Reviewerly/exHarmony huggingface dataset:
Types of Cryptocurrencies

The most well-known cryptocurrency is Bitcoin. This coin has the first mover advantage over all the other coins which were created afterwards. Today there exists a huge variety of digital coins. Some of them have found their way in the digital world, others hardly have any value at all or are scams. This chapter first provides general information on cryptocurrencies with a focus on those with the highest market value. It will detail users, objectives and opportunities of cryptocurrencies. Moreover, it will explain how new cryptocurrencies can be created. Thereafter, this chapter will give an overview of the characteristics of different cryptocurrencies. The chapter concludes with an overview of the different attributes of the mentioned cryptocurrencies.
TSSN: A Trusted Shared Storage Network Based on Blockchain and Bilateral Matching

Confronted with the explosive growth of data in various cyber systems, decentralized storage has gained popularity thanks to its ubiquitousness, since the devices can share their idle storage space for higher scalability. However, current decentralized storage networks suffer the failure risk due to the insufficient trust among nodes, in addition to the lack of a reliable resource allocation mechanism. In this paper, we present a novel trusted shared storage network, namely TSSN, to achieve efficacious decentralized storage with trust. Designed under a shared storage framework, TSSN introduces blockchain via an on/off-chain collaborative protocol, which realizes the decentralized trust with promising efficiency. In the primary novelty of TSSN, we design an allocation mechanism based on trading and multi-attribute bilateral matching to maintain the stability and fluidity of resources. To verify the practical effectiveness of our solution, we implement a TSSN prototype on our testbed based on IPFS and Ethereum. The experimental results demonstrate the performance of TSSN within on-chain storage overhead, throughput, and latency.
Evaluation of Information Security Policy for Small Company

Recently, the use of information technology and communications by people has increased dramatically in various governmental and private institutions and companies, therefore, it became necessary to protect information from various threats and breaches, and turn into establishing a detailed and precise information security policy that everyone must pursue. The target of this paper assessing the policy of the information security of a specific firm, find out the strengths and weaknesses of its security policy based on ENISA criteria.ENISA is the European Network and Information Security Agency which consists of five domains, each domain contains particular objectives for boosting, evaluating, and to distinguish the shortage in the company's security policy requirements.The obtained findings show that using ENISA security criteria has achieved a high performance and significant efficiency in terms of evaluating the measures taken to implement a reliable and robust information security policy approved by the company.
Major activities and provisional results of gold deposit forecasting in the eastern (Russian) part of Fennoscandian shield

The paper discusses the main gold metallogeny features within foreign Precambrian shields (excluding sedimentary-metamorphogene deposits of Au-rich conglomerates and magmatogene Au-rich Cu-Ni and PGE deposits) and regional features of the eastern Fennoscandian shield. Data on geological setting of Finnish gold deposits are summarized and analyzed. The paper briefly reviews gold prospecting knowledge within Karelian-Kola region and work results. The author compiled a digital model of gold-specific forecast-metallogenic map for the eastern Fennoscandian shield; based on this model, further prioritized exploration activities in Karelian-Kola region are proposed, potential for discovering medium-sized and major deposits of various formational and geological/economic types is forecasted.
Intelligent Character Recognition with Shared Features from Resnet

Transcribing handwritten documents is still one of the hardest problems in the field of computer vision. Digitizing handwritten documents have many potential applications. Even today, people do this task manually. Online handwritten recognition, takes advantage of the temporal information, and transcription is easy compared to offline handwritten recognition, where temporal information is absent. Also, there are numerous ways people write, and the handwriting of one person varies from that of others. Generalizing this makes handwritten text recognition much difficult. In this paper, we proposed a single neural network for both word localization and word recognition. Our proposed intelligent character recognition (ICR) method has Resnet architecture to extract features from a handwritten document. These shared features are used for both localization and prediction. To overcome the scaling issues in localization, we considered feature pyramidal networks (FPN). Later the word predictions are corrected with the help of dictionary based on the Levenshtein Distance (LD). Since there are not enough handwritten documents available, we built our own dataset from answer sheets of local students. Our ICR is trained and tested on three datasets. Across the three datasets, we obtained an average character error rate (CER) of 12.06% and a mean Average Precision (mAP) of 0.94.
Talent Evaluator Using Adaptive Testing

The offline aptitude system has been used for a long time, and it has served its purpose quite well. Now, with the increase in the number of students appearing for placements of universities, and other educational centres, there is a need for a scalable approach in terms of conducting and evaluating recruitment. The Evaluator will provide a smart environment to conduct recruitment processes online where the evaluation is done automatically by using adaptive testing. Since the traditional process has always had a generalised testing mechanism, the advent of adaptive testing will provide a different dimension in the evaluation process which will help not only the students but also the recruiters to understand where the student taking the aptitude truly stands. The Evaluator is very practical as it utilises the resources of the modern age while cutting down on evaluation time, and other limitations of the offline recruitment system. The aim of this project is to influence the current placement recruitment system, and hopefully drift it to a more effective and efficient approach.
diaLogic: Interaction-Focused Speaker Diarization

diaLogic is a user-friendly Python program which performs social interaction classification through speaker diarization. The main libraries used include Python's PyQt5 and Keras APIs, Matplotlib, and the computational R language. Speaker diarization is achieved with high consistency due to a simple four-layer convolutional neural network (CNN) trained on the Librispeech ASR corpus. Speaker interactions are modeled through a custom R language script. The data generated by the program allows the characterization of speaker traits within social experiments. Group leaders, followers, and level of speaker contribution can be characterized. These traits can be used to determine overall group performance, as well as the performance of individuals. The interface is designed to be simplistic and intuitive, which allows easy operation by nonengineers. This design consideration allows program operation with minimal training for users in the social sciences disciplines. The program is designed with a modular backend, which is invisible to the user of the program. The backend allows easy expansion through modular algorithms. For future iterations of the program, speaker interaction data collection will be fully automated through machine learning and/or logical constructs. The integration of voice-based emotion recognition will be the next phase for this program. Overall, the diaLogic program is the central workspace for social interaction characterization.
Hard Examples Mining for Adversarial Attacks

This paper focuses on adversarial attacks and security on machine learning and deep learning models. We apply different methods of perturbation on pictures in ImageNet and record the success rate of examples which are successfully attacked and wrongly recognized and then conclude a graph to describe the relationship between the intensity of attack and the accuracy of recognition. Then, we figure out the reason of examples hard to be attacked which is determined by models or determined by examples themselves. Besides, we analyze the pictures which are extremely defensive to the attacks and find out some visual characters to support them stay strong.
Modulation Recognition Algorithm of Communication Signals Based on Artificial Neural Networks

With the development of wireless communication technologies and computer science, modulation recognition of communication signals has attracted more and more attention. This paper studies a modulation recognition method based on artificial neural networks (ANN). Two kinds of communication channels are tested with the modulation recognition method, namely additive white Gaussian noise (AWGN) channel and Rayleigh channel. Simulations are formed in MATLAB environment. According to the simulation results, the modulation recognition method can achieve an average recognition accuracy of 96% in the AWGN channel and 82% in the Rayleigh channel.
Interactive and Intelligent Tutoring of Graphical Solutions

Nowadays online teaching–learning has become an integral part of the education of students ranging from school to college. The present paper proposes a feasible implementation of a student-centric automated system of learning and tutoring of school-level concepts of linear equations. The system can intelligently assess the student’s understanding of the concepts by testing him/her with relevant questions. While the student plots a line in the interactive graphical interface corresponding to a given algebraic equation, the system will evaluate the correctness of the line plotted. The system also checks student’s response regarding the graphical solution of two equations. The score of a student will be evaluated on the basis of the correctness of the response given by the student. The intelligent treatment of the system lies in the fact that by evaluating a student’s understanding the system can automatically test him with harder or easier concepts. The test case cited demonstrates the usefulness and of the system.
English-Hindi Cross Language Query Translation and Disambiguation Using Most Salient Seed Word

Natural languages suffer from two types of ambiguity namely Lexical ambiguity and Syntactic ambiguity. This paper deals only in lexical ambiguity, ambiguity that arises when a word has two or more possible meanings. English language is no excuse. To translate English to Hindi query in Cross Language Information Retrieval, these ambiguous words need to be disambiguated properly for relevant Hindi language documents to be retrieved. This paper aims to find the most salient context word in the English query and use it as a single disambiguation feature in contrast of using the entire query as a context for disambiguation. When the entire query is used as a context for disambiguation, all the terms are assumed as equally important. Ideally this is not true always. All the terms in the source query are not as predicative of the word being translated as others and thus treating all query terms as uniformly important may not always be a wise decision. This paper aims to investigate this claim by proposing two methods which use either statistical mean or contribution ratio to find the best context seed word to disambiguate user query terms. The proposed methods are compared to baseline method which uses entire query as disambiguation feature. The proposed methods achieve 85% precision as compared to baseline method, which is quiet good and thus these methods can be used with high confidence for query translation and disambiguation instead of using entire query context as done by most of the researchers.
Human Speaker Recognition Based Database Method

During the last few years, many attempts were accomplished in the field of human sound and speech processing aiming to build speakers identification systems. The basic views of these systems were different, but the accuracy of the final computer process result for the identification depended on varieties of factors. For the intent of human sound extractions several methods in both the time domain and the frequency domain are used. Popular Linear Prediction Encoding (LPC) is used to parameterize voices, and to be used later in voiced/unvoiced separate extraction method functions. In comparison, direct classical methods are used for the extraction of human sound characteristics. Human voices are so much time varying that one recorded voice signal of a short time can never convey to distinguish speaker identification almost (100%).
Novel Explainable Recommenders Based on Neuro-Fuzzy Systems

In this chapter, three novel recommenders are proposed, denoted as: “Recommender A”, “Recommender B”, and “Recommender C”. Their properties are investigated in detail in Sects. 3.1, 3.2 and 3.3., respectively. The performance of the recommenders is illustrated using the MovieLens dataset []. New algorithms for encoding nominal attribute values are described. Simulation results are discussed. A comparison of the recommenders is presented, and their main characteristics are summarized in Sect. 3.4.
CORRELATES OF CAMPUS BULLYING POLICY

This study aimed to find out the correlates of bullying policy in Central Luzon State University by the use of information dissemination strategies. This research is a qualitative paper. 438 students served as respondents: The results were analyzed through descriptive analysis. This research used Correlation Analysis. The sex of the respondents was highly significant with their awareness level on bullying policy. The relationship between information dissemination strategy and awareness level of the respondents was highly significant to each other. The college-level of implementation and awareness level of the respondents on the bullying policy, based on the results were not significant or had any relationship. However, the rest of the variables were highly significant and shows the relationship in each other.
Food Ordering System in College Canteens Designed Using Python

This document provides an observational solution to the daily observed problem that are faced by multiple students all over India and so did we face in our college. Usually, the average time period for Breakfast and Lunch break are 15 and 40 mins respectively. Due to huge campus infrastructure the department has placed multiple canteens for better reach for students, but increasing number of students in department like Engineering, Medical Architecture, Law, Fashion Designing has hampered the Time Management of Canteens and students meaning the time allotted to students isn’t enough to carry along for the Day and in reciprocating in causing multiple health issues in growing students, In order to bring rest to this un-managed problem we decided to create an app that would solve this problem at the user end as well at the management end saving time and manage scheduling food orders with limited man resource in respective time slot allotted to the students.
Bearing fault diagnosis based on feature fusion

Locally linear embedding algorithm is widely utilized to feature extraction, and it is also an effective tool for fault diagnosis. However, locally linear embedding is sensitive to redundancy dimensions, that is, too high dimensions may degrade the performance of it. In order to address this problem, in this paper, a novel feature fusion method is proposed, which integrates a feature selection algorithm into locally linear embedding. In this paper, we first employ Fisher criterion to roughly select the representative features from original data set, and then locally linear embedding is used to extract the final low-dimensional results. Experiments results show that the proposed method is robust to the value of neighbors and can obtain better classification accuracy.
Investigating Data Distribution for Classification Using PSO with Adversarial Network

Data distribution plays a key role in the performance of various classification algorithms. Artificial neural network (ANN) has been widely applied in considerable complex tasks because of its excellent universal approximation capability. Although the floating centroids method (FCM) provides an effective and diverse output encoding that removes the fixed centroids constraint, the adaptive mechanism between the FCM-based neural network classifier and the data distribution has not been studied. In this paper, we design an adversarial network to investigate the characteristics of FCM and adopt a particle swarm optimization to evolve the data distribution. Experimental results demonstrated that FCM show the characteristics of diversified centroids, flexible clustering, and insensitivity to data scale, thereby obtaining competitive performance.
Reliability of Large Scale GPU Clusters for Deep Learning Workloads

Recent advances on deep learning technologies have made GPU clusters popular as training platforms. In this paper, we study reliability issues while focusing on training job failures from analyzing logs collected from running deep learning workloads on a large-scale GPU cluster in production. These failures are largely grouped into two categories, infrastructure and user, based on their sources, and reveal diverse reasons causing the failures. With insights obtained from the failure analysis, we suggest several different ways to improve the stability of shared GPU clusters designed for DL training and optimize user experience by reducing failure occurrences.
Siamese-ERNIE: Topic Relevance Check for English Writing

Topic relevance is an important criterion for English writing assessment. It has always been hard work for teachers to evaluate the writing assignments from students. The development of deep learning and natural language processing can help by checking the semantic relevance of article title and content. CNN and RNN have shown good performance in semantic text similarity. However, they usually require large corpora and huge computational power for training. In the case of English writing topic relevance checking, it is hard to collect an exhaustive corpus due to the topic variety. BERT has established an outstanding performance in different natural language processing tasks based on transfer learning. ERNIE set a new level for natural language tasks, since it uses entity information based on BERT. Siamese-ERNIE is a modification of the pretrained ERNIE network that uses siamese network structures to derive semantically meaningful sentence embeddings. We use Siamese-ERNIE to train (fine-tuned) the title and content semantic relevance on our corpus based on a pretrained-model. As the article title and content embedding can be calculated by our Siamese-ERNIE model, we can then measure the topic relevance by computing the semantic cosine distance of its embedding.
QOE-Based Neural Live Streaming Method with Continuous Dynamic Adaptive Video Quality Control

In this paper, a quality of experience (QoE)-based neural live streaming method with dynamic adaptive video quality control is developed to improve streaming performance. First, the dynamic adaptive streaming issue is formulated as a Markov decision process (MDP) problem. Second, an reinforcement learning (RL)-based approach is proposed as an appropriate solution, where the client functions as an RL agent and the environment is made up of various networks. User QoE is the reward by mutual consideration of video quality and play-back state. Finally, to optimize the total reward, the RL algorithm chooses the required video quality for each video segment. Experimental results show that the proposed RL-based streaming algorithm outperforms state-of-the-art schemes in terms of both temporal and visual QoE metrics by a noticeable margin while guaranteeing application-level fairness when multiple clients share a bottlenecked network. The code is available on the following website: https://github.com/OpenCode007/ICME2021.
Insider Abuse and Fraud Prediction for U.S. Banks: A Comparison of Machine Learning Approaches

We develop empirical models for the detection of insider abuse and fraud occurring at U.S. commercial banks with the goals of identifying leading indicators of fraud and fraud prediction. We use information from enforcement actions taken by bank supervisors to remove offending bank employees and to prohibit employees from working in the banking industry to identify instances of serious insider abuse and fraud. The time period for our analysis is 2007–2019. To the best of our knowledge, these data—bank supervisory enforcement actions—have not been used in previous studies of insider abuse and fraud at banks. The explanatory variables in our models are measures of banks’ capital adequacy, asset quality, management, earnings, liquidity and sensitivity to market risk.We model fraud using machine learning models—artificial neural networks, deep learning autoencoder networks, gradient boosting, logit regression and random forests. The models are estimated using observed annual incidences of fraud (no fraud) and four quarters of commercial bank financial data. We use these models to make predictions of the probability of insider abuse and fraud at banks in the following calendar year by applying the estimated (fitted) model the four quarters of bank financial data in the subsequent year. Our results indicate that artificial neural networks, gradient boosting and random forests are, in general, more accurate in out-of-sample forecasts of fraud than are deep learning autoencoder network and logit regression models. In terms of the drivers of fraud the estimates of logit regressions suggest that earning assets and capitalization are negatively related to the fraud while loan loss rates and noncore funding dependence are positively related to fraud.
Autonomous Quadrotor Trajectory Planning and Control for In-Flight Aerial Vehicle Capture

Quadrotor vehicles provide great flexibility in autonomous flight planning due to their ability for Vertical Take Off and Landing (VTOL) and high maneuverability in the air. This paper leverages these properties to develop a novel path planning and control architecture for an interceptor air vehicle designed to capture a small target drone which is assumed to be at rest or moving slowly. The developed architecture has three main parts. First, a geometric path planner is developed to determine the trajectory for the quadrotor to travel from its initial location to the capture location. The second component of the architecture is a minimal-time thrust profile generation algorithm. The algorithm represents the quadrotor's acceleration as a B-Spline and uses the convex-hull property of the spline to transform the constraints into functions of the control points so an optimization problem can be formulated to minimize time to capture. The third component is a low-level controller that tracks the orientation and thrust commands. In this architecture, the acceleration profile and geometric path are generated independently. This planning and control architecture provides one key advantage over alternative optimal control approaches: by separating the thrust profile generation from the geometric path generation, the minimal time trajectory generation problem has less variables and constraints to calculate, thus allowing on-board calculation of the thrust profile. Results are presented demonstrating aerial capture of targets in simulation. This architecture provides reasonable tracking performance with errors of less than 10 cm when the interceptor air vehicle is traveling at slower speeds and less than 20 cm at higher speeds. It is also shown that the nonlinear optimization problem can be solved in less than 5 ms for on-board calculation.