Reviewerly/exHarmony huggingface dataset:
NASB: Neural Architecture Search for Binary Convolutional Neural Networks

Binary Convolutional Neural Networks (CNNs) have significantly reduced the number of arithmetic operations and the size of memory storage needed for CNNs, which makes their deployment on mobile and embedded systems more feasible. However, the CNN architecture after binarizing requires to be redesigned and refined significantly due to two reasons: 1. the large accumulation error of binarization in the forward propagation, and 2. the severe gradient mismatch problem of binarization in the backward propagation. Even though the substantial effort has been invested in designing architectures for single and multiple binary CNNs, it is still difficult to find an optimal architecture for binary CNNs. In this paper, we propose a strategy, named NASB, which adopts Neural Architecture Search (NAS) to find an optimal architecture for the binarization of CNNs. Due to the flexibility of this automated strategy, the obtained architecture is not only suitable for binarization but also has low overhead, achieving a better trade-off between the accuracy and computational complexity of hand-optimized binary CNNs. The implementation of NASB strategy is evaluated on the ImageNet dataset and demonstrated as a better solution compared to existing quantized CNNs. With the insignificant overhead increase, NASB outperforms existing single and multiple binary CNNs by up to 4.0% and 1.0% Top-1 accuracy respectively, bringing them closer to the precision of their full precision counterpart. The code and pretrained models will be publicly available.
Creativity in Martin Heidegger's existentialism

In the conditions of globalization with characteristic for this era sociocultural transformations, the anthropological problematic, which emphasizes the role of interrelation between creativity and personal being, comes to the forefront. The object of this study is creativity in the philosophical-cultural concept of M. Heidegger. The subject is the phenomenon of creation in M. Heidegger's existentialism. Reference to the concept of M. Heidegger is not accidental, since the problem of human and creativity is focal in his philosophical writings. In his work &amp;ldquo;The Origin of the Work of Art&amp;rdquo;, Heidegger sets an ontological vector of studying creativity in art, assigning to being as such the core position.&amp;nbsp; The article explores M. Heidegger's understanding of creation, which he describes using the terms of "being", "time", "space", "unconcealment", "truth", "thingness", "beautiful", and "beauty".The theoretical and methodological basis of the study was the cultural and historical method. The appeal to the existential-phenomenological methodology allows identifying the peculiarities of comprehension of creativity in the concept of M. Heidegger. The systemic approach serves as the foundation for examining the phenomenon of creativity as a system. It is determines that Heidegger explains creativity as the artist&amp;rsquo;s way to personal fulfillment. The conclusion is made that according to the philosopher not every work can be attributed to creation, but only those that reflect the system of views of the artist and specificity of time. For Heidegger, art is the space of existence and creation; the essence of art consists in the truth being created in creation.
A Novel Hybrid Dragonfly Algorithm with Modified Conjugate Gradient Method

Dragonfly Algorithm (DA) is a meta-heuristic algorithm, It is a new algorithm proposed by Mirjalili in (2015) and it simulate the behavior of dragonflies in their search for food and migration. In this paper, a modified conjugate gradient algorithm is proposed by deriving new conjugate coefficient. The sufficient descent and the global convergence properties for the proposed algorithm are proved. Novel hybrid algorithm of the dragonfly (DA) was proposed with modified conjugate gradient Algorithm which develops the elementary society that is randomly generated as the primary society for the dragonfly optimization algorithm using the characteristics of the modified conjugate gradient algorithm. The efficiency of the hybrid algorithm was measured by applying it to (10) of the optimization functions of high measurement with different dimensions and the results of the hybrid algorithm were very good in comparison with the original algorithm
Meta Feature Modulator for Long-tailed Recognition

Deep neural networks often degrade significantly when training data suffer from class imbalance problems. Existing approaches, e.g., re-sampling and re-weighting, commonly address this issue by rearranging the label distribution of training data to train the networks fitting well to the implicit balanced label distribution. However, most of them hinder the representative ability of learned features due to insufficient use of intra/inter-sample information of training data. To address this issue, we propose meta feature modulator (MFM), a meta-learning framework to model the difference between the long-tailed training data and the balanced meta data from the perspective of representation learning. Concretely, we employ learnable hyper-parameters (dubbed modulation parameters) to adaptively scale and shift the intermediate features of classification networks, and the modulation parameters are optimized together with the classification network parameters guided by a small amount of balanced meta data. We further design a modulator network to guide the generation of the modulation parameters, and such a meta-learner can be readily adapted to train the classification network on other long-tailed datasets. Extensive experiments on benchmark vision datasets substantiate the superiority of our approach on long-tailed recognition tasks beyond other state-of-the-art methods.
A Meta-Analysis: Factors Affecting Students’ Reading Interest in Indonesia

The increasingly intense industrial era competition needs to be balanced with the ability to process and utilize various sources of information. One of the efforts that needs to be done to obtain this information is indeed through reading. However, the data shows that the interest in reading especially students in Indonesia is still very low. Many factors affect. One of them is the technology that makes students distracted by using various media only for entertainment and social media. Based on this, the purpose of this study is to analyze the things that affect reading interest. The method used is a meta-analysis research by reviewing 15 articles in national journals that were updated starting in 2018, 2019, and 2020. The research results showed that there were 3 things that influenced reading interest. They are literacy, technology, and library infrastructure. Reading literacy is an important factor in fostering interest in reading while library technology and infrastructure are supporting media in motivating readers. The presence of technology is utilized in supporting readers through a variety of familiar media such as smartphones. The use of the internet is positively directed as an effort to improve reading. The completeness of facilities and infrastructure such as the library is also a major factor so that the reader becomes comfortable and interested in reading.
Formation of Open Science Space in the European Union and Africa: Comparative Legal Aspect

The paper explores the process of open science evolvement as one of the most distinct trends in scientific research in the modern world. The author notes that freedom of science has not yet received a direct confirmation in international legal sources in the field of human rights protection; still it is mentioned in the constitutions of a number of states. A comparative analysis of the issue on the European and African continents shows a significant gap. In the European Union, attempts are being made to transfer regulation to the supranational level (for example, the post of European Union Special Envoy for Open Science was established), while free access to research results in Africa is provided largely by non-governmental associations and private projects (such as SOKHA, LIREAGE, SYRAM). The history of the problems and the current state of affairs in the European Union and Africa are revealed, the features and development prospects are highlighted.
Social demand of society as a factor of specifying the goals of foreign language teaching in technical university

The structure of the article is determined by the logic of designing the goals of foreign language teaching in a technical university. The highest level of generality of goals reflects the social demand of society as a whole and is presented in the Federal State Educational Standard of Higher Education. At the second level of goal formulation (Model Discipline Programs), they are specified according to the needs of the spheres of economics. In the development of Work Programs of Discipline within the framework of teaching in a specific university, final clarification of goals is required depending on the requirements of regional employers (the third level of specification of goals). In order to clarify the objectives of teaching foreign language to students of technical universities taking into account the requirements of the regional labour market, a questionnaire was conducted in 2019 for graduates of the National Research University „Moscow Institute of Electronic Technology,” working in a specialty in enterprises in the field of exact technologies in Zelenohrad (Moscow). The questionnaire showed that in addition to the four types of speech activities specified in the Model Programs on Foreign Language for Non-Language Universities, engineers use interpretation and translation in their professional activities. The rating of types of foreign language speech activity in descending order of their importance for professional activity of the engineer is as follows: reading – auditing – translation – writing – speaking. The most demanded professional foreign-language tasks in all types of speech activities (except translation) are solved by modern engineers in communicative situations using information technologies.
Actualization of Ways to Introduce Russian Education into Digital Economy Realities

The object of research is the process of transformation of economy, due to the development of digital technologies.The subject of research is the Russian education as a key social institution of formation and development of digital competence.The purpose is to study trends of modernization of Russian education in accordance with labor market requirements of digital economy.The article describes the evolution of the postindustrial housekeeper from the standpoint of the concepts of knowledge economy, information and digital economy.Based on a critical analysis of approaches to the definition of the digital economy formulated its author's definition.During the study, the following tasks were set and solved: The factors hindering the introduction of digital technologies in the activities of economic entities and population; assessed today Russian reality from the standpoint of the formation of the competitive opportunities of the digital economy; identify problem areas and contradictions inherent in the Russian education at all levels, prevent the formation and development of digital competences; It identified the main directions of transformation of the Russian education and the necessity of qualitative transformations of the educational process for the realization of the objectives set in the National Program "Digital Russian economy."The main results of the study are to identify priority areas of qualitative transformations of national education: substantive content and description of competencies of the digital economy for a variety of subject areas and spheres of professional activity; development of new educational programs and the modernization of the learning content of existing programs in accordance with the requirements of the emerging digital economy; development mechanisms of personal educational routes of students; substantial rethinking of educational technologies and e-learning and the creation of conditions for their effective use; formation in educational institutions of the corporate culture that can support and encourage innovative behavior of employees.
Strengthen Security Culture Through Communications and Awareness Programs

Human error or misconduct of one kind or another must be either the direct cause or an enabling factor behind almost every security breach or outage. Whether it is the user clicking a phishing link, an operator accidentally deleting the corporate directory, a manager approving excessive privileges, a receptionist letting a thief or spy into the building, or an incident responder hitting the snooze button on the wrong malware alarm, the examples are legion.
Real-coded multi-objective genetic algorithm with effective queuing model for efficient job scheduling in heterogeneous Hadoop environment

In the recent past, cloud infrastructure has considerably increased its applicability. This has resulted in effective Big Data processing. Hadoop schedulers are critical components to deliver required levels of efficiency, where MapReduce tasks are assigned to Hadoop nodes by the scheduler. There is a significant challenge in planning the increasing amount of functions and resources in a scalable way. Also, this challenge is further compounded by the potential heterogeneity of the deployed Hadoop. This paper proposes a scheduler that makes scheduling a choice by assessing the entire task group in the job queue. Further, the proposed scheduler uses a new scheduling method based on Real Coded Genetic Algorithm (RCGA). RCGA with MapReduce enables users to create more scalable applications. A higher abstraction is provided in less time. The experimental results indicate that the proposed RCGA scheduler achieves better performance than the existing systems for the following metrics: Execution Time, Total Cost, Resource Utilization, Speedup, Throughput, Scheduling Efficiency, Fairness Relaxation, Scheduling Time, Turnaround Time, CPU Time, Data Locality and Average Node-Locality Ratio.
Commonsense Knowledge Graph Reasoning by Selection or Generation? Why?

Commonsense knowledge graph reasoning(CKGR) is the task of predicting a missing entity given one existing and the relation in a commonsense knowledge graph (CKG). Existing methods can be classified into two categories generation method and selection method. Each method has its own advantage. We theoretically and empirically compare the two methods, finding the selection method is more suitable than the generation method in CKGR. Given the observation, we further combine the structure of neural Text Encoder and Knowledge Graph Embedding models to solve the selection method's two problems, achieving competitive results. We provide a basic framework and baseline model for subsequent CKGR tasks by selection methods.
IMPROVING DISPARITY ESTIMATION BASED ON RESIDUAL COST VOLUME AND RECONSTRUCTION ERROR VOLUME

Abstract. Recently, great progress has been made in formulating dense disparity estimation as a pixel-wise learning task to be solved by deep convolutional neural networks. However, most resulting pixel-wise disparity maps only show little detail for small structures. In this paper, we propose a two-stage architecture: we first learn initial disparities using an initial network, and then employ a disparity refinement network, guided by the initial results, which directly learns disparity corrections. Based on the initial disparities, we construct a residual cost volume between shared left and right feature maps in a potential disparity residual interval, which can capture more detailed context information. Then, the right feature map is warped with the initial disparity and a reconstruction error volume is constructed between the warped right feature map and the original left feature map, which provides a measure of correctness of the initial disparities. The main contribution of this paper is to combine the residual cost volume and the reconstruction error volume to guide training of the refinement network. We use a shallow encoder-decoder module in the refinement network and do learning from coarse to fine, which simplifies the learning problem. We evaluate our method on several challenging stereo datasets. Experimental results demonstrate that our refinement network can significantly improve the overall accuracy by reducing the estimation error by 30% compared with our initial network. Moreover, our network also achieves competitive performance compared with other CNN-based methods.
Towards Visually Explaining Similarity Models

We consider the problem of visually explaining similarity models, i.e., explaining why a model predicts two images to be similar in addition to producing a scalar score. While much recent work in visual model interpretability has focused on gradient-based attention, these methods rely on a classification module to generate visual explanations. Consequently, they cannot readily explain other kinds of models that do not use or need classification-like loss functions (e.g., similarity models trained with a metric learning loss). In this work, we bridge this crucial gap, presenting a method to generate gradient-based visual attention for image similarity predictors. By relying solely on the learned feature embedding, we show that our approach can be applied to any kind of CNN-based similarity architecture, an important step towards generic visual explainability. We show that our resulting attention maps serve more than just interpretability; they can be infused into the model learning process itself with new trainable constraints. We show that the resulting similarity models perform, and can be visually explained, better than the corresponding baseline models trained without these constraints. We demonstrate our approach using extensive experiments on three different kinds of tasks: generic image retrieval, person re-identification, and low-shot semantic segmentation.
Multiobjective aerial surveillance over disjoint rectangles

In Aerial Surveillance Problem (ASP), an air platform with surveillance sensors searches a number of rectangular areas by covering the rectangles in strips and turns back to base where it starts. In this paper, we present a multiobjective extension to ASP, for which the aim is to help aerial mission planner to reach his/her most preferred solution among the set of efficient alternatives. We consider two conflicting objectives that are minimizing distance travelled and maximizing minimum probability of target detection. Each objective can be used to solve single objective ASPs. However, from mission planner’s perspective, there is a need for simultaneously optimizing both objectives. To enable mission planner reaching his/her most desirable solution under conflicting objectives, we propose exact and heuristic methods for multiobjective ASP (MASP). We also develop an interactive procedure to help mission planner choose the most satisfying solution among all Pareto optimal solutions. Computational results show that the proposed methods enable mission planner to capture the trade-offs between the conflicting objectives for large number of alternative solutions and to eliminate the undesirable solutions in small number of iterations.
Cross attentive pooling for speaker verification

The goal of this paper is text-independent speaker verification where utterances come from 'in the wild' videos and may contain irrelevant signal. While speaker verification is naturally a pair-wise problem, existing methods to produce the speaker embeddings are instance-wise. In this paper, we propose Cross Attentive Pooling (CAP) that utilizes the context information across the reference-query pair to generate utterance-level embeddings that contain the most discriminative information for the pair-wise matching problem. Experiments are performed on the VoxCeleb dataset in which our method outperforms comparable pooling strategies.
Coherence measures induced by norm functions

Which matrix norms induce proper measures for quantifying quantum coherence? We study this problem for two important classes of norms and show that (i) coherence measures cannot be induced by any unitary similarity invariant norm, and (ii) the $\ell_{q,p}$-norm induces a coherence measure if and only if $q=1$ and $1 \leq p \leq 2$, thus giving a new class of coherence measures with simple closed forms that are easy to compute. These results extend and unify previously known facts about norm-induced coherence measures, and lead to a broader framework for understanding what functionals can be coherence measures.
New Insights from the Bitcoin Futures Market

Recently, cryptocurrencies have drawn considerable attention from investors around the world.Such digital assets have also raised numerous hot issues in academic fields.Among them, Bitcoin is the most well-known and most notorious.After it was created in 2009, Bitcoin kept rising in price and reached its peak in late 2017.After that, it plunged dramatically.Coincidentally, Bitcoin futures also launched in December 2017.We are curious about the role Bitcoin futures play in the Bitcoin market.In this study, we investigate the relationship between Bitcoin and Bitcoin futures.First, we compare the optimal hedge ratios using three different hedge strategies, the naïve hedge, the ordinary least squares (OLS) method, and dynamic hedging with the bivariate BEKK-GJR-GARCH model.Dynamic hedging is the most effective of the three methods; the level of risk reduction is around 59%. Then we test whether the volatility of Bitcoin would be significantly different before and after Bitcoin futures (BTC) launched.Our results support the hypothesis.
Semi-quantum key distribution protocol based on the hyperentanglement Bell state of polarization-spatial mode

The semi-quantum key distribution protocol based on the hyperentanglement Bell state of polarization-spatial mode is presented in this paper. This protocol is utilized to share the session keys and construct key hierarchy of security systems in high capacity between the legitimate users securely. Different from the previous protocols, two quantum non-demolition detectors are constructed with cross-Kerr nonlinearities and different phase shifts for distinguishing the Bell states in spatial mode degree of freedom. Meanwhile, this protocol can improve the capacity and efficiency when the legitimate users share the session keys. And the technology of the hyperentanglement purification and hyperentanglement concentration can enhance the robustness and stability of this protocol. At last, this protocol proposed in this paper can withstand several kinds of attacks.