Reviewerly/exHarmony huggingface dataset:
Algebraic aspects and coherence conditions for conjoined and disjoined conditionals

We deepen the study of conjoined and disjoined conditional events in the setting of coherence. These objects, differently from other approaches, are defined in the framework of conditional random quantities. We show that some well known properties, valid in the case of unconditional events, still hold in our approach to logical operations among conditional events. In particular we prove a decomposition formula and a related additive property. Then, we introduce the set of conditional constituents generated by n conditional events and we show that they satisfy the basic properties valid in the case of unconditional events. We obtain a generalized inclusion-exclusion formula and we prove a suitable distributivity property. Moreover, under logical independence of basic unconditional events, we give two necessary and sufficient coherence conditions. The first condition gives a geometrical characterization for the coherence of prevision assessments on a family F constituted by n conditional events and all possible conjunctions among them. The second condition characterizes the coherence of prevision assessments defined on F∪K, where K is the set of conditional constituents associated with the conditional events in F. Then, we give a further theoretical result and we examine some examples and counterexamples. Finally, we make a comparison with other approaches and we illustrate some theoretical aspects and applications.
Threat Analysis for Smart Homes

The development and deployment of highly dynamic, cyber+connected operational environments, such as smart homes, smart cities, and smart transportation systems, is increasing. The security analysis of such dynamic environments necessitates the use of dynamic risk assessment methodologies and the modeling of dynamically changing states. In this paper, we focus on the smart home environment, where the deployment of IoT devices increase the attack surface. We examine existing dynamic risk assessment methodologies, and by leveraging a smart home reference architecture we identify the security risks of a smart home’s physical and communication viewpoints, taking into consideration also dynamic operational aspects. Further, we develop a smart home network topology generator and a graph-based attack model to study dependencies among dynamically changing states and the propagation of a malware infection.
Model of optimum integration of diverse geodata for the benefit of management of forestry

Abstract The description of model of optimization of processing of the diverse space distributed data is provided in work. Approach to implementation of consolidation of information from different sources is considered. The control algorithm of diverse data taking into account filtering of fragments of all arrays of information is provided. Key information and technical aspects of application of model of processing of diverse geodata for the solution of tasks of management of forestry are described.
On the Influence of the Supine Posture on Simulation Sickness in Virtual Reality

Virtual Reality (VR) has the capability of fully immersing users into a wide variety of gaming experiences where the sole focus of the user is on that experience itself.One problem that often occurs when it comes to VR experiences is that of simulation sickness (SS), which is particularly prevalent when the user is forced to play at an abnormal posture due to physical limitations or specific treatment procedures.In this paper we report on a between group study comparing the effect of SS mitigating factors for two postures: seated and lying-down on one's back (known as the supine pose).A 3D recreation of the popular game Pacman (Namco, 1980) was developed specifically for VR.Each subject participated a total of six sessions of six minutes each taking place over the course of two to three weeks with a two to five day minimum and maximum between each session, respectively.At the start and end of each session participants were asked to fill-in the Simulator Sickness Questionnaire (SSQ), allowing to effectively rate the intensity of SS per session.Results show a lack of significant differences when examining participants as a whole regarding the SS decrease in relation with the field of view (FOV) and the rest frame factors.However, when considering the participant experience in VR, it appears that the lying-down pose does trigger simulation sickness symptoms for more proficient participants whereas they do not report such symptoms for the seated posture.
Machine Learning-Based Fast Banknote Serial Number Recognition Using Knowledge Distillation and Bayesian Optimization

We investigated a machine-learning-based fast banknote serial number recognition method. Unlike existing methods, the proposed method not only recognizes multi-digit serial numbers simultaneously but also detects the region of interest for the serial number automatically from the input image. Furthermore, the proposed method uses knowledge distillation to compress a cumbersome deep-learning model into a simple model to achieve faster computation. To automatically decide hyperparameters for knowledge distillation, we applied the Bayesian optimization method. In experiments using Japanese Yen, Korean Won, and Euro banknotes, the proposed method showed significant improvement in computation time while maintaining a performance comparable to a sequential region of interest (ROI) detection and classification method.
ConflictNET: End-to-End Learning for Speech-Based Conflict Intensity Estimation

Computational paralinguistics aims to infer human emotions, personality traits and behavioural patterns from speech signals. In particular, verbal conflict is an important example of human-interaction behaviour, whose detection would enable monitoring and feedback in a variety of applications. The majority of methods for detection and intensity estimation of verbal conflict apply off-the-shelf classifiers/regressors to generic hand-crafted acoustic features. Generating conflict-specific features requires refinement steps and the availability of metadata, such as the number of speakers and their speech overlap duration. Moreover, most techniques treat feature extraction and regression as independent modules, which require separate training and parameter tuning. To address these limitations, we propose the first end-to-end convolutional-recurrent neural network architecture that learns conflict-specific features directly from raw speech waveforms, without using explicit domain knowledge or metadata. Additionally, to selectively focus the model on portions of speech containing verbal conflict instances, we include a global attention interface that learns the alignment between layers of the recurrent network. Experimental results on the SSPNet Conflict Corpus show that our end-to-end architecture achieves state-of-the-art performance in terms of Pearson Correlation Coefficient.
A multiple clustering combination approach based on iterative voting process

This paper addresses the problem of clustering ensemble which aims to combine multiple clusterings into a probably better solution in terms of robustness, novelty and stability. The proposed Iterative Combining Clusterings Method (ICCM) processes iteratively the entire dataset, where each iteration is based on two steps framework. In the first step, different clustering algorithms process the common dataset individually and, in the next step, a set of sub-clusters is extracted through a voting process among the data objects. To overcome the ambiguity due to voting, only objects with majority voting are assigned to their correspondent sub-clusters. The remaining objects are then collected and re-clustered in the next iterations. At the end of the iterative process, a clustering algorithm is used to group the obtained sub-cluster centres and extract the final clusters of the dataset. Two gene expression datasets and three real-life datasets have been used to evaluate the proposed approach using external and internal criteria. The experimental results demonstrate the effectiveness and robustness of the proposed method, where an improvement up to 16.89% for iris dataset, and up to 14.98% for wine dataset in DB index has been achieved. The external validity metrics confirm the usefulness of the proposed approach by achieving the highest average NMI (%) score of 81.05%, across the datasets compared to different clustering ensemble methods.
Constructing Click Model for Mobile Search with Viewport Time

A series of click models has been proposed to extract accurate and unbiased relevance feedback from valuable yet noisy click-through data in search logs. Previous works have shown that users search behavior in mobile and desktop scenarios are rather different in many aspects, therefore, the click models designed for desktop search may not be effective in the mobile context. To address this problem, we propose two novel click models for mobile search: (1) Mobile Click Model (MCM), which models click necessity bias and examination satisfaction bias; (2) Viewport Time Click Model (VTCM), which further extends MCM by utilizing the viewport time. Extensive experiments on large-scale real mobile search logs show that: (1) MCM and VTCM outperform existing models in predicting users’ clicks and estimating result relevance; (2) MCM and VTCM can extract richer information, such as the click necessity of search results and the probability of user satisfaction, from mobile click logs; (3) By modeling the viewport time distributions of heterogeneous results, VTCM can bring a significant improvement over MCM in click prediction and relevance estimation tasks. Our proposed click models can help better understand user behavior patterns in mobile search and improve the ranking performance of mobile search engines.
A secure image encryption algorithm based on a parametric switching chaotic system

A Parametric Switching Chaotic System (PSCS) based image cipher is presented in this work. To provide strong sensitivity to the plain image, permutation is done using random pixel exchanging process. A two-point diffusion function is used to provide improved sensitivity. The proposed cipher yields good entropy value, large key space and less correlation between adjacent pixels along all directions. The good UACI (Unified Average Changing Intensity) and NPCR (Number of Pixels Change Rate) values indicate the effectiveness of the proposed cipher against differential attack. A detailed cryptanalysis study is also done to demonstrate the efficiency of the suggested cipher.
A novel demodulation method for quadrate type modulations using a hybrid signal processing method

In this study, the estimation of modulation type and demodulation data from Quadrate type QASK, QFSK and QPSK modulation signals, which are widely used in digital modulation types, has been performed. For this purpose, modulation signals with 1-byte length ranging from 0 to 255 have been obtained for QASK, QFSK and QPSK modulations from multi-level modulation methods. An automatic 3-stage software layer has been developed for the estimation of data from QASK, QFSK and QPSK modulated signals. Quadrate type modulation signals have 4 carrier frequencies. In the software layer, firstly, the modulation signals have been divided into 4 equal parts, and each piece's period has been determined. Moreover, by applying Fourier analysis to each part, amplitude and phase information from the signal has been extracted for the first 10 harmonics. With Fourier analysis, reference signals for QASK, QFSK, and QPSK modulation were generated according to the amplitude, frequency and phase information of each part. Error matrices have been created by comparing each part of the data to the reference signals to be demodulated. According to the least faulty matrices, the binary data value of the signal piece has been found, and then the type of modulation has been determined by comparing the errors totals. Finally, the baseband demodulation data obtained in 4 separate logic 2 bits have been converted to decimal. In this way, the type of any modulation signal could be automatically determined and so that the base band data could be obtained successfully by the developed method. The proposed hybrid signal processing can be easily adapted to microcontroller systems in the real world.
Experience Management in Multi-player Games

Experience Management studies AI systems that automatically adapt interactive experiences such as games to tailor to specific players and to fulfill design goals. Although it has been explored for several decades, existing work in experience management has mostly focused on single-player experiences. This paper is a first attempt at identifying the main challenges to expand EM to multi-player/multi-user games or experiences. We also make connections to related areas where solutions for similar problems have been proposed (especially group recommender systems) and discusses the potential impact and applications of multi-player EM.
A Bayesian mixture model for clustering circular data

Clustering complex circular phenomena is a common problem in different scientific disciplines. Examples include the clustering of directions of animal movement in the wild to identify migration patterns, and the classification of angular positions of meteorological events to investigate seasonality fluctuations. The main goal is to develop a novel methodology for clustering and classification of circular data, under a Bayesian mixture modeling framework. The mixture model is defined assuming that the number of components is finite, but unknown, and that each component follows a projected normal distribution. Model selection is performed by jointly making inferences about the parameters of the mixture model and the number of components, choosing the model with the highest posterior probability. A deterministic relabeling strategy is used to recover identifiability for the components in the chosen model. Estimates of both the posterior classification probabilities and the scaled densities are approximated via the relabeled MCMC output. The proposed methods are illustrated using both simulated and real datasets, and performance comparisons with existing strategies are also given. The results suggest that the new approach is an appealing alternative for the clustering and classification of circular data.
Multi-Channel CNN Based Inner-Attention for Compound Sentence Relation Classification

Relation classification is a vital task in natural language processing, and it is screening for semantic relation between clauses in texts. This paper describes a study of relation classification on Chinese compound sentences without connectives. There exists an implicit relation in a compound sentence without connectives, which makes it difficult to realize the recognition of relation. The major challenges that relation classification modeling faces are how to obtain the contextual representation of sentence and relation dependence features between clauses. To solve this problem, we propose a novel Inatt-MCNN model to extract sentence features and classify relations by combining multi-channel CNN and Inner-attention mechanism. This network structure utilizes CNN to extract local features of sentences and Inner-attention to capture sentence-level feature representations for this relation classification task. Besides, since the Inner-attention is based on Bi-LSTM, the global and long-term dependence semantic information can be well obtained in Inatt-MCNN to promote the model performance. We conduct experiments on two public Chinese discourse datasets: the Chinese compound sentence corpus (CCCS) dataset and the Tsinghua Chinese Treebank(TCT) dataset. Compared with the previous public methods, Inatt-MCNN model has superior performance and achieves the highest accuracy, especially on the CCCS dataset.
Research on School Teaching Platform Based on Blockchain Technology

Blockchain has a good application prospect in the field of education and teaching. To promote the innovation of school education and teaching, this paper analyses the core technology and principle of blockchain, and puts forward its advantages of application in school teaching. Combining with educational practice, this paper explains the composition and function of the framework, core components, and application module in Blockchain Teaching Platform. This article also expounds the method of building Blockchain Teaching Platform, and puts forward some problems and thoughts of the blockchain application in school teaching. Generally speaking, it strives to carry out beneficial research and exploration in the deep integration of information technology and educational practice.
Countering Block Withholding Attack Efficiently

Bitcoin, well-known cryptocurrency, selected Poof-of-Work (PoW) for its security. PoW mechanism incentivizes participants and deters attacks on the network. So far, Bitcoin's PoW has been adopted in many cryptocurrencies. Researchers found, however, some vulnerabilities in PoW such as selfish mining, block withholding attack, and so on. Especially, after Rosenfeld suggested block withholding attack and Eyal made this attack practical, many variants and countermeasures have been proposed. However, most of countermeasures cause many changes in the mining algorithm itself, which makes it impractical. In this paper, we propose new countermeasure to prevent block withholding attack effectively. Mining pools can adapt our method without changing their mining environment.
The development of verb constructions in spoken learner English

Abstract Based on datasets of L1 Italian and Spanish learner language culled from the Trinity Lancaster Corpus Sample, this paper investigates how verb-argument constructions (VACs) develop in the spoken English of L2 learners across proficiency levels. In addition to proficiency and L1 effects, we focus on the potential influence of native English usage on learner VAC production. Insights into learners’ productive knowledge of five target VACs and the verbs used in those VACs are gained through (1) comparisons of normalized entropy scores for verbs in VACs; (2) correlation analyses comparing for each VAC the verbs produced by groups of learners and by native English speakers; and (3) regression analyses comparing learner verb-VAC associations against indices of VAC usage, including verb-VAC frequency, VAC-verb association strength and contingency. Results indicate that, across L1 backgrounds, more proficient learners are more productive in their VAC use and closer to patterns in L1 English usage than less proficient learners.
Sentiment Analysis of Chinese Product Reviews using Gated Recurrent Unit

Despite the explosive growth of Chinese e-commerce platforms in recent years, research focusing on the sentiment classification of Chinese documents pales in comparison to its western counterparts (English documents). This paper looks into the nascent area of Natural Language Processing (NLP) in the Sentiment Analysis of Chinese Text. The proposed Deep Learning method is the use of a sentence-based approach in the sentiment analysis of online reviews to gain more granularity and increased classification accuracy. Experimental results on a balanced (50:50), 2 class (positive, negative) test dataset of 1669 product reviews show an empirical accuracy of 87.66%, while results on an imbalanced (18:82) test dataset of 2519 product reviews show an accuracy of 87.9%, thus demonstrating the effectiveness and robustness of this proposed approach.
Fast Locality-Sensitive Hashing Frameworks for Approximate Near Neighbor Search

The Indyk-Motwani Locality-Sensitive Hashing (LSH) framework (STOC 1998) is a general technique for constructing a data structure to answer approximate near neighbor queries by using a distribution $$\mathcal {H}$$ over locality-sensitive hash functions that partition space. For a collection of n points, after preprocessing, the query time is dominated by $$O(n^{\rho } \log n)$$ evaluations of hash functions from $$\mathcal {H}$$ and $$O(n^{\rho })$$ hash table lookups and distance computations where $$\rho \in (0,1)$$ is determined by the locality-sensitivity properties of $$\mathcal {H}$$ . It follows from a recent result by Dahlgaard et al. (FOCS 2017) that the number of locality-sensitive hash functions can be reduced to $$O(\log ^2 n)$$ , leaving the query time to be dominated by $$O(n^{\rho })$$ distance computations and $$O(n^{\rho } \log n)$$ additional word-RAM operations. We state this result as a general framework and provide a simpler analysis showing that the number of lookups and distance computations closely match the Indyk-Motwani framework. Using ideas from another locality-sensitive hashing framework by Andoni and Indyk (SODA 2006) we are able to reduce the number of additional word-RAM operations to $$O(n^\rho )$$ .
Enhancing Machine Learning Aptitude Using Significant Cluster Identification for Augmented Image Refining

Enhancing the image to remove noise, preserving the useful features and edges are the most important tasks in image analysis. In this paper, Significant Cluster Identification for Maximum Edge Preservation (SCI-MEP), which works in parallel with clustering algorithms and improved efficiency of the machine learning aptitude, is proposed. Affinity propagation (AP) is a base method to obtain clusters from a learnt dictionary, with an adaptive window selection, which are then refined using SCI-MEP to preserve the semantic components of the image. Since only the significant clusters are worked upon, the computational time drastically reduces. The flexibility of SCI-MEP allows it to be integrated with any clustering algorithm to improve its efficiency. The method is tested and verified to remove Gaussian noise, rain noise and speckle noise from images. Our results have shown that SCI-MEP considerably optimizes the existing algorithms in terms of performance evaluation metrics.
Keyword-based approach for recognizing fraudulent messages by keystroke dynamics

In recent years, many approaches that use keystroke dynamics in free text authentication have been proposed. The major drawback of the proposed approaches is that training generally requires several months, thereby resulting in low practicality. In this study, a method to detect U.S. English fraudulent messages by analyzing keyboard users' keystroke dynamics is proposed. To the best of our knowledge, this is the first study to apply keystroke dynamics to detect fraudulent instant messages. In the proposed system, each user requires only approximately 20 min of training in U.S. English keystroke dynamics. Furthermore, a voting-based statistical classifier is presented to improve the recognition accuracy of instant messages and prevent phishing messages. Experimental results indicate that the proposed approach outperforms other relevant published methods in terms of shorter training time, fewer false alarms, and comparable recognition accuracy.
Blockchain Hands on for Developing Genesis Block

This chapter discusses the data processing models which are applicable in the blockchain technology. It also allows providing the distributed ledger technology for handling data. By exercising this chapter, one can create the own methodology for blockchain without hopping onto preexisting knowledge on it. This chapter also provides the underpinnings and practical aspects of blockchain implementation on platforms like Ethereum and Hyperledger Fabric.