Reviewerly/exHarmony huggingface dataset:
Application of Blockchain in Different Segments of Supply Chain Management

Supply Chain management is very fundamental for enhancing the capabilities of the Supply Chain. The traditional Supply Chain struggles with key issues like lack of information sharing, long delays involved in data retrieval, and unreliability within product tracing. Blockchain, the technology behind cryptocurrencies like Bitcoin and Ethereum, is showing the potential to transform Supply Chain functions from tracking of its provenance, re-engineering of business processes to security enhancement. This paper aims to explore the revolutions that could happen in various sectors of the Supply Chain using blockchain and Smart Contracts. A proof of concept is also discussed in the literature survey section that could help us in understanding the roles of individual participants on the blockchain network.
Amharic Handwritten Document Recognition using Deep Learning

Ethiopia is the only African country with its own indigenous writing styles and alphabets called Amharic alphabets. Amharic alphabets have a total of 238 and plus characters. Nowadays, the need for OCR systems is growing gradually. However, finding an OCR system that can be used for handwritten Amharic documents is nearly impossible. In this paper, we have proposed a handwritten document recognition system for the Amharic alphabets. Handwritten character recognition is one of the most challenging image classification approaches. There are different approaches to handwritten character recognition systems; in this paper, we have proposed a convolutional neural network-based recognition system for offline Amharic handwritten character recognition. Like most handwritten character recognition systems, this paper proposes a system that comprises all the necessary steps of handwritten character recognition systems. The paper includes preprocessing, segmentation, feature extraction, and classification phase for the recognition of Amharic handwritten documents. We have prepared a dataset containing more than 42,000 characters and each image is resized to a size of 28*28 pixels and centered. We have achieved an accuracy of 86.57 with a model loss of 0.52 from the prepared dataset. Even though, the prepared dataset is relatively small when compared to some other systems, the accuracy we have achieved is remarkable.
Proof of a conjecture on a special class of matrices over commutative rings of characteristic 2

In this note, we prove the conjecture posed by Keller and Rosemarin at Eurocrypt 2021 on the nullity of a matrix polynomial of a block matrix with Hadamard type blocks over commutative rings of characteristic 2. Therefore, it confirms the conjectural optimal bound on the dimension of invariant subspace of the Starkad cipher using the HADES design strategy. We also give characterizations of the algebraic structure formed by Hadamard matrices over commutative rings.
Selective Image Segmentation Models Using Three Distance Functions

Image segmentation can be defined as partitioning an image that contains multiple segments of meaningful parts for further processing. Global segmentation is concerned with segmenting the whole object of an observed image. Meanwhile, the selective segmentation model is focused on segmenting a specific object required to be extracted. The Convex Distance Selective Segmentation (CDSS) model, which uses the Euclidean distance function as the fitting term, was proposed in 2015. However, the Euclidean distance function takes time to compute. This paper proposed the reformulation of the CDSS minimization problem by changing the fitting term with three popular distance functions, namely Chessboard, City Block, and Quasi-Euclidean. The proposed models were CDSSNEW1, CDSSNEW2, and CDSSNEW3, which applied the Chessboard, City Block, and Quasi-Euclidean distance functions, respectively. In this study, the Euler-Lagrange (EL) equations of the proposed models were derived and solved using the Additive Operator Splitting method. Then, MATLAB coding was developed to implement the proposed models. The accuracy of the segmented image was evaluated using the Jaccard and Dice Similarity Coefficients. The execution time was recorded to measure the efficiency of the models. Numerical results showed that the proposed CDSSNEW1 model based on the Chessboard distance function could segment specific objects successfully for all grayscale images with the fastest execution time as compared to other models.
Evaluation of the Impact of Data Sparsity on Collaborative Filtering Models in Context-Aware Recommendation Systems

Kulkarni, Saurabh Rodd, Sunil F.Recommendation systems are one of the important categories of information retrieval systems helping users in the decision-making process. While doing so, they need to take different contextual factors into consideration which influence user preferences. Adding contexts may make data available to recommendation systems sparser. As the data becomes more sparse, the accuracy of the recommendation system suffers. This work focuses on the impact of data sparsity on collaborative filtering techniques used in recommendation systems and suggests the model which can be suitable at different data sparsity levels.
State preparation error tolerant quantum key distribution protocol based on heralded single photon source

In practical quantum key distribution systems, there inevitably exist errors in the quantum state preparation process due to imperfections in realistic equipment and devices. Those errors would lead to some security loopholes in the quantum key distribution systems. According to the work of Tamaki et al. (&lt;ext-link ext-link-type="uri" xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="http://doi.org/10.1103/PhysRevA.90.0523142014"&gt;&lt;i&gt;Phys. Rev. A&lt;/i&gt; &lt;b&gt;90&lt;/b&gt; 052314&lt;/ext-link&gt;), here in this work we propose a state preparation error tolerant quantum key distribution protocol through using heralded single-photon sources. In this protocol, we characterize the size of the error in the preparation state of Alice and bring it into the security analysis, thereby avoiding possible security loopholes and improving the security of the system. Moreover, we take the three-intensity decoy-state method for example to introduce the method of constructing the model and estimating the parameters, and carry out corresponding numerical simulations. We make a comparison between the loss tolerant protocol with weak coherent source (WCS) and our present protocol using heralded single-photon source (HSPS). Simulation results show that under the same state preparation error, the key generation rate of the protocol based on WCS is higher than that of protocol based on HSPS at short transmission distances (e.g. less than 150 km). The main reason is that the detection efficiency of the local detector used in the latter scheme is low. However, in the case of long transmission distances (e.g. greater than 200 km), the key generation rate of scheme with WCS drops deeply, while the decline of the key generation rate of the present scheme is much flatter. As a result, the former can no longer generate keys after 211 km, while the latter can transmit a maximum distance of 228 km. Moreover, we also make a comparison between the present scheme and the GLLP protocol with HSPS. The simulation results show that the GLLP protocol with HSPS is very sensitive to the state preparation error and its key generation rate will rapidly decrease with the increase of the state preparation error. On the contrary, our present protocol shows almost no performance degradation under practical state preparation errors. It thus verify the robustness against the state preparation errors of our present work. In addition, in principle, the method can also be combined with the measurement-device-independent quantum key distribution protocol and the twin-field quantum key distribution protocol to further increase the secure communication transmission distance that the present system can reach. Therefore, this work may provide an important reference value for the practical application of long-distance quantum secure communication in the near future.
Methods for nonparametric statistics in scientific research. Overview. Part 2

The use of nonparametric methods in scientific research provides a number of advantages. The most important of these advantages are versatility and a wide range of such methods. There are no strong assumptions associated with nonparametric tests, which means that there is little chance of assumptions being violated, i. e. the result is reliable and valid. Nonparametric tests are widely used because they may be applied to experiments for which it is not possible to obtain quantitative indicators (descriptive studies) and to small samples. The second part of the article describes nonparametric goodness-of-fit tests, i. e. Pearson’s test, Kolmogorov test, as well as tests for homogeneity, i. e. chi-squared test and Kolmogorov-Smirnov test. Chi-squared test is based on a comparison between the empirical (experimental) frequencies of the indicator under study and the theoretical frequencies of the normal distribution. Kolmogorov-Smirnov test is based on the same principle as Pearson’s chi-squared test, but involves comparing the accumulated frequencies of the experimental and theoretical distributions. Pearson’s chi-squared test and Kolmogorov test may also be used to compare two empirical distributions for the significance of differences between them. Kolmogorov test based on the accumulation of empirical frequencies is more sensitive to differences and captures those subtle nuances that are not available in Pearson’s chi-squared test. Typical errors in the application of these tests are analyzed. Examples are given, and step-by-step application of each test is described. With nonparametric methods, researcher receives a working tool for statistical analysis of the results.
Object Detection Using SSD MobileNet on Indian Dataset

Driver Assistance System (DAS) is significant to reduce human errors and in turn road accidents, which is a major threat to the Indian road and transport infrastructure. DASs make use of computer vision and machine learning to learn the environment and update the driver with information for further action. The computer vision enables the system to see the environment through the various sensors equipped on the vehicles such as radars, lidars, etc. and machine learning gives the ability to understand it. Deep Learning is a subset of machine learning which works like humans. It processes the data, understands and creates patterns to take decisions. Object detection using deep learning identifies the objects present in an image or a video and classifies those objects as car, person, building, etc. In the present work, pretrained object detection model, SSD MobileNet is implemented on DATS_2020, a dataset for Indian traffic scenario.
Characterising Cybercriminals: A Review

This review provides an overview of current research on the known characteristics and motivations of offenders engaging in cyber-dependent crimes. Due to the shifting dynamics of cybercriminal behaviour, and the availability of prior reviews in 2013, this review focuses on original research conducted from 2012 onwards, although some older studies that were not included in prior reviews are also considered. As a basis for interpretation of results, a limited quality assessment was also carried out on included studies through examination of key indicators.
Reform and Thinking of Computer Network Technology Specialty Based on Internet of Things

In order to further improve the effect and quality of computer teaching in engineering schools, cultivate students’ autonomous learning ability, and obtain successful experience in the construction of computer and network disciplines, it is necessary to analyze the actual needs for the transformation of massive computer networks in colleges and universities in the Internet of things (IoT) era on the basis of summarizing the overview of the Internet of things, and study the specific Transformation Countermeasures in detail. Based on the in-depth research on the educational reform of the computer network technology specialty of the Internet of things, this paper summarizes the teaching status of the computer network technology specialty on the basis of literature. In order to further understand the actual situation, this paper uses the method of questionnaire to investigate the teaching status of the computer network technology specialty at the present stage, Teachers and students majoring in computer network technology believe that it is necessary to carry out teaching reform under the background of Internet of things.
New built-in tools for extending the Planning C language

In this paper, the problem of developing language extensions of Planning C (a dialect of C++) is considered. The review of existing external programs and solutions built into languages that allow translating new constructions introduced into the language into the output code is carried out. Based on the analysis, it is concluded that the most natural solution built into the language will be some combination of improved regular expressions (to highlight new constructions) with code generators based on procedural and syntactic macros. At the same time, it is advisable to use elements of direct logical programming (both in macros and in regular, more precisely, regular-logical expressions). The proposed approach makes it possible to more flexibly allocate replaceable constructs in comparison with template approaches and more simply replace them with output code in comparison with approaches based on manipulations with the syntax tree. The syntax and semantics of the proposed solutions are described. A preprocessing scheme is proposed that implements the selection of initial constructions by scanners (groups of parameterized regular logical expressions) and their replacement with output code implemented by deductive macromodules (with possible multiple matching). This scheme allows you to work with arbitrary input and output syntaxes and provides prompt input of new constructions into Planning C, which is especially valuable, for example, when prototyping new extensions. The paper contains information about the successful testing of the proposed approaches (on the development of a number of syntactically non-trivial extensions of Planning C).
Introduction

This chapter gives an overview of human activity recognition as well as its commonly used sensors. We start from the background and challenges in vision-based human activity recognition. Then the related specific tasks are sorted out and the general solutions are briefly introduced. The instantiated tasks to be elaborated in the following chapters are finally discussed in a compact manner.
Bi-objective Task Scheduling in Cloud Data Center Using Whale Optimization Algorithm

Workflow scheduling in clouds refers to mapping workflow tasks to the cloud resources to optimize some objective function. Workflow scheduling is a crucial component behind the process for optimal workflow enactment. It is a well-known NP-hard problem and is more challenging in the heterogeneous computing environment. Cloud environments confront several issues, including energy consumption, implementation time, emissions of heat and CO $$_2$$ and running costs. The increasing complexity of the workflow applications forces researchers to explore hybrid approaches to solve the workflow scheduling problem. Efficient and effective cloud workflow planning is one of the most important approaches to address the above difficulties and make optimal use of resources. This study suggests energy awareness, based on the methodology whale optimization algorithm (WOA). Our objective is to decrease the energy consumption and maximize the throughput of computational workflows which impose a considerable loss on the quality of service guarantee (QoS). The proposed method is compared with other standard state-of-the-art techniques to analyze its performance.
Data and Biomaterial Archives in Radioecology and Radiobiology; the Importance of STOREing

Abstract In this commentary we consider the importance of ready access to ecological data, existing resources and approaches for radiological datasets and material, and wider public policy developments in regard to data access and reuse. We describe the development and operation of the STORE database for radiobiology, radioecology and epidemiology as a central data sharing resource, and finally we consider the issues of financial and scientific sustainability of material resource archives and the future of resources like STORE.
Perspective Chapter: Distinguishing Encrypted from Non-Encrypted Data

Discriminating between encrypted and non-encrypted information is desired for many purposes. Much of the efforts in this direction in the literature is focused on deploying machine learning methods for the discrimination in streamed data which is transmitted in packets in communication networks. Here, however, the focus and the methods are different. The retrieval of data from computer hard drives that have been seized from police busts against suspected criminals is sometimes not straightforward. Typically the incriminating code, which may be important evidence in subsequent trials, is encrypted and quick deleted. The cryptanalysis of what can be recovered from such hard drives is then subject to time-consuming brute forcing and password guessing. To this end methods for accurate classification of what is encrypted code and what is not is of the essence. Here a procedure for discriminating encrypted code from non-encrypted is derived. Two methods to detect where encrypted data is located in a hard disk drive are detailed using passive change-point detection. Measures of performance of such methods are discussed and a new property for evaluation is suggested. The methods are then evaluated and discussed according to the performance measures.
Language Politeness of Preschool Children in the Market Environment

Politeness is an important aspect of language because it can help people communicate more quickly. Children learn politeness through their social environment from an early age. Children are taught how to greet people politely, express their desires in an ethical manner, express curiosity, and express disagreement without screaming or using high voices, among other things. This study involved phenomenological qualitative research, which examined the natural phenomena of language. Data were collected by listening to the use of language without participating in the discussion process and using other free listening techniques. The purpose of the note technique that was used was to observe linguistic phenomena as they occurred. Students in classes B1 and B2 at Raudhatul Athfal (RA) Ash-Syafi’iyah T / A 2018/2019 in Kendari were the data sources. According to the findings, children’s language politeness in the family environment was heavily influenced by the situation and circumstances of the environment. Children who had grown up in a market environment had different communication language. Students at RA Ash-Syafi’iah learned about language in the sense of using it to communicate with other people or animals. Children used greetings, permission, thanks, and prohibitions as examples of imperative speech as a sign of politeness. Furthermore, the politeness of language in the children from the market environment had an effect on the intonation of the child’s voice when speaking, as well as the child’s kinesthetic signals.&#x0D; Keywords: politeness, preschool children’s language, market environment
Laravel Blade Templating Engine Implementation (Case Study: Muhammadiyah Lamongan Hospital Job Management System)

Laravel is one of several frameworks owned by the Hypertext Preprocessor (PHP) programming language. Laravel itself was created to facilitate programmers in creating a website with a framework that laravel is expressive and has syntax that is easy to understand. Laravel can also automate common things that are commonly used when building an application such as: Database library, Templating Engine, Session Management, etc. So much used to create a web application. Hypertext Markup Language (HTML), Cascading Style Sheets (CSS) and PHP programming languages: Hypertext Preprocessor is the most widely used programming language for creating a web sistem, using the Blade Templating Engine feature in the Laravel Framework will make it easier for programmers to create dynamic interfaces where programmers do not need to repeat the same code on each page, like; Navbar, Sidebar, footer and so on. By using directive yield, section and extends to call the template that has been created.
Self-Supported Pdni Dendrite on Ni Foam for Improving Monohydric Alcohol and Polyhydric Alcohols Electrooxidation

Despite considerable efforts to study direct alcohol fuel cells (DAFCs), the development of efficient bifunctional catalysts for monohydric alcohol and polyhydric alcohols remains a great challenge. This work reports the preparation of self-supported PdNi dendrites on Ni foam (self-supported PdNi DNTs/ NF) with a large specific surface area by a simple self-sacrificing template and hydrothermal method. The presence of NF with enrichment of nucleation sites is crucial for the generation of selfsupported PdNi DNTs. The as-obtained self-supported PdNi DNTs/ NF exhibits an excellent property for ethanol and glycerol electrooxidation with the mass activity of 1245 mA mg−1 and 1169.6 mA mg−1 and the specific activity of 16.70 mA cm−2 and 8.95 mA cm−2, respectively, in alkaline media. Furthermore, the PdNi DNTs/ NF exhibit outstanding long-term durability in the current–time test for 3600 s with superior anti-CO poisoning performance, possessing a potential application in DAFCs.
Vision-Based Human-Following Robot

Vision-based human-following robot is a combination of a general wheel-based motion device with the concept of a human-following robot. Such robots can help humans in a plethora of ways, whether it is shopping in a supermarket using trollies or carrying luggage in an airport. We aim to create a system that allows efficient and effective tracking using a combination of algorithms, which communicate with minimal hardware devices. We have used vision-based trackers for detecting humans and following their movements, which give smooth and accurate tracking abilities.
Vision-Based Body Activity Recognition

The chapter systematically sorts out the human body-related activity recognition tasks based on the visual sensors, including body pose estimation, action recognition, and body reconstruction. The corresponding state-of-the-art approaches are summarized and introduced. As an important interaction cue, human body activity has broad application scenarios. Therefore, it has attracted considerable attention to improving the accuracy, efficiency, and robustness of the recognition and address the challenges of its diversity and complexity. Basically, there are two kinds of approaches for it: model-based and data-driven. It can clearly see that the data-driven implicit modelling will potentially unify these two approaches, which is a promising direction.
Adversarial Examples in Image Forensics

Abstract We describe the threats posed by adversarial examples in an image forensic context, highlighting the differences and similarities with respect to other application domains. Particular attention is paid to study the transferability of adversarial examples from a source to a target network and to the creation of attacks suitable to be applied in the physical domain. We also describe some possible countermeasures against adversarial examples and discuss their effectiveness. All the concepts described in the chapter are exemplified with results obtained in some selected image forensics scenarios.