Reviewerly/exHarmony huggingface dataset:
Fast Sparse Deep Neural Networks: Theory and Performance Analysis

In this paper, fast sparse deep neural networks that aim to offer an alternative way of learning in a deep structure are proposed. We examine some optimization algorithms for traditional deep neural networks and find that deep neural networks suffer from a time-consuming training process because of a large number of connecting parameters in layers and layers. To reduce time consumption, we propose fast sparse deep neural networks, which mainly consider the following two aspects in the design of the network. One is that the parameter learning at each hidden layer is given utilizing closed-form solutions, which is different from the BP algorithm with iterative updating strategy. Another aspect is that fast sparse deep neural networks use the summation method of a multi-layer linear approximation to estimate the output target, which is a different way from most deep neural network models. Unlike the traditional deep neural networks, fast sparse deep neural networks can achieve excellent generalization performance without fine-tuning. In addition, it is worth noting that fast sparse deep neural networks can also effectively overcome the shortcomings of the extreme learning machine and hierarchical extreme learning machine. Compared to the existing deep neural networks, enough experimental results on benchmark datasets demonstrate that the proposed model and optimization algorithms are feasible and efficient.
iQCAR

Resource interferences caused by concurrent queries is one of the key reasons for unpredictable performance and missed workload SLAs in cluster computing systems. Analyzing these inter-query resource interactions is critical in order to answer time-sensitive questions like 'who is creating resource conflicts to my query'. More importantly, diagnosing whether the resource blocked times of a 'victim' query are caused by other queries or some other external factor can help the database administrator narrow down the many possibilities of query performance degradation. We introduce iQCAR, an inter-Query Contention Analyzer, that attributes blame for the slowdown of a query to concurrent queries. iQCAR models the resource conflicts using a multi-level directed acyclic graph that can help administrators compare impacts from concurrent queries, identify most contentious queries, resources and hosts in an online execution for a selected time window. Our experiments using TPCDS queries on Apache Spark show that our approach is substantially more accurate than other methods based on overlap time between concurrent queries.
Explainable Convolutional Neural Networks for Multi-Sensor Data

In this paper, we propose a new convolutional neural networks (CNNs) structure to overcome the interpretation problem posed by a series of the nonlinear transformation processes in CNNs. Convolutional neural networks (CNNs) have been successfully used in various applications including images, signals, and texts. Despite their superior performance in various tasks, the main limitation of CNNs is the lack of interpretability. Recently, deep neural network models including CNNs have been effectively used for the analysis of multi-sensor data. Although the original characteristics of deep neural network models offer high accuracy in classification for activity recognition, they are difficult to identify the critical sensors that play a significant role in discrimination of various equipment conditions. The proposed CNNs offers good performance and interpretability simultaneously. The effectiveness and applicability of the proposed method were demonstrated by the real sensor data collected from construction equipment for activity recognition.
The Effect of Scrambling CNFs

It has been an ongoing, decades-long debate about how SAT solvers and in general different or new algorithms should be evaluated and compared both in competitions and more importantly in papers. Evaluations are usually performed on existing benchmarks. Cross-validation and other means to avoid over-fitting are rarely used. In this paper we revisit the old idea of scrambling benchmarks also used in early competitions. Scrambling has the goal to make results of such evaluations more robust. We present a new method for scrambling CNFs, which allows to gradually increase the effect of scrambling, from keeping the scrambled CNF close to the original CNF, to complete random permutation of variables, clauses, and phases of literals. We used this method to scramble benchmarks from the last two SAT competitions and solved them with the best solvers in the main track of the last SAT competition. As expected our experimental results suggest that scrambling has a substantial effect on the performance of individual solvers but surprisingly has little effect on rankings among solvers. As a consequence we argue that only using our method of scrambling is not enough to increase robustness of competitions and evaluations in general.
Application of an Improved Correlation Method in Electrostatic Gait Recognition of Hemiparetic Patients

Hemiparesis is one of the common sequelae of neurological diseases such as strokes, which can significantly change the gait behavior of patients and restrict their activities in daily life. The results of gait characteristic analysis can provide a reference for disease diagnosis and rehabilitation; however, gait correlation as a gait characteristic is less utilized currently. In this study, a new non-contact electrostatic field sensing method was used to obtain the electrostatic gait signals of hemiplegic patients and healthy control subjects, and an improved Detrended Cross-Correlation Analysis cross-correlation coefficient method was proposed to analyze the obtained electrostatic gait signals. The results show that the improved method can better obtain the dynamic changes of the scaling index under the multi-scale structure, which makes up for the shortcomings of the traditional Detrended Cross-Correlation Analysis cross-correlation coefficient method when calculating the electrostatic gait signal of the same kind of subjects, such as random and incomplete similarity in the trend of the scaling index spectrum change. At the same time, it can effectively quantify the correlation of electrostatic gait signals in subjects. The proposed method has the potential to be a powerful tool for extracting the gait correlation features and identifying the electrostatic gait of hemiplegic patients.
Gender Stereotypes Differ between Male and Female Writings

Written language often contains gender stereotypes, typically conveyed unintentionally by the author.Existing methods used to evaluate gender stereotypes in a text compute the difference in the co-occurrence of gender-neutral words with female and male words.To study the difference in how female and male authors portray people of different genders, we quantitatively evaluate and analyze the gender stereotypes in their writings on two different datasets and from multiple aspects, including the overall gender stereotype score, the occupation-gender stereotype score, the emotion-gender stereotype score, and the ratio of male words used to female words.We show that writings by females on average have lower gender stereotype scores.We also find that emotion words in writings by males have much lower stereotype scores than the average score of all words, while in writings by females the scores are similar.We study and interpret the distributions of gender stereotype scores of individual words, and how they differ between male and female writings.
Further Lower Bounds for Structure-Preserving Signatures in Asymmetric Bilinear Groups

Structure-Preserving Signatures (SPSs) are a useful tool for the design of modular cryptographic protocols. Recent series of works have shown that by limiting the message space of those schemes to the set of Diffie-Hellman (DH) pairs, it is possible to circumvent the known lower bounds in the Type-3 bilinear group setting thus obtaining the shortest signatures consisting of only 2 elements from the shorter source group. It has been shown that such a variant yields efficiency gains for some cryptographic constructions, including attribute-based signatures and direct anonymous attestation. Only the cases of signing a single DH pair or a DH pair and a vector from $$\mathbb {Z}_p$$ have been considered. Signing a vector of group elements is required for various applications of SPSs, especially if the aim is to forgo relying on heuristic assumptions. An open question is whether such an improved lower bound also applies to signing a vector of $$\ell > 1$$ messages. We answer this question negatively for schemes existentially unforgeable under an adaptive chosen-message attack (EUF-CMA) whereas we answer it positively for schemes existentially unforgeable under a random-message attack (EUF-RMA) and those which are existentially unforgeable under a combined chosen-random-message attack (EUF-CMA-RMA). The latter notion is a leeway between the two former notions where it allows the adversary to adaptively choose part of the message to be signed whereas the remaining part of the message is chosen uniformly at random by the signer. Another open question is whether strongly existentially unforgeable under an adaptive chosen-message attack (sEUF-CMA) schemes with 2-element signatures exist. We answer this question negatively, proving it is impossible to construct sEUF-CMA schemes with 2-element signatures even if the signature consists of elements from both source groups. On the other hand, we prove that sEUF-RMA and sEUF-CMA-RMA schemes with 2-element (unilateral) signatures are possible by giving constructions for those notions. Among other things, our findings show a gap between random-message/combined chosen-random-message security and chosen-message security in this setting.
Unified Semantic Parsing with Weak Supervision

Semantic parsing over multiple knowledge bases enables a parser to exploit structural similarities of programs across the multiple domains.However, the fundamental challenge lies in obtaining high-quality annotations of (utterance, program) pairs across various domains needed for training such models.To overcome this, we propose a novel framework to build a unified multi-domain enabled semantic parser trained only with weak supervision (denotations).Weakly supervised training is particularly arduous as the program search space grows exponentially in a multi-domain setting.To solve this, we incorporate a multipolicy distillation mechanism in which we first train domain-specific semantic parsers (teachers) using weak supervision in the absence of the ground truth programs, followed by training a single unified parser (student) from the domain specific policies obtained from these teachers.The resultant semantic parser is not only compact but also generalizes better, and generates more accurate programs.It further does not require the user to provide a domain label while querying.On the standard OVERNIGHT dataset (containing multiple domains), we demonstrate that the proposed model improves performance by 20% in terms of denotation accuracy in comparison to baseline techniques.
12. A survey of group invariant Butson matrices and their relation to generalized bent functions and various other objects

This survey concerns the following closely related concepts: - Group invariant Butson matrices, - generalized bent functions, - cyclic n-roots, - generalized Hadamard matrices, - abelian splitting semiregular difference sets. We explain the connections between these notions and show that group invariant Butson matrices can be viewed as their “common denominator.” We also review the most relevant known results on these objects, some of which are quite recent.
Quantization for uniform distributions on stretched Sierpiński triangles

In this paper, we have considered a uniform probability distribution supported by a stretched Sierpiński triangle. For this probability measure, the optimal sets of n-means and the nth quantization errors are determined for all $$n\ge 2$$ . In addition, it is shown that the quantization coefficient for such a measure does not exist though the quantization dimension exists.
Wide baseline pose estimation from video with a density-based uncertainty model

Robust wide baseline pose estimation is an essential step in the deployment of smart camera networks. In this work, we highlight some current limitations of conventional strategies for relative pose estimation in difficult urban scenes. Then, we propose a solution which relies on an adaptive search of corresponding interest points in synchronized video streams which allows us to converge robustly toward a high-quality solution. The core idea of our algorithm is to build across the image space a nonstationary mapping of the local pose estimation uncertainty, based on the spatial distribution of interest points. Subsequently, the mapping guides the selection of new observations from the video stream in order to prioritize the coverage of areas of high uncertainty. With an additional step in the initial stage, the proposed algorithm may also be used for refining an existing pose estimation based on the video data; this mode allows for performing a data-driven self-calibration task for stereo rigs for which accuracy is critical, such as onboard medical or vehicular systems. We validate our method on three different datasets which cover typical scenarios in pose estimation. The results show a fast and robust convergence of the solution, with a significant improvement, compared to single image-based alternatives, of the RMSE of ground-truth matches, and of the maximum absolute error.
Exploring Research-Methods Blogs in Psychology: Who Posts What About Whom, and With What Effect?

During the methods crisis in psychology and other sciences, much discussion developed online in forums such as blogs and other social media. Hence, this increasingly popular channel of scientific discussion itself needs to be explored to inform current controversies, record the historical moment, improve methods communication, and address equity issues. Who posts what about whom, and with what effect? Does a particular generation or gender contribute more than another? Do blogs focus narrowly on methods, or do they cover a range of issues? How do they discuss individual researchers, and how do readers respond? What are some impacts? Web-scraping and text-analysis techniques provide a snapshot characterizing 41 current research-methods blogs in psychology. Bloggers mostly represented psychology’s traditional leaderships’ demographic categories: primarily male, mid- to late career, associated with American institutions, White, and with established citation counts. As methods blogs, their posts mainly concern statistics, replication (particularly statistical power), and research findings. The few posts that mentioned individual researchers substantially focused on replication issues; they received more views, social-media impact, comments, and citations. Male individual researchers were mentioned much more often than female researchers. Further data can inform perspectives about these new channels of scientific communication, with the shared aim of improving scientific practices.
Collaborative Approaches to Integrate Repositories within the Research Information Ecosystem: Creating Bridges for Common Goals

Open Access (OA) has many tendrils running across the wider research information landscape. There are more researchers, organizations, and systems than ever before engaging with (or being asked to engage with) OA throughout the research ecosystem. However, too often OA activities and processes within repositories remain siloed from research information management systems (RIMS) and tasks, creating an undue burden of time and duplicating effort, thereby undermining the overall effectiveness of OA. By investing in interoperable metadata standards and practices, and creating a networked landscape of systems and community, technology ecosystems can be created that encourage researchers to make even more of their research open while streamlining research information management activities. By unifying the community around a more sustainable, systems-agnostic approach focused on flexible interoperability, it is possible to create an environment in which organizations can choose the tools relevant to their needs, bring those tools together in a complementary dynamic, and maximize data reuse.
Human vs Machine Attention in Neural Networks: A Comparative Study.

Human visual system can selectively attend to parts of a scene for quick perception, a biological mechanism known as Human attention. Inspired by this, recent deep learning models encode attention mechanisms to focus on the most task-relevant parts of the input signal for further processing, which is called Machine/Neural/Artificial attention. Understanding the relation between human and machine attention is important for interpreting and designing neural networks. Many works claim that the attention mechanism offers an extra dimension of interpretability by explaining where the neural networks look. However, recent studies demonstrate that artificial attention maps do not always coincide with common intuition. In view of these conflicting evidence, here we make a systematic study on using artificial attention and human attention in neural network design. With three example computer vision tasks, diverse representative backbones, and famous architectures, corresponding real human gaze data, and systematically conducted large-scale quantitative studies, we quantify the consistency between artificial attention and human visual attention and offer novel insights into existing artificial attention mechanisms by giving preliminary answers to several key questions related to human and artificial attention mechanisms. Overall results demonstrate that human attention can benchmark the meaningful `ground-truth' in attention-driven tasks, where the more the artificial attention is close to human attention, the better the performance; for higher-level vision tasks, it is case-by-case. It would be advisable for attention-driven tasks to explicitly force a better alignment between artificial and human attention to boost the performance; such alignment would also improve the network explainability for higher-level computer vision tasks.
Digitization of library resources and the formation of digital libraries: Special reference in green stone digital library software

This paper discusses the new activities, methods and technology used in digitization and formation of digital libraries. It set out some key points involved and the detailed plans required in the process, offers pieces of advice and guidance for the practicing librarians and information scientists. Digital libraries are being created today for diverse communities and in different fields e.g. education, science culture, development, health, governance and so on. With the availability of several free digital Library software packages at the recent time, mainly green stone digital library software is the one of them to creation and sharing of information through the digital library collections has become an attractive and feasible proposition for library and information professionals around the world. Keywords: Digitization, Digital libraries, Librarians, Information scientists, Library software, Green stone digital library, Information professionals.
Effect of Self-Invertible Matrix on Cipher Hexagraphic Polyfunction

A cryptography system was developed previously based on Cipher Polygraphic Polyfunction transformations, C i × j ( t ) ≡ A i × i t P i × j m o d N where C i × j , P i × j , A i × i are cipher text, plain text, and encryption key, respectively. Whereas, ( t ) is the number of transformations of plain text to cipher text. In this system, the parameters ( A i × i , ( t ) ) are kept in secret by a sender of messages. The security of this system, including its combination with the second order linear recurrence Lucas sequence (LUC) and the Ron Rivest, Adi Shamir and Leonard Adleman (RSA) method, until now is being upgraded by some researchers. The studies found that there is some type of self-invertible A 4 × 4 should be not chosen before transforming a plain text to cipher text in order to enhance the security of Cipher Tetragraphic Trifunction. This paper also seeks to obtain some patterns of self-invertible keys A 6 × 6 and subsequently examine their effect on the system of Cipher Hexagraphic Polyfunction transformation. For that purpose, we need to find some solutions L 3 × 3 for L 3 × 3 2 ≡ A 3 × 3 mod N when A 3 × 3 are diagonal and symmetric matrices and subsequently implement the key L 3 × 3 to get the pattern of A 6 × 6 .
Learning high-level robotic soccer strategies from scratch through reinforcement learning

The field of automated learning has been steadily growing in robotic tasks. This phenomenon is supported by the evolution of computational resources and new reinforcement learning algorithms. Researchers have drawn their attentions to methods that are easy to implement and tune, while achieving state-of-the-art performance. This trend also affects the world of robotic soccer, where new papers delve systematically into the optimization of basic skills. However, when learning higher-level strategies, there is space for improvement on two fronts. First, the simulation environment should allow the agent to abstract from low-level details. Second, the existing methods to train this kind of behaviors are still scarce. This paper contributes with innovative problem-solving methods, specifically in the rewards field. To test alternative approaches, an extended version of the RoboCup's official Soccer Server simulator was used. The results have confirmed the importance of the proposed reward components and their relationship with the episodes' initial conditions.
Accelerating Sparse Matrix Operations in Neural Networks on Graphics Processing Units

Graphics Processing Units (GPUs) are commonly used to train and evaluate neural networks efficiently.While previous work in deep learning has focused on accelerating operations on dense matrices/tensors on GPUs, efforts have concentrated on operations involving sparse data structures.Operations using sparse structures are common in natural language models at the input and output layers, because these models operate on sequences over discrete alphabets.We present two new GPU algorithms: one at the input layer, for multiplying a matrix by a few-hot vector (generalizing the more common operation of multiplication by a one-hot vector) and one at the output layer, for a fused softmax and top-N selection (commonly used in beam search).Our methods achieve speedups over state-of-theart parallel GPU baselines of up to 7× and 50×, respectively.We also illustrate how our methods scale on different GPU architectures.