Reviewerly/exHarmony huggingface dataset:
Requirements for In-App Feedback Tools - a Mapping Study

Mobile apps are becoming increasingly important in everyone’s daily life. The success of an app is linked to high user acceptance. Therefore, it is necessary to capture users’ expectations, needs, and problems regarding an app in any situation. For this reason, we conducted a systematic mapping study to obtain an overview of the current state of the art of in-app feedback collection approaches. We analyzed 36 publications and derived requirements for in-app feedback tools. We categorized the requirements into these groups: initiation of data collection, data collection enhancements, integrated communication regarding feedback provided, and transparency.
Behavior Decision System

This chapter describes the principles and architecture of the Behavior Decision System, providing artificial intelligence (AI) programmers with the necessary information to implement a similar system and extend it to meet the needs of their own game. When constructing their scoring system, they considers various schemes for automatic normalization or scaling of scoring values. For that reason, target selection is a necessary part of the evaluation step; to accurately represent the value of an action, they must consider all the potential targets of that action, and then select the target which provides the greatest utility value. During the development of AI programmers hostile creature factions, they found that an ability or behavior which was developed for a specific AI character type could be easily shared with others, assuming that the relevant assets also available for the new characters.
Rejoinder—A Gibbs Sampler for a Class of Random Convex Polytopes

We are very grateful to all commenters for their stimulating remarks, questions, as well as useful pointers to the literature which span a wide range of statistical methods over decades of research. We have neither the space nor the knowledge to answer many of the questions raised, and we only aim to offer some clarifications. We hope that readers will be as enthusiastic as ourselves about research on the topics discussed by the commenters. In the following, we refer to Diaconis and Wang as DW, Hoffman, Hannig and Zhang as HHZ, Lawrence and Vander Wiel as LV, Ruggeri as R, Shafer as S, and Williams as W.
Mutual Information of Neural Network Initialisations: Mean Field Approximations

The ability to train randomly initialised deep neural networks is known to depend strongly on the variance of the weight matrices and biases as well as the choice of nonlinear activation. Here we complement the existing geometric analysis of this phenomenon [1] with an information theoretic alternative. Lower bounds are derived for the mutual information between an input and hidden layer outputs. Using a mean field analysis we are able to provide analytic lower bounds as functions of network weight and bias variances as well as the choice of nonlinear activation. These results show that initialisations known to be optimal from a training point of view are also superior from a mutual information perspective.
An Efficient Framework for Resource Allocation and Dynamic Pricing Scheme for Completion Time Failure in Cloud Computing

Cloud computing, as an infrastructure less service, has gained a lot of attention over a decade now. The surge for the resource allocation and pricing have been at the centre stage of the research for a while in cloud computing. In this paper, we have proposed an efficient resource allocation and dynamic pricing algorithm for completion time failure in cloud computing (RADPACTF). Theoretical analysis is also provided in support of the proposed algorithm.
The Norwegian COVID-19 Tracing App Experiment: Lessons for Governance and Civic Activism

The rollout of a host of COVID-19 tracing apps has been described as a global digital experiment. This development has been followed by a proliferation of scholarship, analyzing and assessing the performance of these apps in fighting the pandemic . This includes discussions of their legal, ethical and technical properties, as well as more detailed investigations of the surveillance implications and the effectiveness of national initiatives. In this commentary, I am interested in what this experiment tells us about the digital transformation of health governance-and perhaps, also of governance, in general-at the national level.
Design and Analysis of Convolutional Neural Network for RF Signal Modulation Classification for In-Orbit Deployment

To effectively transmit data to and from satellites requires a complex and robust RF communication system. Using adaptive link techniques, several different types of signal modulations may be used to maximize satellite efficiency depending on a variety of unexpected channel impairments. We propose a neural network algorithm capable of learning these RF signal modulations using a supervised learning technique designed for low power, high-efficiency in-orbit deployment. Thus, as signal modulation changes, a receiving satellite would be able to recognize this modulation update without adjustment from an operator, or any delay due to control signal transmission.The work presented demonstrates a convolutional neural network (CNN) capable of learning and recognizing a set of modulation schemes commonly used to transmit RF information. We are capable of recognizing the modulation scheme from the I and Q data channels directly, with no preprocessing or data conversion required other than breaking the incoming signal into a set of uniform normalized samples. We perform a network design and size analysis, showing that we can obtain reasonably high accuracy using networks with a relatively low number of trainable parameters. Given that a user of a system such as this may wish to receive a signal using a modulation scheme that the network has not previously learned, we demonstrate that transfer learning can learn new modulation schemes by retraining only the fully connected layers in the CNN. Thus, this type of network would excel in outer space deployment using high-efficiency transfer learning hardware. Modulation recognition can be performed through rapid feedforward computation, and the CNN training process is significantly simplified when learning new modulations is required.
«Il Vangelo mi prende alla gola». Gli alfabeti filosofico-religiosi in Claudio Magris

The essay deals with a still-unexplored field in Claudio Magris’ writing, which is also the highest dimension of his work, namely, the philosophical-religious alphabets.
Improving the Performance of Imbalanced Learning and Classification of a Juvenile Delinquency Data

The social environment has changed over recent years. Most parents have working lives and, as a result, they might not have enough time to pay sufficient attention to their children. In addition, the tendency of juvenile delinquency has increased over time. Although the policy of the Department of Juvenile Observation and Protection is to help children develop a more positive attitude about themselves and their world, the recidivism rates of children are still high. In order to solve this issue, the possibility of recidivism must be understood. The research aims to predict number of delinquents who would commit crime in the future. The experimental dataset, the Juvenile Delinquency Dataset, was considered as imbalanced data. The research employed a proposed algorithm, namely Cluster and Over (CaO), which is a notable resampling technique for imbalanced data. The results showed that the conventional prediction models without resampling techniques yielded both low accuracy and low recall. When CaO was applied with a decision tree, the values of Gmean increased by 5.3 and 3.9 percent compared with SMOTE and KmeansSMOTE, respectively. When CaO was conducted with KNN, the values of Gmean increased by 3.8 and 3.1 percent compared with SMOTE and KmeanSMOTE, respectively. Therefore, CaO yielded more efficient results than SMOTE and KmeansSMOTE.
Gold Mineralisation in Eastern Mahakoshal Belt of Central India: A Reappraisal Based on Mineral System

Gold exploration in the Mahakoshal belt has been carried out for the past thirty years and mostly quartz veins were identified as the host targets. The recent study based on the concept of mineral system has indicated that gold occurs in mafic and ultramafic rocks also, besides the quartz veins. The mineralisation in Agori Formation is generally confined to Algoma type BIF, metavolcanics and phyllites, while in Parsoi Formation it occurs predominantly in quartz veins. The metavolcanics have acted as source rocks and the crustal deep faults have acted as conduits for mineralizing fluids, in the continental back arc type tectonic setting with two mineral systems viz. (i) basin related fluid flow with active magmatism, and (ii) magmatic related hydrothermal system.
Pengaruh Penggunaan Media Audio Visual Terhadap Kemampuan Menulis Teks Puisi Siswa Kelas X di SMA Manggala Sakti

This research was conducted to determine learning outcomes in writing poetry texts using audio visuals for X grade students of SMA Manggala Sakti in the 2020/2021 academic year. This research is a quasi-experimental research conducted in class X SMA Manggala Sakti. The research subjects were all students of class X with a total of 16. This study was a quasi-experimental population in this study were students of class X even semester of SMA Manggala Sakti. The data of this study were taken from the poetry text writing test. Based on the results of the tests that have been carried out, it was found that there were differences in the results of writing poetry by students using audio-visual media. The results show that Ho is rejected, and Hi is accepted from the t-test hypothesis testing at a significant level of 0.05 with T count (3.91) and T table (0.68), so the results show a significant difference. The first average value was 59.06 to 78.43. This shows a significant increase with a difference of 19.37 increase. Thus, giving treatment in the form of audio-visual media in class X SMA Manggala Sakti got results that were in line with expectations.
An In-Browser Collaborative System for Functional Annotations

In this work, we used the Web IndeX system, which converts words into hyperlinks in arbitrary Web pages, to implement a system for sharing annotations registered on keywords within a limited group. This system allows group members to view all the written annotations by simply mousing over the keyword when it appears on a web page, facilitating sharing information and awareness in collaborative research and work. In this study, we define our system as a sticky note type annotation sharing system. In contrast to the sticky note type, our system is positioned as a functional type, but it may display annotations that are not necessary since it allows browsing on any page. To improve this point, we propose a usability judgment method that shows only the most useful posts.
Dual mutations collaboration mechanism with elites guiding and inferiors eliminating techniques for differential evolution

Differential evolution (DE) is a powerful evolutionary algorithm for global optimization problems. Generally, appropriate mutation strategies and proper equilibrium between global exploration and local exploitation are significant to the performance of DE. From this consideration, in this paper, we present a novel DE variant, abbreviated to DMIE-DE, to further enhance the optimization capacity of DE by developing a dual mutations collaboration mechanism with elites guiding and inferiors eliminating techniques. More specifically, an explorative mutation strategy DE/current-to-embest with an elite individual serving as part of the difference vector and an exploitative mutation strategy DE/ebest-to-rand with selecting an elite individual as the base vector are employed simultaneously to achieve the balance between local and global performance of the whole population instead of only one mutation strategy in classical DE algorithm. The control parameters F and CR for above mutation strategies are updated adaptively to supplement the optimization ability of DMIE-DE based on a rational probability distribution model and the successful experience from the previous iterations. Moreover, an inferior solutions eliminating technique is embedded to enhance the convergence speed and compensate cost of the fitness evaluation times during the evaluation process. To evaluate the performance of DMIE-DE, experiments are conducted by comparing with five state-of-the-art DE variants on solving 29 test functions in CEC2017 benchmark set. The experimental results indicate that the performance of DMIE-DE is significantly better than, or at least comparable to the considered DE variants.
Toward an Open Web (of Things)

Thirty years of untamed growth of the World Wide Web have contributed to a sprawl of communication platforms and web applications that are generating tons of data. Yet, there are no rules about its usage and storage exist. As a result, the web has entered an unfair culture where big tech companies offer free applications in exchange for the right to sell our user-generated content. This imbalance has led to several data scandals that have shown how these companies can exercise economic and political control on our society.
Multi-class Text Classification and Publication of Crime Data from Online News Sources

Over the past decade, India’s major cities have been deemed as some of the most unsafe places in the world for women. We know that crimes against women occur every day as newspapers show reports of the activities that occur to not just women but also minor girls. As a result of this, women’s safety is a growing concern for the government. The women in our lives deserve to feel secure wherever they go. This paper aims to help women select the states they travel to or relocate to based on recent criminal activity. The proposed methodology is a web application utilizing the Django framework. The web application provides a map with the crime hotspots of India sourced from reputed news articles on the Internet. Articles are first scraped using a web crawler we have designed. The scraped article is classified into a corresponding category to depict them on the heatmap. Classifying news articles is a multi-class classification problem that requires a robust and powerful machine learning model. A novel hybrid architecture of Convolutional Neural Network (CNN) and Gated Recurrent Unit (GRU) is adopted and proposed in this paper. The ensemble model performs incredibly well on news sources collated from reputed websites and successfully classifies news articles with an accuracy of 95.1%.
Architecture Design of Application Oriented Undergraduate Innovation and Entrepreneurship Guidance Service Platform Based on Big Data Technology

A large number of homogeneous university innovation and entrepreneurship information platform construction is facing low utilization rate, which cannot support the activities and sustainable development of teachers and students. Therefore, based on the development situation of big data era, this paper establishes an innovation and entrepreneurship guidance service platform based on B/S architecture for the guidance service of innovation and entrepreneurship education system in application-oriented universities. The scheme mainly uses the advanced J2EE environment, and combined with big data to build a new model of innovation and entrepreneurship education system in application-oriented university. We also design and implement the key modules of the system combined with data sharing and Internet technology. The development process involves the storage application of Servlet, JavaBean, JSP, JavaScript and SQL technology. The system operation test results prove the feasibility of the scheme, which can effectively play the role of big data and improve the entrepreneurship rate of college graduates.
Evaluating Recommender Systems when Applied to Arabic Content

In the era of big data, many works have proposed recommender systems (RSs) to handle the information overload issue. Among them, very few studies have focused on Arabic content. Recently, the Arabic content on the Internet has remarkably expanded due to the increasing number of Arabic web users. Consequently, it is become necessary to have RSs for such content. To overcome this challenge, we decided to develop a new pipeline allowing to use RSs in an Arabic context. Our main goal is to assess RSs when applied to Arabic content. For this reason, we built four new publicly available large scale Arabic datasets for recommendation purposes. Then, we proposed a relevant scheme for preprocessing the constructed datasets. Finally, we evaluated and analyzed the impact of exploiting Arabic content by two widely used recommendation algorithms from the literature. The experiment results showed that the proposed pipeline ensures promising findings, which may inspire the research community to conduct additional studies in this direction.
Medical Image Compression Method Using Lightweight Multi-Layer Perceptron for Mobile Healthcare Applications

As video compression is one of the core technologies required to enable seamless medical data streaming in mobile healthcare applications, there is a need to develop powerful media codecs that can achieve minimum bitrates while maintaining high perceptual quality. Versatile Video Coding (VVC) is the latest video coding standard that can provide powerful coding performance with a similar visual quality compared to the previously developed method that is High Efficiency Video Coding (HEVC). In order to achieve this improved coding performance, VVC adopted various advanced coding tools, such as flexible Multi-type Tree (MTT) block structure which uses Binary Tree (BT) split and Ternary Tree (TT) split. However, VVC encoder requires heavy computational complexity due to the excessive Rate-distortion Optimization (RDO) processes used to determine the optimal MTT block mode. In this paper, we propose a fast MTT decision method with two Lightweight Neural Networks (LNNs) using Multi-layer Perceptron (MLP), which are applied to determine the early termination of the TT split within the encoding process. Experimental results show that the proposed method significantly reduced the encoding complexity up to 26% with unnoticeable coding loss compared to the VVC Test Model (VTM).
A Kind of Sea-fog Removal Algorithm Fusing Image Enhancement and End-to-end Network

Generally, for images with sea fog, the distribution of fog density is uneven, and the whole scenes tend to the darkness, if only the image enhancement algorithm is used to defog, it could cause color distortion of the overall image, and the details blurring of the target image; the end-to-end defogging algorithm has obvious defogging effects for the uniform mist image on the land, but it is not good for sea fog images with dense fog and dark scenes. In this paper, a kind of single sea fog image defogging method is proposed, which fuses the image enhancement with end-to-end network. Firstly, the image enhancement based on Multi Scale Retinex was adopted, to improve the contrast and brightness of the uneven fog image; Then, the end-to-end network was used to extract the transmittance, and then the transmittance was optimized by the guided filter; Finally, through removing fog based on the atmospheric scattering model, the edge blur and color distortion of the object are improved. We had experimental analysis compared our proposed method with several mainstream defogging algorithms, to evaluate the defogging quality from both subjective and objective aspects separately, the experimental results show that the proposed single-image sea fog removal algorithm has obvious defogging effects and good image restoration ability.
Design and Implementation of A Mobile Learning Resource Sharing System for Ideological and Political Education

To solve the problem of storage and transmission of mobile learning resources for ideological and political courses, this paper analyzes the characteristics and current situation of the construction of mobile learning resources, and designs and develops a set of mobile teaching resource sharing system based on open source Android technology and C/S architecture. The system adopts mature ASP.net with MySQL technology solution for development. We also introduce HTML5 audio and video technology to form a knowledge map, and establish the mobile database model based on Agent. After the practical application in the teaching test of Ideological and political course, the system runs well, which can meet the students' personalized and fragmented learning needs, and effectively complete the construction of the interaction process between teachers and students.
Low latency high throughput Montgomery modular multiplier for RSA cryptosystem

With the advancements of communication technology, security threats are also increasing. RSA is a robust cryptosystem to protect classified information. The efficiency of RSA cryptosystem depends on the efficient execution of Montgomery modular multiplication. A low latency and throughput efficient Montgomery modular multiplier is proposed in this paper. The input and final output of this multiplier are binary, but intermediate input and output are carry-saved i.e. sum and carry bits are stored in separate registers. Sum and carry bits are obtained from the addition of operands using carry-save adder (CSA). Montgomery multipliers require quotient calculation for subsequent iteration. The proposed multiplier computes two subsequent quotients in parallel with carry-save addition. Output of carry-save addition is right shifted by two bits to obtain the next-to-next intermediate output. This calculation also requires integer multiples of the inputs. Computation of next output is skipped. Format conversion of output is performed using a carry look-ahead unit (CLU). CLU along with one cycle of carry save addition is also utilized for pre-computation of integer multiples of inputs. The proposed multiplier is implemented on NEXYS4DDR and VIRTEX VII FPGA and the following result is obtained. NEXYS4DDR: Area (LUT + REG): 38903, Delay: 5.84 ns, Cycles: 1158, Latency: 6.76 µs, Throughput: 302.96 Mbps. VIRTEX VII: Area (LUT): 16447, Delay: 1.62 ns, Cycles: 1158, Latency: 1.88 µs, Throughput: 1089.4 Mbps. Results verify the improved area, latency and throughput performance of the proposed work.
Prosody-Enhanced Mandarin Text-to-Speech System

The end-to-end Text-to-Speech (TTS), which can generate speech directly from a given sequence of graphemes or phonemes, has shown superior performance over the conventional TTS. It has been able to generate high-quality speech, but it is still unable to control the local prosody such as word-level emphasis. Although the prominence of synthesized speech can be adjusted by explicit prosody tags, the acquisition of such tags is often time-consuming and laborious. This paper focuses on a deep neural prominence prediction module, using Continuous Wavelet Transform (CWT) to analyze the prosodic signal of input data, get the corresponding continuous prominence values of Chinese characters in the text to guide the training of a prominence prediction network, so that it can realize the mapping from the input text to the corresponding prominence value of each Chinese character in the text. The proposed method does not need to label the training data manually, so a fully automatic prosody control system is realized. Experiments show that the proposed system can generate more natural and expressive speech.