Reviewerly/exHarmony huggingface dataset:
Increasing the Effective Volume of Digital Watermark Used in Monitoring the Program Code Integrity of FPGA-Based Systems

The paper deals with monitoring the program code integrity of FPGA-based systems. An approach to the integrity monitoring considered in the paper is based on embedding the digital watermark into the information object of FPGA chips program code. A digital watermark, which is embedded into the program code, contains a monitoring hash sum. Such kind of embedding does not change the program code size and operation of device, which is programmed with the help of this program code. In using this approach the integrity monitoring is provided by the condition that the digital watermark extraction and the recovery of initial state of program code information object occur simultaneously. In the paper a method, which allows increasing the embedded digital watermark effective volume, is proposed. Increasing the effective volume of the digital watermark gives the possibility to use a broader set of hash functions to monitoring the integrity. This allows using the hash functions possessing a big cryptographically strong in the process of the integrity monitoring. Increasing the effective volume is achieved due to the preliminary preparation of the FPGA program code information object, which (preparation)is performed before the embedding of digital watermark. In the course of this preparation the information object bits set by the embedding key are led to some predetermined state. In the paper an experimental research of the proposed method efficiency in the point of increasing the effective volume of the digital watermark is presented.
Predicting the Timing and Quality of Responses in Online Discussion Forums

We consider the problem of jointly predicting the quality and timing of responses to questions asked in online discussion forums. While prior work has focused on identifying users most likely to answer and/or to provide the highest quality answers to a question, the promptness of the response is also a key factor of user satisfaction. To address this, we propose point process and neural network-based algorithms for three prediction tasks regarding a user's response to a question: whether the user will answer, the net votes that will be received on the answer, and the time that will elapse before the answer. These algorithms learn over a set of 20 features we define for each pair of user and question that quantify both topical and structural aspects of the forums, including discussion post similarities and social centrality measures. Through evaluation on a Stack Overflow dataset consisting of 20,000 question threads, we find that our method outperforms baselines on each prediction task by more than 20%. We also find that the importance of the features varies depending on the task and the amount of historical data available for inference. At the end, we design a question recommendation system that incorporates these predictions to jointly optimize response quality and timing in forums subject to user constraints.
Thermal Vision Based Fall Detection via Logical and Data driven Processes

Inadvertent falls can cause serious, and potentially fatal injuries, to at risk individuals. One such community of at-risk individuals is the elderly population where age related complications, such as osteoporosis and dementia, can further increase the incidence and negative impact of such falls. Notably, falls within that community has been identified as the leading cause of injury related preventable death, hospitalization and reduction to quality of life. In such cases, rapid detection of, and reaction, to fall events has shown to be critical to reduce the negative effects of falls within this community. Currently, a range of fall detection solutions exist, however, they have several deficiencies related to the core approach that has been adopted. This study has developed an ensemble of thermal vision-based, big data facilitated, solutions which aim to address some of these deficiencies. An evaluation of these logical and data-driven processes has occurred with the promising results presented within this manuscript. Finally, opportunities future work and real-world evaluation have occurred and are underway.
Interlanguage of Automatic Speech Recognition

Phonological features in Automatic Speech Recognition (ASR) has been found from people experiencing the interlanguage. Numerous studies have used ASR as a tool to convert speech form into text form. Initially, it extracts features and elements of a speech, including the phonological features. The negative transfer is a phenomenon caused by L2 learners try to, intentionally or not, bring their respective L1 regularities into the L2. Furthermore, it creates a new language in the process, called the interlanguage. One can found the negative transfer phenomenon in the interlanguage, which is commonly experienced by L2 learners. This phenomenon will be most likely to be examined in its phonological features of respective first language (L1). In this paper, we investigate the negative transfer in the phonological features from different ASRs using different models, techniques, and algorithms. In addition, we also present the respective language phonology model to highlight the why factor causing the negative transfer phenomenon. This paper is a literature review regarding the effort of phonological features extraction for Automatic Speech Recognition (ASR) in terms of interlanguage.
Efficient Discovery of Expressive Multi-label Rules Using Relaxed Pruning

Being able to model correlations between labels is considered crucial in multi-label classification. Rule-based models enable to expose such dependencies, e.g., implications, subsumptions, or exclusions, in an interpretable and human-comprehensible manner. Albeit the number of possible label combinations increases exponentially with the number of available labels, it has been shown that rules with multiple labels in their heads, which are a natural form to model local label dependencies, can be induced efficiently by exploiting certain properties of rule evaluation measures and pruning the label search space accordingly. However, experiments have revealed that multi-label heads are unlikely to be learned by existing methods due to their restrictiveness. To overcome this limitation, we propose a plug-in approach that relaxes the search space pruning used by existing methods in order to introduce a bias towards larger multi-label heads resulting in more expressive rules. We further demonstrate the effectiveness of our approach empirically and show that it does not come with drawbacks in terms of training time or predictive performance.
Inferactive data analysis

Abstract We describe inferactive data analysis , so‐named to denote an interactive approach to data analysis with an emphasis on inference after data analysis. Our approach is a compromise between Tukey's exploratory and confirmatory data analysis allowing also for Bayesian data analysis. We see this as a useful step in concrete providing tools (with statistical guarantees) for current data scientists. The basis of inference we use is (a conditional approach to) selective inference , in particular its randomized form. The relevant reference distributions are constructed from what we call a DAG‐DAG—a Data Analysis Generative DAG, and a selective change of variables formula is crucial to any practical implementation of inferactive data analysis via sampling these distributions. We discuss a canonical example of an incomplete cross‐validation test statistic to discriminate between black box models, and a real HIV dataset example to illustrate inference after making multiple queries on data.
Ternary Parity Codes: Features

For organizing offline and online diagnostics systems, redundant coding is widely used. Codes are focused on error detection in the codeword bits. At present, the automation devices function in binary logic and consequently binary redundant codes are used in the development of technical diagnostic tools. Nevertheless, several specialists note the ternary number system and automation devices advantage that implement its principles. This work is devoted to the simplest ternary codes - ternary parity codes. Some ternary parity codes properties are described, and the need for a more detailed account of the error type in the ternary redundant code's data vectors is noted. It is proved that the described ternary parity code, built based on using a single convolution function modulo three, has the data vectors uniform distribution property between check single-trit vectors. In addition, was introduced the concept of code with the least total number of undetectable errors in the ternary code data vectors for the lengths fixed values of data and check vectors. It is proved that such a code has an data vectors uniform distribution between all possible check vectors. Formulas for calculating the total number of undetectable errors in such codes are given.
Cognitive Computing Approaches for Human Activity Recognition from Tweets—A Case Study of Twitter Marketing Campaign

Big data analytics is a growing area of research and has in the recent years introduced many applications for business sector to create value from data. Cognitive computing provides new approaches for analysing social media data, e.g. text analytics and machine vision, available via APIs and software libraries. This study focuses on cognitive computing approaches for recognizing and categorizing valence, arousal and human activity from tweets. The aim is to identify human activities from tweets and map these activities on pleasure and arousal dimensions using cognitive computing services in order to derive new insights for business. A case study of Twitter marketing campaign of a company providing stress management services is presented. In the marketing campaign participants submitted images and text on events that help them to recover from stress. These tweets are analysed with cognitive computing services to recognize activities that participants of the campaign performed to recover from stress and these are compared against qualitative analysis. The caption recognition was successful at detecting some of the activities of the users and overall results indicate that many participants cope with stress by e.g. performing outdoor activities. The two sentiment detection algorithms were also successful in detecting consumer sentiments from the text component of the tweets. Finally, implications for business are drawn from the results of analysis.
Digital Video Source Acquisition Forgery Technique Based on Pattern Sensor Noise Extraction

Digital camera of a smartphone is a component frequently used by people to capture a large number of videos, which can illustrate situations that compromise their presumption of innocence in court cases.Usually, these videos circulate on the Internet, prone to intentional manipulation to prosecute one person or exempt another.In this sense, digital videos are a matter of great importance for forensic science, because they can be useful to verify the authenticity of such evidence in judicial processes, helping to make sound decisions.However, it is possible that criminals or attackers know weaknesses of forensic techniques and use anti-forensic techniques to manipulate videos without leaving any trace of the procedure performed.Forensic science confronts anti-forensic techniques, analyzes them rigorously and applies anti-measures in the development of techniques to detect anti-forensic operations.In this paper, anti-forensic techniques are proposed to perform the source anonymization and forgery in MP4 videos.
A Topic-Based Unsupervised Learning Approach for Online Underground Market Exploration

Cyber fraud has become a lucrative form of illicit business by leveraging the Internet as a communication channel and as a result, causes significant losses to the economy. Criminals in the cyber fraud underground economy use online underground markets and other forms of social media to exchange and trade illegitimate information. Due to the high variability in the marketplaces and actors therein, analyzing these underground markets is challenging. To understand more about the underground economy of cyber fraud and its actors, we propose a topic-based hierarchical self-organizing map, which can well represent and visualize actors' similarity and thus, uncover their roles in the underground markets. We compare the proposed method with a topic-based social network analysis method for identifying the key users and their roles in the cyber fraud value chain. Experiments conducted on data from several online underground markets suggest that the proposed method can aid in identifying key actors in terms of roles, influence levels, and their social relationships.
Geometric Interpretation of Errors in Multi-Parametrical Fitting Methods Based on Non-Euclidean Norms

The paper completes the multi-parametrical fitting methods, which are based on metrics induced by the non-Euclidean Lq-norms, by deriving the errors of the optimal parameter values. This was achieved using the geometric representation of the residuals sum expanded near its minimum, and the geometric interpretation of the errors. Typical fitting methods are mostly developed based on Euclidean norms, leading to the traditional least–square method. On the other hand, the theory of general fitting methods based on non-Euclidean norms is still under development; the normal equations provide implicitly the optimal values of the fitting parameters, while this paper completes the puzzle by improving understanding the derivations and geometric meaning of the optimal errors.
Machine Learning Algorithm on Keystroke dynamics Fused pattern in biometrics

Nowadays, Multimodal biometric is being deployed to overcome the challenges of spoofing which is encountered by unimodal biometrics. However, security is still compromised using multi modal biometric, thus requiring the attention of researchers. In view to increase security, an attempt of deploying multiple machine learning techniques on fused pattern have been explored and developed. Two enhanced algorithms of Chaotic Neural Network and NeuroEvolution of the augmenting topology have been devised and applied on the fused features. To verify the trustworthiness of the proposed system, several experiments have been deployed on both unimodal and multimodal systems. After several experiments performed, the algorithms reveal that multi biometric system exhibit higher recognition rates and lower false rejection rates compared to unimodal systems. In this research, we have also shown that algorithms have the possibility to countermeasure the loopholes that may occur at the matching module. We have achieved a good recognition rate of 99.1% by adopting score level fusion by using the proposed NeuroEvolution of the augmenting topology algorithm compared to previous research work conducted. Thus, the proposed solution has proved to be stable while using fused features.
Understanding unconventional preprocessors in deep convolutional neural networks for face identification

Abstract Deep convolutional neural networks have achieved huge successes in application domains like object and face recognition. The performance gain is attributed to different facets of the network architecture such as: depth of the convolutional layers, activation function, pooling, batch normalization, forward and back propagation and many more. However, very little emphasis is made on the preprocessor’s module of the network. Therefore, in this paper, the network’s preprocessing module is varied across different preprocessing approaches while keeping constant other facets of the deep network architecture, to investigate the contribution preprocessing makes to the network. Commonly used preprocessors are the data augmentation and normalization and are termed conventional preprocessors. Others are termed the unconventional preprocessors, they are: color space converters; grey-level resolution preprocessors; full-based and plane-based image quantization, Gaussian blur, illumination normalization and insensitive feature preprocessors. To achieve fixed network parameters, CNNs with transfer learning is employed. The aim is to transfer knowledge from the high-level feature vectors of the Inception-V3 network to offline preprocessed LFW target data; and features is trained using the SoftMax classifier for face identification. The experiments show that the discriminative capability of the deep networks can be improved by preprocessing RGB data with some of the unconventional preprocessors before feeding it to the CNNs. However, for best performance, the right setup of preprocessed data with augmentation and/or normalization is required. Summarily, preprocessing data before it is fed to the deep network is found to increase the homogeneity of neighborhood pixels even at reduced bit depth which serves for better storage efficiency.
Hybrid projective nonnegative matrix factorization based on α-divergence and the alternating least squares algorithm

Nonnegative Matrix Factorization (NMF) is a linear dimensionality reduction technique for extracting hidden and intrinsic features of high-dimensional data sets. Recently, several Projective NMF (P-NMF) methods have been proposed for the purpose of resolving issues associated with the standard NMF approach. Experimental results show that P-NMF algorithms outperform the standard NMF method in some aspects. But some basic issues still affect the existing NMF and P-NMF methods, these include slow convergence rate, low reconstruction accuracy and dense basis factors. In this article, we propose a new and generalized hybrid algorithm by combining the concept of alternating least squares with the multiplicative update rules of the α-divergence-based P-NMF method. We have conducted extensive numerical experiments on 7 real-world data sets and compared the new algorithm with several state-of-the-art methods. The attractive features and added advantages of the new algorithm include remarkable clustering performances, providing highly "orthogonal" and very sparse basis factors, and extracting distinctive and better localized features of the original data than its counterparts.
A Novel Formula for Estimating Oil Compressibility Below Bubble Point Pressure Using Group Method of Data Handling: A Comparative Approach

Abstract Oil compressibility (co) plays a vital role in vast aspects ranging from upstream to downstream. For reservoir with pressure below bubble point, the effect of co to the fluid flow is insignificant as it is overshadowed by the presence of large gas compressibility (cg). This study aims to increase the range of applicability and accuracy of the formula used for estimating the co by eliminating the limitations of other existing correlations. A new formula for the estimation of oil compressibility below bubble point pressure is devised using Group Method Data Handling (GMDH). The approach is a combination of neural networks and some high-level statistical methods which rely on generating simple relations among the input parameters and the dependent parameter. The relations then result in eliminating some parameters with low impact on the output. A series of consecutive layers with the link is generated, and polynomial terms are created. A total number of 322 data points were collected from different sources from literature. Systematic trend analysis has been conducted to verify that the proposed GMDH model honours the exact physical behaviour. The new proposed model found to follow the correct trend, which implies its soundness. Besides, a comparative study was carried out using the best available correlations to confirm the significance of the results of the oil compressibility prediction using GMDH. Different statistical analyses have been conducted to verify the robustness of the newly developed model. The statistical analyses showed a positive outcome whereby the proposed model obtained the lowest average absolute percentage relative error of 5.17% and the highest correlation coefficient of 96.8%. The best model tested among the other models has five input parameters and an average absolute percentage relative error of 10.955% and a correlation coefficient of 95.6%. The new approach managed to reduce the curse of dimensionality as four input parameters have found to have a strong dependency on co (solution gas-oil ratio, oil density, reservoir temperature, and reservoir pressure). The new proposed model overcome the limitations described by the locality of some correlations as they depend on data from specific locations.
Exploiting Belief Bases for Building Rich Epistemic Structures

We introduce a semantics for epistemic logic exploiting a belief base abstraction. Differently from existing Kripke-style semantics for epistemic logic in which the notions of possible world and epistemic alternative are primitive, in the proposed semantics they are non-primitive but are defined from the concept of belief base. We show that this semantics allows us to define the universal epistemic model in a simpler and more compact way than existing inductive constructions of it. We provide (i) a number of semantic equivalence results for both the basic epistemic language with "individual belief" operators and its extension by the notion of "only believing", and (ii) a lower bound complexity result for epistemic logic model checking relative to the universal epistemic model.
Research and Applications of Key Technologies of Quantum Secure Communication in Energy Internet

With the rapid development of energy Internet, interactions between the power communication network and the cyber Internet become increasingly frequent, resulting in information security threats. Power grid encryption algorithms are mainly based on computational complexity, which will be facilely cracked by the development of quantum computing. Meanwhile, the quantum security communication technology, which is absolutely safe in in theory, can cope with the challenges brought by the improvement of computing. Basing on quantum security communication technology, this paper proposes a general architecture of quantum security communication system in power grid. Problems existing in the application of quantum secure communication technology in power system and key technologies that required to be developed are analyzed concretely. Eventually, several typical energy Internet functions, such as major power-securing events and Unified meter reading and charging service (UMRCS) are proposed to verify the rationality and feasibility.
Fully dynamic depth-first search in directed graphs

Depth-first search (DFS) is a fundamental and important algorithm in graph analysis. It is the basis of many graph algorithms such as computing strongly connected components, testing planarity, and detecting biconnected components. The result of a DFS is normally shown as a DFS-Tree. Given the frequent updates in many real-world graphs (e.g., social networks and communication networks), we study the problem of DFS-Tree maintenance in dynamic directed graphs. In the literature, most works focus on the DFS-Tree maintenance problem in undirected graphs and directed acyclic graphs. However, their methods cannot easily be applied in the case of general directed graphs. Motivated by this, we propose a framework and corresponding algorithms for both edge insertion and deletion in general directed graphs. We further give several optimizations to speed up the algorithms. We conduct extensive experiments on 12 real-world datasets to show the efficiency of our proposed algorithms.