Reviewerly/exHarmony huggingface dataset:
Data Manipulation Avoidance Schemes for Distributed Machine Learning

Distributed machine learning (DML) is widely used in resource-constrained networks where the intelligent decision is needed, since no single node can work out the accurate results from massive dataset within an acceptable time complexity. However, this will inevitably expose more potential targets to adversaries compared with non-distributed environment, especially when the dataset is periodically sent to distributed nodes for updating the decision model, which should be adapted to changing environments and requirements. In order to prevent the dataset from manipulation, we put forward a novel scheme, which depends on partial training results, to detect and correct manipulated train samples for DML. Based on variable training loops, the proposed scheme utilizes a cross-learning mechanism and a manipulated data detection algorithm to ensure the data security and the learning model correctness in an unsafe environment. Furthermore, a mathematical model is established, in order to find the optimal training loops. Simulation experiments show the mathematical model matches simulation results well, in which the optimal training loops achieve the best performance. Moreover, based on the optimal training loops, the proposed data manipulation avoidance schemes can significantly improve the accuracy of the final trained model.
A Methodology to Find Artifacts of the Hacker in Man-in-the-Browser Attack

Man-in-the-browser attack is an evolved version of man-in-the-middle attack which mainly targets the internet banking. These attacks fall under the category of session hijacking, so, it is difficult to detect and stop the malicious actions as they are performed using legitimate session. Computer forensics plays a prominent role in finding the traces left behind by the hacker while compromising a computer. These traces will explain how the attack was carried out, which could serve as an evidence in the court proceedings.
Studying L-Diversity and K-Anonymity Over Datasets with Sensitive Fields

Data mining has been popular for its possibilities and what it has to offer. There is a need to consider the privacy of the people which data will be used when publishing datasets. The main issue is to be able to publish datasets without compromising privacy of the people in them. That’s when techniques like k-anonymity and l-diversity can be used to protect privacy of every tuple in those datasets. There are a lot of techniques which would help protect the privacy of a given dataset, but here only two techniques were considered, l-diversity and k-anonymity. In this paper, first the two main techniques were introduced. Secondly, experimentations comparing k-anonymity to l-diversity over 3 main possible attacks scenario were run: the prosecutor scenario, the journalist scenario and the marketer scenario. Those experimentations considered datasets with sensitive data and quasi-identifier. In the end the author offers a conclusion stating which one of the two techniques is the best regarding the security and the utility of the dataset.
The Analysis of the Socio-Technical Environment (STE) of Online Sextortion Using Case Studies and Published Reports from a Cybersecurity Perspective

This paper examines the Socio-Technical Environment (STE) of online Sextortion in the Digital Society. Sextortion refers to: “the broad category of sexual exploitation in which abuse of power is the means of coercion, as well as to the category of sexual exploitation in which threatened release of sexual images or information is the means of coercion”. STE can be defined as he complex interrelationships between people, information, processes and technology and online networks. It addresses issues that are central to the domains of online sextortion and the interactivities between tasks, information and social. The methods chosen for this research project to address the sextortion problems and online presence have been identified in the research and developed here to address the STE of online sextortion from a cybersecurity perspective using Checklands Soft System Methodology (SSM) and a modified version of the Event Analysis of Systemic Team-work (EAST) model to illustrate it. The results of this project discuss the impact of sextortion on our socio-technical environment, and then how and where, law enforcement, governments, NGOs and technology companies are tackling these online threats. This paper further investigates they could take to prevent online sextortion from happening, protecting potential victims and better risk managing this environment. The research suggests that any cybersecurity measures taken, need to be considered in a holistic light. To prevent online sextortion because through the STE shows that the platforms, services and wide-ranging geographic nature of the groups and individuals of the victims and perpetrators because no single measure could mitigate all the vulnerabilities that lead to the proliferation of sextortion.
User-Centered Research and Design of a K-5 Digital Literacy Curriculum

A research team engaged in a user-centered research and development approach to the creation of “Professor Garfield’s 21st Century Literacy Curriculum,” a comprehensive, web-based tool for K-5 teachers. This process consisted of a four-phase approach that included thorough exploration of the state-of-the-art in digital literacy tools, a survey of K-5 teachers designed to understand their technology use and perceptions of digital literacy, design thinking and empathy research with more than 30 K-5 teachers, and summative user experience testing with teachers from across the country. This approach illuminated key requirements for an interactive system that supports digital literacy instruction, as well as the primary factors that motivate teachers to adopt new online teaching tools.
Exploring The Elements of The Grounding Problem of Video Surveillance System

Abstract The analysis of the grounding problem of video surveillance system and the study of its corresponding design methods are the key to ensure that such systems fully play the expected role. This paper will first discuss the role of system grounding, and then analyze it on this basis. The classification and design principles of the grounding of the video surveillance system are the last part which mainly combines these contents, and proposes the construction strategy corresponding to the system grounding.
Prediction Analysis of Forced Shutdowns in Brazilian Type I Hydropower Plants

Risk management involves both objective and subjective factors. In principle, objective factors are expressed by numerical values being classified by limit values or ranges, while subjective factors express values that can hardly be represented numerically. The problem of evaluation tends to worsen when events must be compared, and a selection or ordering must be performed. The current work presents a proposal of solution to help manage the demands related to the process of prediction, evaluation and classification of shutdowns in Brazilian Hydropower Plants for a regulatory agency. The method, by weighing a relevant set of factors, aims to indicate the projects which should be prioritized by the inspection team, based on criteria data mining techniques such as logistic regression. Its application allowed to measure and guarantee the consistency of the judgments along the process, minimizing risks, promoting more rationalization and administrative transparency.
On the genesis of deposits of the Pervy Severny iron ore district

Research subject . This study was aimed at examining iron ore and ore-bearing strata in the Pervy Severny iron ore district in the Northern part of the Tagil depression. This area features several ore occurrences and three small industrial objects, which have been almost completely exhausted, namely the Pervoe Severnoe, Sukhodoyskoye and Peshchernoe deposits. Materials and methods . The research was based on the analysis of published materials and available production reports on the aforementioned objects, as well as the author’s own field observations and search for similar deposits in literature sources. Results . It has thus far been believed that magnetite ores were formed by contact-metasomatic interaction of Givetian limestone and the intrusions of dolerites and gabbrodiorites. However, the area under study is characterized by very few typical skarns. In addition, the ore bodies here are deposited in accordance with sedimentary and volcanic rocks. Iron ore deposits in the Lan-Dill Region in Germany seem to be the closest analogue, which suggests an alternative (volcanogenic-sedimentary) model of the formation of iron ore deposits in the Pervy Severny iron ore district. Conclusion . The use of the volcanogenic-sedimentary model allows search criteria to be specified, thus significantly increasing prospects of detection of new industrial iron ore deposits.
Compression Based Modeling for Classification of Text Documents

Classification of text data one of the well known, interesting research topic in computer science and knowledge engineering. This research article, address the classification of text files issue using lzw text compression algorithms. LZW is a lossless compression technique which requires two pass on the input data. These two passes are treated separately as training stage and text stage for classification of text data. The proposed compression based classification technique is tested on publically available datasets. Results of the experiments shows the effectiveness of the proposed algorithm.
Naturally Interact with Mobile Virtual Reality by CAT

Traditionally, users used to adopt external device as the controller for the virtual environment. Nowadays we are more interested in natural interaction, such as visual interaction within the virtual reality (VR). More natural human-machine interaction methods would be employed to improve the accessibility of interaction when the user immerses in the virtual or augmented reality. This paper introduces our work on a project called “Collision as Trigger (CAT)” in the mobile virtual reality field, cooperating with a VR helmets vender, a mobile phone manufacturer and a digital entertainment content provider. Our outcomes include stereopsis, motion sensing, scene switching based on collisions as the trigger, as well as some experience improvements on the noise suppression.
An Incremental Clustering Approach to Personalized Tag Recommendations

Volumes of user-generated contents have caused the problem of information overload and hindered Internet users from browsing and retrieving information. Social tagging that allows users to annotate resources with free preferred keywords to ease the access to their collecting resources. Though social tagging benefits users managing their resources, it always suffers the problems such as diverse and/or unchecked vocabulary and unwillingness to tag because tags are freely and voluntarily assigned by users. Tag recommender systems, which follow some criteria to select from the tag space the most relevant tags to the user’s annotating resource, drastically transfer the tagging process from generation to recognition to reduce user’s cognitive effort and time. This study takes personalized tag recommendation as an incremental clustering problem and proposes a Progressive Expansion-based Tag (PET) recommendation technique. The incremental clustering assumes each object appears in sequence and then is incrementally clustered into either an appropriate existing category or a created new category. The PET technique can classify each resource into multiple categories (i.e., tags) or label it as new. While a resource is labelled as new, it will recommend a set of tags that have been used by other users and are relevant to the target user’s practices. Finally, our empirical evaluation results suggest that the proposed PET technique outperforms the traditional popularity-based tag recommendation methods, while the performance rates achieved by both techniques are not satisfying.
Multiobjective multi unit-type neuroevolution for micro in RTS games

We used multiobjective genetic algorithms with neuroevolution of augmenting topologies (NEAT) to evolve effective micro behaviors for opposing groups with heterogeneous compositions in StarCraft II, an RTS game. We used the Fast Nondominated Sorting Genetic Algorithm to maximize damage done and minimize damage received in a skirmish, and used this two objective fitness to guide NEAT to evolve the structure and weights of a neural network based controller. The evolved NEAT network controls the movement and attack commands for each unit. We show that non-dominated selection and NEAT can be used together to generate effective micro for groups with two types or three types of units on each side. The evolved micro also generalized well to random configurations, doing well along both objectives. We also manually co-evolved against the best performing individuals produced during a run for multiple cycles and show that this improves micro resulting in better performance against the default Starcraft II AI.
Touch-Based Ontology Browsing on Tablets and Surfaces

Semantic technologies and Linked Data are increasingly adopted as core application modules, in many knowledge domains and involving various stakeholders: ontology engineers, software architects, doctors, employees, etc. Such a diffusion calls for better access to models and data, which should be direct, mobile, visual and time effective. While a relevant core of research efforts investigated the problem of ontology visualization, discovering different paradigms, layouts, and interaction modalities, a few approaches target mobile devices such as tablets and smartphones. Touch interaction, indeed, has the potential of dramatically improving usability of Linked Data and of semantic-based solutions in real-world applications and mash-ups, by enabling direct and tactile interactions with involved knowledge objects. In this paper, we move a step towards touch-based, mobile interfaces for semantic models by presenting an ontology browsing platform for Android devices. We exploit state of the art touch-based interaction paradigms, e.g., pie menus, pinch-to-zoom, etc., to empower effective ontology browsing. Our research mainly focuses on interactions, yet providing support to different visualization approaches thanks to a clear decoupling between model-level operation and visual representations. Presented results include the design and implementation of a working prototype application, as well as a first validation involving habitual users of semantic technologies. Results show a low learning curve and positive reactions to the proposed paradigms, which are perceived as both innovative and useful.
Innovation of Cluster Method for Mixed Data Based on Specific Initialization Process and Attribute Weighting

This paper proposes one improved K-Prototype algorithm based on innovations of controlling initialization process and attribute weighting (KP-IW) in order to deal with mixed data containing numeric and categorical attributes. Making initialization of clustering fixed and giving weightings to attributes are two common principles for improving algorithms. However, there are rarely methods regarding numeric or categorical proportion as one new attribute, which will affect the initialization consequence and weight value assigning to attribute because that density distribution of instances is calculated by the combing each attributes and those entire two proportions instead of only the former. There are some more detailed innovations for initialization and weighting, involving auxiliary point, auxiliary clusters and weightings combing linear and exponential effect. And it can be concluded that the KP-IW algorithm is suitable according to the clustering evaluation scores from KP-IW compared with others algorithms.
Blockchain &amp; distributed financial data

Purpose The purpose of this paper is to investigate the emergence of blockchain to determine its feasibility for electronic transfer payments. An integrated conceptual framework is developed for blockchain electronic transfer. The three “markers” of classic money (store of value, medium of exchange and unit of account and efficiency) are assessed in the case of blockchain technology. A survey is also conducted among the executives of financial vs manufacturing sectors with respect to five major variables for the emerging distributed financial data based on blockchain. Design/methodology/approach Conducting a literature review and using prior knowledge from the works of those well-versed and knowledgeable in the field. This research is also supported by collecting and analyzing the results of a behavioral survey of the executives in two different industries. Findings Blockchain technology has the potential to have widespread change in how firms operate and implement a new method for electronic payments transfer through cryptocurrencies. Although the community for blockchain technology is deregulated and has drawbacks, it is expected that the limitations will gradually be mitigated through its growth. The results of the behavioral survey show that there are significant differences between the expectations/perceptions of participants in the sectors of finance vs manufacturing. Namely, the knowledge/awareness of participants about blockchain, the value-added convenience for end users, and participants’ willingness to embracing and accepting new applications are significantly different between the two sectors. Originality/value The reach and applications for Blockchain are not limited to business or any particular sector. Blockchain technology may contribute to the operations of different types of organizations and industry sectors. Since the perception of participants about blockchain is different between selected industries, this research suggests the need for more education and building awareness among the participants in different sectors of the economy.
The Method for Automated Determination of Certainty Coefficients Values of Initial Facts in Decision-making Systems Based on Fuzzy Approximate Reasoning Networks

The article considers the approach for automated determining the values of confidence coefficients to the initial facts in approximate reasoning networks. The proposed method is based on construction of membership functions for each coefficient and using of the mathematical apparatus of fuzzy logic to fine-tune the initial rough estimate of the coefficient value given at design stage of an intelligent decision-making system. The developed method makes it possible to automate the procedure for determining the exact values of coefficients as much as possible, reducing the need for their a priori expert assessment and allowing the approximate reasoning network to correspond as closely as possible to the conditions of a particular real situation in which a decision is made.
A classification-based selection for evolutionary optimization

For evolutionary algorithms (EAs), selection is one of the main components which decides solutions for the new population. Most selection strategies are fitness-based and prodigal in fitness evaluations, since many evaluated solutions are discarded immediately due to their worse values. It is desirable to predict the quality of new solutions without the evaluations before selection, thus the efficiency of EAs can be improved. Naturally selection can be considered as a classification problem: selected solutions belong to the 'good' class and the discarded ones belong to the 'bad' class. This paper demonstrates this idea by introducing a classification-based selection (CBS) strategy for EAs. The CBS is integrated into a state-of-the-art algorithm and studied on a test suite. The experimental results evidence the efficiency of CBS on saving the number of fitness evaluations when compared with the original algorithm.
Modelling of Stability of Wireless Sensor Network Control Systems in case of uncertainty

Main factor of Sensor network control system is to provide system optimal functionality support method that take in account all its industrial and economic criteria. In this paper stochastic modelling method of the stability of Sensor network control system (SCS) is observed. The process of deterministic and stochastic modelling of SCS stability is considered in the conditions of uncertainty using benchmarking and dynamic programming method by Bellman.
A Novel Reversible Data Hiding Scheme for Encrypted VQ-Compressed Image

VQ-compressed images have been widely used as an important branch of multimedia, however, there are few reversible data hiding schemes based on VQ-compressed image in encrypted domain. In this paper, a reversible data hiding scheme based on encrypted VQ-compressed image is proposed. On the content owner side, codebook is supposed to be reorganized firstly, and codewords in codebook are then grouped into codeword pair after reorganization. After this process, index table and the new codebook are encrypted separately, using modulo operation to encrypt index table and stream cipher to encrypt codebook. Additional data will be embedded in specific position of index table by modifying their index value. As to the receiver, encrypted index table with addition data and encrypted codebook are decrypted by the encryption key. The additional data can be extracted and image can be recovered effectively according to spatial correlation in natural image.
Proposal and design of digital booklet through university extension activities

Technological innovations have been constantly developed, not only as theoretical evidences of concepts, but also bringing short, medium and long term economic and social benefits to society. An example is the use of mobile devices in health. Interesting alternative in well-being and education situations, where there is a need to involve a large number of people and there are difficulties in accessing information, either because of the distance from urban centers or the lack of health professionals and educational actions, especially for the prevention and improvement of quality of life. In this way, this study focuses on: (i) social aspects - access to preventive and health promotion actions through training programs and continuous digital inclusion; (ii) technological - the development of a specific application for the contexts involved in the programs of permanent training and digital inclusion, encouraging action research and teaching applied to real situations, through mobile technologies. Considering the technological advances and the identification of different pedagogical strategies, this paper presents a proposal of online booklet for elderly people about functional exercises.
Optimization of Computing Time for Sequential MapReduce Jobs

In order to obtain high cost-effective computing capability and improve resource utilization efficiency, MapReduce clusters are usually shared by multiple jobs, so the scheduling of multiple MapReduce jobs will have an important impact on cluster performance. Aiming at optimizing the average job computing time, this paper studies the scheduling schemes of multiple jobs arriving in Poisson process, and proposes an easy-to-implement median scheduling algorithm. Through simulation experiments, it can be seen that the proposed algorithm can effectively reduce the average computing time of MapReduce jobs.