Reviewerly/exHarmony huggingface dataset:
Black-box caches fingerprinting

We propose the first methodologies for remotely inferring and fingerprinting the software of DNS caches in the Internet based solely on the exchange of queries/responses with the DNS platform. Our techniques are robust and cannot be altered in transit, e.g., by firewalls, which does not hold for the existing fingerprinting techniques. In particular, the only way to alter the outcome of our fingerprinting methods is by modifying the DNS software itself.
Cognitive Writing: A New Approach to Organizing Technical Material

Philosophers have always concerned themselves with epistemology, the theories of knowledge. Plato believed that ideas were immortalized in universal forms, while his student Aristotle called for classifications from direct experience. Since 1948, when John Von Neumann compared the electronic computer, which was brand new, to the brain, a much older data processing apparatus, many computer science projects have built models of the mind. Working with experts in a variety of disciplines, including space exploration, medicine, and mathematics, knowledge engineers design expert systems with the goal of producing a pattern of output that could be considered intelligent “thinking.” Anthropologists are searching startlingly diverse cultures in hopes of defining universal systems of knowledge classification. Neuroscience and anthropology can be thought of as at the opposite end of the spectrum, yet neuro-scientists also experiment with cognitive theories. A sequence is an ordered set of items. Ducklings following a mother duck and the passage of the seasons are examples of natural sequences.
Privacy-Preserving Computation of the Earth Mover’s Distance

The Wasserstein distance, also known in computer science as the Earth Mover’s Distance (EMD) is a distance metric between two probability distributions. EMD has often been used as a distance metric to compare images and documents, and is central to privacy models such as t-closeness. In this work, we show that, given one-dimensional discrete probability distributions, the computation of EMD can be reduced to the computation of the cardinality of the intersection of two sets. We then use a private matching scheme to create a privacy-preserving computation protocol for EMD: two parties can compute EMD between their privately-owned documents without revealing them to the other party. We demonstrate our proposal by implementing a privacy-preserving reverse image search, where images are kept encrypted at an external server.
Optimal model for a Markov chain with Markov covariates

In this paper, we develop a model selection procedure for paired stochastic processes (Yt, Xt) where the Markov chain Xt chooses the set of transition probabilities to be used by Yt at time t. More precisely Xt is an order p Markov chain and given the value of Xt at time t, the (response) process Yt is an order o Markov chain with transition probabilities that depend on the specific value of Xt. This situation is different from Hidden Markov models and Double Markov chains (see [1]) since in our case, both processes Xt and Yt are observed. We define a family of models for this situation and investigate a consistent model selection procedure such that the final model has a minimal number of parameters. The methodology used to select a model is based on the Bayesian Information Criterion (BIC) by Schwarz [2]. The procedure consists of finding a partition of the joint state space determining the relationship between the joint processes of covariate and the response. The model and the model selection methodology, itroduced here, are generalizations of the Partition Markov models by Garćıa and González-López [3], [4], [5], and [6].
Distribution of Gold and Associated Ore-Bearing Elements in Gold-Copper-Pyrite Ores of the Kyzylbulag Deposit (Lesser Caucasus, Azerbaijan)

The article presents the results of studies of the distribution of gold and associated ore elements in gold-copper pyrite ores of the Kyzylbulag deposit. It was established that the elemental composition of ores and host rocks of the deposit includes Cu, Pb, Au, Ag, Ni, As, Sb, Bi, and Mo, of which Au, Cu, Ag, and Zn are the more stable. The behavior of elements was also studied for the entire ore body, as a result of which they are divided by cluster analysis at R (5 %) - 0.1 into two groups: 1) Au, Cu and Ag; 2) Pb and Zn. The closest relationship in the first group was found between Au and Cu, Au and Ag, Ag and Cu; and in the second, between Pb and Zn. Acquired results confirm that gold and the copper are the major components of ore in the field expressed generally in the mineral paragenesis of chalcopyrite - native gold shown in the independent hydrothermal stage of ore deposition.
Recommendation function for smart data analytics toolbox to support semantic merging of middle-of-life data streams

Continuous enhancements of connected products make them able to generate and communicate a huge amounts of middle-of-life data streams to their producers. This affordance also creates a challenge for current data analytics tools unable to keep up with the heterogeneous nature and characteristics of these type of data. Accordingly, a function able to combine data from multiple data streams and analyze them as one source of information is definitely needed in a next-generation data analytics toolbox to support product enhancements by designers. As a result of a recent Ph.D. project, this paper presents the conceptualization and the implementation of a novel function of merging middle-of-life data streams. The implemented computational mechanism (i) acquires middle-of-life data streams, (ii) pre-processes them individually, (iii) merges information from the concerned streams, (iv) derives recommendation based on the merged information, and (v) send a recommendation as a message to the designer. The performance of the computational implementation was tested in an application case of data steaming and management to white goods designers for enhancing a connected washing machine. From a computational point of view, the testing proved that the set of proprietary algorithms designed for the realization of computational merging, together with the existing ones taken from the literature, were able to efficiently perform the subtasks. The advantages of merges were: (i) it provides more information than the one obtained by processing sensors' data individually, (ii) it reflects the condition of the product with a higher fidelity, (iii) it communicates information about the product while it is in use by the customer, (iv) it reduces the sensors analyses time and effort, and (v) it provides recommendation as an action plan concerning the product at hand. The outcomes of this study will be used in a follow up research to develop a comprehensive smart data analytics toolbox to support product designers in product innovation.
Study of the influence of the unauthorized blocks number on the speed and RAM expenses during the data analysis process

Abstract In order to the proper operation of the hardware and software systems and for increase of the reliability of legal data, it is necessary for the hardware device to receive data only from the corresponding software. Otherwise, the data received from extraneous device can lead to errors in the operation of the device or even to a complete loss of its functionality or data. In order to identify the challenges of the transfer of blocks, this study focuses on the influence of the number of unauthorized blocks, on the speed and RAM expenses during the data analysis process. The described method allows to reduce the costs of hardware, which exchanges and analyzes blocks of information. This is done with the help of a buffer to store information and with a set of mathematical equations. And measure the extent of the effect of intensity of receiving unauthorized blocks and hash field length.
Introduction to Side Channel Attacks and Investigation of Power Analysis and Fault Injection Attack Techniques

Over the last few years industry is facing problems against new kind of attacks known as hardware attacks / physical attacks / side channel attacks. Engineers faces a challenge for building resistant free embedded system against attacks called as side channel attacks. Leaking information from computer devices (such as voltage fluctuations, power consumption, etc.) rather than software algorithms come under the category of hardware attacks or side channel attacks. Countless embedded devices are becoming victim to side channel attacks every day, and malicious attackers have become smart enough to impose threats on them. Therefore, there is a strong need to address issues related to side channel attacks. This chapter will provide an insight into the field of hardware security; it will provide a deep investigation of various types of side channel attacks and better understanding of various power analysis tools and fault injection techniques, which will further give researchers a vision to build efficient and secure systems in order to thwart attacks. This chapter focuses both on passive attacks as well as active attacks since embedded systems needs security against all kinds of attacks.
Unmanned Aerial Vehicle for Human Surveillance by Using Face and Upperbody Classifiers

The security and surveillance system frameworks are becoming essential part of our life. In thus framework an unmanned aerial vehicle based surveillance mechanism is proposed. The designed framework passes through several stages which are the modular design, system integration and calibration. The system optimization is realized by adjusting the flight board parameters and defining an effective flight plan. The designed system embeds a camera along with the face and the upper body classifiers in order to find out the human population in the concerned zone of aerial surveillance. A system prototype is successfully implemented and Validated.
The thesis topic similarity test with TF-IDF method

This research is to clarify how to test the thesis topic similarity, make it easy to check the topic thesis, whether it has been made by a student before. In this regard, an important issue that can be raised is how to make a thesis topic similarity test the manual way to be automated. The purpose of this study is a research method similarity of thesis topics using the TF-IDF method. In this research the system has two stages of process, the first mining the text that is categorizing the thesis that has been categorized using the TF-IDF algorithm, which is to read the appearance of each word in the contents of the document. The second stage results from the TF-IDF algorithm are reprocessed with the VSM algorithm. The end result of this program will get the names of documents that have a degree of similarity with keywords.
Multi-level Graph Label Propagation for Image Segmentation

This article introduces a multi-level automatic image segmentation method based on graphs and Label Propagation (LP), originally proposed for the detection of communities in complex networks, namely MGLP. To reduce the number of graph nodes, a super-pixel strategy is employed, followed by the computation of color descriptors. Segmentation is achieved by a deterministic propagation of vertex labels at each level. Several experiments with real color images of the BSDS500 dataset were performed to evaluate the method. Our method outperforms related strategies in terms of segmentation quality and processing time. Considering the Covering metric for image segmentation quality, for example, MGLP outperforms LPCI-SP, its most similar counterpart, in 38.99%. In term of processing times, MGLP is 1.07 faster than LPCI-SP.
Utilizing Gated Convolutional Picker to Enhance Relation Extraction

Distant supervision is an useful way to automatically gather training data for relation extraction. Aiming at the question of label marking error in distant supervision process, certain solutions are proposed. Nevertheless, the existing ways rarely think about the relation when deleting noisy data, and ignore that when the relation contained in a sentence mismatched with its label, the sentence is regarded as noise. In this research, we come up with a way to improve the relation classifier by utilizing sentence picker to eliminate the noise problem. Specifically, the gating convolution unit of the sentence picker could output the relational features, which will be utilized to determine if a sentence expresses a relationship. The relation classifier uses the high-quality training data picked by the sentence picker to enhance its performance. The two parts are trained alternately, and good performance is achieved. Experimental results tell that our module could significantly enhance the performance of relation classifier and is superior to the competitive baseline method.
Load Balancing for Machine Learning Platform in Heterogeneous Distribute Computing Environment

With the recent rapid development of computing power, interest in machine learning research on large data sets is increasing significantly. The machine learning is used in a wide variety of fields, from information retrieval, data mining, and speech recognition to human-computer interaction and application development by non-experts using machine learning platforms. However, there is not enough research on load balancing for distributed systems composed of heterogeneous servers with different performances and architectures that process machine learning tasks.
The Vacuity of the Open Source Security Testing Methodology Manual

The Open Source Security Testing Methodology Manual (OSSTMM) provides a “scientific methodology for the accurate characterization of operational security” [Her10, p.13]. It is extensively referenced in writings aimed at security testing professionals such as textbooks, standards and academic papers. In this work we offer a fundamental critique of OSSTMM and argue that it fails to deliver on its promise of actual security. Our contribution is threefold and builds on a textual critique of this methodology. First, OSSTMM’s central principle is that security can be understood as a quantity of which an entity has more or less. We show why this is wrong and how OSSTMM’s unified security score, the rav, is an empty abstraction. Second, OSSTMM disregards risk by replacing it with a trust metric which confuses multiple definitions of trust and, as a result, produces a meaningless score. Finally, OSSTMM has been hailed for its attention to human security. Yet it understands all human agency as a security threat that needs to be constantly monitored and controlled. Thus, we argue that OSSTMM is neither fit for purpose nor can it be salvaged, and it should be abandoned by security professionals.
Performance Evaluation of The Cepstral-Based STPS Features with Res-TCNetworks for ASR in Adverse Environments

In this paper, we evaluate the performance of Automatic speech recognition (ASR) using cepstral-based Smoothed and Thresholded Power Spectrum (STPS-LP) features with residual Temporal Convolution Networks (Res-TCN). The experiments were performed in comparison with cepstral-based LP features, where both features were obtained from AMR bit-stream parameters. In terms of Word Error Rate (WER%), results show that using the STPS-LP cepstral features, the performance of speech recognition was on average 6.22% better in comparison with the conventional LP cepstral features, depending on the type of environmental noise.
Efficient fault‐tolerant logical Hadamard gates implementation in Reed–Muller quantum codes

Abstract We investigate the implementation of fault‐tolerant logical Hadamard gates in Reed–Muller quantum codes (RMQCs). During the realization, we consider the influences of random single‐qubit errors and some error‐detecting stabilizers are simplified by the existing syndromes. We first identify the errors and modify the gauge‐fixing syndromes, then refer to the modified syndromes to select the fix operations, and finally perform the error‐correcting and fix operations together. Furthermore, we establish a graph model for the RMQCs and exhibit a progress of finding the fix operations for the unsatisfied stabilizers. For the circuit design, we optimize the choice of gauge operators, the positions of the ancillary qubits and design a parallel circuit for implementing a fault‐tolerant logical Hadamard gate in 15‐qubit RMQC. We simulate the progress of finding corresponding fix operations for 31‐qubit and 63‐qubit RMQCs and the whole process of realizing logical Hadamard gate with random single‐qubit errors for 15‐qubit and 31‐qubit RMQCs. Results show that correct fix operations can be obtained and fault‐tolerant logical Hadamard gates can be realized as expected. The performance comparisons are also given and the results show that our method can achieve a higher success rate with a reasonable higher cost. With the implementation of the logical Hadamard gate, a universal fault‐tolerant gate set is achieved in single RMQC.
Research on sentiment classification of Chinese short text

Abstract Sentiment classification can be widely used in various fields because of the value it has gained, and has received extensive attention in recent years. No matter the traditional methods based on language rules, machine statistical learning, or the current popular deep learning have achieved great success in this respect, but up to now, because of the particularity of Chinese, there are still few methods for Chinese text sentiment classification. Moreover, the traditional method is time-consuming and labor-consuming, with poor versatility. The neural network method saves resources and is generally applicable, but the application of language knowledge is not sufficient. For this reason, we propose a method combining traditional method with neural network, which combines sentiment lexicon, attention mechanism and stacked BILSTM. Word vectors are generated using word2vec, and the context semantics are captured multiple times by stacked BILSTM to get richer semantic information, for sentiment classification, sentiment words often have more influence on the result of classification, so we use sentiment lexicon which contains rich sentiment word to monitor the attention mechanism, and make the attention model pay more attention to sentiment words. Combining the advantages of traditional methods and neural networks, we use the proposed method to do the sentiment binary classification experiments on two data sets. The experimental results show that our proposed method is superior to the previous method.
Comment on secure file storage and retrieval in cloud

In 2015, Thangavel et al. proposed the MRSA scheme which differs from the work by Ivy et al. Thangavel's scheme has error since they misused RSA algorithm. In this paper the author would like to examine this error and also correct it.
TEXTS OF THE STUDENTS IN PEDAGOGICS AS A MONOLOGUE AND A SOLILOQUY

The research analyses practically proved model of text centered model of pedagogic and phycological profile of the students based on maximal effect in using of static and dynamic features of created/creating texts. Traditionally in university education in Bulgaria is related active participation with written textsgroup work, solitary work, assuming resume or syllabus, report or research on specific problem, developing of conspectus of lessons, analysis of viewed lessons, summary of texts used in different situations, etc. The article suggests a model of slightly different model of analyzing and using of texts, created by students in the context of realization of the educational model based on reflexibility. The research is based on 284 texts created from March 2017 to October 2018 by students first and second year of education in The University of the Town of Shumen Bishop Konstantin Preslavski – Primary school teaching with a foreign language, Preparatory class teaching and Preparatory and primary teaching. The texts of the students have been viewed in two contexts static as a whole verbal product and dynamic as realized actions of producing and analyzing of the texts. The analysis of students’ texts is done due to division either as a monologue or soliloquy with specific content, structure, formal and functional specifics. The effect of research and evaluating the texts with pedagogical themes is proved in the context of ruling educational motives-activity, reflexibility, m otivation,responsibility in self education.
Multi-sampling Classifiers for the Cooking Activity Recognition Challenge

We propose multi-sampling classifiers (MSC), a collection of multi-class and binary classifiers, to address the cooking activity recognition challenge (CARC), CARC consists of macro and micro labels. To deal with these labels, MSC uses a multi-class classifier to recognize the macro labels and 10 binary classifiers to examine whether each micro label exists. To shield the MSC model from sampling noise, we generate three distinct resampling rates of the temporal sensor data. All predictions of the three data sampling rates are gathered together using a soft-majority voting ensemble.
A Study on Nuclei Shape Features at the Classification of Glioma Disease Stage Using CNN

Recently, a lot of studies using Deep Learning techniques have been reported in the field of Digital Histopathology. For instance, there are ideas using deep Convolutional Neural Network (CNN) for disease stage classification and segmentation. These methods are expected to reduce pathologists’ work and realize quantitative analysis. However, at the disease stage classification using CNN, even if we can obtain high classification accuracy, it is difficult for us to understand how CNN decides the disease stage. In this paper, we discussed the relationship between features of cell nuclei shape and the disease stage classification using CNN.
A Convex Optimization Based Method for Color Image Reconstruction

In this paper, we present an efficient method for reconstructing color images based on the convex optimization. Current convex optimization methods are all applied to one channel images, so as the number of channels increases, the number of convex optimization problems increase. We present a solution to reconstruct color images represented by three color channels RGB by only one convex optimization problem and use a high-performance algorithm BFGS for solving the problem. We also perform several experiments to compare our method with others on reality datasets.
Modified YOLOv3-Tiny Using Dilated Convolution for Driver Distraction Detection

In this paper, the proposed YOLOv3-tiny-dilated network is presented. The well-known YOLO series neural network is famous for its rapidity, and the lightweight version is more suitable for mobile devices or embedded systems. However, the accuracy of one-stage network is slightly lower. Therefore, scale variation problem is investigated in this paper and we replaced the original Feature Pyramid Network (FPN) of the original YOLOv3-tiny network by three dilated convolution branches of different dilation rates to increase the accuracy of the network. As a result, the mean average precision (mAP) was improved by about 2% in the COCO dataset test. Besides, the proposed network was used to identify driving distraction behavior and obtain good accuracy of 95.47%.
Deceptive actions and demonstrations of intentions for robot collision avoidance

Efficiency and effectiveness of many robotics tasks depend crucially on the quality of the solution of the problem of collision avoidance. In many cases, traditional path planning and obstacle avoiding approaches and algorithms can not guarantee a sufficiently high quality of the solution of the problem of collision avoidance for multi-robot and human-robot interactions. In this paper, we consider demonstrations of true intentions and demonstrations of deceptions for the problem of collision avoidance.