Reviewerly/exHarmony huggingface dataset:
A Bitcoin Wallet Security System (BWSS)

The Bitcoin technique faces several security challenges such as bitcoin wallet damage and attacks. This research presents a Bitcoin Wallet Security System (BWSS) to ensure the confidentiality and integrity of bitcoin wallet. The proposed system functions are distributed into two components and then implemented using Java programming language. A two-factor authentication is used to provide a more confidential bitcoin wallet while a backup mechanism is used to back up the wallet automatically or manually. Finally, the system is evaluated and refined after evaluation to come out with the required security features in a secure manner. The proposed system enables the bitcoin wallet owner to first protect his/her bitcoin wallet(s) with a second security layer using a two factor authentication and, second, back up his/her wallet in a secure and flexible ways. The system can be improved in the future to secure bitcoin wallets in mobile devices.
Hybrid Feature Vector-Assisted Action Representation for Human Action Recognition Using Support Vector Machines

Human action recognition (HAR) is one of most significant research topics, and it has attracted the concentration of many researchers. Automatic HAR system is applied in several fields like visual surveillance, data retrieval, healthcare, etc. Based on this inspiration, in this chapter, the authors propose a new HAR model that considers an image as input and analyses and exposes the action present in it. Under the analysis phase, they implement two different feature extraction methods with the help of rotation invariant Gabor filter and edge adaptive wavelet filter. For every action image, a new vector called as composite feature vector is formulated and then subjected to dimensionality reduction through principal component analysis (PCA). Finally, the authors employ the most popular supervised machine learning algorithm (i.e., support vector machine [SVM]) for classification. Simulation is done over two standard datasets; they are KTH and Weizmann, and the performance is measured through an accuracy metric.
Spiker-Converter: A Semi-Supervised Framework for Low-Resource Speech Recognition with Stable Adversarial Training

Labeling large amounts of speech is laborious and expensive. The scarcity of speech with the accent or in specific scenes hangs the further applications of the ASR system in practice. On the contrary, collecting speech and domain-related text corpus is more achievable. In this work, we propose an end-to-end model called Spiker-Converter for the low-resource speech recognition task. It decomposes the ASR task by introducing additional acoustic supervision, dramatically reduce the demand for labeled samples. Besides, we provide a semi-supervised training method, which consumes a few labeled speech samples but large amounts of unlabeled speech and domain-related text. Specifically, we use a Discriminator to produce learning signals for the ASR model with unlabeled speech as input. Note that we apply adversarial training to part of the ASR model, ensuring stability. Experiments show the significant effectiveness of our semi-supervised training method. For now, our method can only be used for Chinese-like languages, but it shows a potential direction to solve low-resource speech recognition tasks.
Transforming MOOCs for University 4.0: Prospects and Limitations

The large-scale transition to distance learning driven by the lockdown during the pandemic in the spring of 2020, which, among other aspects, affected higher education, and the related challenges again brought to the fore the need to systematically integrate educational content, which is both high in quality (recognized by the international educational community, relevant and up-to-date, meeting local and/or international standards, etc.) and easily accessible (uninterrupted access, the use of e-devices, etc.), into university curriculums. We believe that massive open online courses (MOOCs) from Coursera, Futurelearn, EdX and others, which gradually become a meaningful component of the curricula, satisfy the requirements. However, their educational potential should be analyzed with due regard to a larger-scale transformation in the system of higher education, the emergence of university 4.0. The article focuses on the key aspects of the new trend, which are then applied to evaluate the usability of MOOCs for future university curriculums and possible limitations. The article draws the conclusion that MOOCs will have to rethink the content and especially the goals to stay relevant in the new educational paradigm.
Video Steganography: Storing Data in the Transition of Frames

In today’s fast-paced world, security has attained an extremely important place in the technological sphere. In the last couple of decades, a huge boom was seen in the number of techniques of steganography that were developed. Steganography is the science of concealing data in an image, an audio, or a video file, such that the data is not at all discernible to the human senses. This paper intends to delve into the relatively new technique of video steganography. New algorithms are explored to hide information not in the individual frames of the video, but in the transition of the frames. The least significant bit technique is used in our algorithm. This method will provide a more secure method as compared to the existing techniques, as it is a novel approach to hiding data in plain sight.
Decision Support Technique for Prediction of Acute Lymphoblastic Leukemia Subtypes Based on Artificial Neural Network and Adaptive Neuro-Fuzzy Inference System

Leukemia is usually characterized as a potentially fatal disease with different types and subtypes. It pretends as one of the major reason for death in developed and developing nations like Bangladesh, almost a hundred thousand people around the world are losing the fight against leukemia in every decade. An early prediction and diagnosis of a leukemia type have become inexorable in treatment, as it can reduce deadly risk to the valuable life of affected patients. This paper's objective is to design and development a smart decision support prediction system of Acute Lymphoblastic Leukemia Subtypes using Artificial Neural Network (i.e. ANN) and Adaptive Neuro-Fuzzy Inference System (i.e. ANFIS). For this prediction system, 500 datasets (main database) of different subtypes of Acute Lymphoblastic Leukemia patients are used. Training and testing are the two main steps of ANN and ANFIS, where 445 and 55 datasets are elected for training and testing respectively. According to the experimental result, it is apparent that the Adaptive Neuro-Fuzzy Inference System (i.e. ANFIS) approach performs better than the Artificial Neural Network (i.e. ANN) in both training and testing. Analysis reveals that a simple and well ergonomic back-propagation learning technique of Artificial Neural Network (i.e. ANN) and a hybrid optimization learning technique of the Adaptive Neuro-Fuzzy Inference System (i.e. ANFIS) can easily predict blood disorder efficiently and effectively in terms of hematological parameters.
The Factors Influencing Usage Intention of Urban Poor Population in India Towards Mobile Financial Services (Mobile Payment/Money)

Considering the promise mobile telephony offers and the dramatic increase in mobile telephone usage in India, several initiatives have emerged in extending financial services to the marginalised population. However, these mobile financial services have not resulted in the desired acceptance among this targeted population. Following the contextual inquiry methodology, this paper presents the outcome of two empirical studies that aimed to identify factors that underlie this lack of usage of mobile payment options among the urban poor population. While the first study examines mobile phone features and services’ usage patterns and services, the second study examines the prerequisites to access and use BHIM and Paytm, the two most popular mobile payment/money in India. The two studies’ outcomes help understand the acceptance and adoption levels and their shortcomings in the existing design features. The study suggests desired features that may guide in designing of these financial services.
Too big to fail? An analysis of the Colombian banking system through compositional data

Although still incipient in economics and finance, compositional data analysis (in which relative information is more important than absolute values are) has become more relevant in statistical analysis in recent years. This article constructs a concentration index for financial/banking systems via compositional analysis to establish the potential existence of “too big to fail” financial entities. The intention is to provide an early warning tool for regulators about this type of institution, so they can define thresholds and measures depending on their risk appetite and the systems’ specificities. The index has been applied to the Colombian banking system and assessed over time with a forecast to determine whether the system is becoming more concentrated. Results found that the concentration index has been decreasing in recent years and the model predicts this trend will continue. Regarding the methodology used, compositional models were shown to be more stable and to lead to better prediction of the index compared to the classical multivariate methodologies.
DeepMoM: Robust Deep Learning With Median-of-Means

Data used in deep learning is notoriously problematic. For example, data are usually combined from diverse sources, rarely cleaned and vetted thoroughly, and sometimes corrupted on purpose. Intentional corruption that targets the weak spots of algorithms has been studied extensively under the label of “adversarial attacks.” In contrast, the arguably much more common case of corruption that reflects the limited quality of data has been studied much less. Such “random” corruptions are due to measurement errors, unreliable sources, convenience sampling, and so forth. These kinds of corruption are common in deep learning, because data are rarely collected according to strict protocols—in strong contrast to the formalized data collection in some parts of classical statistics. This article concerns such corruption. We introduce an approach motivated by very recent insights into median-of-means and Le Cam’s principle, we show that the approach can be readily implemented, and we demonstrate that it performs very well in practice. In conclusion, we believe that our approach is a very promising alternative to standard parameter training based on least-squares and cross-entropy loss.
Comprehensive Energy Online Service Recommendation Algorithm based on Random Forest

Under the environment of fierce market competition and the upgrading of the power market, the integrated energy service industry will gradually become a new trend in future power development. Integrated energy online e-commerce service is a key component of future grid smart energy, and it is also an important way for the transition of energy sources from grid energy integration services to multi-energy service providers' competitive supply strategy transformation. An effective integrated energy service recommendation method plays an important role for integrated energy companies to occupy an advantageous position in the energy competition. According to the user's interest characteristics and the user's behavior data in the system, this paper analyzes the user's interest preferences. Part of the data is taken to establish a user interest map and perform spectral clustering, and the clustered data is imported into the random forest model for scoring prediction training. Part of the data is taken to establish a user interest map and perform spectral clustering, and the clustered data is imported into the random forest model for scoring prediction training.
Automated Text Categorization

One of the central motivations behind NLP (Natural Language Processing) is distinguishing designs. Agreed a content report, errand of recognizing the setting is referred to be as theme order. Text classification is an important advance in the characteristic non-standard speech process. It is also performed utilizing totally different classification algorithms. Its cut back is wide utilized in text classification to perform classification on large live of text information. However, Map Reduce needed plenty of your time to perform the tasks thereby increasing latency and since the information is distributed over the cluster it builds time and so reducing process speed. Also, It utilizes a long queue of code. driven by this, we propose a basic nonetheless compelling machine learning methodology that uses a Naïve mathematician classifier for text information. In the Machine Learning approach, the classifier is made mechanically by learning the properties of classes from a collection of predefined coaching information. Hence, it will method complicated moreover, multi assortment information in dynamic things. Here we tend to propose a naïve Bayes classifier that scales directly with a range of indicators and information points that may be used for each binary and multiclass classification issue. We tend to enforce the conferred schemes victimization Machine Learning tool. The experiment results demonstrate the performance improvement within the classification technique. .This manuscript investigates the performance of three various classifiers to be specific Decision Tree, and Naive Bayes and KNN on a theme characterization commission (within six subject divisions). An Assessment is finished based on meticulousness, truthfulness, recall, and f1-attain based outcomes. Along with those three previously mentioned classifiers, we have selected the Naïve Bayes as our best model utilizing which we achieved 87.1% exactness.
Neither physical nor juridical persons: electronic personhood and an evolving theory of archival diplomatics

In 2017 the European Union (EU) adopted P8_TA-PRO0051, a resolution outlining a series of recommendations for the Commission on Civil Law Rules for Robotics. Despite its ostensibly futuristic premise, the resolution is firmly grounded in the concerns of the present: mapping the legal and ethical implications of autonomous robots for European lawmakers. Included in the EU resolution is the proposal to investigate creating a legal status for electronic persons so that autonomous robots can be responsible for damages they cause. One inconspicuous site that stands to be transformed by a legislative instrument granting electronic personhood is archival diplomatics: the prospect of electronic persons represents a peculiar challenge to some of its foundational ideas. That is, the definition of persons — the central element of any document — within diplomatics has not yet been discussed in relation to the possibility of electronic persons or personhood. Drawing from legal scholarship on robotics, I will provide an exploratory overview of what a status as persons for autonomous robots and AI systems might entail for an evolving theory of archival diplomatics.
Integrated Validation System (IVS) Design for Proposal Submission of Student Activities at PPNS

The concept of Industry 4.0 is a combination of new technological developments that aim to facilitate human-computer interaction. Higher Education Institutions are currently not separated from the development of the industrial revolution involvement 4.0. Technology has a tremendous influence in the running of all activities of daily living, not least the performance of management in institutions of Higher Education Institutions. The demand to be able to work more effectively and efficiently in producing a product both in the form of goods and services makes PPNS have to keep up with technological developments. PPNS is expected to produce a product, both goods and services, in accordance with the expectations of the stakeholders (students, society and lecturers-employees). One of the problems that often becomes a problem is the bureaucracy that has a long flow. In this study, problems that occur in the PPNS Student Affairs Sector will be raised, namely regarding the approval of student activity proposals from the Student Association (HIMA) by the PPNS Management in Student Affairs under Deputy Director III. There are several steps that can be assisted in the process by using technology, so as to increase the effectiveness of performance in student affairs. Based on these problems and opportunities, a study is proposed with the title Integrated Validation System (IVS) Development to Optimize Student Service Performance in PPNS. With this system, it is hoped that it will help optimize the performance in the field of student affairs so that it can be more effective and efficient in serving requests for student activities at PPNS
Finding Similar sub-lattice of A<sub>5</sub> Lattice

Undoubtedly, the current developments in the world of technology are very closely related to the basic sciences, and these advances are due to the results that these sciences have achieved and provided to us over the years. Linear algebra and its laws have long solved many technical problems for us, one of which is to perform complex calculations to implement efficient models for encrypting digital data of any kind by performing some techniques in the field of algebra. Linearly, effective steps have been taken to optimize the results obtained. One of the cryptographic techniques is the use of lattice properties, which has recently received a lot of attention and effective measures have been taken in this field. In this article, we demonstrate the very important features of A <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">5</sub> lattices, and we introduce a new method for finding the generators of similar sub lattices of A <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">5</sub> Lattice.
MeidyaCoM‐policy: Approach for modelling and checking repair policies for self‐healing systems

The architecture of distributed systems is subject to certain failures: component failure, downed connections etc. These failures come from the dynamicity and complexity of these systems. As a solution to cure this weakness, adaptation plans can be added. However, the main difficulty of self-adaptation emerges while considering the soundness of the adaptation and preservation of stylistic constraints of the system. The MeidyaCoM-Policy, an architecture-centric approach for modelling and checking repair policies to guarantee the success of the adaptation, is presented. This approach is a solution for applying self-healing. A new unified modelling language (UML) profile that provides a visual notation for modelling repair policies is proposed to automatically transform UML models to Z notation using transformation rules coded in eXtensible styles language transformation (XSLT) language. These specifications are implemented under the Z/EVES theorem prover to prove specification soundness and consistency to guarantee the success of the adaptation. MeidyaCoM-Policy applies to all architectural styles. It is instantiated for the publish/subscribe style and illustrated with a case study. An outline of the developed software environment is provided.
Multiple Hypothesis Tracking with Mixed Integer Linear Programming

The multiple hypothesis tracking (MHT) algorithm is widely used in many tracking applications. In the track-oriented approach, the reconstruction of the global hypotheses is closely related to the maximum weight independent set problem, which is NP-hard. Its high computational complexity makes it not scale to practical large problems. In this paper, we propose a mixed integer linear programming (MILP)-based approach to enumerate m-best global hypothesis efficiently. Compared to the MHT implementation that only generates the best hypothesis, MILP-based approach exhibits better overall tracking performance in the simulation experiment. By efficiently generating multiple optimal global hypotheses, MILP-MHT is able to prune impossible track hypothesis as early as possible.
Forensic Analysis on WhatsApp/LinkedIn

The fast growth in usage and application of Social Networking structures reason them to a capability purpose through cyber criminals to conduct malicious sports activities which includes identification theft,piracy,unlawful buying and selling, unlawful income, sexual harassment, cyber harassment, and cyber-terrorism. Many SN structures are extending their services to mobile structures, making them an essential supply of evidence in cyber research cases. On this paper, I observe 2 popular SN packages: WhatsApp and LinkedIn on Android and iOS structures, to uncover the remnants of user activities that may be of forensic interest. This paper makes a speciality of performing a forensic evaluation on social networking applications broadly used on Smartphone’s along with WhatsApp and LinkedIn. The subject of this research is an analysis of the artifacts produced by the WhatsApp and LinkedIn application on Android. This study gives analysts and professionals the benefit of helping to find digital proof of WhatsApp on Android. WhatsApp and LinkedIn app artifacts on Android, such as user account records, message exchange documentation, and user uploaded activity traces were successfully described in the results of this analysis.
Fast Biometric Authentication System Based on Audio-Visual Fusion

In the past few years, face and voice recognition are attracting much attention. Fusion of face and voice authentication in a system will ensure secure human-machine interactions rather than the traditional password. In this age of lockdown due to the Covid-19 pandemic, more transactions take place online and it is important to undertake these transactions securely and safely. Traditional password systems and single authentication systems can be extended to the fusion of face recognition and voice detection. This paper introduces fast biometric authentication based on audio-visual fusion.
Agro Chain - The Life of Wealth in Agriculture

To develop a application software for automating the agricultural land documents of the farmers along with their sustainable needs. At present agricultural documents are maintained by the government physically which makes it difficult to access and get back the data efficiently. SHA-256-based approach for creating a Cryptocurrency system for storing largest crop documents (field chart, income reports, encumbrance certificate) in a bitcoin blockchain (Secured hashing algorithm). The administrator account can update the records on a daily basis as well as the user will view the property records in the blockchain network via their private key. Because the blockchain network cannot be hacked, it ensures the manipulation of land records is avoided, helping to maintain the ledger. The proposed structure consists of a block chain network under the SHA algorithm that cannot be compromised and stores land data. All data are often contained in an unified, easy-to-follow block chain Ethereum uses a meta mask wallet to perform transactions in the blockchain system. Even in the database, the agricultural land government relevant documents of small landowners are not being adequately maintained. At this modern environment, property data's are inconsistent, and property forgery is common. So, by using SHA 265 algorithm, we've developed a blockchain technology to preserve the details of the farmland government document, which is the most trustworthy application. As a result, the particulars of The public land record is kept secure in the blockchain.