Reviewerly/exHarmony huggingface dataset:
Deep Hash with Improved Dual Attention for Image Retrieval

Recently, deep learning to hash has extensively been applied to image retrieval, due to its low storage cost and fast query speed. However, there is a defect of insufficiency and imbalance when existing hashing methods utilize the convolutional neural network (CNN) to extract image semantic features and the extracted features do not include contextual information and lack relevance among features. Furthermore, the process of the relaxation hash code can lead to an inevitable quantization error. In order to solve these problems, this paper proposes deep hash with improved dual attention for image retrieval (DHIDA), which chiefly has the following contents: (1) this paper introduces the improved dual attention mechanism (IDA) based on the ResNet18 pre-trained module to extract the feature information of the image, which consists of the position attention module and the channel attention module; (2) when calculating the spatial attention matrix and channel attention matrix, the average value and maximum value of the column of the feature map matrix are integrated in order to promote the feature representation ability and fully leverage the features of each position; and (3) to reduce quantization error, this study designs a new piecewise function to directly guide the discrete binary code. Experiments on CIFAR-10, NUS-WIDE and ImageNet-100 show that the DHIDA algorithm achieves better performance.
THE CYBERSECURITY OF HEALTHCARE

This article is dealing mainly with cybersecurity of healthcare with a special emphasis on the Benešov hospital (Czech Republic), which was hit by the Ryuk ransomware virus. The case shows that even a well-managed middle-size hospital with a relatively good level of cybersecurity might be significantly hit by the attack. The article first explores the general context of cybersecurity of healthcare with reference to the forms of cyber-attacks involving ransomware and then explores the case of the Benešov hospital (Hospital of Rudolf and Stefanie). The main aim of this article is to derive lessons learned from the case, which might be well used for the management of further incidents of similar nature, which is increasingly important at the times of fight with COVID-19 infection creating new pressure on the system.
Predictors of the Use of ICTS in Higher Education: Relevance and Readiness of Universities for Their Implementation

Nowadays higher education that does not implement information and communication technologies (ICTs) is inconceivable; this places new demands on the level of qualification of teachers and the development of professional competencies in students. Therefore, the study of the readiness of universities for the introduction of ICTs is a relevant issue. The purpose of the research is to determine the predictors of the readiness of higher educational institutions for the introduction of ICTs. For this purpose, 218 students and 196 teachers of Moscow City University (Russia) were interviewed. The respondents were asked to fill in a specially designed questionnaire that included 40 key questions aimed at assessing the use of ICTs and their impact on student performance. The data analysis involved the following two steps: determining the level of ICT use in the educational process and comparing the indicators of the two groups of respondents - students and teachers. Lack of awareness of students on the introduction of new methods of learning in the educational institution was revealed in the course of the study. Although students and teachers note the steady development of the use of ICT in the educational process, they are not inclined to assess this area as the leading strategic goal of the institution. The primary task of ICT use in higher education is to train teachers and improve their skills. The use of ICT improves the quality of education and competitiveness of higher educational institutions, contributes to the development of professional competencies, but does not have a significant impact on the academic performance of students. Predictors of the use of ICTs in higher education are blended learning focused on the use of online technologies and independent work of students, virtual laboratories, as well as the development of open education and the MOOC system. The development of standardized quality control criteria will ensure the equality of diplomas in online and offline education.
Audio steganography based on least significant bits algorithm with 4D grid multi-wing hyper-chaotic system

Although variety in hiding methods used to protect data and information transmitted via channels but still need more robustness and difficulty to improve protection level of the secret messages from hacking or attacking. Moreover, hiding several medias in one media to reduce the transmission time and band of channel is the important task and define as a gain channel. This calls to find other ways to be more complexity in detecting the secret message. Therefore, this paper proposes cryptography/steganography method to hide an audio/voice message (secret message) in two different cover medias: audio and video. This method is use least significant bits (LSB) algorithm combined with 4D grid multi-wing hyper-chaotic (GMWH) system. Shuffling of an audio using key generated by GMWH system and then hiding message using LSB algorithm will provide more difficulty of extracting the original audio by hackers or attackers. According to analyses of obtained results in the receiver using peak signal-to-noise ratio (PSNR)/mean square error (MSE) and sensitivity of encryption key, the proposed method has more security level and robustness. Finally, this work will provide extra security to the mixture base of crypto-steganographic methods.
Improving Perceived Quality of Live Adaptative Video Streaming

Quality of service (QoS) requirements for live streaming are most required for video-on-demand (VoD), where they are more sensitive to variations in delay, jitter, and packet loss. Dynamic Adaptive Streaming over HTTP (DASH) is the most popular technology for live streaming and VoD, where it has been massively deployed on the Internet. DASH is an over-the-top application using unmanaged networks to distribute content with the best possible quality. Widely, it uses large reception buffers in order to keep a seamless playback for VoD applications. However, the use of large buffers in live streaming services is not allowed because of the induced delay. Hence, network congestion caused by insufficient queues could decrease the user-perceived video quality. Active Queue Management (AQM) arises as an alternative to control the congestion in a router’s queue, pressing the TCP traffic sources to reduce their transmission rate when it detects incipient congestion. As a consequence, the DASH client tends to decrease the quality of the streamed video. In this article, we evaluate the performance of recent AQM strategies for real-time adaptive video streaming and propose a new AQM algorithm using Long Short-Term Memory (LSTM) neural networks to improve the user-perceived video quality. The LSTM forecast the trend of queue delay to allow earlier packet discard in order to avoid the network congestion. The results show that the proposed method outperforms the competing AQM algorithms, mainly in scenarios where there are congested networks.
Development of OSONO, a service robot with reference to“Joruri puppet”, and its choreography

It is expected that various service robots will be widely used and utilized in commercial facilities where coexistence and affinity with humans are required, as well as business spaces such as public facilities, tourist facilities, nursing care and welfare facilities. These service robots are required to attract people and to enable to exchange messages with each other. For one of this solution, by our original methodology, we have developed a physical robot named OSONO, with referencing to the Japanese traditional puppet theater, which is registered as a World Intangible Cultural Heritage. In this paper, we propose our method to develop a robot with fine design, extract and analyze choreography of Joruri puppet, and reconstruct choreography. Particularly, we verify that this method realizes excellent choreography even a robot has a small number of actuators.
Transfer Learning in Data Fusion at Autonomous Driving

one of the major challenges for Autonomous Driving (AD) system while adapting it in various environments at any time is the perception of and prediction of objects and road user behavior and their actions, A special attention is put into data and sensor fusion methods for object detection and identification and scene understanding. In addition, we highlight on the widely used deep learning network architectures in autonomous driving. In this research, we focus on different data fusion methods including early fusion and late fusion methods and how to implement the late data fusion technique from a source domain such as a reference vehicle or a stationary car with well-trained dataset and large amount of labelled data by using transfer learning at late stages of Neural Network on the target domain which has a few labelled data. In fact, the late fusion method is useful in enhancing the training rate for the target domain with assist of the well-trained source domain instead of training target domain from scratch. Also, the data fusion using transfer learning will increase the performance and accuracy level that can be achieved in the target domain while it learns the target task with assistance from the source domain.
Modernising education: unlearned lessons from Frederick Taylor

Nowadays, the term “modernisation” is usually associated with the term “digitalisation”, which, in its turn, is adopting semantics of the terms “modern”, “progressive”, “future”. Frederick Taylor was one of the first scholars to face the limits of the latter and came close to a number of questions concerning their boundary indicators, content characteristics and guidelines for their establishment. Nowadays, these unexplained questions tend to disguise themselves as something “inevitable”, “obvious”, “insurmountable” in the educational process. The methods of an integrated approach allowed for demonstrating that the understanding of the content of the so-called “unlearned lessons” from Frederick Taylor regarding the problem of “human capacity” in terms of its mental component can help sufficiently in finding an answer to the question about the ways of developing critical thinking among the inhabitants of this space, and about the specific “trivium” and “quadrivium” for them. Therefore, it was concluded that the problem of “human capacity” is crucial for the development of the ideology of education as a system of ideas about the role and purpose of education in society, and, thus, about what is an “educated person” and how it is possible to create one today.
Automatic Multi-Stain Registration of Whole Slide Images in Histopathology

Joint analysis of multiple biomarker images and tissue morphology is important for disease diagnosis, treatment planning and drug development. It requires cross-staining comparison among Whole Slide Images (WSIs) of immuno-histochemical and hematoxylin and eosin (H&amp;E) microscopic slides. However, automatic, and fast cross-staining alignment of enormous gigapixel WSIs at single-cell precision is challenging. In addition to morphological deformations introduced during slide preparation, there are large variations in cell appearance and tissue morphology across different staining. In this paper, we propose a two-step automatic feature-based cross-staining WSI alignment to assist localization of even tiny metastatic foci in the assessment of lymph node. Image pairs were aligned allowing for translation, rotation, and scaling. The registration was performed automatically by first detecting landmarks in both images, using the scale-invariant image transform (SIFT), followed by the fast sample consensus (FSC) protocol for finding point correspondences and finally aligned the images. The Registration results were evaluated using both visual and quantitative criteria using the Jaccard index. The average Jaccard similarity index of the results produced by the proposed system is 0.942 when compared with the manual registration.
On the Effectiveness of Transfer Learning for Code Search

The Transformer architecture and transfer learning have marked a quantum leap in natural language processing, improving the state of the art across a range of text-based tasks. This paper examines how these advancements can be applied to and improve code search. To this end, we pre-train a BERT-based model on combinations of natural language and source code data and fine-tune it on pairs of StackOverflow question titles and code answers. Our results show that the pre-trained models consistently outperform the models that were not pre-trained. In cases where the model was pre-trained on natural language "and" source code data, it also outperforms an information retrieval baseline based on Lucene. Also, we demonstrated that the combined use of an information retrieval-based approach followed by a Transformer leads to the best results overall, especially when searching into a large search pool. Transfer learning is particularly effective when much pre-training data is available and fine-tuning data is limited. We demonstrate that natural language processing models based on the Transformer architecture can be directly applied to source code analysis tasks, such as code search. With the development of Transformer models designed more specifically for dealing with source code data, we believe the results of source code analysis tasks can be further improved.
Learning Embeddings from Knowledge Graphs With Numeric Edge Attributes

Numeric values associated to edges of a knowledge graph have been used to represent uncertainty, edge importance, and even out-of-band knowledge in a growing number of scenarios, ranging from genetic data to social networks. Nevertheless, traditional knowledge graph embedding models are not designed to capture such information, to the detriment of predictive power. We propose a novel method that injects numeric edge attributes into the scoring layer of a traditional knowledge graph embedding architecture. Experiments with publicly available numeric-enriched knowledge graphs show that our method outperforms traditional numeric-unaware baselines as well as the recent UKGE model.
IMENet: Joint 3D Semantic Scene Completion and 2D Semantic Segmentation through Iterative Mutual Enhancement

3D semantic scene completion and 2D semantic segmentation are two tightly correlated tasks that are both essential for indoor scene understanding, because they predict the same semantic classes, using positively correlated high-level features. Current methods use 2D features extracted from early-fused RGB-D images for 2D segmentation to improve 3D scene completion. We argue that this sequential scheme does not ensure these two tasks fully benefit each other, and present an Iterative Mutual Enhancement Network (IMENet) to solve them jointly, which interactively refines the two tasks at the late prediction stage. Specifically, two refinement modules are developed under a unified framework for the two tasks. The first is a 2D Deformable Context Pyramid (DCP) module, which receives the projection from the current 3D predictions to refine the 2D predictions. In turn, a 3D Deformable Depth Attention (DDA) module is proposed to leverage the reprojected results from 2D predictions to update the coarse 3D predictions. This iterative fusion happens to the stable high-level features of both tasks at a late stage. Extensive experiments on NYU and NYUCAD datasets verify the effectiveness of the proposed iterative late fusion scheme, and our approach outperforms the state of the art on both 3D semantic scene completion and 2D semantic segmentation.
Clustering ensemble selection based on the extended Jaccard measure

Clustering ensemble selection has shown high efficiency in the improvement of the quality of clustering solutions. This technique comprises two important metrics: diversity and quality. It has been empirically proved that ensembles of higher effectiveness can be achieved through taking into consideration the diversity and quality simultaneously. However, the relationships between these two metrics in base clusterings have remained uncertain. This paper suggests a new hierarchical selection algorithm using a diversity/quality measure based on the Jaccard similarity measure. In the proposed algorithm, the selection of the subsets of the clustering partitions is done based on their diversity measures. The proposed diversity measure (in two types of pair-wise diversity and hybrid diversity) is applied to the proposed algorithm. Hypergraph-partitioning algorithm (HGPA), cluster-based similarity partition algorithm (CSPA), and meta-clustering algorithm (MCLA) were used to obtain the consensus solution and cluster ensemble selection results with a hierarchical method. The experimental results on 14 datasets showed that selecting a subset of base clusterings using the proposed algorithm led to more accurate results compared to those of the full ensemble. The effectiveness and robustness of the proposed algorithm were demonstrated in comparison with the full ensemble. The comparative results showed that the proposed method by new diversity measure outperformed the full ensemble.
A Survey on Response Selection for Retrieval-based Dialogues

Building an intelligent dialogue system capable of naturally and coherently conversing with humans has been a long-standing goal of artificial intelligence. In the past decade, with the development of machine/deep learning technology and the explosive growth of available conversation data in social media, numerous neural models have been developed for context-response matching tasks in retrieval-based dialogue systems, with more fluent and informative responses compared with generative models. This paper presents a comprehensive survey of recent advances in response selection for retrieval-based dialogues. In particular, we first formulate the problem of response selection and review state-of-the-art context-response matching models categorized by their architecture. Then we summarize some recent advances on the research of response selection, including incorporation with extra knowledge and exploration on more effective model learning. Finally, we highlight the challenges which are not yet well addressed in this task and present future research directions.
A New Approach for Network Steganography Detection based on Deep Learning Techniques

One of the techniques that current cyber-attack methods often use to steal and transmit data out is to hide secret data in packets.This is the network steganography technique.Because millions of packets are sent and received every hour in internet activity, so it is very difficult to detect the theft and transmission of system data out using this form.Recent approaches often seek ways to compute and extract abnormal behaviors of packets to detect a steganography protocol or technique.However, such methods have the difficult problem of not being able to detect abnormal packets when an attacker uses other steganography techniques.To solve the above problem, this paper proposes a network steganography detection method using deep learning techniques.The highlight of this study is some new proposed features based on different components of the packet.By combining these many components, this proposal will not only provide the ability to detect many steganography techniques in the network, but also improve the ability to accurately detect abnormal packets.Besides, this study proposes to use deep learning for the task of detecting normal and abnormal packets.The authors want to take advantage of the big data analysis and processing capabilities of deep learning models in order to improve the ability to analyze and detect network steganography techniques.The experimental results in Section IVD have proved the effectiveness of this proposed method compared with other approaches.
A Note on Time and Space Complexity of RSA and ElGamal Cryptographic Algorithms

The computational complexity study of algorithms is highly germane to the design and development of high-speed computing devices.The whole essence of computation is principally influenced by efficiency of algorithms; this is more so the case with the algorithms whose solution space explodes exponentially.Cryptographic algorithms are good examples of such algorithms.The goal of this study is to compare the computational speeds of RSA and ElGamal cryptographic algorithms by carrying out a survey of works done so far by researchers.This study has therefore examined some of the results of the studies already done and highlighted which of the RSA and ElGamal algorithms performed better under given parameters.It is expected that this study would spur further investigation of the behaviour of cryptographic structures in order to ascertain their complexity and impact on the field of theoretical computer science.The experimental results of many of the papers reviewed showed that RSA cryptographic algorithm performs better as regards to energy usage, time complexity and space complexity of text, image and audio data during encryption process while some studies showed that ElGamal performs better in terms of time complexity during decryption process.
Quantum teleportation is a reversal of quantum measurement

We introduce a generalized concept of quantum teleportation in the framework of quantum measurement and reversing operation. Our framework makes it possible to find an optimal protocol for quantum teleportation enabling a faithful transfer of unknown quantum states with maximum success probability up to the fundamental limit of the no-cloning theorem. Moreover, an optimized protocol in this generalized approach allows us to overcome noise in quantum channel beyond the reach of existing teleportation protocols without requiring extra qubit resources. Our proposed framework is applicable to multipartite quantum communications and primitive functionalities in scalable quantum architectures.
On the Predictability of Bitcoin Price Movements: A Short-term Price Prediction with ARIMA

Daily transactions in cryptocurrencies have long been following an ascending tendency, with Bitcoin leading the charge. Daily transactions recorded in the system increased from 7000 trade per day in 2012to more than 1 million nowadays. The study aims to examine the utility of cryptocurrencies specific to Bitcoin and diagnose how predictable its price fluctuations and the volatility of the crypto market. Because the dilemma between risk aversion and return maximization became evident for investors with high yielded digital assets in a zero-lower bound environment. Hence the predictability of its price movements in the short run may shed some light on the price formation of Bitcoin. Using an ARIMA model in forecasting Bitcoin price due to its response to short-term data, the study revealed that ARIMA (1,1,0) is efficient in forecasting quarterly price movements for the last two quarters of 2020, and the deviation of its price in this period might suggest a change in its perceived investment value to investors as a digital asset after the outbreak of COVID-19.&nbsp;