Reviewerly/exHarmony huggingface dataset:
An algorithm based on evidence theory and fuzzy entropy to defend against SSDF

In cognitive radio networks, spectrum sensing is one of the most important functions to identify available spectrum for improving the spectrum utilization.Due to the open characteristic of the wireless electromagnetic environment, the wireless network is vulnerable to be attacked by malicious users (MUs), and spectrum sensing data falsification (SSDF) attack is one of the most harmful attacks on spectrum sensing performance.In this article, an algorithm based on the evidence theory and fuzzy entropy is proposed to resist SSDF attacks.In this algorithm, secondary users (SUs) obtain the corresponding degree of membership function and basic probability assignment function based on the local energy detection result.The new conflicting coefficient is calculated based on the evidence distance and classical conflicting coefficient, and the conflicting weight of the evidence is obtained.The fuzzy weight is calculated by the fuzzy entropy.The credibility weight is obtained by updating the credibility.On this basis, the probability assignment function of the evidence is corrected, and the final result is obtained by using the fusion formula.Simulation results show that the proposed algorithm has a higher detection probability and lower false alarm probability than other algorithms.It can effectively defend against SSDF attacks and improve the performance of spectrum sensing.
Applying Knowledge Graphs as Integrated Semantic Information Model for the Computerized Engineering of Building Automation Systems

During the life cycle of a smart building, an extensive amount of heterogeneous information is required to plan, construct, operate and maintain the building and its technical systems. Traditionally, there is an information gap between the different phases and stakeholders, leading to information being exchanged, processed and stored in a variety of mostly human-readable documents. This paper shows how a knowledge graph can be established as integrated information model that can provide the required information for all phases in a machine-interpretable way. The knowledge graph describes and connects all relevant information, which allows combining and applying it in a holistic way. This makes the knowledge graph a key enabler for a variety of advanced, computerized engineering tasks, ranging from the planning and design phases over the commissioning and the operation of a building. The computerized engineering of building automation systems (BAS) with an advanced software tool chain is presented as such a use case in more detail. The knowledge graph is based on standard semantic web technologies and builds on existing ontologies, such as the Brick and QUDT ontologies, with various novel extensions presented in this paper. Special attention is given to the rich semantic definition of the entities, such as the equipment and the typically thousands of datapoints in a BAS, which can be achieved as a combination of contextual modeling and semantic tagging.
Fela: Incorporating Flexible Parallelism and Elastic Tuning to Accelerate Large-Scale DML

Distributed machine learning (DML) has become the common practice in industry, because of the explosive volume of training data and the growing complexity of training model. Traditional DML follows data parallelism but causes significant communication cost, due to the huge amount of parameter transmission. The recently emerging model-parallel solutions can reduce the communication workload, but leads to load imbalance and serious straggler problems. More importantly, the existing solutions, either data-parallel or model-parallel, ignore the nature of flexible parallelism for most DML tasks, thus failing to fully exploit the GPU computation power. Targeting at these existing drawbacks, we propose Fela, which incorporates both flexible parallelism and elastic tuning mechanism to accelerate DML. In order to fully leverage GPU power and reduce communication cost, Fela adopts hybrid parallelism and uses flexible parallel degrees to train different parts of the model. Meanwhile, Fela designs token-based scheduling policy to elastically tune the workload among different workers, thus mitigating the straggler effect and achieve better load balance. Our comparative experiments show that Fela can significantly improve the training throughput and outperforms the three main baselines (i.e. dataparallel, model-parallel, and hybrid-parallel) by up to 3.23×, 12.22×, and 1.85× respectively.
Use and Abuse of General Criterion for Discrimination of Causal Correlations from Individual Peculiarities and Provisional Correlations

A fundamentally novel approach to the issue about existence of a general criterion for autonomous discrimination of causal correlations from individual peculiarities and provisional correlations at stable complex systems is put forward.It is grounded on a recently proven by the author decomposition theorem whose subject has no cross section with the subject of the Central Limit Theorem.The fundamental advantage of that criterion lies in insensitivity to the details of the underlying dynamics and to the details of the hierarchical structure regardless of the nature of the corresponding system.It holds in an unspecified ever-changing environment.It also holds when information is incomplete and/or uncertain.Another advantage of the criterion is the ability to forecast a change in a system.The limitation of the criterion is substantiated as a ban over predictability whether that change would develop in an adaptation or in destruction.It is worth noting that while the criterion itself holds in the frame of the recently proposed theory of boundedness, the ban over prediction of the nature of a change is model-free.
Markov Boundary Learning With Streaming Data for Supervised Classification

In this paper, we study the problem of Markov boundary (MB) learning with streaming data. A MB is a crucial concept in a Bayesian network (BN) and plays an important role in BN structure learning. In addition, in the supervised learning setting, the MB of a class attribute is optimal to feature selection for classification. Almost all existing MB learning algorithms focus on static data, but few efforts have been proposed to learning MBs with streaming data. In this paper, by linking dynamic AD-trees with streaming data, we proposed a new SDMB (streaming data-based MB) algorithm for learning MBs with streaming data. Specifically, given a target variable, SDMB employs a dynamic AD-tree to summarize the historical data, then the SDMB sequentially learns the MB of the target upon all available data by calculating independence tests using the dynamic AD-tree. In experiments, using the synthetic and real-world data sets, we evaluate the SDMB algorithm and compared it with the state-of-the-art online feature selection algorithms and data stream mining methods, and the experimental results validate the SDMB algorithm.
Campuses of the Future in University Cities in Russia and France in the Context of the Digital Economy, Innovation and a Barrier-Free Environment

А.М.Елисеев, Ю.В.Подопригора, Т
Software as a Service in Cloud Computing

Software as a Service (SaaS) is one of the delivery model of cloud computing in which service are provided by the service providers and client make use of it through Internet.Here we present a saas in pcloud.pcloud is a distributed system which contains a number of cooperated nodes.In pcloud, distribute the programming software to a number of nodes so that queries can be executed in parallel manner.In this distributed system, communication overhead between the co-operated nodes is very low.Programming code is send as a user queries to pcloud where it is processed and retrieved through PIR protocol.The concept used by the PIR protocol is the way that accesses of sequential segments are made to different physical storage devices.To minimize the effect of node failures, pCloud includes data replication.The Load Balancing can be achieved by loosely coupled and nearest neighbor property of clouds.
Keeping the Learner at the Focal Point: The Use of Needs Assessment and Persona Construction to Develop an Instructional Resource Center For Instructional Designers

This design case shares how personas were utilized to create an online career resource center intended to help instructional design students in making informed career decisions. The outcome of this project has helped identify instructional opportunities to improve services currently provided to instructional design students and graduate student members of the Association for Education Communications and Technology (AECT ). By evaluating the mechanisms and supports already in place and contrasting those with the needs of the students who are seeking career guidance, the intent of the project was to bridge the gap between graduate student members’ career needs and the resources available to them within a professional association. We discuss how&#x0D; we gathered information through a needs assessment for the creation of a Career Resource Center to further educate and inform instructional design students of resources for career decision-making.
Performance evaluation of decision tree classification algorithms using fraud datasets

Text mining is one way of extracting knowledge and finding out hidden relationships among data using artificial intelligence methods. Surely, taking advantage of different techniques has been highlighted in previous researches however, the lack of literature focusing on cybercrimes implies the lack of utilization of data mining in facilitating cybercrime investigations in the Philippines. This study therefore classifies computer fraud or online scam data coming from Police incident reports as well as narratives of scam victims as a continuation of a prior study. The dataset consists mainly of unstructured data of 49,822 mainly Filipino words. Further, five (5) decision tree algorithms namely, J48, Hoeffding Tree, Decision Stump, REPTree, and Random Forest were employed and compared in terms of their performance and prediction accuracy. The results show that J48 achieves the highest accuracy and the lowest error rate among other classifiers. Results were validated by Police investigators where J48 was likewise preferred as a potential tool to apply in cybercrime investigations. This indicates the importance of text mining in the field of cybercrime investigation domains in the country. Further work can be carried out in the future using different and more inclusive cybercrime datasets and other classification techniques in Weka or any other data mining tool.
DEEP-AD: A Multimodal Temporal Video Segmentation Framework for Online Video Advertising

In this paper, we introduce the DEEP-AD framework, a multimodal advertisement insertion system dedicated to online video platforms. The framework is designed from the viewer's perspective, in terms of commercial contextual relevance and degree of intrusiveness. The main contribution of the paper concerns a novel multimodal temporal video segmentation algorithm into scenes/stories, which makes it possible to determine automatically the temporal instants that are the most appropriate for inserting advertisement clips. The proposed algorithm exploits various deep convolutional neural networks, involved at several stages. The video stream is first divided into shots based on a graph partition method. The video shots are then clustered into scenes/story units with the help of an agglomerative clustering methodology taking as input visual, audio and semantic features. Furthermore, in order to facilitate the user's access to multimedia documents a novel thumbnail extraction method is proposed based on both semantic representativeness and visual quality information. Finally, the optimal advertisement insertion points are determined based on the ads temporal distribution, commercial diversity and degree of intrusiveness. The experimental results, carried out on a large dataset of more than 30 videos, taken from the French National Television and US TV series validate the proposed methodology with average accuracy and recognition rates superior to 88%. Moreover, when compared with other state of the art methods, the proposed temporal video segmentation yields gains of more than 6% in precision and recall rates.
Decentralized electronic voting system based on blockchain technology developing principals

Electronic trust services are becoming an integral part of the information space.With the reliable implementation of basic services as an electronic signature and electronic authentication, it is possible to build more complex systems that rely on them, particularly the electronic voting system.In the paper, the new concept for developing a decentralized electronic voting system using blockchain technology is proposed.The two-level architecture provides a secure voting process without redundancy of existing (not based on blockchain) systems.The presented blockchain-based voting protocol has six steps that ensure all requirements that are put forward to such types of protocols including voting transparency and anonymity.
Adaptive dictionary learning based on local configuration pattern for face recognition

Abstract Sparse representation based on classification and collaborative representation based classification with regularized least square has been successfully used in face recognition. The over-completed dictionary is crucial for the approaches based on sparse representation or collaborative representation because it directly determines recognition accuracy and recognition time. In this paper, we proposed an algorithm of adaptive dictionary learning according to the inputting testing image. First, nearest neighbors of the testing image are labeled in local configuration pattern (LCP) subspace employing statistical similarity and configuration similarity defined in this paper. Then the face images labeled as nearest neighbors are used as atoms to build the adaptive representation dictionary, which means all atoms of this dictionary are nearest neighbors and they are more similar to the testing image in structure. Finally, the testing image is collaboratively represented and classified class by class with this proposed adaptive over-completed compact dictionary. Nearest neighbors are labeled by local binary pattern and microscopic feature in the very low dimension LCP subspace, so the labeling is very fast. The number of nearest neighbors is changeable for the different testing samples and is much less than that of all training samples generally, which significantly reduces the computational cost. In addition, atoms of this proposed dictionary are these high dimension face image vectors but not lower dimension LCP feature vectors, which ensures not only that the information included in face image is not lost but also that the atoms are more similar to the testing image in structure, which greatly increases the recognition accuracy. We also use the Fisher ratio to assess the robustness of this proposed dictionary. The extensive experiments on representative face databases with variations of lighting, expression, pose, and occlusion demonstrate that the proposed approach is superior both in recognition time and in accuracy.
Acoustic and Thermal Insulation of Nanocomposites for Building Material

This work aims to enhance acoustic and thermal insulation properties for polymeric composite by adding nanoclay and rock wool as reinforcement materials with different rations. A polymer blend of (epoxy+ polyester) as matrix materials was used. The Hand lay-up technique was used to manufacture the castings. Epoxy and polyester were mixed at different weight ratios involving (50:50, 60:40, 70:30, 80:20, and 90:10) wt. % of (epoxy: polyester) wt. % respectively. Impact tests for optimum sample (OMR), caustic and thermal insulation tests were performed. Nano clay (Kaolinite) with ratios ( 5 and 7.5% ) wt.% , also hybrid reinforcement materials involving (Kaolite 5 &amp; 7.5 % wt.% + 10% volume fraction of rockwool ) were added as reinforcement materials to the optimum sample. Results of impact test prove that the optimum sample has (80:20) wt. % of mixing ratio of (epoxy: polyester) wt. % for using as matrix materials. Moreover, the adding of nanoclay (Kaolinite) with ratio (7.5 wt.%) leads to the highest sound insulation. The sound intensity started at (99.8) db at 100 Hz, and reached to (101.3) db at 10000 Hz., which is much lower than the values obtained from the un-reinforced blend, of which the sound intensity started at (107.2) db and reached to (108.7) db., at the same range of frequencies. Thermal conductivity results show that the optimum matrix with (7.5 %) wt. % has the lowest value about (0.443 k.w\m .c).&#x0D; The results show that the blend reinforced with nano clay in a weight fraction (7.5)% has the best sound insulation, so that the sound intensity started at (99.8) db at 100 Hz., and reached (101.3) db at 10000 Hz., which is much lower than the values obtained from the unreinforced blend, of which the sound intensity started at (107.2) db and reached (108.7) db., applying the same range of frequencies. The same casting (blend+7.5% nanoclay) showed the lowest value of thermal conductivity (xxxx) W.m°C in comparison with castings that were made of unreinforced blend and those hybridized with rockwool.
Measuring the Similarity between the Sanskrit Documents using the Context of the Corpus

Identifying the similarity between two documents is a challenging but important task.It benefits various applications like recommender systems, plagiarism detection and so on.To process any text document one of the popularly used approaches is document term matrix (DTM).The proposed approach processes the oldest, untouched, one of the morphologically critical languages, Sanskrit and builds a document term matrix for Sanskrit (DTMS) and Document synset matrix Sanskrit (DSMS).DTMS uses the frequency of the term whereas DSMS uses the frequency of synset instead of term and contributes to the dimension reduction.The proposed approach considers the semantics and context of the corpus to solve the problem of polysemy.More than 760 documents including Subhashitas and stories are processed together.F1 Score, precision, Matthews Correlation coefficient (MCC) which is the most balanced measure and accuracy are used to prove the betterment of the proposed approach.
Document Clustering Using K-Means with Term Weighting as Similarity-Based Constraints

In similarity-based constrained clustering, there have been various approaches on how to define the similarity between documents to guide the grouping of similar documents together. This paper presents an approach to use term-distribution statistics extracted from a small number of cue instances with their known classes, for term weightings as indirect distance constraint. As for distribution-based term weighting, three types of term-oriented standard deviations are exploited: distribution of a term in a collection (SD), average distribution of a term in a class (ACSD), and average distribution of a term among classes (CSD). These term weightings are explored with the consideration of symmetry concepts by varying the magnitude to positive and negative for promoting and demoting effects of three standard deviations. In k-means, followed the symmetry concept, both seeded and unseeded centroid initializations are investigated and compared to the centroid-based classification. Our experiment is conducted using five English text collections and one Thai text collection, i.e., Amazon, DI, WebKB1, WebKB2, and 20Newsgroup, as well as TR, a collection of Thai reform-related opinions. Compared to the conventional TFIDF, the distribution-based term weighting improves the centroid-based method, seeded k-means, and k-means with the error reduction rate of 22.45%, 31.13%, and 58.96%.
The distillability of entanglement of bipartite reduced density matrices of a tripartite state

The relation between the distillability of entanglement of three bipartite reduced density matrices from a tripartite pure state has been studied in Hayashi and Chen [2011 Phys. Rev. A 84 012325]. We extend this result to the tripartite mixed state of rank at most three. In particular we show that if the state has two bipartite reduced density operators with rank two, then the third bipartite reduced density operator additionally having non positive partial transpose (non-PPT) is distillable. In contrast, we show that the tripartite PPT state with two reduced density operators of rank two is a three-qubit fully separable state. We obtain these facts by proving a conjectured matrix inequality for the bipartite matrix M with Schmidt rank at most three. This is one of the main results of this paper. We also prove it for some M with arbitrary Schmidt rank.
Deep Learning Based Code Completion Models for Programming Codes

With the fast development of Information Technology, program software and mobile applications have been widely used in the world, and are playing important roles in human's daily life. Thus, writing programming codes has been important work in many fields. however, it is a hard and time-cost task which presents a great amount of workload to programmers. To make programmers' work easier, intelligent code completion models have been a popular research topic in recent years. This paper designs Deep Learning based models to automatically complete programming codes, which are LSTM-based neural networks, and are combined with several techniques such as Word Embedding models in NLP (Natural Language Processing), and Multihead Attention Mechanism. Moreover, in the models, this paper raises a new algorithm of generating input sequences from partial AST (Abstract Syntax Tree) that have most relevance with nodes to be predicted which is named as RZT (Reverse Zig-zag Traverse) Algorithm, and is the first work of applying Multihead Attention Block into this task. This paper makes insight into codes of several different programming languages, and the models this paper presents show good performances in accuracy comparing with the state-of-art models.
EPNN Based High Secure Intensive Hidden Digital Watermark Application in Telemedicine

This paper presents a novel and secure hybrid approach to securely transmit multimedia medical content over the web to help diagnosis from distance.This approach is a combination of a novel encryption algorithm and Extended Euclid's based watermarking algorithm.This approach provides authentication, confidentiality, and integrity.The user's authentication data is converted into ciphertext and then inserted into the watermark image, a 2-level LWT based decomposition of a host image is performed to retrieve (LL, LH, HL & HH) coefficients.The watermark and cover image one-level LL coefficients are divided into 16*16 blocks and watermark image blocks are embedded into a cover image using Extended Euclid's algorithm.Then the watermarked image and cover image is given as input to PNN to obtain the optimized image matrix.The experimental results show better Peak Signal to Noise Ratio, Mean Square Error and Normalized Correlation with respect to existing methodology.Also, the watermarked image quality is analyzed with different image quality metrics.
A New design of fuzzy logic controller optimized By PSO-SCSO applied To SFO-DTC induction motor drive

In this paper, a new strategy for the design of fuzzy logic controllers (FLC) is proposed. This strategy is based on the optimization, by the particle swarm optimization (PSO) Algorithm and by sine-cosine based swarm optimization (SCSO) Algorithm, of the input-output gains and the geometric forms of the triangular membership functions of the proposed FLC. This latter is obtained by the minimizing a cost function based on combining two criterions, the Integral Time Absolute Error (ITAE) and Integral Absolute Error (IAE). A comparison, between the conventional FLC and the proposed FLC is carried out. The PSO-FLC shows a remarkable improvement in the performances of the controlled induction motor.