Reviewerly/exHarmony huggingface dataset:
Secure Electronic Voting System based on Mobile-app and Blockchain

Introduction: The notion of electronic voting has evolved over a period of time replacing the traditional system which was based on paper ballots. Several types of electronic voting systems exist, still the implementation is partial and there is a scope for improvement for making it more secure and user-friendly.Method In this paper, a proof-of-concept is presented which aims to address the issues and challenges in the electoral system, by using the concept of Ethereum blockchain and smart contracts. Result: These electronic electoral processes propose a centralized solution that can be easily tampered, thus increasing the problem of distrust in the citizens. To overcome this blockchain technology can be used for implementing mobile based electronic voting system. Blockchain technology is aiding in the development of novel digital services that are more secure and reliable. Discussion: The main objective of this paper is to depict how a feasible, secure and reliable mobile voting system can be built by implementing the concept of blockchain and smart contracts. Conclusion: The issue of security and transparency in the voting system can be addressed using blockchain technology. The present study aims to fulfil these gaps partially by providing use-case for the voting process which is based on mobile and blockchain technology.
Reversible Logic Circuit Synthesis for Quantum Computing via a Gate Pattern-based Optimization Model

Quantum computing is the latest computing concept that can substantially reduce the computational burden of some difficult NP problems. Boolean reversible logic is frequently used as a key component in many quantum algorithms. This study proposes an approach based on a mixed integer programming model for reversible circuit synthesis. The proposed optimization model maps a given reversible function into a multiple-control Toffoli (MCT) circuit with minimum quantum costs. Our model exploits the relationship between MCT gate patterns and the operations on qubit states. We also report our experience in computational experiments on the circuit synthesis of reversible benchmark problems.
Deep Learned Super Resolution for Feature Film Production

Upscaling techniques are commonly used to create high resolution images, which are cost-prohibitive or even impossible to produce otherwise. In recent years, deep learning methods have improved the detail and sharpness of upscaled images over traditional algorithms. Here we discuss the motivation and challenges of bringing deep learned super resolution to production at Pixar, where upscaling is useful for reducing render farm costs and delivering high resolution content.
Valid and Invalid Bitcoin Transactions

In this paper, we describe valid and invalid bitcoin transactions. Bitcoin is one of the most widespread currencies in the network, which can be used to perform purchases and transactions among nodes. Because of its digital feature, it can undergo some problems and attacks such as double spending (DS), Denial of Service(DoS), packet sniffing and so on. One of the most common challenges is double spending. DS implies spending a balance of given cryptocurrency more than once, viably making a difference between the spending record and the sum of available cryptocurrency. It is suggested that it can be prevented by including Timestamp server, which adds precise time data that is never repetitive. In this paper, the given solution was implemented using Java platform. The experiment results show the progress in preventing DS of bitcoin as soon as we connected the TSA server.
Test-Driven Anonymization in Health Data: A Case Study on Assistive Reproduction

Artificial intelligence (AI) is a broad field whose prevalence in the health sector has increased during recent years. Clinical data are the basic staple that feeds intelligent healthcare applications, but due to its sensitive character, its sharing and usage by third parties require compliance with both confidentiality agreements and security measures. Data Anonymization emerges as a solution to both increasing the data privacy and reducing the risk against unintentional disclosure of sensitive information through data modifications. Although the anonymization improves privacy, the diverse modifications also harm the data functional suitability. These data modifications can affect applications that employ the anonymized data, especially those that are data-centric such as the AI tools. To obtain a trade-off between both qualities (privacy and functional suitability), we use the Test-Driven Anonymization (TDA) approach, which anonymizes incrementally the data to train the AI tools and validates with the real data until maximizing its quality. The approach is evaluated on a real-world dataset from the Spanish Institute for the Study of the Biology of Human Reproduction (INEBIR). The anonymized datasets are used to train AI tools and select the dataset that gets the best trade-off between privacy and functional quality requirements. The results show that TDA can be successfully applied to anonymize the clinical data of the INEBIR, allowing third parties to transfer without transgressing user privacy and develop useful AI Tools with the anonymized data.
An Optimized Hardware/Software Co-Design Framework for Real-Time Pedestrian Detection

Real-time Pedestrian Detection is an important feature of on-board intelligent Advanced Driver Assistance System (ADAS). The system employs feature detection and classification to identify human objects for pedestrian detection and their collision avoidance. However, these processes are computationally expensive and too complex for real-time implementation on a general-purpose processor. To address the issue of real-time deployment of such systems, this paper presents an approach using hardware/software co-design based on Xilinx Zynq FPGA and optimized OpenCV implementation. It uses Histogram of Oriented Gradients (HOG) features for human object detection and multi-stage Support Vector Machine (SVM) for object classification. The results show that our proposed co-design approach is at least 7× lower in latency than the reported software-based implementations and 6.6× faster than the previously reported HW/SW co-design approaches. In addition, our solution achieves 94% recall and 92% precision for $640\times 480$ resolution images. The results have been verified using INRIA and MIT datasets.
Span Programs and Quantum Time Complexity.

Span programs are an important model of quantum computation due to their tight correspondence with quantum query complexity. For any decision problem $f$, the minimum complexity of a span program for $f$ is equal, up to a constant factor, to the quantum query complexity of $f$. Moreover, this correspondence is constructive. A span program for $f$ with complexity $C$ can be compiled into a bounded error quantum algorithm for $f$ with query complexity $O(C)$, and vice versa. In this work, we prove an analogous connection for quantum time complexity. In particular, we show how to convert a quantum algorithm for $f$ with time complexity $T$ into a span program for $f$ such that it compiles back into a quantum algorithm for $f$ with time complexity $\widetilde{O}(T)$. While the query complexity of quantum algorithms obtained from span programs is well-understood, it is not generally clear how to implement certain query-independent operations in a time-efficient manner. We show that for span programs derived from algorithms with a time-efficient implementation, we can preserve the time efficiency when implementing the span program. This means in particular that span programs not only fully capture quantum query complexity, but also quantum time complexity. One practical advantage of being able to convert quantum algorithms to span programs in a way that preserves time complexity is that span programs compose very nicely. We demonstrate this by improving Ambainis's variable-time quantum search result using our construction through a span program composition for the OR function.
Coalgebraic Reasoning with Global Assumptions in Arithmetic Modal Logics

We establish a generic upper bound ExpTime for reasoning with global assumptions (also known as TBoxes) in coalgebraic modal logics. Unlike earlier results of this kind, our bound does not require a tractable set of tableau rules for the instance logics, so that the result applies to wider classes of logics. Examples are Presburger modal logic, which extends graded modal logic with linear inequalities over numbers of successors, and probabilistic modal logic with polynomial inequalities over probabilities. We establish the theoretical upper bound using a type elimination algorithm. We also provide a global caching algorithm that potentially avoids building the entire exponential-sized space of candidate states, and thus offers a basis for practical reasoning. This algorithm still involves frequent fixpoint computations; we show how these can be handled efficiently in a concrete algorithm modelled on Liu and Smolka’s linear-time fixpoint algorithm. Finally, we show that the upper complexity bound is preserved under adding nominals to the logic, i.e., in coalgebraic hybrid logic.
Integral Characteristics of the Entrepreneurial Leader: A Pedagogical Experience

The objective of this study was to systematize the results of a pedagogical experience carried out with second year students of the Universidad Cesar Vallejo, Peru (UCV). The academic subject was Personal Development (curriculum B and C of the UCV educational model). It was developed through 33 experiential leadership workshops conducted by lecturers of the above-mentioned subject. Population: 1522 students. Sample: 1316; attendance rate: 86%. As a teaching-learning methodology, the Flipped Classroom was used, through which students researched (semi-presential) and discussed (presential) the characteristics of the entrepreneurial leader. For this purpose, work teams were formed (between 5 and 6 members). This allowed the students to systematize their individual findings until they reached collective and definite conclusions. Finally, the study establishes a hierarchy of the fundamental (level 1), relevant (level 2) and complementary (level 3) characteristics of the entrepreneurial leader, by grouping them into a ranking of integral characteristics.
Fog-Assisted Privacy Preservation Scheme for Location-Based Services Based on Trust Relationship

With advancements in smart mobile devices and their capabilities, location-based services have gained utmost importance, as its individual and social benefits are enormous. Users of location-based services have a concern to the security issues posed by its usage as the location service providers track the users' interests, behavior, and identity information. Most of the location-based services are launched from mobile phones that have stringent resources; hence incorporating encryption schemes becomes tedious, and further, dual identity attacks uncover the encrypted message. A fog-assisted privacy protection scheme for location-based service (FPriLBS) employs a semi-trusted third party as a fog server to eliminate redundant queries submitted to the location service provider in addition to the trusted helper selection scheme which hides the real identity of the user from the fog server. The experimental results show that the proposed FPriLBS outperforms the existing schemes in terms of processing time and processing cost.
More is not always better: The negative impact of A-box materialization on RDF2vec knowledge graph embeddings

RDF2vec is an embedding technique for representing knowledge graph entities in a continuous vector space. In this paper, we investigate the effect of materializing implicit A-box axioms induced by subproperties, as well as symmetric and transitive properties. While it might be a reasonable assumption that such a materialization before computing embeddings might lead to better embeddings, we conduct a set of experiments on DBpedia which demonstrate that the materialization actually has a negative effect on the performance of RDF2vec. In our analysis, we argue that despite the huge body of work devoted on completing missing information in knowledge graphs, such missing implicit information is actually a signal, not a defect, and we show examples illustrating that assumption.
A Medical Blockchain Privacy Protection Model Based on Mimicry Defense

With the advent of the medical big data era, the electronic medical records of patients will generate huge medical value and social benefits in the process of production, authorization, circulation and sharing. How to realize the efficient circulation of medical data under the premise of ensuring patients’ privacy is the key problem that needs to be solved in the process of medical informationization. In order to solve the problem, this paper presents a dynamic heterogeneous medical alliance chain model named MAPM. By introducing the idea of mimicry defense, this model adds dynamic random attributes on the basis of the original isomorphic redundant blockchain consensus mechanism. The diversified workload demonstrates the consensus algorithm, improves the blockchain system’s ability to withstand unknown attacks, and achieves privacy protection for patients during medical data sharing.
Artificial Intelligence: A Threat to Privacy?

Klaus Schwab has observed that, “The Fourth Industrial Revolution, finally, will change not only what we do but also who we are. It will affect our identity and all the issues associated with it: our sense of privacy, our notions of ownership, our consumption patterns, the time we devote to work and leisure, and how we develop our careers, cultivate our skills, meet people, and nurture relationships.” Advances in Artificial intelligence have transformed our world. John McCarthy from the Computer Science Department of Stanford University coined the termand defined it as, the science and engineering of making intelligent machines. Artificial intelligence is where a machine possesses the intelligence as that of a human being. Such machines with artificial intelligence, like anyother human being can react to and contemplate the environment it is in and react accordingly. It collects information around it and has the ability to take decisions accordingly. This system of artificial intelligence though sounds helpful on prima facie understanding; it has been a threat to the privacy of an individual. These artificial intelligence mechanisms are controlled by softwares which are developed by human entities. Such owners have a control over the action and reaction of the artificial intelligence mechanism. In today’s digitalised world every individual in one or the other way is subject to the use of technology. Enormous amount of personal data is stored as digital data, which the artificial intelligence mechanism is making use of, in view of the betterment of standard of living. On the flip side, all personal data including our finger prints, travel details, frequent interaction with a particular persons, medical reports are collected, stored, processed, profiled with the help of Artificial Intelligence. This invades a person’s privacy. In this background, the paper tries to analyze the invasion of privacy by Artificial Intelligence and the ill-effects of the same. In the guise of public good even the government has adopted AI mechanisms which lead to questioning the governmental action. Likewise, there is hardly any legislation that regulates these aspects either on the national or on the international platform. The paper focuses on India and lack of any legislation till date to protect an individual’s Privacy. Since the Supreme Court of India has upheld that right to privacy is a fundamental right under Article 21 of the Constitution of India, though not an absolute right, it is high time a comprehensive Privacy legislation is enacted in India.
Efficiently Reusing Old Models Across Languages via Transfer Learning.

Recent progress in neural machine translation (NMT) is directed towards larger neural networks trained on an increasing amount of hardware resources. As a result, NMT models are costly to train, both financially, due to the electricity and hardware cost, and environmentally, due to the carbon footprint. It is especially true in transfer learning for its additional cost of training the “parent” model before transferring knowledge and training the desired “child” model. In this paper, we propose a simple method of re-using an already trained model for different language pairs where there is no need for modifications in model architecture. Our approach does not need a separate parent model for each investigated language pair, as it is typical in NMT transfer learning. To show the applicability of our method, we recycle a Transformer model trained by different researchers and use it to seed models for different language pairs. We achieve better translation quality and shorter convergence times than when training from random initialization.
Semi-supervised Induction of Morpheme Boundaries in Czech Using a Word-Formation Network

This paper deals with automatic morphological segmentation of Czech lemmas contained in the word-formation network DeriNet. Capturing derivational relations between base and derived lemmas, and segmenting lemmas into sequences of morphemes are two closely related formal models of how words come into existence. Thus we propose a novel segmentation method that benefits from the existence of the network; our solution constitutes new state-of-the-art for the Czech language.
Mammographic CADe And CADx for Identifying Microcalcification Using Support Vector Machine

The second most frequent cause of death and disease in women worldwide is breast cancer. Effective detection is a crucial element in the successful treatment of cancer. X-ray mammography is the majorityfrequent method for breast cancer. Screenings because of its convenience, portability, and cost-effectiveness. The CADe and CADx techniques were designed to assist radiologists in improving the detection and screening of breast cancer. The goal is for breast cancer to be changed. These instruments provide a radiologist with a second opinion to help interpret and understand the mammogram images. One of the fundamental basis of death from cancer in women is breast cancer. Optical mammograms accomplish early detection. This analysis should create systems to detect symptoms in breast cancer. The graduating of images in the MIAS database is the image set, the area of interest is defined using the morphology algorithm, the gray level co-occurrence matrix functions were found, and the latter classifies SVM (Support vector machine). The quality of abnormalities (normal or abnormal) in automatic mammograms, with each test in use during the diagnostic process, otherwise has a significance that is 85% inside training data, 60% outside of training data, and 67,5% inside and outside training data. Anomalies are of considerable consistency. Such findings affect the percentage of abnormality accuracy by calculating proof used during the classification process. The extreme levels of the classification measures (benign and malignant) will be calculated for the future. Keywords:Support Vector Machine, Biopsy, Digital Mammogram, CAD, Gray Level Matrix Cite this Article: G. S. Pradeep Ghantasala, Nalli Vinaya Kumari. Mammographic CADe and CADx for Identifying Microcalcification Using Support Vector Machine. Journal of Communication Engineering & Systems. 2020; 10(2): 9–16p.
Challenges Regarding the Use of Human Capital in the Contemporary Economy

Abstract The paper addresses a topical and important issue for national economic activity, highlighting the important role that human capital has in ensuring economic development and growth. Given the increasingly dynamic and turbulent economic and social context, as a result of the expansion of the phenomena circumscribed by globalization, continuous training through education and the level of health of the population remain the main pillars in ensuring the competitiveness of human capital. Considering this reality, the paper presents a detailed analysis of Romania's position regarding the investment in education and health in a European context and the main elements that characterize the national human capital. By identifying the vulnerabilities associated with the qualitative and quantitative structure of the national human resource, a set of proposals and recommendations are formulated meant to constitute possible solutions for the reported problems. The main conclusions deriving from the study strengthen the hypothesis according to which the return of different forms of professional training is ensured by investing in education and health and represents the conditions of a competitive economic environment in which human capital is properly capitalized.
Detection of pedestrian and different types of vehicles using image processing

Nowadays, the control of the traffic in the urban roads and in the highway has been a big challenge as the number of increase in the auto mobiles. So to overcome this problem we use the detection and tracking the vehicles using the traffic surveillance system. We can manage and control the traffic more easily. It is very complicated and a challenging task to identify the vehicle or a moving object in a complex environment with various background. The ratio detected of such algorithms depends on the quality of the foreground mask generated. Therefore this project is to present the detection and tracking the vehicles and the pedestrians in an efficient method which focus on trajectory motion of the vehicles and the pedestrians. In this proposed method, the pixels in the background are preserved which can be cars, bikes, buses, pedestrian, etc., the rest is discarded as the noise. Hence, our proposed method detects the vehicles and the pedestrians as mentioned and discards the rest noise as well in the same time. Here the quality of the generated foreground mask is more to increase the detection ratio. The performance is compared with other standard methods qualitatively and quantitatively.
SECURED SMART CARD SIMULATION

Different methods of encryption that are widely used in smart card have been presented. Because of the usefulness and widespread application of Food Ration Card throughout our country, three models of designing and simulations are developed. A comparison between the different models is done. The first model is the Food Ration Card without using any security method. The second model is the Food Ration Card with using an AES algorithm as a security method. The third model is the Food Ration Card with using RSA method. All models are implemented and simulated using BasicCard Development kit Environment. For the first model, a Compact BasicCard version ZC1.1 is used. While for the second and third models, a Professional BasicCard version ZC4.5 is used. From obtained results, we noticed that AES-method is faster than RSA and takes less memory size, but RSA is more securethan AES, since it uses two different keys instead of one as in AES.
Towards Verbal Explanations by Collaborating Robot Teams

In this paper, we describe ongoing work on understandable teams of robots collaborating to solve a common task while communicating their current, suggested or planned actions in natural language to ...
Automated Screening of Sickle Cells Using a Smartphone- Based Microscope and Deep Learning

We present a deep learning-based framework for performing automatic screening of sickle cells using a smartphone-based microscope. We achieved 98% accuracy when blindly testing 96 human blood smear slides, including 32 with sickle cell disease.