Reviewerly/exHarmony huggingface dataset:
Hardware lightweight design of PRESENT block cipher

In present days the demand for security is increased, so that people gained interest to study on lightweight symmetric ciphers in computing fields like IoT. There are many different security primitives. By that it becomes very difficult to choose the particular primitive for particular application. There are different algorithms for different security primitives. Nowadays embedded device plays major role in the society, which has the ability to transfer data by converting into simple ubiquitous computing devices. They include some category such ass retile, IT, Health, Bio Science, Industrial and networking. As increase in the technology IoT faces many challenges, like handling huge amounts of data, processing power deal with energy consumption, address security threats. In This paper presents a novel design for the lightweight block cipher PRESENT. Which is an implement from AES cipher, which is also a one of the block ciphers. AES is best for almost all applications, and all are mostly preferred block cipher. Now for updated world AES is not applicable for the some of the networks which are like RFID, sensors and some other networks. So that to avoid these network problems PRESENT is the ultra-light weight block cipher which has the equal importance to both security and hardware planning. By this updated advance design will permits to examine the size, occupied space, speed etc. Hence in this we implemented in a compact size.
Probabilistic Neural Network - parameters adjustment in classification task

This work presents a comparative analysis of probabilistic neural network training methods applied to achieve best performance in various classification tasks. Two result from classical mathematical methods based on the theory of kernel density estimators: the plug-in method and cross-validation procedure. The other two methods are more advanced: a metaheuristic algorithm of particle swarm optimization, and a procedure based on reinforcement learning. Ten data sets, regarded in eleven classification problems, taken from the UCI repository are used for the numerical analysis. A comparative analysis of probabilistic neural network learning methods leads to interesting conclusions. Although it does not allow for unambiguous selection of the best learning method, it provides a possibility of choosing a method that is adequate for the given conditions. The description of this is included in the work.
Review of The Risk Analysis Using MEHARI Model: The Guideline To Analyze Risk For Startup Educational Platform

The risk analysis supported by technical assets can be more expensive and not enough to analyze information security requirements. The old method adaptation depend mostly on the calculations estimatations of probabilities and financial loss during a security breach. The purpose of this paper is to propose alternative ways to conduct risk analysis for small companies. Risk analysis defined in the paper is an analysis to identify such comprehensive risks set by concentrating by equal to the information and technology also not undermine the people and process area too. This method considers the essential business processes that focus for the relevance of the analysis. Crucial items for this practice consist of business-driven studies for identify relevant IT assets and risk method scenarios for capturing procedure details, and security. People involvement and tools selected for the analysis will be expected for result from set of risks that identified more comprehensively also for high increase of the security awareness to all of the organization.
Hybrid Model of Eye Movement Behavior Recognition for Virtual Workshop

Purpose This paper is oriented to the virtual workshop scenario, discusses the creation of a feasible eye movement recognition model and provides new ideas for eye movement recognition and eye movement interaction in the virtual workshop. Method Obtain user’s eye movement data through eye tracker in real time, create blink behavior recognition model and gaze/saccade behavior recognition model based on convolutional neural network (CNN) and bidirectional long short-term memory network (Bi-LSTM). A data set was created by collecting eye movement data of three subjects for comparative experiments to verify the performance of the proposed model. Results The experimental results showed that the recognition accuracy, Kappa coefficient, F1 score and running time of the proposed model had certain advantages over other models. Conclusion The created hybrid model of eye movement recognition for virtual workshops has high reliability and effectiveness, which will create a foundation for future research on building eye movement interaction systems.
Affine Deformation Model Based Intra Block Copy for Intra Frame Coding

In intra frame coding, many methods are proposed to reduce spatial redundancy, including angular intra prediction and intra block copy. However, these two methods cannot deal with complex structure redundancy, such as scaling and rotation relationship where spatial structures are the same but sizes and orientations are different, leading to limited coding efficiency. In this paper, we first mathematically prove that 4-parameter affine deformation model can describe this complex relationship. Then we propose an affine deformation model based intra block copy (ADMIBC) scheme. In the scheme, we focus on several problems, including a fast displacement vector validity judgement algorithm to reduce encoding time, a padding method for unavailable reference pixels during sub-pixel interpolation to improve prediction accuracy and candidate list building methods of control point displacement vector predictor to reduce coding bits. Experimental results show that ADMIBC achieves up to -2.16% BD-rate reduction and -0.99% on average in all intra configuration, compared to the latest video coding standard Versatile Video Coding (VVC).
Towards Explainable Direct Marketing in the Telecom Industry Through Hybrid Machine Learning

Direct marketing enables businesses to identify customers that could be interested in product offerings based on historical customer transactions data. Several machine learning (ML) tools are currently being used for direct marketing. However, the disadvantage of ML algorithmic models is that even though results could be accurate, they lack relevant explanations. The lack of detailed explanations that justify recommendations has led to reduced trust in ML-based recommendations for decision making in some critical real-world domains. The telecommunication domain has continued to witness a decline of revenue in core areas such as voice and text messaging services which make direct marketing useful to increase profit. This paper presents the conceptual design of a machine learning process framework that will enable telecom subscribers that should be targeted for direct marketing of new products to be identified, and also provide explanations for the recommendations. To do this, a hybrid framework that employs supervised learning, case-based reasoning and rule-based reasoning is proposed. The operational workflow of the framework is demonstrated with an example, while the plan of implementation and evaluation are also discussed.
Visual-to-Semantic Hashing for Zero Shot Learning

Hashing-based multimedia retrieval are facing the problem of the dramatic increase of data, especially new unseen categories. It is time-consuming, expensive, and sometimes impractical to label new samples and retrain the hashing model. Recently, several zero-shot hashing methods are proposed to generate the hash function with good generalization for unseen classes, via exploring semantic information and similarity relationship. However, the performance of existing solutions is still not satisfying. Therefore, we propose a modified two-stage framework, called Visual-to-Semantic Hashing (VSH). To fully exploit the semantic information, visual feature is firstly mapped to the semantic space, and then generate its hash codes. To transfer supervised knowledge from seen classes to unseen classes, a margin-based ranking loss is employed to learn the semantic structure. To boost the discriminability of semantic mapping, a classification module is adopted to distinguish between different semantic mapping vectors. Plenty of experiments demonstrate that the proposed VSH is superior to state-of-the-art methods.
Joint Entity Linking and Relation Extraction with Neural Networks for Knowledge Base Population

Relation extraction and entity linking are two fundamental procedures to extend knowledge bases. Most existing methods typically treat them separately and ignore the semantic relevance between entities and relations. In this paper, we pioneer a general joint learning framework for relation extraction and entity linking, which allows these two tasks boost each other. Based on the framework, a demonstration model is proposed with neural networks. We conduct experiments on variants of a standard benchmark dataset (NYT-10) to verify the effectiveness of our approach. Experimental results show that our approach significantly outperforms traditional separate methods without reducing efficiency, especially on datasets with many ambiguous entity mentions. Furthermore, various mainstream methods for relation extraction and entity linking can be easily integrated into our loosely-coupled framework due to its flexible architecture.
AmbiNet Modeling of Spatial Aspects of Things

In recent years, the authors have been developing the Virtual-Physical Space (ViPS) reference architecture that can be adapted to various CPSS-like applications. ViPS supports the virtualization of physical “things” by considering factors such as events, time, space, and location. This article examines the ViPS AmbiNet component responsible for modeling the spatial aspects of “things” implemented in accordance with the Context-Aware Ambient Calculus (CCA) formalism, the approximate modeling language, and supporting tools.
Effective Sentiment Analysis for Multimodal Review Data on the Web

Multimodal review data on the web mainly involve videos, audios, and texts, which have been the major form of review data on E-business, social networks, etc. Extracting the sentimental opinions of the multimodal review data on the web is helpful for many web-based services, such as online customer services, product recommendation, and personalized web search. However, the multiple modalities of the multimodal review data on the web introduces new challenges for sentiment analysis. The key issue is how to make fusion of multimodal data to improve the effectiveness of sentiment analysis. In this paper, we present a novel two-staged self-attention-based approach for multimodal sentiment analysis. At the first stage, we perform an inter-utterance learning by using a self-attention neural network. At the second stage, we conduct a cross-model fusion to integrate information from different modalities. To the best of our knowledge, this is the first work that utilizes the self-attention network to achieve inter-utterance sentiment analysis for multimodal data. We conduct experiments on two public datasets and compare our proposal with several state-of-the-art methods. The results suggest the effectiveness and robustness of our proposal.
Template Matching and Decision Diagrams for Multi-agent Path Finding

We propose a polyhedral cutting plane procedure for computing a lower bound on the optimal solution to multi-agent path finding (MAPF) problems. We obtain our cuts by projecting the polytope representing the solutions to MAPF to lower dimensions. A novel feature of our approach is that the projection polytopes we used to derive the cuts can be viewed as ‘templates’. By translating these templates spatio-temporally, we obtain different projections, and so the cut generation scheme is reminiscent of the template matching technique from image processing. We use decision diagrams to compactly represent the templates and to perform the cut generation. To obtain the lower bound, we embed our cut generation procedure into a Lagrangian Relax-and-Cut scheme. We incorporate our lower bounds as a node evaluation function in a conflict-based search procedure, and experimentally evaluate its effectiveness.
Data Quality in a Decentralized Environment

One of the most important aspects of applying distributed ledger technologies in the field of big data is ensuring the necessary data quality and calculating the corresponding metrics. The paper proposes a conceptual framework for working with Master Data in a decentralized environment. The greatest effect of this framework methods is increasing a real-time integrity for the data segments that have the direct impact on overall data quality. The proposed approach provides the result thanks to a special platform architecture similar to the blockchain and built-in artificial intelligence agents - oracles.
Video fingerprinting based on quadruplet convolutional neural network

In order to achieve fast and accurate retrieval of video copies, this paper proposes a compact video fingerprinting based on quadruplet convolutional neural network. The algorithm consists of four branch networks with shared weights, each branch network contains feature extraction and quantization coding. The projection and excitation network is combined with 3D convolution for feature extraction, which mainly learns feature weights to improve useful features and suppresses valueless features. The deep features learned are mapped into approximate real vectors in a fully connected form and quantized to generate binary codes. The model employs an improved quadruplet loss to divide the feature distance between the copied videos and the non-copied, and a quantization error term is added to ensure that the fingerprint codes contains as much similar information as the original data. The experimental results performed on the public dataset show that the algorithm can effectively improve the robustness and distinctiveness, and the average detection accuracy under multiple compound attacks is better than the compared algorithms.
Noise robust hybrid algorithm for segmenting image with unequal cluster sizes based on chaotic crow search and improved fuzzy c-means

Adding spatial penalty to fuzzy C-means (FCM) model is an important way to reduce the influence of noise in image segmentation. However, these improved algorithms easily cause segmentation failures when the image has the characteristics of unequal cluster sizes. Besides, they often fall into local optimal solutions if the initial cluster centers are improper. This paper presents a noise robust hybrid algorithm for segmenting image with unequal cluster sizes based on chaotic crow search algorithm and improved fuzzy c-means to overcome the above defects. Firstly, each size of clusters is integrated into the objective function of noise detecting fuzzy c-means algorithm (NDFCM), which can reduces the contribution of larger clusters to objective function and then the new membership degree and cluster centers are deduced. Secondly, a new expression called compactness, representing the pixel distribution of each cluster, is introduced into the iteration process of clustering. Thirdly, we use two- paths to seek the optimal solutions in each step of iteration: one path is produced by the chaotic crow search algorithm and the other is originated by gradient method. Furthermore, the better solutions of the two-paths go to next generation until the end of the iteration. Finally, the experiments on the synthetic and non–destructive testing (NDT) images show that the proposed algorithm behaves well in noise robustness and segmentation performance.
Toward Construction of Measurement/Characterization Platform for Open Innovation

Recent Japanese activities to construct measurement/characterization platform were introduced based on the output of the special committee of JSPS and of the NEDO project. The concept and trial of common data format for combined analysis were also introduced.
Fast Computation of the Exact Number of Magic Series with an Improved Montgomery Multiplication Algorithm

The numbers of magic series of large orders are computed on Intel Xeon Phi processors with an improved and optimized Montgomery multiplication algorithm. The number of magic series can be efficiently computed by Kinnaes’ formula, of which the most time-consuming element is modular multiplication. We use Montgomery multiplication for faster modular multiplication, and the number of operations is reduced through procedural simplifications. Modular addition, subtraction, and multiplication operations are vectorized by using the following instructions: Intel Advanced Vector Extensions (AVX), Intel Advanced Vector Extensions 2 (AVX2), and Intel Advanced Vector Extensions 512 (AVX-512). The number of magic series of order 8000 is computed on multiple nodes of an Intel Xeon Phi processor with a total execution time of 1806 days. Results are compared with salient studies in the literature to confirm the efficacy of the approach.
Secure cloud storage system based on ciphertext retrieval

The development of cloud computing greatly raises the utilization of computing and storage resources, and the users' access to data also becomes more convenient. However, the openness of cloud computing environments also make user data security face greater challenges, and this now becomes one of the main problems hindering the development of cloud computing technology. This paper presents a secure cloud storage system based on attribute encryption that supports ciphertext retrieval. Users first request an attribute key from the trusted center, then outsource encrypted private data to the cloud server. Authorized users generate keyword traps with attribute keys that allow them to search for cloud-encrypted data only if the authorized user's attributes satisfy the specified access control tree. Security analysis shows that the system can effectively protect user privacy and data security. Performance analysis shows that the system has high efficiency.
Toward a conceptual framework for scholarly information seeking

This article reviews literature on information seeking theories and discusses the relevance of those theories to the information seeking habits of faculty with research expectations. A new model is constructed to depict the factors influencing scholarly information needs. The model incorporates career advancement factors, the influence of discovery tools, and the influence of interpersonal networks. Theoretical frameworks from T.D. Wilson and James Krikelas play important roles in building the model to describe scholarly information needs. This model can be of use to librarians, discovery tool designers, and university administrators supporting the information seeking, research, and teaching of faculty.
Human Motion Generation by Stochastic Conditioning of Deep Recurrent Networks On Pose Manifolds

Human motion generation is a stochastic process. The 3D motion generation task requires efficient regulation of stochasticity and a controlled approach for error-accumulation. Current generation approaches either fail to check error-amplitude or to preserve the signal. In this paper, we present a stochastic approach for 3D human motion generation. To this end, we design a fully differentiable, end-to-end, block-based autoregressive recurrent neural network (RNN) architecture. The proposed model incorporates variable auto-conditioning length along with probabilistic variational inference on the RNN hidden-state, to regulate stochasticity. We separately train an auto-encoder to bound skeletons on a known manifold of valid-poses. We extensively test the proposed approach on publicly available Motion-Capture benchmarks. The quantitative and qualitative evaluations indicate the superiority of the proposed approach in comparison to state-of-the-art on long-term motion generation while achieving comparable performance on short-term prediction task.
Unsupervised Neural Aspect Extraction with Related Terms

The tasks of aspect identification and term extraction remain challenging in natural language processing. While supervised methods achieve high scores, it is hard to use them in real-world applications due to the lack of labelled datasets. Unsupervised approaches outperform these methods on several tasks, but it is still a challenge to extract both an aspect and a corresponding term, particularly in the multi-aspect setting. In this work, we present a novel unsupervised neural network with convolutional multi-attention mechanism, that allows extracting pairs (aspect, term) simultaneously, and demonstrate the effectiveness on the real-world dataset. We apply a special loss aimed to improve the quality of multi-aspect extraction. The experimental study demonstrates, what with this loss we increase the precision not only on this joint setting but also on aspect prediction only.
Multilevel Interaction Reasoning For Complex Event Recognition

Event as a complex process contains many factors. Objects, environments, and their interactions vary with time. Recognizing event remains a challenging task in computer vision. In this paper, a multilevel interaction reasoning framework is proposed for complex event recognition. Firstly, we construct a 3D ConvNet to extract the spatial-temporal feature to present global scene. Then a graph ConvNet is built to reasoning about the multilevel interaction: object-object and object-environment interaction, via graphs that contain object feature in video, and projection of global scene feature in 3D ConvNet. The proposed method effectively explore the nature of events occurring and developing, and experimental results on challenging UCF-Crime dataset achieve state-of-the-art with a 3.5% gain over other models.
Towards standardization in frameworks, tools and approaches dedicated to ontology building and management

The widespread popularity and spread of ontologies in many areas increasingly requires the successful integration of multiple such ontologies. The ontology integration problem has been investigated during last years and it is still a challenging task. Aligning and merging existing ontologies, which are usually handled manually, is often a big and tedious part of the sharing process. Due to the fact that there are different approaches, tools and strategies that help to avoid mistakes during modeling especially large domains and creating a new single coherent ontology, this article attempts to build a common knowledge repository in the form of an open and publicly available tool. The overall goal of the proposed open knowledge repository is to provide free access to basic data and to compare the features offered. It is worth mentioning that this form of knowledge representation is publicly available and focuses on the possibility of updating and reusing. It is an attempt to structure knowledge that can be successfully extended with additional groups of solutions and parameters. The proposed approach can be easily implemented as a light library and used in any type of OWL/RDF or OWL/XML management application or adapted in a given management information system.
A DLT Based Innovative Investment Platform

Digital transformation has affected many industries and has become a mega-trend in the information technology systems. However, the venture capital investment process is still rigid. The use of distributed ledger technology and artificial intelligence in the field of finance and investment requires a detailed framework and a rethinking of the interaction model of market participants. The article shows the DLT based approach with attached of F-BFT consensus to improve the efficiency of the investment process in the modern world.