Reviewerly/exHarmony huggingface dataset:
Instant-Hybrid Neural-Cryptography (IHNC) based on fast machine learning

Abstract Nowadays, cryptographic systems’ designers are facing significant challenges in their designs. They have to constantly search for new ideas of fast unbreakable algorithms with a very powerful key generator. In this paper, we propose a novel hybrid neural-cryptography methodology. It depends on new rule of very fast Backpropagation (BP) instant machine learning (ML). This proposed Hybrid Cryptography system is constructed from Encryptor and Decryptor based on the asymmetric Autoencoder type. The Encryptor encrypts and compresses a set of data to be instant code (i-code) using public key. While the Decryptor recovers this i-code (ciphered-data) based on two keys together. The first is the private key and the other is called instant-key (i-key). This i-key is generated from 3 factors as well (the original data itself, the generated i-code and the private key). The i-key is changing periodically with every transformation of plain data set, so it is powerful unpredictable key against the brute force.
Sprinkling on the magic dust

This chapter looks at how communications became the ‘magic dust’ for development; the assumptions underpinning what its transformative effects might be; the debate over its appropriateness and cost-effectiveness; the shift from information communication technology for development (ICT4D) to mobile for development (M4D); the transition to a wider palette of technologies; and the long challenges to ICT4D in Africa: education and agriculture. The chapter opens with a description of how one of the world’s largest refugee camps (Dadaab) was connected to mobile voice and internet communications, and some of the changes it brought about, some transformative and others within the boundaries of existing cultures. The opening snapshot illustrates two themes of the chapter. Firstly, how development agencies’ operating costs and efficiencies can often be improved by using the same technologies used elsewhere, not least by connecting camps to fibre networks and delivering aid money directly to refugees by mobile money. Secondly, how development agencies use technologies and their use, or lack of use, by ‘beneficiaries’.
Bayesian Networks for Decision Support in Emergency Response: A Model for Missing Person Investigations

The successful operation of Emergency services (Police, Fire, Medical Emergency) relies heavily upon Information Systems and particularly Decision Support Systems. Missing person cases consume resources from the already overstretched resources of Police Forces. Such cases predominantly come from at-risk groups such as children in care, people suffering from depression, or elderly people suffering from dementia. This chapter reviews current practices used for missing person cases and describes a decision support model based on Bayesian networks.
Detection of Link Communities in Attributed Graphs via an Approximate Bayesian Generative Model

Attributed graphs are complex networks with non-trivial topological structures and rich node contents. Recently, a variety of algorithms have been proposed to detect meaningful communities from a given attributed graph by combining these topology and content information. However, few of them is capable of detecting the communities of links, which are better to interpret the node behaviors in forming communities. Furthermore, since nodes are possible to grouped into more than one communities, community detection upon links is natural and parameter-free to incorporate overlap, thus revealing the intrinsic organization of attributed graphs in a more reasonable way. In this work, we propose a novel Bayesian probabilistic model to approximately simulate the generative process of an attributed graph given the prior knowledge regarding the distribution of community labels over links. An efficient variational algorithm, namely VBLCD, is developed to solve the inference problem of the proposed model, thus completing the task of detecting link communities. To evaluate the performance of VBLCD, we have applied it to address two practical applications including document classification and social community detection, and also compared VBLCD with several state-of-the-art algorithms. Experimental results demonstrate the promising performance of VBLCD.
Neural Style Transfer with Twin-Delayed DDPG for Shared Control of Robotic Manipulators

Neural Style Transfer (NST) refers to a class of algorithms able to manipulate an element, most often images, to adopt the appearance or style of another one. Each element is defined as a combination of Content and Style: the Content can be conceptually defined as the “what” and the Style as the “how” of said element. In this context, we propose a custom NST framework for transferring a set of styles to the motion of a robotic manipulator, e.g., the same robotic task can be carried out in an “angry”, “happy”, “calm”, or “sad” way. An autoencoder architecture extracts and defines the Content and the Style of the target robot motions. A Twin Delayed Deep Deterministic Policy Gradient (TD3) network generates the robot control policy using the loss defined by the autoencoder. The proposed Neural Policy Style Transfer TD3 NPST <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3 alters the robot motion by introducing the trained style. Such an approach can be implemented either offline, for carrying out autonomous robot motions in dynamic environments, or online, for adapting at runtime the style of a teleoperated robot. The considered styles can be learned online from human demonstrations. We carried out an evaluation with human subjects enrolling 73 volunteers, asking them to recognize the style behind some representative robotic motions. Results show a good recognition rate, proving that it is possible to convey different styles to a robot using this approach.</sup>
Aggregating Correlation Filter with Multiple Kernels Learning for Robust Visual Object Tracking

Recently, Discriminative Correlation Filter based trackers have increasingly become popular in the domain of visual object tracking, which is benefited by their effective and robustness in terms of tracking performance. However, the significant variations of target appearance, such as Occlusion or Deformation, always suppress the performance improvement. Although the multi-feature fusion strategy partly alleviates the above factors, the synthetic scheme also ambiguates the overall presentation ability of multiple features and results in degradation of discriminative power during classification filter learning. In this paper, we present a novel tracking framework based on the correlation filter with multiple kernel learning. Instead of directly integrating different features, we decompose a tracking process into several module, including low-dimension modules and high-dimension modules. The handcraft features extracted from low-dimension modules are robust to the small variation of target while the semantic convolutional features from the high-dimension modules can distinguish the target object from the relative complex tracking environment. In addition, we also design a reliability detection mechanism based on the confidence map to accurately measure the important degree of each kernel learning and precisely estimate the target state in the subsequent frames. For verifying our proposed tracker, we conduct extensive experiments on several standard sequence benchmark. Experiment results show that our proposed trackers perform favorably against other state-of-the-art trackers.
PENGARUH MEDIA KOMIK CERITA ANAK TERHADAP KEMAMPUAN MENULIS TEKS NARASI SISWA KELAS V SDN 1 KEDIRI SELATAN TAHUN PELAJARAN 2020/2021

The problem raised in this study is the influence of children's story comics media on the ability to write narrative texts for fifth grade students at SDN 1 Kediri Selatan in the 2020/2021 school year. The aim is to determine the effect of children's story comics media on the ability to write narrative texts for fifth grade students at SDN 1 Kediri Selatan in the 2020/2021 school year. The results of this study are expected to be useful theoretically and practically. The concept used in this research is the media of children's story comics and writing skills. This type of research is a quantitative descriptive study, the data sampling technique is the Simple Random Sampling technique (aims to take several samples from several populations). The data collection technique used a written test. Hypothesis testing was carried out using the T test with the help of SPSS version 25. The results showed that the lowest value of the experimental pretest was 37 while the highest was 54 and the lowest was for the control class 39 and the highest was 48, then for the experimental posttest, the lowest value was 76 and the highest score was 85. , for the posttest control class the lowest score is 80 and the highest is 92. This shows that there is an influence of children's story comics media on the ability to write narrative texts for the fifth grade students of SDN 1 Kediri Selatan in the 2020/2021 school year.
CPU-GPU Heterogeneous Parallel Algorithm for Metal Solidification Molecular Dynamics Simulation Based on Artificial Intelligence

AI (Artificial Intelligence) technology is one of the most popular technologies in today’s society, and its development and application are getting more and more attention. Especially in recent years, AI technology has been greatly developed, and its shadow can be seen everywhere in our lives. Now its application range is very wide. In order to explore the influence of AI technology on CPU-GPU heterogeneous parallel algorithm of metal solidification molecular dynamics simulation, we set up an experimental group and a control group as the experimental research object, and then the experimental group applied big data technology in its CPU-GPU heterogeneous parallel algorithm of metal solidification molecular dynamics simulation, while the control group still used the conventional method. Then the experimental results show that the calculation efficiency and accuracy of the experimental group are much higher than those of the control group, and the calculation efficiency and accuracy of the experimental group can reach 97.80% and 99.51% respectively, while the corresponding data results of the control group are 85.10% and 91.80% respectively.
Optimal Resource Allocation for Crowdsourced Image Processing

Crowdsourced image processing has the potential to vastly impact response timeliness in various emergency situations. Because images can provide extremely important information regarding an event of interest ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">hits</i> ), sending the right images to an analyzer as soon as possible is of crucial importance. In this paper, we consider the problem of optimally assigning resources, both local (CPUs in phones) and remote (network-based GPUs) to mobile devices for processing images, ultimately sending those of interest to a centralized entity while also accounting for the energy consumption at the distributed nodes. To that end, we use the dual-path Network Utility Maximization (NUM) framework, coupled with a hit-ratio estimator and energy costs, to enable a distributed implementation of the system. We include analysis of different hit-ratio estimators using realistic trace data, first considering immediate and then delayed feedback. We address accuracy concerns when estimating the likelihood of future image <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">hits</i> and provide a window-based heuristic for scenarios when hit-ratio feedback is severely delayed. Our TCP-inspired window-method predicts both image <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">hit</i> likelihood and current wireless network congestion with great effectiveness. Results are validated using both synthetic simulations and real-life traces.
Process-Based Knowledge Organization

To overcome some limitations of existing models that organize knowledge on the World Wide Web for online learning, this paper proposes a novel method of using graphical process models to organize knowledge on the Web and to guide the learning process. Process models can not only visually represent the logical relationships (i.e., if … then, all, at least one, and exactly one) of knowledge elements, but also display learning paths that consist of logically connected knowledge elements. This paper also presents a design of an innovative learning system that integrates process models with some existing knowledge organization models to facilitate online learning. This paper makes a useful contribution by proposing process-based knowledge organization as well as providing a visual representation of learning paths that explicitly prompt people to follow a logical process for effective learning.
Special Issue: DOMAIN ADAPTABLE MODEL FOR SENTIMENT ANALYSIS, 81-86.

An increase in user data in social network websites has shown anincreasing power of social networks for expressing user opinionson various domains which has turned online opinions into a veryvaluable asset.
Parallel Symbiotic Lion Swarm Optimization Algorithm Based on Latin Hypercube Distribution

In order to optimize the search process of lion swarm optimization algorithm, improve the accuracy of the result and save the time, we propose the parallel symbiotic lion swarm algorithm based on Latin Hypercube. First, Latin Hypercube is used to initial population. Secondly, the mutualistic symbiosis mechanism is proposed to increase the communications between agents. Then, reverse learning strategy of dimensional keyhole imaging is added to increase diversity of the optimization process. Finally, on the premise of ensuring the quality of the solution, the parallel computation is combined with the improved lion swarm optimization algorithm to improve the convergence rate. Several benchmark functions are used to evaluate the performance of the improved lion swarm algorithm, and the running time of the parallel algorithm is compared. Experimental results show that the efficiency, stability and convergence rate of the improved lion swarm algorithm are greatly improved.
An Extension of Multi-Attribute Group Decision Making Method Based on Quantum-Like Bayesian Network Considering the Interference of Beliefs

Multi-attribute group decision making (MAGDM) problem has become one of the most remarkable topics recently. MAGDM is made up of multiple decision makers (DMs) who perform opinions on a set of alternatives and then choose the appropriate one. In the classical decision-making process, the multiple DMs are usually regarded as independent and the opinions usually come from the preference of DMs when determining weights of attributes or giving evaluation of alternatives. However, especially in real-life problems, MAGDM is a complicated cognitive process that involves the diverse and uncertainty cognition of DMs from different backgrounds and fields, and t he opinions from DMs are likely to interfere with each other. This paper proposes an extension method based on quantum-like Bayesian network (QLBN) and belief entropy considering the interference of beliefs on the basis of our previous research. The purpose is to model subjectivity sourced from the interference of DMsâ€™ beliefs at different decesion-making stages, including the aggregation of attributes probabilities and alternatives probabilities. In this paper, a QLBN for MAGDM problem is constructed firstly. The beliefs of DMs are in a superposition state in it, that is, the opinions of DMS are regarded as various wave functions occurring at the same time. Then the probabilities of attributes and the probabilities of alternatives in QLBN are aggregated across all DMs, and the alternatives are sorted. In the process of aggregation, the beliefs from DMs will interfere with each other. Belief entropy, an index to calculate the uncertainty of probability is introduced to calculate the interference value. When the interference between DMs is considered only in a certain decision-making stage, the proposed method will degenerate into the existing QLBN methods; and when all DMs are regarded as independent, the QLBN will degenerate into a classical BN. Finally, a supplier selection problem is introduced to illustrate and validate our model. The comparison analyses show that the proposed approach can fully consider the interference between DMs and the ranking result are more reasonable and and realistic.
RGB Image Guided Depth Hole-Filling Using Bidirectional Attention Mechanism

Depth maps obtained by commercial depth sensors are more likely to have missing values due to the occlusion effect, low-reflection objects, etc. Filling holes of depth maps is an important way to meet the demands of depth related computer vision tasks. In this paper, we propose an efficient end-to-end network that takes RGB image and mask of the hole to jointly guide the depth hole-filling. Previous algorithms indistinguishably treat valid pixels and holes, resulting in inaccurate depth values prediction and blurred boundaries. Nevertheless, the proposed algorithm uses the bidirectional attention mechanism which takes the surrounding valid values as the auxiliary information to focus on the process of depth hole-filling from edge to center. The proposed method achieves competitive performance on existing public datasets.
Mobile Application mTransporters

mTransporteri (mTransporters) is mobile application. It is intended for getting contact data of transport firms from remote database on smartphone. Obtained dataset is displayed in table with fields as follows: Firma (Firm), telefon (telephone), SMS and email. It also enables direct contact of transport firms by tap on appropriate contact data: phone, SMS or email. The application is made in JAVA programming language and works on smartphones with operating system Android. The version of the Android operating system for the application must be at least 25.
A Survey of Anti-forensic for Face Image Forgery

Deep learning related technologies, especially generative adversarial network, are widely used in the fields of face image tampering and forgery. Forensics researchers have proposed a variety of passive forensic and related anti-forensic methods for image tampering and forgery, especially face images, but there is still a lack of overview of anti-forensic methods at this stage. Therefore, this paper will systematically discuss the anti-forensic methods for face image tampering and forgery. Firstly, this paper expounds the relevant background, including the relevant tampering and forgery methods and forensic schemes of face images. The former mainly includes four aspects: conventional processing, fake face generation, face editing and face swapping; The latter is mainly the relevant forensic means based on spatial domain and frequency domain using deep learning technology. Then, this paper divides the existing anti-forensic works into three categories according to their method characteristics, namely hiding operation traces, forgery reconstruction and adversarial attack. Finally, this paper summarizes the limitations and prospects of the existing anti-forensic technologies.
Cross-Knowledge Graph Entity Alignment via Neural Tensor Network

Abstract With the expansion of the current knowledge graph scale and the increase of the number of entities, a large number of knowledge graphs express the same entity in different ways, so the importance of knowledge graph fusion is increasingly manifested. Traditional entity alignment algorithms have limited application scope and low efficiency. This paper proposes an entity alignment method based on neural tensor network (NtnEA), which can obtain the inherent semantic information of text without being restricted by linguistic features and structural information, and without relying on string information. In the three cross-lingual language data sets DBP FR−EN , DBP ZH−EN and DBP JP−EN of the DBP15K data set, Mean Reciprocal Rank and Hits@k are used as the alignment effect evaluation indicators for entity alignment tasks. Compared with the existing entity alignment methods of MTransE, IPTransE, AlignE and AVR-GCN, the Hit@10 values of the NtnEA method are 85.67, 79.20, and 78.93, and the MRR is 0.558, 0.511, and 0.499, which are better than traditional methods and improved 10.7% on average.
Differential faultt attack on DEFAULT

&lt;p style='text-indent:20px;'&gt;Block cipher DEFAULT has been proposed as a differential fault analysis immune cipher at Asiacrypt 2021. In this paper, we consider the initial version of DEFAULT with no permutation involved in the last round and show that one can find the key in this version with complexity &lt;inline-formula&gt;&lt;tex-math id="M1"&gt;\begin{document}$ 2^{16} $\end{document}&lt;/tex-math&gt;&lt;/inline-formula&gt; by injecting 112 faults. However, our idea does not work for the modified version of the cipher (where a key scheduling algorithm is involved).&lt;/p&gt;
Anti-electromagnetic interference method based on high-order cumulant method and PRI sorting

Facing the increasingly complex electromagnetic environment, how to make the detection instrument still have better detection performance is one of the current research hotspots. This paper proposes an anti-electromagnetic interference method based on high-order cumulant and PRI sorting. The former identifies the modulation type of the signal, the latter sorts the signals with different modulation parameters of the same modulation type, and finally selects the signal to be processed. The simulation results show that the algorithm is accurate and effective.
Docalog: Multi-document Dialogue System using Transformer-based Span Retrieval

Information-seeking dialogue systems, including knowledge identification and response generation, aim to respond to users with fluent, coherent, and informative answers based on users' needs.This paper discusses our proposed approach, Docalog, for the DialDoc-22 (Multi-Doc2Dial) shared task.Docalog identifies the most relevant knowledge in the associated document, in a multi-document setting.Docalog, is a three-stage pipeline consisting of (1) a document retriever model (DR.TEIT), ( 2) an answer span prediction model, and (3) an ultimate span picker deciding on the most likely answer span, out of all predicted spans.In the test phase of MultiDoc2Dial 2022, Docalog achieved f1scores of 36.07%and 28.44% and SacreBLEU scores of 23.70% and 20.52%, respectively on the MDD-SEEN and MDD-UNSEEN folds.