Reviewerly/exHarmony huggingface dataset:
ROMANet: Fine-Grained Reuse-Driven Data Organization and Off-Chip Memory Access Management for Deep Neural Network Accelerators

Many hardware accelerators have been proposed to improve the computational efficiency of the inference process in deep neural networks (DNNs). However, off-chip memory accesses, being the most energy consuming operation in such architectures, limit the designs from achieving efficiency gains at the full potential. Towards this, we propose ROMANet, a methodology to investigate efficient dataflow patterns for reducing the number of the off-chip accesses. ROMANet adaptively determine the data reuse patterns for each convolutional layer of a network by considering the reuse factor of weights, input activations, and output activations. It also considers the data mapping inside off-chip memory to reduce row buffer misses and increase parallelism. Our experimental results show that ROMANet methodology is able to achieve up to 50% dynamic energy savings in state-of-the-art DNN accelerators.
Selective Training: A Strategy for Fast Backpropagation on Sentence Embeddings

Representation or embedding based machine learning models, such as language models or convolutional neural networks have shown great potential for improved performance. However, for complex models on large datasets training time can be extensive, approaching weeks, which is often infeasible in practice. In this work, we present a method to reduce training time substantially by selecting training instances that provide relevant information for training. Selection is based on the similarity of the learned representations over input instances, thus allowing for learning a non-trivial weighting scheme from multi-dimensional representations. We demonstrate the efficiency and effectivity of our approach in several text classification tasks using recursive neural networks. Our experiments show that by removing approximately one fifth of the training data the objective function converges up to six times faster without sacrificing accuracy.
A hybrid classification-based model for automatic text summarisation using machine learning approaches: CBS-ID3MV

A hybrid approach for the generation of automatic text summarisation is achieved through CBS-ID3MV. A classification-based model using ID3 and multivariate (CBS-ID3MV) approach produces summaries from the text documents through classification and multiple linear regression. Efficient feature selection and extraction methods identify text features from each sentence, for the purpose of classifying summary sentences. The CBS-ID3MV model is trained with DUC 2002 training documents and the proposed approach's performance is measured at several compression rates namely 10%, 20% and 30% on the text data. The results got by the proposed framework works better when compared with other summarisers after evaluation using ROUGE metrics.
The Impact of Knowledge Management on CRM Approaches

The purpose of the current paper is to examine the impact of knowledge management and its tools on the processesand approaches of the customer’s relation management(CRM) within the banks sector in Kuwait.The methodological approach followed in the current study is a quantitative .The population of the current study wasall the CRM managers within the operating Kuwaiti banks which are working within the capital of Kuwait. Thequestionnaire was distributed on the sample of the study through visiting the banks and presenting the main aim ofthe paper. The convenience sample was (50) managers. However, the researchers retrieved (30) questionnaire forms(i.e. the response rate is (60%).The researchers found that there is a significant impact of knowledge management and its tools on processes andapproaches of customer relation management within banks sector in Kuwait.
Concept Development and Design of Self-Transfer Devices for Wheelchair Users

Movement disability not only affects mobility but also affects other daily activities of a person with disability which includes accessing toilet, moving to a chair, etc. A wide variety of mobility assistive devices are commercially available but, the devices that enable self-transfer of a wheelchair user from one surface to another, specifically addressing transfer to a toilet commode, is the need of the hour. In order to develop a manual and a powered self-transfer device, a methodical search of the entire solution space has been made by constructing a concept classification tree to decide on the choice of transfer posture, sequence of transfer motion, actuators, mechanisms and power source. Prior to this decision making, the procedure adopted for transferring a disabled person by a caregiver and the general techniques followed by the disabled people to transfer themselves in toilets are investigated. Later, after the conceptual design which is common to both manual and powered version is finalized, the design criteria and target specifications are put forward. Then, the self-transfer devices are developed that satisfy these design criteria and target specifications. This work is licensed under a  Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License .
Ernst von Aster ve Çağdaş Felsefe Ders Notları

This study is composed of short biographic information about Ernst Von Aster, 20th Century German historian of philosophy, and the lecture notes about “Contemporary Philosophy” that is given by him at Istanbul University Faculty of Literature Philosophy Department in the 1943-1944 academic period. The text which is the lecture notes taken by Cahit Tanyol, a student at the Philosophy Department in the above-mentioned period, in Ottoman Turkish has been translated into Latin alphabet and made ready to be published. Lecture notes are started with the Descartes philosophy, especially his existence approach. Then the views of Descartes and Newton about universe and mechanics are compared. Right after the relations between Newton physics and Kant philosophy are examined. Subsequently, Kant metaphysics and the philosophy of Brentano are compared with each other. After the comparison between Neo-Kantianism and Schelling, the relation between Kant and Husserl is examined. On the other hand, time and space in the Heidegger’s ontology are dealt with. Also, the existing approach in Descartes and Heidegger is investigated. Lastly, Vitalism of Hans Driesch and Spiritualism of Bergson are dealt with. The lecture notes end with the New Positivism after comparison is made between evolution approaches of Bergson and Darwin.
Exploration of Keystroke Dynamics Based Authentication on Fixed-Text and on Free-Text

International Journal of Computer Sciences and Engineering (A UGC Approved and indexed with DOI, ICI and Approved, DPI Digital Library) is one of the leading and growing open access, peer-reviewed, monthly, and scientific research journal for scientists, engineers, research scholars, and academicians, which gains a foothold in Asia and opens to the world, aims to publish original, theoretical and practical advances in Computer Science,Information Technology, Engineering (Software, Mechanical, Civil, Electronics & Electrical), and all interdisciplinary streams of Computing Sciences. It intends to disseminate original, scientific, theoretical or applied research in the field of Computer Sciences and allied fields. It provides a platform for publishing results and research with a strong empirical component. It aims to bridge the significant gap between research and practice by promoting the publication of original, novel, industry-relevant research.
Machine Learning With Avatar-Based Management of Sleptsov Net-Processor Platform to Improve Cyber Security

The literature review of known sources forming the theoretical basis of calculations on Sleptsova networks and on the basis of authors' developments in machine learning with avatar-based management established the basis for the future solutions to hyper-computations to support cyber security applications. The chapter established that the petri net performed exponentially slower and is a special case of the Sleptsov network. The universal network of Sleptsov is a prototype of the Sleptsov network processor. The authors conclude that machine learning with avatar-based management at the platform of the Sleptsov net-processor is the future solution for cyber security applications in Russia.
THE INFLUENCE OF THE PEDAGOGICAL STYLE OF COMMUNICATION ON THE MOTIVATION OF STUDENTS TO LEARN

1 Moscow State University of Food Production (RUSSIAN FEDERATION)2 Russian State Social University (RUSSIAN FEDERATION)
A Novel Approach to Vector-based Video Compression through Motion Tracking

As the age of technology progresses, the demand for video with higher resolution and bitrate increases accordingly, and as video compression algorithms approach neartheoretically perfect compression, more bandwidth is necessary to stream higher-quality video. Higher pixel resolutions do not change the fact that scaling individual frames using bilinear or bi cubic filtering naturally causes the video to lose detail and quality. In addition, as the number of pixels per frame continues to increase, so does the file size of each frame and ultimately the file size of the fully rendered video. Over time, as file size increases and required bandwidth increases, the cost of hardware systems multiplies, requiring a solution for further compression. Using core concepts of computer vision and Bezier models, this project proposes a method of converting pixel-based frames into a graphical vector format and applying motion tracking methods to compress the rendered video past current compression techniques. The algorithm uses the canny operator to break down pixel-based frames into points and then obtains Bezier curves through taking the matrix pseudo inverse. By tracking the motion of these curves through multiple frames, we group curves with similar motion into “objects” and store their motion and components, thus compressing our rendered videos: adding scalability without losing quality. Through this approach, we are able to achieve an average compression rate of 88% over industry-standard compression algorithms for ten sample H.264-encoded animation videos. Future work with such approaches could include modeling different lighting or shading with similar Bezier splines as well as bypassing pixel-based recording altogether by introducing a method to record video in a directly scalable format.
A Customized Non-Exclusive Clustering Algorithm for News Recommendation Systems

Clustering is one of the main tasks in machine learning and data mining and is being utilized in many applications including news recommendation systems. In this paper, we propose a new non-exclusive clustering algorithm named Ordered Clustering (OC) with the aim is to increase the accuracy of news recommendation for online users. The basis of OC is a new initialization technique that groups news items into clusters based on the highest similarities between news items to accommodate news nature in which a news item can belong to different categories. Hence, in OC, multiple memberships in clusters are allowed. An experiment is carried out using a real dataset which is collected from the news websites. The experimental results demonstrated that the OC outperforms the k-means algorithm with respect to Precision, Recall, and F1-Score.
A QoE-Driven Rate Adaptation Approach for Dynamic Adaptive Streaming Over HTTP

In this paper, we aim to optimize the quality of experience (QoE) of Dynamic adaptive streaming over HTTP (DASH) through better bitrate adaptation. To this end, we first propose a quantitative QoE evaluation model based on playback continuity, segment media quality and perceptual quality fluctuations caused by bitrate switching to evaluate users’ subjective perception accurately. Large quantities of subjective mean-opinion-score (MOS) tests show that there exists a good correlation between our model score and subjective perception. Second, we formulate the rate adaptation in DASH services as the optimization problem of multi-stage decision process, and the goal of optimization is to maximize the reward of decision that measured by the QoE model. Numerous experiment results demonstrate that our proposed rate adaptation approach has good performance in different network environments.
Surveillance system with motion and face detection using histograms of oriented gradients

&lt;span&gt;With the rapidly increasing crime rate in recent years, community safety issues aroused a wide concern among public community. Various security technologies had been invented and carried out, for example password door lock, alarm system, and closed-circuit televisions (CCTVs). Although the installation of CCTVs is common in most premises, they require extensive man power to manually monitor the videos. Moreover, the reliability of human operator greatly deteriorates when they are in fatigue condition. In view of this, our project aims to develop an automated computer vision based surveillance system. Unlike ordinary CCTV system that requires human operator to manually observe and detect intruder, a computer vision based surveillance system automatically monitor the security of premises and trigger actions once an intrusion is detected. Basically, it is a simple surveillance camera system that will be setup at the entrance of the house. The reliability is being enhanced by applying the motion detection and face recognition algorithm, using histogram of oriented gradients that could detect the existence of people at the main entrance and try to validate the user. Apart from recognizing the user, the propose system also support mobile interaction whereby user can monitor the camera, activate alarm, and even received notification when a stranger was being detected at the entrance of the house. By including such functionalities, proposed system had highly surpassed the existing surveillance system by not only support monitoring, but also try to recognize the people and inform the user at the exact moment when stranger detected, so that user could take immediate action about it, for example activating the alarm or report to police. The project was executed with expected outcome and objectives had been accomplished.&lt;/span&gt;
Online user misconduct and an evolving infrastructure of practices : a practice-based study of information infrastructure and social practices

Introduction. By conceptualising practices as the building blocks of an information infrastructure which has evolved around the issue of dealing with online misconduct, this study challenges an und ...
Coherent control of light scattering

In this chapter, we discuss these coherent scattering phenomena in photonics. We first establish a unified description of such exotic scattering effects in electromagnetics and show that the origin of all these effects can be traced back to singularities of the scattering matrix. Then we discuss these effects paying particular attention to their physical essence and coherent nature
Publisher Correction: Barcoding reveals complex clonal behavior in patient-derived xenografts of metastatic triple negative breast cancer

The original version of this Article contained an error in Fig. 4. In the left histogram of the right panel of Fig. 4d, several data points were inadvertently deleted from the histogram during the production process. This error has been corrected in both the PDF and HTML versions of the Article. The original, incorrect version of Fig. 4 is presented in the accompanying Publisher Correction.
Graph Based Aspect Extraction and Rating Classification of Customer Review Data

This paper introduces graph-based aspect and rating classification, which utilizes multi-modal word co-occurrence network to solve aspect and sentiment classification tasks. Our model consists of three components: (1) word co-occurrence network construction, with aspect and sentiment labels as different modes; (2) dispersion computation for aspects and sentiments, and; (3) feedforward network for classification. Our experiment shows that proposed model outperforms baseline models, Word2Vec and LDA, in both aspect and sentiment classification tasks. Our classification model uses comparatively smaller vector size for representing words and sentences. The proposed model performs better in classifying out of vocabulary contexts.
Revolutionizing Global Supply Chains One Block at a Time: Growing International Trade with Blockchain: Are International Rules Up to the Task?

There is growing interest in blockchain’s potential to transform the logistics of international trade through its application to supply chains to increase efficiencies and minimize vulnerabilities. Yet for blockchain to truly transport supply chains on a global scale, laws and regulations that facilitate and support its implementation are required. This article explores potential regulatory barriers to the development of blockchain technology in supply chains and asks whether there is a role for international trade rules to address these barriers.
On Twitter Bots Behaving Badly: Empirical Study of Code Patterns on GitHub

Bots, i.e., algorithmically driven entities that behave like humans in online communications, are increasingly infiltrating social conversations on the Web. If not properly prevented, this presence of bots may cause harm to the humans they interact with. This paper aims to understand which types of abuse may lead to harm and whether these can be considered intentional or not. We manually review a dataset of 60 Twitter bot code repositories on GitHub, derive a set of potentially abusive actions, characterize them using a taxonomy of abstract code patterns, and assess the potential abusiveness of the patterns. The study does not only reveal the existence of 31 communication-specific code patterns – which could be used to assess the harmfulness of bot code – but also their presence throughout all studied repositories.
Correction to: Automatic and Accurate Detection of Webshell Based on Convolutional Neural Network

In the version of this paper that was originally published, the acknowledgment of the NSFC Foundation was missing. The paper has been updated to include these corrections.
Control of vocal loudness in singing

Vocal loudness in sones is quantified on the basis of changes in spectral slope and harmonic tuning in a range of singing fundamental frequencies of 125 Hz to 1000 Hz. Spectral slope of the mouth output pressure is bracketed in the range of -3 dB/octave to -12 dB/octave to reflect a typical glottal spectrum from breathy to pressed adduction. To approximate formant tuning of harmonics, the SPL level of the first three harmonics is raised by 10, 20, and 30 dB. It is shown that spectral slope change is more effective in increasing vocal loudness than tuning a single harmonic with a vocal tract resonance. Some applications to amplified and unamplified vocal production and respective training are given.
&lt;i&gt;Indic&lt;/i&gt; script identification from handwritten document images

Script identification plays an important role in document image processing especially for multilingual environment. This paper hires two conventional textural methods for recognition of the scripts of the handwritten documents inscribed in different Indic scripts. The first method extracts well-known Haralick features from spatial grey-level dependence matrix (SGLDM) and the second method computes fractal dimension by using segmentation-based fractal texture analysis (SFTA). Finally, a 104-element feature vector is constructed from each page image by these two methods. The proposed technique is then evaluated on a total dataset comprising 360 handwritten document pages written in 12 Indian official scripts namely Bangla, Devanagari, Gujarati, Gurumukhi, Kannada, Malayalam, Manipuri, Oriya, Tamil, Telugu, Urdu and Roman. Experimentations using multiple classifiers reveal that multilayer perceptron (MLP) shows the highest identification accuracy of 96.94%. The encouraging outcome confirms the efficacy of customary textural features to handwritten Indic script identification.
Preface to Research on Applied Ethics (Cybersecurity)

This is the second special section on applied ethics for AJIS. As was the case for the first special section on ethics, of the various submissions, only three have been accepted for publication. This is not an indication that little work is being done in relation to cybersecurity ethics, but rather a reflection of the difficulty of getting published in a high quality journal. A great deal of research is being done in the area of ethics as regards cybersecurity, particularly in Europe as a result of the recent toughening of its privacy legislation and the implications that has for all manner of ethics and technology, from blockchain, to wearable robots and through to cybersecurity. An overview of those three articles follows, after which the guest editor backgrounds are described.
Implementation of SQ3R to Increase Reading Interest, Critical Thinking Skills, and Ability to Understand Indonesian Language Reading of 6th Grade Indonesia A Students

The purposes of this research was to analyzing the development of reading interest, critical thinking skills and reading comprehension skills by applying SQ3R (Survey, Question, Read, Recite and Review). This research method is Classroom Action Research, data collection was done through observation, written tests, interviews and field notes which were analyzed descriptive qualitative. There was an increase in student's interest in reading in Cycle I by 50% to 100% in Cycle II and III. In students' critical thinking skills, namely from 87.5% in Cycle I to 100% in Cycle II and III. Student reading comprehension ability also show improvement, in Cycle I was 67.4; Cycle II was 84.2 and Cycle III was 88.6. From student interviews and questionnaires showed that students agreed through the application of SQ3R to increase students' reading interest, critical thinking skills and reading comprehension skills.
A Short Comment on the use of R_adj^2 in Social Science

It is a common practice to prefer , over  to assess the explainability power of a statistical regression model among social scientists, especially for one having more than one independent variables. However, this preference is not advantageous at all times because the usage of  may end up in negative coefficients making them non-interpretable. A Monte Carlo simulation experiment is used to appraise the behavior of these adjusted versions of for different numbers of independent variables. It has been found that almost all of the selected adjusted version of  produces negative coefficients.
The Partial-Value Association Discovery Algorithm and Applications

Existing techniques for machine learning and data mining have shortcomings in handling data of different types, data without a priori knowledge of data dependence, and data with variable relations varying in different value ranges. This paper illustrates how the Partial-Value Association Discovery (PVAD) algorithm overcomes shortcomings of existing machine learning and data mining techniques. The paper also demonstrates how the PVAD algorithm was used to analyze engineering student data and computer network data for identifying characteristics of engineering retention and network traffic normalcy.