Reviewerly/exHarmony huggingface dataset:
Multi-layer LSTM Parallel Optimization Based on Hardware and Software Cooperation

LSTM’s special gate structure and memory unit make it suitable for solving problems that are related to time series. It has excellent performance in the fields of machine translation and reasoning. However, LSTM also has some shortcomings, such as low parallelism, which leads to insufficient computing speed. Some existing optimization ideas only focus on one of the software and hardware. The former mostly focuses on model accuracy, and CPU accelerated LSTM doesn’t dynamically adjust to network characteristics; While the latter can be based on the LSTM model structure. Customized accelerators are often limited by the structure of LSTM and cannot fully utilize the advantages of the hardware. This paper proposed a multi-layer LSTM optimization scheme based on the idea of software and hardware collaboration. We used the pruning by row scheme to greatly reduce the number of parameters while ensuring accuracy, making it adapt to the parallel structure of the hardware. From the perspective of software, the multi-layer LSTM module was analyzed. It was concluded that some neurons in different layers could be calculated in parallel. Therefore, this paper redesigned the computational order of the multilayer LSTM so that the model guaranteed its own timing properly and it was hardware friendly at the same time. Experiments showed that our throughput increased by 10x compared with the CPU implementation. Compared with other hardware accelerators, the throughput increased by 1.2x-1.4x, and the latency and resource utilization had also been improved.
The Principles of Scientific Humanism I.T. Frolov in the Context of the Development of Modern Humanistic Thought

The postmodern trend of modern philosophical thought has actualized the problem of human nature. The attention of researchers is attracted by traditional philosophical alternatives, including alternatives within the humanistic direction. Hence the interest in the philosophical creativity of the representative of this trend in the USSR in the 70–80 years of the XX century, I.T. Frolov. The scientist proposed a synthesis of natural science and socio-humanitarian knowledge based on philosophical anthropology. The principles formulated by him formed the basis of a new form of philosophical humanism–scientific humanism. The purpose of the study is a comparative analysis of the work of I.T. Frolov and other major humanist thinkers of the XX century in the course of their reflection on socioanthropological challenges. The analysis of the philosophical heritage of I.T. Frolov in the context of the development of modern humanistic thought allows us to reach a new level of conceptualization of theories related to human philosophy and, thereby, improve the quality of scientific research.
Fuzzy Logic Based Image Dehazing System

Image dehazing has emerged as a popular research field in recent years because of its&#x2019; practical significance. It deals with restoration of visibility of images which are captured during hazy weather conditions. The degradation of visibility of these images occurs mainly due to scattering and attenuation of scene light caused by the presence of aerosol particles in the atmosphere. In this work, we have designed a novel image dehazing system to perform effective visibility restoration of images by exploiting their properties in multiple color spaces. The knowledge base of the designed system comprises of 125 novel Fuzzy Inference rules whose input fuzzy linguistic variables are chosen to be the selected parameters from multiple color spaces. The selection of parameters is carried out purely on experimental basis depending upon their relevance in detecting the intra-variation of visibility degradation among different regions of an image. Enhancement constant is considered as output fuzzy linguistic variable of the rules. Contrast enhancement of each pixel is performed using the unique enhancement constant value estimated based on its&#x2019; characteristics. Comprehensive comparative analyses using several benchmark databases are carried out to validate the efficiency of the proposed system.
Experiments on Static Data Summarization Techniques

The persistent usage of relational data leads to a huge data growth. This immense growth requires faster access and analysis of data. This can be done by either fragmenting the data or by scanning less amount of data as in data blocking and skipping. Data Summarization describes data in a compact way, such that when a query gets executed it only needs to scan the summary of the block and not the entire block to identify which block to scan. Some of the static data summarization techniques such as MinMax, Global Range Encoding (GRE), and Fingerprint Headers are evaluated in this work. The experiment is performed on a benchmark TPC-H dataset, which is a business-oriented dataset. The identified evaluation parameters are: Query Excecution Time (QET) and Percentage of Tuples Skipped. The evaluation is further investigated in terms of Algorithm Execution Time (AET). The QET of Fingerprint Headers is less compared to other techniques as these techniques scans less data compared to the rest of techniques. AET for data sizes x, 1.5x, and 2x is recorded and it is observed that GRE, and two-level Fingerprint Headers records highest AET. This is due to the number of partitions generated by these techniques. Two-level Fingerprint Headers skip the highest number of tuples i.e. 76&#x0025; as compared to Full Scan as these techniques scan less amount of data.
Parts of Speech Tagging in Bangla Sentences using Supervised Learning: A Performance Comparison between Viterbi and Bidirectional-LSTM Models

Parts of speech (POS) tagging is a crucial preprocessing step for many Natural Language Processing applications. Though numerous works have been done on English corpus with high accuracy, very few works have been done on Bangla Corpus due to scarcity of resources and the ambiguity of the language. In this paper we have created a POS tagger using Hidden Markov Model(HMM) with Viterbi Algorithm for decoding and a deep learning model called Bidirectional Long-short term memory (BiLSTM). We used similar datasets to compare the performance of the two approaches. It can be inferred from the results that increasing the size of dataset has greater positive impact on the performace of Bi-LSTM model than on the HMM model.
A Study of Adversarial Attacks on Malaria Cell Image Classification

In all mosquito borne diseases in the world no other disease is as dangerous Malaria. According to the WHO 409,000 people died of this disease in 2020. Early detection and diagnosis can aid in saving precious human lives. In order to identify Malaria infected cells properly, expertise is needed from people. Deep learning approaches can aid in such tasks since they can accurately identify these cell images in large-scale scenarios without any fatigue. In recent studies, it has been shown that, in spite of the success that deep learning has achieved in varying applications, it is unreliable. Adversarial perturbations when added to the input data forces the model to provide wrong answers. We used VGG16, ResNet18 and Wide-ResNet-28-10 model to correctly classify infected Malaria cells from normal images. We also performed Fast Gradient Sign Method (FGSM) attack to evaluate the robustness of these models. FGSM adversarial training was performed on these models to defend it from such attacks as well. We studied in depth the effects of this attack against these models by varying maximum allowed perturbations. We achieved 95.74&#x0025;, 95.94&#x0025; and 95.30&#x0025; accuracy on clean data and 92.71&#x0025;, 90.64&#x0025;, and 88.73&#x0025; accuracy on adversarial images after performing adversarial training for VGG16, ResNet and Wide-ResNet respectively.
Color Image Segmentation using Fuzzy Histon

Fuzzy histon, an extension of histon, is a contour plotted on the top of the histograms of the color components such that the pixels with high degree of correlation are visualized as belonging to one singular value. In this paper, we propose a fuzzy histon roughness index based thresholding algorithm for the segmentation of color images. The quantitative evaluation of the proposed approach against the existing thresholding technique and Compression-based Texture Merging (CTM) algorithm proves its effectiveness.
OPEN SCIENCE: CURRENCY CHALLENGES AND DEVELOPMENT PROSPECTS IN UKRAINE

Science is an important part of the intellectual infrastructure of the modern world. It stimulates social development, enriches the information system of education, politics, promotes innovation. Open Science is a change in the existing system of scientific research, which allows to improve scientific activity through open and joint ways of cooperation and exchange of knowledge and data in the process of research, transmission and exchange of results. This new approach is affecting research institutions and research practices in general, opening up new ways of funding, evaluating and providing material incentives to researchers. Open science increases the quality and impact of science, promoting its reproducibility and interdisciplinarity. This makes science more efficient through the rational allocation of resources, more reliable through meaningful testing and more sensitive to the needs of society. Openness of scientific activity involves providing access to research results in digital format to a wide range of users through the use of information and communication technology tools. On the one hand, online platforms create new opportunities for organizing and publishing the content of research projects, scientific developments and large data sets, which makes them available to other scientists and researchers, as well as potential users in the business community and society as a whole. On the other hand, information and communication technologies allow to collect large amounts of data and information that can be the basis for scientific experiments and research. The article examines the theoretical aspects of the development of open science in the European scientific space, its content and identified by the European Commission areas of implementation. The issues of regulation of Open Science policy implementation processes are analyzed and its main participants are identified. The advantages and obstacles in the implementation of the provisions of open science by the subjects of scientific activity, as well as the features of managing the processes of its implementation in modern institutions of higher education, identified promising areas of Open Science in the scientific environment of Ukraine.
CSWS—Suppress confusion subregion in indoor image fingerprint localization

Abstract Image data can provide rich content information, which has attracted a lot of attention in the field of indoor fingerprint positioning. However, indoor image information from different locations is characterized by high content repetition, and these repetitive regions can cause the problem of insufficient differentiation of adjacent image fingerprints or even fingerprint misclassification. To solve this problem, this article proposes a confusion subregion weighted suppression strategy in the image fingerprint database. First, duplicate regions (confusion subregions) are extracted from the fingerprint database using an appropriate salient region detection method. Then the similarity of these duplicate regions in Euclidean space is defined, and the degree of influence of these regions on the distinguishability of the fingerprint database is measured. Finally, the suppression of these duplicate regions is achieved in the original image fingerprint database by introducing weighted suppression coefficients. Experiments show that the algorithm proposed in this article can achieve significant results in the fingerprint localization task of real indoor scenes and effectively improve the localization accuracy.
Modern Lesson: Teacher and Student

The article is devoted to the problem of finding resources for the development of a modern lesson in the context of the implementation of the federal project «Teacher of the Future» and the certification procedure for teaching staff. The shortcomings that have developed in the practice of conducting lessons under the traditional system of education are revealed. The content and technology of the pedagogical council on the topic "Resources for the development of a modern lesson" are considered.
HDNet: A dual-stream network with progressive fusion for image hazing detection

Image hazing refers to artificially adding haze to a clear image to produce a natural haze effect. However, it might also be used as an image forgery for malicious purposes such as misleading tourists or spreading fake weather information. Up to the present, there are still no works specially designed for fake hazy image detection. Inspired by the fact that almost all artificial hazy images are synthesized based on the atmospheric scattering model (ASM), we observe that though synthetic and real hazy images are very difficult to be differentiated by naked eyes, there still exist some ASM-related imprints in synthesized hazy images. Specifically, the subtle differences between synthetic hazy images and real hazy ones are image saturation and haze distribution. In this work, we propose a dual-stream image hazing detection network, namely HDNet, to distinguish synthetic hazy images from natural hazy ones. One stream explicitly learns features from the image saturation channel, and the other one learns haze distribution features from the dark channel and gray level co-occurrence matrix. Moreover, to fuse well the learned features from two streams, we propose a progressive attention mechanism. A stage-wise learning strategy is exploited for network training, in which the learning process is divided into four sub-tasks that are completed stage-by-stage. Extensive experimental results prove that the proposed HDNet achieves desirable detection accuracy and robustness.
Dijital Kütüphanelerin E-Öğrenmedeki Rolü ve Önemi

Digital libraries and e-learning (electronic learning) systems are the information retrieval systems of the modern information society of the 21st century. Modern digital libraries offer many opportunities in today's information infrastructure with their resources that can support e-learning. In the study, the interaction of digital library and e-learning in the digital age is examined on the basis of the concepts of digital library and e-learning. The aim of the study, which is based on the literature, is to explore the role that digital libraries play in e-learning. Within the scope of the study, the functions, functioning, advantages and disadvantages of digital libraries are addressed and discussed from the perspective of e-learning. The study findings point out the increasing role of digital libraries on e-learning with the facilitating effect of developments in information technologies. Keywords: Digital libraries, E-learning, Information, Resources, Digital divide
Efficient binary diffusion matrix structures for dynamic key-dependent cryptographic algorithms

In this paper, we propose a new mechanism for the generation of dynamic binary diffusion matrices, with a flexible dimension (n×n), to be used in the design of new symmetric cryptographic algorithms. The proposed framework defines four primary invertible and non-invertible binary diffusion matrix forms. The advantages of these forms stem from the dynamic key approach, where each primary matrix requires the construction of two pseudo-random sub-matrices (Mu and Mv) with a size that depends on two variables (m and l). A cryptographic analysis was carried out to detect the optimal size of m and l for the construction of the matrices to achieve the best possible cryptographic performance and thus, to provide better immunity against different types of attacks. The results showed that the optimal size for m is ∈{n2−1,n2,n2+1}, and for l=n−m. Accordingly, the proposed scheme was designed with an optimal size of m and l, which resulted an acceptable linear branch number and low fixed numbers. In comparison to the existing static diffusion techniques, the proposed solution offers a higher security level since the diffusion matrices are constantly changing and they depend of the dynamic key, which is unknown to attackers.
Abrupt visibility modifications affect specific subjective (not objective) aspects of body ownership

The sense of body ownership builds on proper multisensory integration mechanisms. The Rubber-Hand Illusion (RHI) paradigm exploits a visuo-tactile multisensory conflict to induce illusory body ownership toward a fake hand, assessed by multidimensional subjective ratings and univocal objective measurements. Considering the controversy as to whether viewing the rubber hand is necessary or not to induce the illusion, we investigated the effects of targeted manipulations of visibility on subjective and objective aspects of the RHI. To this aim, we collected questionnaire and proprioceptive drift data from thirty participants receiving visuo-tactile stimulation in a setup that allowed for increasing and decreasing the visibility (illumination) of the rubber hand. We found that specific subjective ratings (Movement and Loss of Ownership) were sensitive to the interaction between rubber hand's visibility and illusory ownership. The interaction was not significant for the Embodiment subjective component and for the objective one (proprioceptive drift). Since different degrees of visibility did not differentially affect the RHI, these findings highlight that relatively abrupt changes in the visibility of the rubber hand can differentially impact subjective versus objective components of body ownership. This understanding may be critical for neuroscientific theories on the relationship between multisensory integration and body consciousness.
NVRAM as an Enabler to New Horizons in Graph Processing

Abstract From the world wide web, to genomics, to traffic analysis, graphs are central to many scientific, engineering, and societal endeavours. Therefore an important question is what hardware technologies are most appropriate to invest in and use for processing graphs, whose sizes now frequently reach terabytes. Non-Volatile Random Access Memory (NVRAM) technology is an interesting candidate enabling organisations to extend the memory in their systems typically by an order of magnitude compared to Dynamic Read Access Memory (DRAM) alone. From a software perspective, it permits to store a much larger dataset within a single memory space and avoid the significant communication cost incurred when going off node. However, to obtain optimal performance one must consider carefully how best to integrate this technology with their code to cope with NVRAM esoteric properties such as asymmetric read/write performance or explicit coding for deeper memory hierarchies for instance. In this paper, we investigate the use of NVRAM in the context of shared memory graph processing via vertex-centric. We find that NVRAM enables the processing of exceptionally large graphs on a single node with good performance, price and power consumption. We also explore the techniques required to most appropriately exploit NVRAM for graph processing and, for the first time, demonstrate the ability to process a graph of 750 billion edges whilst staying within the memory of a single node. Whilst the vertex-centric graph processing methodology is our main focus, not least due to its popularity since introduced by Google over a decade ago, the lessons learnt in this paper apply more widely to graph processing in general.
Toward Device-free and User-independent Fall Detection Using Floor Vibration

The inevitable aging trend of the world’s population brings a lot of challenges to the health care for the elderly. For example, it is difficult to guarantee timely rescue for single-resided elders who fall at home. Under this circumstance, a reliable automatic fall detection machine is in great need for emergent rescue. However, the state-of-the-art fall detection systems are suffering from serious privacy concerns, having a high false alarm, or being cumbersome for users. In this article, we propose a device-free fall detection system, namely G-Fall, based on floor vibration collected by geophone sensors. We first decompose the falling mode and characterize it with time-dependent floor vibration features. By leveraging Hidden Markov Model (HMM), our system is able to detect the fall event precisely and achieve user-independent detection. It requires no training from the elderly but only an HMM template learned in advance through a small number of training samples. To reduce the false alarm rate, we propose a novel reconfirmation mechanism using Energy-of-Arrival (EoA) positioning to assist in detecting the human fall. Extensive experiments have been conducted on 24 human subjects. On average, G-Fall achieves a 95.74% detection precision on the anti-static floor and 97.36% on the concrete floor. Furthermore, with the assistance of EoA, the false alarm rate is reduced to nearly 0%.
A secure and efficient mechanism for scheduling tasks in cloud computing environments

Abstract This work presents secure, efficient and power‐aware dynamic allocators for Virtual Machines (VMs) in Cloud Data Centers (DCs) taking advantage of the Software‐Defined Networking (SDN) paradigm. Each VM request is characterized by four parameters: CPU, RAM, disk, and bandwidth. We design the allocators to accept as many VM requests as possible, taking into account the power consumption of the network devices. In this paper, we introduce 10 different allocation strategies and compare them with a baseline that consists of using the first available server (First Fit). The allocators differ in terms of allocation policy (Best Fit/Worst Fit), allocation strategy (Single/Multi‐objective optimization), and joint/disjoint selection of IT and network resources. For both joint and disjoint approaches, we evaluate the behavior of all possible pair policy‐strategies by varying the load of the DC and the number of VMs to be allocated. Moreover, the experimental results highlight that joint approaches outperform disjointed ones.
Between Interpretation and the Need for New Regulation of Human Rights in the Virtual World

The subject of the study is to initiate a scientific discussion on the adequacy of human rights, their content and systematics. The reason for the concept of this topic is the fact that most of human activity is carried out in a virtual world built through the use of ICT devices. The aim of the study is to show the extent of increasing human activity in cyberspace. The second goal is to show the similarities and differences between the real and the virtual world. As a research hypothesis which was accepted is the claim, according to which the current wording of human rights does not fully correspond with human situation in the virtual world. This claim is even more justified in the perspective of dynamically developing artificial intelligence, which supports people in decisionmaking processes, but also displaces them from these processes. The study includes the following methods: the statistical method – thanks to which it was possible to analyze statistical data and the descriptive method – thanks to which the phenomena occurring in the virtual world were described. The result of the work is to demonstrate the need to start scientific research and discussion on human rights in virtual reality.