Reviewerly/exHarmony huggingface dataset:
Bilateral Breast Geometry Analysis A Preliminary Tool for Detection of Breast Abnormality

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.
Deutsch Algorithm

In 1992, David Deutsch (Fig. 10.4) and Richard Jozsa discovered the first problem that a quantum computer
Optimal Privacy Preserving Scheme Based on Modified ANN and PSO in Cloud

In the cloud, various privacy-preserving and security threats on data retrieval processes exist. In this article, the authors propose an efficient method for secure privacy preserving in cloud. Initially, the shared file is encrypted using a Vigenere encryption algorithm before uploading. For creating the privacy map, the efficient classification algorithm is recommended. Here, a Modified Artificial Neural Network (MANN) is used to generate the privacy map. The weight value of the neural network is optimized using a Particle Swarm Optimization (PSO) algorithm. While retrieving files initially, the authorization of the person is verified by providing basic information, then the OTP of the respective files is verified. Since the user can retrieve the files only after authorization, verification and decryption of the files is highly secured and privacy is preserved. The performance of the proposed method is evaluated in terms of time and accuracy.
Smart Technologies for Emergency Response and Disaster Management

Disasters are the convergence of hazards that strikes a vulnerable community which is insufficient to withstand with its adverse effects and impact. Completely avoiding natural or anthropogenic disaster is not possible but its impact can be minimized by generating timely warning. The real-time earth observation is very important for generating such early warning. The earth observation is improving through the advancement in remote sensing technologies. Sensing technology provides real time monitoring and risk assessment. It helps in fast communication of an event occurrence. Disaster detection in urban areas is greatly improved by using remote sensing techniques. This chapter discus various devices used for real time earth monitoring of disaster events like Flood, Tsunami, Tornadoes, Droughts, Extreme Temperatures, Avalanches and Landslide. These devices gather information by continuous monitoring in their deployed location. The sensor information thus gathered must be communicated and processed to extract the disaster information.
A Direct and Robust Method for Ego-Motion Estimation

In general, the motion of the a camera is recovered through dense optical flow or sparse optical flow methods. However, these methods have high computational complexity, and sparse feature points are always usually unreliable in real video sequences. In this paper, we propose an effective and robust ego-motion estimation method based on geometric and photometric constraints, which can directly estimate the parameters of camera motion from video and avoid to calculate optical flow. Additionally, in order to improve the robustness against outliers, we propose a coarse-to-fine iteratively reweighted least squares strategy by introducing error entropy and textural degree to measure the confidence of image patch matching. Experimental results on synthetic and real challenging videos demonstrate that our method can effectively and reliably estimate ego-motion, especially in when there are partial occlusions such as blockage of vehicles and shaded areas.
A Communication Platform for Group Decision Support System

In a group decision support system, the various decision-makers have their own information, constrains, decision strategies, preferences, and objectives which are generally not shared or communicated. This implies that the group decision process is distributed between the different entities implicated and impacted by various group members' characteristics. Solution to this problem is to find a decision that would be acceptable to all the decision-makers, following the necessity of a negotiation process that allows the elaboration of a common agreement for a group that faces a conflict on the decision to take. In the current study, the authors propose to establish a communication platform for a group decision support system (GDSS) based on web services, incorporating a multicriteria analysis methods and a negotiation protocol.
China’s Transition to a Digital Currency: Does It Threaten Dollarization?

This article provides a detailed introduction to China’s launching of a digital currency. We conduct a comparative analysis concerning whether digital currency is a more stable and reliable currency than cryptocurrency and investigate whether a digital renminbi (or yuan) could replace the US dollar as a medium of exchange in international transactions. China has gained a first-mover advantage by rolling out a central bank digital currency (CBDC). But the outcome will depend on the US response as well as the future evolution of the US and Chinese economies. Most other articles on this topic focus on domestic use of the Chinese CBDC. But this study is unique in analyzing the prospects of a digital renminbi as a replacement for the US dollar in international commerce.
Introduction

Images contain repetitions of structure. Very often, these similar things are ordered along certain laws, which are long known and referred to as Gestalt laws. Things are arranged in patterns. Moreover, often there is hierarchy in such arrangements. Small things are arranged in patterns within bigger things, and these in turn may again be part of an even greater aggregate, and so forth. This chapter introduces the analysis of such hierarchies informally by discussing a small probe of example images. Thus motivated a domain is introduced in which these things can be manipulated by machines. We call them Gestalten. They feature location, scale, orientation, and so forth. But their most important property is their assessment—a number between zero and one. This chapter also reviews related approaches to the topic. There are several important books that give alternative views, and continuous competition workshops on symmetry recognition are held along the major machine vision conferences, and of course, there are key publications in major journals.
Peramalan Nilai Tukar Rupiah Terhadap Mata Uang Negara Asia Menggunakan Metode Quantum Neural Network

Currency is a standardized payment instrument used all around the world. Almost each country has their own currency, and it has a variable value. Asia is one of the biggest continent in the world. Most tourists whom visit Indonesia come from Asia. Thus, that makes the most currency exchanged in Indonesia are currencies from Asia. Currency exchange rate difference between one currency and another affected by several factor. One of those factor is inflation in its country. To overcome that issue, one needs a prediction system that can be used to predict the exchange rate of currencies in the future. Quantum Neural Network is used in this research to predict Indonesian Rupiah exchange rate value to other currency in Asia. Singapore, Hongkong and Japan currencies are particularly used in this research. Results obtained from this research are accuracies. Quantum Neural Network produces 99.78% accuracy on Singapore Dollar to Indonesian Rupiah exchange rate, 99.57% on Hongkong Dollar to Indonesian Rupiah, and 99.60% on Japanese Yen to Indonesian Rupiah.
A Quantum Particle Swarm Optimization Algorithm Based on Self-Updating Mechanism

The living mechanism has limited life in nature; it will age and die with time. This article describes that during the progressive process, the aging mechanism is very important to keep a swarm diverse. In the quantum behavior particle swarm (QPSO) algorithm, the particles are aged and the algorithm is prematurely convergent, the self-renewal mechanism of life is introduced into QPSO algorithm, and a leading particle and challengers are introduced. When the population particles are aged and the leading power of leading particle is exhausted, a challenger particle becomes the new leader particle through the competition update mechanism, group evolution is completed and the group diversity is maintained, and the global convergence of the algorithm is proven. Next in the article, twelve Clement2009 benchmark functions are used in the experimental test, both the comparison and analysis of results of the proposed method and classical improved QPSO algorithms are given, and the simulation results show strong global finding ability of the proposed algorithm. Especially in the seven multi-model test functions, the comprehensive performance is optimal.
An Analysis of the Possibilities of the Use of Technology for the Learning of Languages

Being able to communicate in a foreign language is a very pleasant experience. Moreover, in different working environments and scenarios, it is necessary. For this reason, there is a tremendous need to continue the research and development of techniques, tools, applications, strategies, and experiences related to the teaching and learning of foreign languages, adapted to the different needs, abilities, and interests of the learners. This chapter explores the important role that technology is playing in supporting the learning of languages through the facilitation of ubiquitous learning, where techniques such as informatics technology, mobile technology, or cloud computing, and tools such as learning management systems (LMSs) and massive open online courses (MOOCs) can be of great help.
Introduction

Belief revision is a topic of philosophy and then of computer science. In computer science, to update databases, truth maintenance systems were proposed by Doyle [6], and belief revision becomes one branch of artificial intelligence; and in philosophy, the logical research of the logic of belief needs consider how human’s belief is updated and revised.
A Privacy-Aware Multi-Preference-Based Collaborative Filtering Recommendation System with LSH

Recommendation systems have been widely used in many internet communities, as they could provide users with personalized items based on the big data analysis technologies. Collaborative Filtering (CF) recommendation algorithm is a well-known and widely used technique in the recommendation systems. However, traditional collaborative filtering methods usually make predictions only based on the rating-based features, which could not make a better understanding of user preferences. Moreover, with the growing disclosure of online personal information, privacy protection is another important issue to be considered in dynamic cyber networks. Thus, a security-aware prediction mechanism is necessary to deal with the data privacy in recommendation systems. In view of these challenges, this paper proposes a privacy-aware multi-preference-based collaborative filtering recommendation method with Locality-Sensitive Hashing (LSH). Firstly, two types of user representations are obtained based on both rating features and latent factors. To protect the privacy data of users, both the LSH technique and differential privacy data perturbation mechanism are integrated into our recommendation model. Besides, the employment of LSH in establishing users' nearest neighbor matrix also improve the similarity searching efficiency of the CF recommendation algorithm. Final recommendation is based on a SGD-based hybrid collaborative prediction model. Experimental results show that our proposed algorithm can improve the accuracy of the recommendation system and reduce the time cost on the basis of ensuring user privacy.
Takeda Pharm. Co. Ltd. v. Torrent Pharm. Ltd. United States Court of Appeals of the Federal Circuit, 2021 No. 2020-1552, 2021 WL 560763 (nonprecedential)

Biotechnology Law ReportVol. 40, No. 3 As presented by Chris HolmanTakeda Pharm. Co. Ltd. v. Torrent Pharm. Ltd. United States Court of Appeals of the Federal Circuit, 2021 No. 2020-1552, 2021 WL 560763 (nonprecedential)Edited for Biotechnology Law Report by CHRISTOPHER M. HOLMAN*Published Online:8 Jun 2021https://doi.org/10.1089/blr.2021.29232.cmhAboutSectionsView articleView Full TextPDF/EPUB Permissions & CitationsPermissionsDownload CitationsTrack CitationsAdd to favorites Back To Publication ShareShare onFacebookTwitterLinked InRedditEmail View articleFiguresReferencesRelatedDetails Volume 40Issue 3Jun 2021 InformationCopyright 2021, Mary Ann Liebert, Inc., publishersTo cite this article:Takeda Pharm. Co. Ltd. v. Torrent Pharm. Ltd. United States Court of Appeals of the Federal Circuit, 2021 No. 2020-1552, 2021 WL 560763 (nonprecedential).Biotechnology Law Report.Jun 2021.211-213.http://doi.org/10.1089/blr.2021.29232.cmhPublished in Volume: 40 Issue 3: June 8, 2021Online Ahead of Print:April 26, 2021PDF download
Flow-Pronged Defense against Adversarial Examples

Recent studies have shown that deep neural networks are susceptible to interference from adversarial examples. Adversarial examples are adding imperceptible noise to the data. Currently, there are many types of adversarial examples in image classification, and these adversarial examples can easily lead to DNN misclassification. Therefore, it is essential to design AEs detection methods to allow them to be rejected. In the paper, we propose Flow-Pronged Defense (FPD) for adversarial examples, which is a framework for protecting neural network classification models from adversarial examples. FPD does not need to modify the protected classifier, which includes a FLOW model and a residual network classifier. The Flow model transforms the adversarial examples so that the classifier can better classify the adversarial examples and clean examples. The residual network strengthens the difference between disturbance and clean data through cross-layer connections. Compared with the state-of-the-art method, many experiments show that FPD has higher accuracy and generalization ability.
Cloud Based Reasoning Health Data using Homomorphic Encryption

Cloud computing is an abundant heterogeneous paradigm. The clients are given access to cloud for storing large amount of data for many purposes. The major cloud security issues are data breaches, insider threat and insufficient due diligence etc. Most of the service providers save the Client data as a plain text format which makes the data less secured. Aim of the system is to protect the health data that are outsourced for storing in cloud. In this system, the data is encrypted using paillier cryptosystem before outsourcing, which preserves the privacy of patient’s health data. Computations are performed over this encrypted data using decision tree algorithm. The results are displayed on the client machine. Hence, it ensures the privacy preservation and cautions the patient about his health.
Wearable Technology

Wearable technology is very popular in recent years. Since the advent of Glass Google, lots of kinds of wearable devices have been developed. There is more and more wearable technology in education. Through the literature and field survey, the researchers conducted studies on wearable technology and its application in education. We find that the scope of application involves many fields, such as medical, entertainment, health, military, education, and so on. The effective use of wearable technology in education needs to improve the devices to meet the needs of Education.
Research on the Construction of Underwater Platform Combat Deduction System Based on Service-Oriented and Multi-Agent Technology

Object-oriented intelligent modeling, model management, etc. are difficult problems in the designing and development of underwater platform combat deduction system. The command and control description model based on OODA loop depicted the business process of underwater platform combat deduction using service-oriented and agent modeling technology and established an underwater platforms deduction system architecture, effectively solving the problem of intelligence, reusing, and extensibility in combat deduction modeling. The chapter has reference value in the designing and development of underwater platforms deduction systems.
Exploring Myths in Digital Forensics

Digital forensic methodology deviates significantly relative to the methods of other forensic sciences for numerous practical reasons, and it has been largely influenced by factors derived from the inception and evolution of this relatively new and rapidly changing field. Digital forensics methodology was developed more by practitioners in its early days rather than by computer scientists. This led to accepted best practices in the field that may not represent the best or, at least, tested, science. This paper explores some of these differences in the practice and evolution between digital and other forensic sciences, and recommends scientific approaches to apply to many digital forensic practice rituals.
Effect of Core Stability and Functional Mobility Exercises on Muscle Strength after Lumbar Spinal Cord Injury

Background: Many studies suggest that when people are confined to wheelchair, central neuropathies such as spinal cord injury usually reduce strength of core muscles and corresponding functional abilities for standing and reaching.Studies that have aimed to compare different strategies to improve functional capacity or stability have produced controversial results.Furthermore such studies have focused solely on individual component.In contrast, the present study describes the effect of core stability as well as functional mobility exercises on muscle strength after lumbar spinal cord injury. Aim:To analyze the effect of core stability and functional mobility exercises on muscle strength after lumbar spinal cord injury.Methodology: 25 patients with lumbar spinal cord injury were recruited on a volunteer basis.Ethics approval was obtained from Institutional ethical committee, KIMSDU.The purpose and procedure of the study was explained to each participant before giving their consent to participate in the study.Neurological levels and impairment scales were determined according to the American Spinal Injury Association (ASIA) standards.Descriptive statistics such as percentages, mean, and standard deviation were used for data analysis. Result:The p-value for each component is <0.0001 and is extremely significant.For each variable of the study (muscle groups) the post assessment values were more than pre assessment values.The intervention used in this study produced significant improvement in core strength and lower extremity strength of participants after lumbar spinal cord injury. Conclusion: Core stability and functional
Computer Intelligent Evaluation Model and Research on Education System Using BP Neural Network and Main Component Analysis

With the development of globalization, the internationalization of higher education has become the mainstream trend of educational development all over the world. So how to accurately evaluate the health and sustainability of the higher education system and make correct reform has become a necessary problem. In this paper, the health evaluation model and sustainability evaluation model of the higher education system are established. And evaluate them in six countries which are the United States, Australia, Germany, Japan, India and South Africa. The results show the sustainability rating is Ⅴ, Ⅱ, Ⅲ, Ⅳ, Ⅱ, Ⅰ. In order to summarize the factors affecting the higher education system as far as possible, this paper screens 12 secondary indicators, such as the education fund and the number of institutions of higher learning, and replaces 12 secondary indicators with 3 first-level indicators using the main component analysis model, respectively, the cultural background of national higher education, the scale and investment of national higher education and the development prospects of national higher education. Using these three first-level indicators as input layers, the health status of the six countries mentioned above is evaluated by using the BP neural network evaluation model, then the higher education health evaluation model is obtained.
The Use of Blockchain Technology in Supply Chain Management to Increase Traceability between Vendors and Suppliers

This study will explore Blockchain technology and how it can be implemented in supply chainmanagement. The focus of this will be on answering the question of how Blockchain technologycan help increase traceability between suppliers and vendors within the supply chain. IBM'sBlockchain will be looked at in the case of Walmart, with the incident they had with theirRomaine lettuce E-Coli breakout. An analysis of the case study of Walmart will be conducted tofurther understand why supply chains should start adopting Blockchain technology for more thanjust monitoring payments transactions. Exploration of how blockchain can help with verification of products, the security risks that may follow with using this technology, and managing false transactions. As blockchain is a key piece of technology when it comes to product traceability as well as product verification, it is hugely beneficial in the agricultural industry in helping suppliers, vendors, and consumers verify and track where their food came from. Furthermore it is incredibly helpful to vendors as they can stop any outbreaks related to food borne diseases.
Analysis of the Dynamic Characteristics of the Firefly Algorithm

The firefly algorithm is a meta-heuristic algorithm, the fundamental principle of which mimics the characteristics associated with the blinking of natural fireflies. This chapter presents a rigorous analysis of the dynamics of the firefly algorithm, which the authors performed by applying a deterministic system that removes the stochastic factors from the state update equation. Depending on its parameters, the individual deterministic firefly algorithm exhibits chaotic behavior. This prompted us to investigate the relationship between the behavior of the algorithm and its parameters as well as the extent to which the chaotic behavior influences the searching ability of the algorithm.
Impacts of feature selection on classification of individual activity recognitions for prediction of crowd disasters

We examined the possibility of feature selection using statistical based time frequency domain (SBTFD) extracted features for human activity recognition. This is to reduce the dimensionality of features on monitoring devices to improve accuracy and minimize false negative alarm for crowd disasters. We analysed 54 SBTFD features obtained from 22,350 instances comprising of a climb down, climb up, peak shake while standing, standing, still, and walking; as classes V1, V2, to V8. Also, the benchmark dataset of 274,214 instances from nine users for accelerometer signals. Both datasets were subjected to minimum redundancy maximum relevance with information gain (MRMR-IG), correlation and chi-square techniques to select the relevant SBTFD features. We applied ten-fold cross validation using WEKA with four classifiers to classify individual behaviour classes V1 to V8. We achieved 97.8% accuracy and false negative rate of 9.5% to save human lives from crowd disasters with seven features of MRMR-IG using RF.