4abc-9692-04fdb62a7f74	Notably, instead of using knowledge graphs directly in some tasks, we can model them by knowledge graph embedding methods, which represent entities and relations as embedding vectors in semantic space, then model the interactions between them to solve the knowledge graph completion task. There are many approaches [1]} ...	i
38cf6de4-fd73-40f8-b931-114da0e747e1	For theoretical analysis, we first analyze the state-of-the-art knowledge graph embedding model CP\( _h \) [1]} in comparison to the popular word embedding model word2vec skipgram [2]} to explain its components and provide understandings to its semantic structures. We then define the semantic queries on the knowledge ...	i
4bc58645-1d7f-431c-a254-22ba0bdf2eaf	Based on our theoretical results, we design a general framework for data exploration on scholarly data by semantic queries on knowledge graph embedding space. The main component in this framework is the conversion between the data exploration tasks and the semantic queries. We first outline the semantic query solutions...	i
388310ad-34a1-4321-b88d-de5d4f83751a	In this paper, we studied the application of knowledge graph embedding in exploratory data analysis. We analyzed the CP\( _h \) model and provided understandings to its semantic structures. We then defined the semantic queries on knowledge graph embedding space to efficiently approximate some operations on heterogeneo...	d
764a6cda-e150-4341-971b-9e971c906cee	This paper is dedicated to the theoretical foundation of a new approach and discussions of emerging tasks, whereas experiments and evaluations are left for the future work. There are several other promising directions for future research. One direction is to explore new tasks or new solutions of traditional tasks using...	d
82e5ee19-669b-425c-87bd-1e6f8f7e7b70	In recent years, machine learning algorithms have been increasingly used to inform decisions with far-reaching consequences (e.g. whether to release someone from prison or grant them a loan), raising concerns about their compliance with laws, regulations, societal norms, and ethical values. Specifically, machine learn...	i
274c01a8-01f4-4118-9db4-166c0b5c7d41	Fair learning algorithms typically need access to the sensitive data in order to ensure that the trained model is non-discriminatory. However, consumer privacy laws (such as the E.U. General Data Protection Regulation) restrict the use of sensitive demographic data in algorithmic decision-making. These two requirement...	i
5d0af241-6a11-4aa4-a538-97564180275c	The works of [1]}, [2]} proposed a solution to this quandary using secure multi-party computation (MPC), which allows the learner to train a fair model without directly accessing the sensitive attributes. Unfortunately, as [3]} observed, MPC does not prevent the trained model from leaking sensitive data. For example, ...	i
b7f3d840-9d11-446b-883d-9c0863e156a3	Since [1]}, several follow-up works have proposed alternate approaches to DP fair learning [2]}, [3]}, [4]}, [5]}, [6]}, [7]}. As shown in fig: related work table, each of these approaches suffers from at least two critical shortcomings. In particular, none of these methods have convergence guarantees when mini-batche...	i
9a11b4e1-1524-4c43-9243-1799245cd66a	Our Contributions: In this work, we propose a novel algorithmic framework for DP fair learning. Our approach builds on the non-private fair learning method of [1]}. We consider a regularized empirical risk minimization (ERM) problem where the regularizer penalizes fairness violations, as measured by the Exponential Rén...	i
7a3e3a77-3253-43e7-8c7e-f3bf397601a2	Guaranteed convergence for any privacy and fairness level, even when mini-batches of data are used in each iteration of training (i.e. stochastic optimization setting). As discussed, stochastic optimization is essential in large-scale machine learning scenarios. Our algorithm is the first stochastic DP fair learning m...	i
9e737725-9354-43db-b300-bcc4d256d71c	Empirically, we show that our method outperforms the previous state-of-the-art methods in terms of fairness vs. accuracy trade-off across all privacy levels. Moreover, our algorithm is capable of training with mini-batch updates and can handle non-binary target and non-binary sensitive attributes. By contrast, existing...	i
ec259b81-6d2f-4e81-af8e-e1a77f3cafd2	A byproduct of our algorithmic developments and analyses is the first DP convergent algorithm for nonconvex min-max optimization: namely, we provide an upper bound on the stationarity gap of DP-SGDA for solving problems of the form \(\min _{\theta } \max _{W} F(\theta , W)\) , where \(F(\cdot , W)\) is non-convex. We...	i
8264eb8d-21ec-406d-a29c-0bc4a813b2f9	In this section, we evaluate the performance of our proposed approach (DP-FERMI) in terms of the fairness violation vs. test error for different privacy levels. We present our results in two parts: In Section REF , we assess the performance of our method in training logistic regression models on several benchmark tabu...	m
95322e5f-18a1-4653-93a5-c0963a4d6e4e	Motivated by pressing legal, ethical, and social considerations, we studied the challenging problem of learning fair models with differentially private demographic data. We observed that existing works suffer from a few crucial limitations that render their approaches impractical for large-scale problems. Specifically...	d
298d9497-25cd-47fc-b2f7-785d95521f43	The study of differentially private fair learning algorithms was initiated by [1]}. [1]} considered equalized odds and proposed two DP algorithms: 1) an \(\epsilon \) -DP post-processing approach derived from [3]}; and 2) an \((\epsilon , \delta )\) -DP in-processing approach based on [4]}. The major drawback of their ...	w
bf2aabcd-99b3-47a4-bbd9-21252209eca5	Following [1]}, several works have proposed other DP fair learning algorithms. None of these works have managed to simultaneously address all the shortcomings of the method of [1]}. The work of [3]} proposed DP and fair binary logistic regression, but did not provide any theoretical convergence/performance guarantees. ...	w
52f44d2d-dce4-49a1-ac6d-bbe03471f633	Nam dui ligula, fringilla a, euismod sodales, sollicitudin vel, wisi. Morbi auctor lorem non justo. Nam lacus libero, pretium at, lobortis vitae, ultricies et, tellus. Donec aliquet, tortor sed accumsan bibendum, erat ligula aliquet magna, vitae ornare odio metus a mi. Morbi ac orci et nisl hendrerit mollis. Suspendiss...	i
67c47121-3a1c-4195-9489-4cb56370daa8	Nulla malesuada porttitor diam. Donec felis erat, congue non, volutpat at, tincidunt tristique, libero. Vivamus viverra fermentum felis. Donec nonummy pellentesque ante. Phasellus adipiscing semper elit. Proin fermentum massa ac quam. Sed diam turpis, molestie vitae, placerat a, molestie nec, leo. Maecenas lacinia. Nam...	i
580035ca-35ab-4e20-90a2-4c76e1a94bdf	Concurrent with steady progress towards improving the accuracy and efficiency of 3D object detector algorithms [1]}, [2]}, [3]}, [4]}, [5]}, [6]}, [7]}, [8]}, [9]}, [10]}, [11]}, LiDAR sensor hardware has been improving in maximum range and fidelity, in order to meet the needs of safe, high speed driving. Some of the l...	i
b8e291bf-5052-45b9-9ebc-b15fd15d9e8e	Grid based methods [1]}, [2]}, [3]}, [4]}, [5]} divide the 3D space into voxels or pillars, each of these being optionally encoded using PointNet [6]}. Dense convolutions are applied on the grid to extract features. This approach is inefficient for large grids which are needed for long range sensing or small object det...	i

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Dataset Card for unarXive IMRaD classification

Dataset Summary

The unarXive IMRaD classification dataset contains 530k paragraphs from computer science papers and the IMRaD section they originate from. The paragraphs are derived from unarXive.

The dataset can be used as follows.

from datasets import load_dataset

imrad_data = load_dataset('saier/unarXive_imrad_clf')
imrad_data = imrad_data.class_encode_column('label')  # assign target label column
imrad_data = imrad_data.remove_columns('_id')         # remove sample ID column

Dataset Structure

Data Instances

Each data instance contains the paragraph’s text as well as one of the labels ('i', 'm', 'r', 'd', 'w' — for Introduction, Methods, Results, Discussion and Related Work). An example is shown below.

{'_id': '789f68e7-a1cc-4072-b07d-ecffc3e7ca38',
 'label': 'm',
 'text': 'To link the mentions encoded by BERT to the KGE entities, we define '
         'an entity linking loss as cross-entropy between self-supervised '
         'entity labels and similarities obtained from the linker in KGE '
         'space:\n'
         '\\(\\mathcal {L}_{EL}=\\sum -\\log \\dfrac{\\exp (h_m^{proj}\\cdot '
         '\\textbf {e})}{\\sum _{\\textbf {e}_j\\in \\mathcal {E}} \\exp '
         '(h_m^{proj}\\cdot \\textbf {e}_j)}\\) \n'}

Data Splits

The data is split into training, development, and testing data as follows.

Training: 520,053 instances
Development: 5000 instances
Testing: 5001 instances

Dataset Creation

Source Data

The paragraph texts are extracted from the data set unarXive.

Who are the source language producers?

The paragraphs were written by the authors of the arXiv papers. In file license_info.jsonl author and text licensing information can be found for all samples, An example is shown below.


{'authors': 'Yusuke Sekikawa, Teppei Suzuki',
 'license': 'http://creativecommons.org/licenses/by/4.0/',
 'paper_arxiv_id': '2011.09852',
 'sample_ids': ['cc375518-347c-43d0-bfb2-f88564d66df8',
                '18dc073e-a48e-488e-b34c-e5fc3cb8a4ca',
                '0c2e89b3-d863-4bc2-9e11-8f6c48d867cb',
                'd85e46cf-b11d-49b6-801b-089aa2dd037d',
                '92915cea-17ab-4a98-aad2-417f6cdd53d2',
                'e88cb422-47b7-4f69-9b0b-fbddf8140d98',
                '4f5094a4-0e6e-46ae-a34d-e15ce0b9803c',
                '59003494-096f-4a7c-ad65-342b74eed561',
                '6a99b3f5-217e-4d3d-a770-693483ef8670']}

Annotations

Class labels were automatically determined (see implementation).

Considerations for Using the Data

Discussion and Biases

Because only paragraphs unambiguously assignable to one of the IMRaD classeswere used, a certain selection bias is to be expected in the data.

Other Known Limitations

Depending on authors’ writing styles as well LaTeX processing quirks, paragraphs can vary in length a significantly.

Additional Information

Licensing information

The dataset is released under the Creative Commons Attribution-ShareAlike 4.0.

Citation Information

@inproceedings{Saier2023unarXive,
  author        = {Saier, Tarek and Krause, Johan and F\"{a}rber, Michael},
  title         = {{unarXive 2022: All arXiv Publications Pre-Processed for NLP, Including Structured Full-Text and Citation Network}},
  booktitle     = {Proceedings of the 23rd ACM/IEEE Joint Conference on Digital Libraries},
  year          = {2023},
  series        = {JCDL '23}
}