Reviewerly/exHarmony huggingface dataset:
An Investigation of Various Task Allocating Mechanism in Cloud

The ever increasing demand for computing in every field of the modern era leads to the emergence of the cloud computing environment which has become a boon to the world of communication and the Internet. The huge pool of resources, on-demand services, and pay per use access are some of the features which made cloud computing one of the demanding platforms in this virtual world. Because of its huge number of user access and task requests, efficient resource management is a challenging issue. Thus scheduling tasks in an efficient manner to obtain maximum utilization of resources will help in improving performance and profit. In this research work, we have highlighted job allocation techniques namely heuristic and hybrid algorithms. A comparative analysis of various heuristic scheduling algorithms and hybrid scheduling algorithms have been carried out on two parameters namely makespan and time flow as they are the beneficial parameters for job scheduling. This research presents a systematic study of existing job allocation algorithms to show their advantages and limitations.
Short Paper: A Quantum Circuit Obfuscation Methodology for Security and Privacy

In the Noisy Intermediate-Scale Quantum (NISQ) realm, efficient quantum circuit compilation is critical to ensure successful computation. Several third-party compilers are improving the compilation times and depth/gate counts. Untrusted third parties or a particular version of a trusted compiler may allow an attacker to steal, clone, and/or reverse engineer the quantum circuit. We propose to obfuscate quantum circuits by employing dummy CNOT gates to prevent such threats. If the adversary clones the obfuscated design, he/she will get faulty results. We propose a metric-based dummy gate insertion process to ensure maximum corruption of functionality measured using Total Variation Distance (TVD) and validated using IBM's noisy simulators. Our metric guided dummy gate insertion process achieves TVD of up to 28.83%, and performs 10.14% better than the average TVD and performs within 12.45% of the best obtainable TVD for the test benchmarks. The removal of dummy gates by the designer post-compilation to restore functionality as well as other finer details have been addressed.
Joint Selection using Deep Reinforcement Learning for Skeleton-based Activity Recognition

Skeleton-based human activity recognition has attracted lots of attention due to its wide range of applications. Skeleton data includes two or three dimensional coordinates of body joints. Not all of the joints are effective in recognizing an activity. In this paper, we propose a novel framework for identifying relevant joints per activity and using them for the purpose of activity recognition. We propose to formulate the joint selection problem as a Markov Decision Process (MDP) where we employ deep reinforcement learning to find the most informative joints per frame. The proposed joint selection method is a general framework that can be employed to improve the existing human activity classification methods. Experimental results on two benchmark activity recognition data sets using three different classifiers demonstrate the effectiveness of the proposed joint selection method.
A Hierarchical Coding Scheme for Glasses-free 3D Displays Based on Scalable Hybrid Layered Representation of Real-World Light Fields

This paper presents a novel hierarchical coding scheme for light fields based on transmittance patterns of low-rank multiplicative layers and Fourier disparity layers. The proposed scheme learns stacked multiplicative layers from subsets of light field views determined from different scanning orders. The multiplicative layers are optimized using a fast data-driven convolutional neural network (CNN). The essential factor for multiplicative layers representation, which has not been considered in previous compression approaches, is the origin of redundancy, i.e., the low-rank structure of light field data. The spatial correlation in layer patterns is exploited with varying low ranks through factorization derived from singular value decomposition on a Krylov subspace. Further, encoding with HEVC efficiently removes intra-view and inter-view correlation in low-rank approximated layers. The initial subset of approximated decoded views from multiplicative representation is used to construct Fourier disparity layer (FDL) representation. The FDL model synthesizes the second subset of views identified by a pre-defined hierarchical prediction order. The correlations between the prediction residue of synthesized views are further eliminated by encoding the residual signal. The set of views obtained from decoding the residual is employed to refine the FDL model and predict the next view subsets with improved accuracy. This hierarchical procedure is repeated until all light field views are encoded. The critical advantage of the proposed hybrid layered representation and coding scheme is that it utilizes not just spatial and temporal redundancies but efficiently exploits the strong intrinsic similarities among neighboring sub-aperture images in both horizontal and vertical directions as specified by different predication orders. Besides, the scheme is flexible to realize a range of multiple bitrates at the decoder within a single integrated system. The compression performance analyzed with real light field shows substantial bitrate savings, maintaining good reconstruction quality.
WHS Cost Function for Generating S-boxes

Nonlinear substitution boxes (S-boxes) use in most modern symmetric cryptoalgorithms. They are designed to mix input data and take the essential part in ensuring resistance against known cryptoanalytic attacks (differential, linear, algebraic and other methods of cryptoanalysis). However, the random formation of nonlinear substitutions with the required rates is an extremely complex mathematical problem. We observe the heuristic techniques of S-boxes informed search, in particular, we consider the cost function used in most known algorithms (eg, local search, gradient lift, annealing simulation, genetic search, etc.). The purpose of our research is to determine the specific parameters of the heuristic function, which on the one hand do not reduce the degree of awareness of search nodes, and on the other hand do not require significant computational costs. We investigate the influence of individual parameters on the value of the cost function and on the complexity of its calculation. We also provide specific recommendations for the formation of parameters for the heuristic search of S-boxes.
Assessing Approaches of IT Infrastructure Audit

The paper presents a brief overview of information security components criteria. Further analysis of common schemas in financial and commerce sectors determining common items as IT security subjects. Lead and general security standards are analyzed for the approach of determining the scope for auditing IT infrastructure. Schemes typical for financial and commerce sectors are analyzed for identifying typical and individual IT subjects. A new approach for determining scope is purposed. Comparison of new and general approaches is shown on examples of basic schemas of companies. Testing of the approach described.
Deep Learning Framework Fuzzing Based on Model Mutation

Deep learning(DL) frameworks are widely used for neural network model training and prediction in a lot of areas such as computer vision, natural language processing, medical image and so on. However, DL frameworks may contain bugs that lead to serious security problems, e.g., resource leakage, crashes, computational errors and abnormal behaviors, etc. In this work, we proposed FAME, a DL framework fuzzing system extended from LEMON with API mutation and optimizations for layer and weight mutation. FAME takes inconsistency as feedback to guide fuzzing and it&#x0027;s also able to detect two types of bugs: NaN bugs and crashes. In detail, NaN bugs represent computational logic defects in DL framework while crashes means serious problem in program logic or implementation. Compared with LEMON, FAME is able to adopt API mutation to generate models which could cover a mass of available values of parameters for selected layers. To be more clear, illegal parameters could cause model construction failure or DL frameworks&#x0027; abnormal behaviors. In addition, we analyzed the disadvantages of some layer mutations and weight mutations of LEMON and proposed optimized methods to overcome them. We conducted FAME on five DL frameworks, i.e., TensorFlow, Pytorch, CNTK, Theano and MXNet to evaluate the effectiveness of our approach. The results of 7-day experiments demonstrate that FAME is effective to detect bugs in DL frameworks with totally 4 NaN and Crash bugs found.
The Survey on the development of Secure Multi-Party Computing in the blockchain

This paper introduces the mainstream secure multi-party computing technology in the blockchain, which provides a reference for further research in the blockchain field. First, we analyze the privacy protection issues in the blockchain and conclude that secure multi-party computing can make up for the security flaws in the blockchain. Secondly, we summarize the developing secure multi-party computing technology based on blockchain, which are the Zero-knowledge proof scheme, the secret sharing scheme, and the homomorphic encryption scheme. At the same time, we have conducted a security analysis of the multi-party computing technology in the blockchain, and believe that these three technologies can well solve the privacy protection problem in the blockchain. Finally, we look forward to the development trend of the secure multi-party computing in the blockchain, which is expected to solve the problems of poor scalability, key distribution, and non-resistance to quantum attacks. Although the secure multi-party computing technology in the blockchain is still in the development stage, the combination of cryptography and blockchain will become the mainstream development trend in the future.
A Complete Chatbot based Architecture for answering user's Course-related queries in MOOC platforms

MOOCs, after first being introduced in 2008, have since drawn attention around the world for their advantages and also criticism of their drawbacks. Interactivity with the instructor and personalized experience while learning are some of the main aspects in which the current MOOCs show scope of improvement. In this paper, we are proposing a novel chatbot architecture that can act as teaching assistance to answer queries faced by learners in MOOCs. The chatbot will be trained on the course material using the popular ALBERT model to develop its knowledge base. The chatbot will answer a large number of student&#x0027;s queries, thus reducing the workload of the instructor(s) to answer all the queries. This will open a new avenue for instructor-chatbot-learner interaction, where all of these three will compound each other&#x0027;s value.
Deepfake Image Detection using CNNs and Transfer Learning

Headways in deep learning has enabled the creation of fraudulent digital content with ease. This fraudulent digital content created is entirely indistinguishable from the original digital content. This close identicalness has what it takes to cause havoc. This fraudulent digital content, popularly known as deepfakes having the potential to change the truth and decay faith, can leave impressions on a large scale and even our daily lives. Deepfake is composed of two words, the first being deep: deep learning and the second being fake: fake digital content. Artificial intelligence forming the nucleus of any deepfake formulation technology empowers it to dodge most of the deepfake detection techniques through learning. This ability of deepfakes to learn and elude detection technologies is a matter of significant concern. In this research work, we focus on our efforts towards the detection of deepfake images. We follow two approaches for deepfake image detection, and the first is to build a custom CNN based deep learning network to detect deepfake images, and the second is to use the concept of transfer learning.
Application of Association Rule Mining in offshore HVAC transmission topology optimization

This work develops a hybrid optimization method for determining optimal radial transmission topologies for the connection of offshore wind farms combining Association Rule Mining (ARM) and a greedy algorithm. The method is capable of optimally placing offshore substations and accounts for Capital Expenditures (CAPEX), Corrective Maintenance (CM), losses and Expected Energy Not Transmitted (EENT). The stochastic nature of wind is also considered. First, an inequality based apriori algorithm is applied to a randomly generated population of Offshore Wind Power Plant (OWPP) pairs within a specified search domain. This way, a set of simple constraints is obtained reducing the effective combinatorial search space. A verified optimal greedy algorithm is then applied to efficiently search the reduced search space for the lowest cost radial topology to connect offshore wind. The hybrid approach is shown to introduce minimal error given a sufficient sample population while greatly extending the feasible problem size of the greedy search algorithm. • An original hybrid algorithm for offshore wind transmission topology optimization. • Association rule mining is applied as a clustering strategy. • Generation of synthetic data for machine learning analysis in offshore wind optimization. • Results show increased tractable problem size while maintaining solution quality.
A new methodology for anomaly detection of attacks in IEC 61850-based substation system

Smart substation is a crucial Cyber-Physical system and is prone to cyber-attack. In this paper, we propose a novel anomaly detection mechanism tailored for detecting the IEC 61850-based network traffic. Three types of traffic features are taken into account for comprehensively characterizing the network traffic during a time window. To eliminate the subjectivity of manually selecting the traffic features, we exploit Discrete Wavelet Transform (DWT) algorithm to secondarily extract the deep features. An improved Locally Linear Embedding (LLE) algorithm is proposed to reduce the dimension of deep feature vectors with more effective dimensionality reduction ability. By doing so, the LSTM (Long Short Term Memory)-based Autoencoder network that can learn to reconstruct the normal traffic time-series behavior, and thereafter uses the reconstruction error to detect the anomalies. We assess the performance of our proposed mechanism with the comprehensive experiments on the real smart substation. The results indicate that the proposed mechanism can be performed in a fast way with satisfactory detection performance.
News Text Mining-Based Business Sentiment Analysis and Its Significance in Economy

The purpose of business sentiment analysis is to determine the emotions or attitudes expressed toward the company, products, services, personnel, or events. Text analysis are the simplest and most developed types of sentiment analysis so far. The text-based business sentiment analysis still has some unresolved challenges. For example, the machine learning algorithms are unable to recognize double meanings, jokes and allusions. The regional differences between language and non-native speech structures cannot be explained. To solve this problem, an undirected weighted graph is constructed for news topics. The sentences in an article are modeled as nodes, and the normalized sentence similarity is used as the link of the nodes, which can help avoid the influence of sentence length on the summary results. In the topic extraction process, the keywords are not limited to a single word, to achieve the purpose of improving the readability of the abstract. To improve the accuracy of sentiment classification, this work proposes a robust news mining-based business sentiment analysis framework, called BuSeD. It contains two main stages: (1) news collection and preprocessing, and (2) feature extraction and sentiment classification. In the first stage, the news is collected by using crawler tools. The news dataset is then preprocessed by reducing noises. In the second stage, topics in each article is extracted by using traditional topic extraction tools. And then a convolutional neural network (CNN)-based text analyzing model is designed to analyze news from sentence level. We conduct comprehensive experiments to evaluate the performance of BuSeD for sentiment classification. Compared with four classical classification algorithms, the proposed CNN-based classification model of BuSeD achieves the highest F1 scores. We also present a quantitative trading application based on sentiment analysis to validate BuSeD, which indicates that the news-based business sentiment analysis has high economic application value.
The accuracy of Random Forest performance can be improved by conducting a feature selection with a balancing strategy

One of the significant purposes of building a model is to increase its accuracy within a shorter timeframe through the feature selection process. It is carried out by determining the importance of available features in a dataset using Information Gain (IG). The process is used to calculate the amounts of information contained in features with high values selected to accelerate the performance of an algorithm. In selecting informative features, a threshold value (cut-off) is used by the Information Gain (IG). Therefore, this research aims to determine the time and accuracy-performance needed to improve feature selection by integrating IG, the Fast Fourier Transform (FFT), and Synthetic Minor Oversampling Technique (SMOTE) methods. The feature selection model is then applied to the Random Forest, a tree-based machine learning algorithm with random feature selection. A total of eight datasets consisting of three balanced and five imbalanced datasets were used to conduct this research. Furthermore, the SMOTE found in the imbalance dataset was used to balance the data. The result showed that the feature selection using Information Gain, FFT, and SMOTE improved the performance accuracy of Random Forest.
How fakes make it through: the role of review features versus consumer characteristics

Purpose This study aims to examine how characteristics of an online review and a consumer reading the review influence the probability that the consumer will assess the review as authentic (real) or inauthentic (fake). This study further examines the specific factors that increase or decrease a consumer’s ability to detect a review’s authenticity and reasons a consumer makes these authenticity assessments. Design/methodology/approach Hypothesized relationships were tested using an online experiment of over 400 respondents who collectively provided 3,224 authenticity assessments along with 3,181 written self-report reasons for assessing a review as authentic or inauthentic. Findings The findings indicate that specific combinations of factors including review valence, length, readability, type of content and consumer personality traits and demographics lead to systematic bias in assessing review authenticity. Using qualitative analysis, this paper provided further insight into why consumers are deceived. Research limitations/implications This research showed there are important differences in the way the authenticity assessment process works for positive versus negative reviews and identified factors that can make a fake review hard to spot or a real review hard to believe. Practical implications This research has implications for both consumers and businesses by emphasizing areas of vulnerability for fake information and providing guidance for how to design review systems for improved veracity. Originality/value This research is one of the few works that explicates how people assess information authenticity and their consequent assessment accuracy in the context of online reviews.
Dynamics of Living Standards Based on Factors of the Remuneration System

The concepts “standard of living” and “quality of life” play an essential role in society. They are indicators of assessing the socio-economic situation in a country. In the paper the model the dynamics of living standards which makes it possible to substantiate management decisions on the formation of a remuneration system at enterprises, taking into account the financial security of employees is developed. The use of the model showed that the financial indicators of the salary system effectiveness at enterprises do not reflect its adequate impact on the living standard of employees. Namely, the positive tendencies of an increase in average salary and an increase in the salary bill do not fully ensure an increase in the level indicator reflecting the volume of consumption of basic food products. This is due to the fact that the level of the real salary index is insufficient to fully meet the needs of employees.
Designing Deep Learning Hardware Accelerator and Efficiency Evaluation

With the swift development of deep learning applications, the convolutional neural network (CNN) has brought a tremendous challenge to traditional processors to fulfil computing requirements. It is urgent to embrace new strategies to improve efficiency and diminish energy consumption. Currently, diverse accelerator strategies for CNN computation based on the field-programmable gate array (FPGA) platform have been gradually explored because they have edges of high parallelism, low power consumption, and better programmability. This paper first illustrates state-of-the-art FPGA-based accelerator design by emphasizing the contributions and limitations of existing research works. Subsequently, we demonstrated significant concepts of parallel computing (PC) in the convolution algorithm and discussed how to accomplish parallelism based on the FPGA hardware structure. Eventually, with the proposed CPU+ FPGA framework, we performed experiments and compared the performance against traditional computation strategies in terms of the operation efficiency and energy consumption ratio. The results revealed that the efficiency of the FPGA platform is much higher than that of the central processing unit and graphics processing unit.
Transparent but Accurate Evolutionary Regression Combining New Linguistic Fuzzy Grammar and a Novel Interpretable Linear Extension

Abstract Scientists must understand what machines do (systems should not behave like a black box), because in many cases how they predict is more important than what they predict. In this work, we propose a new extension of the fuzzy linguistic grammar and a mainly novel interpretable linear extension for regression problems, together with an enhanced new linguistic tree-based evolutionary multiobjective learning approach. This allows the general behavior of the data covered, as well as their specific variability, to be expressed as a single rule. In order to ensure the highest transparency and accuracy values, this learning process maximizes two widely accepted semantic metrics and also minimizes both the number of rules and the model mean squared error. The results obtained in 23 regression datasets show the effectiveness of the proposed method by applying statistical tests to the said metrics, which cover the different aspects of the interpretability of linguistic fuzzy models. This learning process has obtained the preservation of high-level semantics and less than 5 rules on average, while it still clearly outperforms some of the previous state-of-the-art linguistic fuzzy regression methods for learning interpretable regression linguistic fuzzy systems, and even to a competitive, pure accuracy-oriented linguistic learning approach. Finally, we analyze a case study in a real problem related to childhood obesity, and a real expert carries out the analysis shown.