Reviewerly/exHarmony huggingface dataset:
Methods for Efficiently Constructing Text-dialogue-agent System using Existing Anime Characters

Starting from their early years, many persons dream of being able to chat with their favorite anime characters. To make such a dream possible, we propose an efficient method for constructing a system that enables users to text chat with existing anime characters. We tackled two research problems to generate verbal and nonverbal behaviors for a text-chat agent system utilizing an existing character. A major issue in creating verbal behavior is generating utterance text that reflects the personality of existing characters in response to any user questions. To cope with this problem we propose use of role play-based question-answering to efficiently collect high-quality paired data of user questions and system answers reflecting the personality of an anime character. We also propose a new utterance generation method that uses a neural translation model with the collected data. Rich and natural expressions of nonverbal behavior greatly enhance the appeal of agent systems. However, not all existing anime characters move as naturally and as diversely as humans. Therefore, we propose a method that can automatically generate whole-body motion from spoken text in order to give the anime characters natural, human-like movements. In addition to these movements, we try to add a small amount of characteristic movement on a rule basis to reflect personality. We created a text-dialogue agent system of a popular existing anime character using our proposed generation methods. As a result of a subjective evaluation of the implemented system, our methods for generating verbal and nonverbal behavior improved the impression of the agent's responsiveness and reflected the personality of the character. Since generating characteristic motions with a small amount of characteristic movement on the basis of heuristic rules was not effective, our proposed motion generation method which can generate the average motion of many people, is useful for generating motion for existing anime characters. Therefore, our proposed methods for generating verbal and nonverbal behaviors and the system-construction method are likely to prove a powerful tool for achieving text-dialogue agent systems for existing characters.
Sparse Graph Embedding Based on the Fuzzy Set for Image Classification

In recent years, many face image feature extraction and dimensional reduction algorithms have been proposed for linear and nonlinear data, such as local-based graph embedding algorithms or fuzzy set algorithms. However, the aforementioned algorithms are not very effective for face images because they are always affected by overlaps (outliers) and sparsity points in the database. To solve the problems, a new and effective dimensional reduction method for face recognition is proposed—sparse graph embedding with the fuzzy set for image classification. The aim of this algorithm is to construct two new fuzzy Laplacian scattering matrices by using the local graph embedding and fuzzy k-nearest neighbor. Finally, the optimal discriminative sparse projection matrix is obtained by adding elastic network regression. Experimental results and analysis indicate that the proposed algorithm is more effective than other algorithms in the UCI wine dataset and ORL, Yale, and AR standard face databases.
TileNET: Hardware accelerator for ternary Convolutional Neural Networks

Convolutional Neural Networks (CNNs) are popular in Advanced Driver Assistance Systems (ADAS) for camera perception. The versatility of the algorithm makes it applicable in multiple applications like object detection, lane detection and semantic segmentation. For image processing to be viable in driver assistance systems, the throughput requirement ranges in the order of a few tens of TeraMACs per second (TMACs). In addition, high accuracy levels of image detection and recognition cannot be compromised for the need for high throughput. In this paper, we present TileNET, a novel tiled architecture for ternary-weighted CNNs. TileNET is modular and scalable across variations in network organization and device configurations. Two modes of the implementation are presented, viz., systolic and streaming. A high-level estimation technique has been developed that facilitates fast performance evaluation through design space exploration among a range of target devices and varying CNN models. Performance has been verified for area and throughput estimation for Xilinx Virtex, Artix, Kintex and Zynq devices. TileNET implemented on Virtex-7 (XC7VX1140T) results in a throughput of about 16 Tera-operations per second (TOPs) for LeNet, AlexNet, ResNet-50 and VGG-16. In addition, the 45nm standard cell implementation of TileNet shows a throughput of about 30 TOPs respectively.
Optimum Spending on Cybersecurity Measures: Part II

The purpose of this research is to investigate the decision-making process for cybersecurity investments in organizations through development and utilization of a digital cybersecurity risk management framework.The initial article, Optimum Spending on Cybersecurity Measures is published on Emerald Insight at: https://www.emerald.com/insight/1750-6166.htm, contains the detailed literature review, and the data results from Phase I and Phase II of this research [1].This article will highlight the research completed in the area of organizational decision-making on cybersecurity spend.In leveraging the review of additional studies, this research utilizes a regression framework and case study methodology to demonstrate that effective risk-based decisions are necessary when implementing cybersecurity controls.Through regression analysis, the effectiveness of current implemented cybersecurity measures in organizations is explored when connecting a dependent variable with several independent variables.The focus of this article is on the strategic decisions made by organizations when implementing cybersecurity measures.This research belongs to the area of risk management, and various models within the field of 1) information security; 2) strategic management; and 3) organizational decision-making to determine optimum spending on cybersecurity measures for risk taking organizations.This research resulted in the development of a cyber risk investment model and a digital cybersecurity risk management framework.Using a case study methodology, this model and framework were leveraged to evaluate and implement cybersecurity measures.The case study methodology provides an in-depth view of a risk-taking organization's risk mitigation strategy within the bounds of the educational environment focusing on five areas identified within a digital cyber risk model: 1) technology landscape and application portfolio; 2) data centric focus; 3) risk management practices; 4) cost-benefit analysis for cybersecurity measures; and 5) strategic development.The outcome of this research provides greater insight into how an organization makes decisions when implementing cybersecurity controls.This research shows that most organizations are dili-
Learning Spatial Search using Submodular Inverse Reinforcement Learning

Finding an optimal search path is a NP-hard problem. Since search is one of human central activities, learning spatial search behavior from human operators is a way to solve search problems. Utilizing the submodularity of search problems, this research proposes a submodular inverse reinforcement learning (SIRL) algorithm to learn humans' search behavior. The experiments demonstrate that the performance of the learned search paths outperform that of state of the art approaches (e.g., MaxEnt IRL and DIRL).
When to Consult Precision-Recall Curves

ROC curves are commonly used to evaluate predictions of binary outcomes. When there are a small percentage of items of interest (as would be the case with fraud detection, for example), ROC curves can provide an inflated view of performance. This can cause challenges in trying to determine which set of predictions is better. This article discusses the conditions under which precision-recall curves may be preferable to ROC curves. As an illustrative example, we compare two commonly used fraud predictors (Beneish's (1999) M-score and Dechow et al.'s (2011) F-score) using both ROC and precision-recall curves. To aid the reader with using precision-recall curves, this article also introduces a Stata module to plot them. This module is now available on the Boston College Statistical Software Components (SSC) archive and can be installed by typing ssc install prtab in Stata.
CNN-based Camera Model Classification and Metric Learning Robust to JPEG Noise Contamination

Pattern noise-based source camera identification is a promising technology for preventing crimes such as illegal uploading and secret photography. In order to identify the source camera model of an input image, recently, highly accurate camera model classification methods based on convolutional neural networks (CNNs) have been proposed. However, the pattern noise in an image is typically contaminated by JPEG compression, and the degree of contamination depends on the quality factor (Q-Factor). Therefore, it could be that JPEG compression of different Q-factors from that of training samples degenerates the accuracy for CNN-based camera model classification. In this paper, we propose a CNN-based camera model classification and metric learning trained with the JPEG-base a noise suppression technique. In the experiments, we evaluate camera model classification accuracy and metric learning performance for various Q-Factors. We demonstrate that JPEG-based noise suppression improves camera model classification accuracy from 87.25% to 99.89% on average. We also demonstrate JPEG-based noise suppression improves the robustness of metric learning to JPEG contamination.
Research on Reversible Visible Watermarking Algorithms Based on Vectorization Compression Method

Abstract In current research on reversible visible watermarking algorithm, the original visible watermark image plays an important auxiliary role, and some algorithms also entirely depend on it to restore host image without any distortion. Therefore, in order to realize semi-blind reversible visible watermarking algorithm, the conventional reversible watermarking algorithm is used to embed compressed visible watermark image data into non-visible-watermarked region of host image. However, the amount of compressed image data obtained by conventional image compression algorithm is relatively large. Therefore, a method based on vectorization compression for the visible watermark image is proposed in this paper. Firstly, it performs edge detection on visible watermark image to obtain a discrete points set $\Gamma $ of vector contour curve. Then, the discrete points in $\Gamma $ are simplified by improved Douglas–Peucker algorithm, after that it obtains compressed vector contour data of visible watermark image. In addition, a reversible visible watermarking algorithm based on convolutional relief and image alpha fusion is proposed, which realizes reversible embedding of visible watermark image and lossless restoration of host image. The experimental results show that the proposed vectorization compression method has more advantages than traditional image compression algorithms, which greatly reduces the storage space of visible watermark image with high fidelity. Additionally, the embedded watermarking image has translucent 3D relief effect, and the fusion of host image and visible watermark image becomes more natural and harmonious.
Digital Forensic Evidence - The missing link in Threat Modeling

Threat modeling is a set of methodologies to analyze the potential threats in a digital system, in order to mitigate them. Digital forensics, on the other hand, is used in order to find the true origin of an event with the help of forensic evidence. Digital forensics is based on Locard's Principle and dictates that even digital crime leaves behind some form of remnants. Both the domains, threat modeling and digital forensics, have separately existed, but have to our knowledge not been used together. In this research we establish the importance of forensic evidence and how it can aid threat modeling by providing more comprehensive threat intelligence. We provide practical examples of how the two fields can be combined, based on attack graphs and Bayesian networks.
Cloud computing: a walk through in models, challenges, and energy solutions

Abstract The paradigm of cloud computing in recent times has engrossed a lot of working populace in its service stack offerings. This considerable rise of human traffic landing onto cloud platform for service extraction has consequently led to exponential rise in energy consumption during dynamic and heterogeneous workload situations encompassing huge cloud infrastructure in form of CPU, Hard Disks, network devices etc. The energy released not only poses costs on its stakeholders but is affecting the environment due to rise in carbon footprints. As per available stats [1], the total power consumption of cloud has significantly arisen from 632 billion kilowatt hours in year 2007 to 163 billion kilowatt hours in 2020 which is adequate to leave a devastating effect on environment. Though research is being carried out in this direction, still efficacious measures are far away from its pragmatic implementations. Therefore, the research study conducted here discusses about existing solutions in the direction of energy management in cloud computing. The latter half of paper presents abrief discussion on gaps and future directions.
Study on Security Protection Technology of Multi-Network Integration

With the wide application of information technology, the network architecture presents heterogeneous characteristic. As different security requirements, how to realize connection and security management of heterogeneous networks has become an inevitable challenge. Accordingly, it is important to study information security technology in the situation of multi-network integration. Based on the technologies of security supervision, physical dependence and network virtualization, this paper discusses the methods of security protection for multi-network integration. Furthermore, strict management regulations are established to improve the multi-network security performance. The above-mentioned methods were expounded and have been applied to a certain multi-network structure. The result indicates that the security protection scheme presented in this paper is effective and feasible.
Research on Optimization of Big Data Fusion QM Model Based on Block-chain Distribution

Abstract In terms of poor classification and detection effect of big data fusion QM model as well as weak feature mining ability, a big data fusion QM model based on block-chain distribution optimization is proposed in the paper. Moreover, a block storage structure model for the distribution of big data is first established on the block-chain to perform shared scheduling according to the attribute distribution of the distributed big data on the block-chain, and then the big data of the distributed big data on the block-chain is mined. On this basis, the differentiated feature distributed retrieval method is used to cluster the distributed big data information on the block-chain, and the rough set feature quantity is extracted from the fuzzy cluster center. Meanwhile, combining the fuzzy correlation feature detection method, the information of the block-chain distributed big data is reorganized to extract the statistical features of each module. In addition, the quantitative recursion in the process of block-chain distributed big data fusion is analyzed, so that the optimization design of the QM model of block-chain distributed big data fusion can be realized. The simulation results show that the feature resolution ability of block-chain distributed big data fusion with the method proposed in the paper is better, and the classification detection and feature mining capabilities of block-chain distributed big data are improved, which has high application value in the block-chain distributed big data information retrieval and scheduling.
Homomorphic Encryption: The ‘Holy Grail’ for Big Data Analytics &amp;amp; Legal Compliance in the Pharmaceutical and Healthcare Sector?

The pharmaceutical and healthcare sector is a number one target for cybercriminals around the world. These cyber-attacks represent significant challenges in the context of data protection and data security. The General Data Protection Regulation (GDPR) imposes strict rules regarding the processing and analysis of personal data. In conventional approaches, data analysts request data from various sources. Then, they anonymise or pseudonymise the data using various tools and techniques. These methods often use powerful algorithms to ensure a high level of security. However, these methods tend to either reduce the quality of data for further analysis or they expose the data again while decrypting it for analysis. Homomorphic Encryption (HE) has recently been touted as the ‘Holy Grail’ of cryptography since it allows the analysis of big data without ever needing to decrypting and thus compromising the confidentiality of the data. This provides a whole new layer of protection and at the same time allows the processing of data for secondary use and scientific research. While HE is not a new technology, it is still in its early stages of development. In this piece, we introduce a new automated tool for searching and analysing the encrypted data using HE techniques, which is being developed within the scope of the EnergyShield project.
Differential Privacy and Prediction Uncertainty of Gossip Protocols in General Networks

Recent advances in social media and information technology have enabled much faster dissemination of information, while at the same time raise concerns about privacy leakage after various privacy breaches. Therefore, the privacy guarantees of information dissemination protocols have attracted increasing research interests, among which the gossip protocols assume vital importance in various information exchange applications. Very recently, the rigorous framework of differential privacy has been introduced to measure the privacy guarantees of gossip protocols in the simplified complete network scenario. In this work, we extend the study to general networks. First, lower bounds of the differential privacy guarantees are derived for the gossip protocols in general networks in both synchronous and asynchronous settings. The prediction uncertainty of the source node given a uniform prior is also determined. It is found that source anonymity is closely related to some key network structure parameters in the general network setting. Then, we investigate information spreading in wireless networks with unreliable communications, and quantity the tradeoff between differential privacy guarantees and information spreading efficiency. Finally, considering that the attacker may not be present in the beginning of the information dissemination process, the scenario of delayed monitoring is studied and the corresponding differential privacy guarantees are evaluated.
Supporting Collective Intelligence of Human-Machine Teams in Decision-Making Scenarios

Efficient collaboration of humans and machines has the great potential for improving many knowledge-intensive processes in variety of applications. Therefore, developing means supporting such collaboration and making it efficient is an important area of research. The paper presents a part of research aimed on the development of a collective intelligence environment that would support joint work of humans and machines on decision support problems, allowing participants to self-organize (define and adapt the plan of actions). In particular, it describes an approach to solving semantic interoperability issues in supporting human-machine collective intelligence for decision-making scenarios. The proposed approach is based on using multi-aspect ontologies and ontology-based smart spaces.
Parallel solution of 3D forward and inverse problems of airborne electromagnetic survey

Abstract The paper is devoted to the comparison of different approaches to parallel solution of 3D forward and inverse problems of airborne electromagnetic (EM) surveys when multidimensional processing of data obtained on large areas is needed. The mathematical models for the solution of 3D forward and inverse problems and different methods of their parallelization are considered. The results of numerical experiments for the airborne EM survey problem, which is typical of prospecting the kimberlite bodies in Eastern Siberia, are presented. It is shown that the combination of different techniques of parallelization in a distributed computational system, taking into account the specifics of the numerical schemes implementation for forward and inverse problems solution, makes it possible to achieve the speedup close to linear.
Blockchain-Based Access Control Mechanism in Electronic Evidence

With rapid development of the information age, the proportion of electronic evidence involved in judicial cases has been rising, and the importance of electronic evidence is increasingly prominent. In the real judicial process, there are strict requirements put forward for the authenticity identification, tamper proof storage and legal sharing methods of electronic evidence, bringing many security challenges. If such conditions are not met, the legal force of electronic evidence will be seriously reduced. However, the existing technical solutions can not meet these above requirements. Blockchain technology has the characteristics of decentralization and tamper proof, which provides a new method for the storage and extraction of electronic evidence. To solve these problems, this paper proposes a blockchain-based storage access control model for electronic evidence and designs the corresponding storage access control protocol. In this scheme, multi-entity cooperation is allowed to store evidence, and a zero-knowledge proof protocol is applied to achieve access control and data sharing. Through the security and experimental analysis, it can be proved that the scheme satisfies security features such as known key security and non-repudiation, and can effectively resist collusion, camouflage, replay and man-in-the-middle attacks. Finally, related experiments show that the scheme has high computational and communication efficiency. In this way, the problem of secure storage and sharing of electronic evidence will be resolved commendably, which meets the security requirements in actual scene.
Survey of image segmentation methods based on clustering

With the rapid development and application of clustering algorithms, the problem of image segmentation has attracted more and more scholars and industry attention. This paper studies the clustering-based image segmentation method, summarizes the basic idea, and divides it into two categories, namely partition method and agglomeration method. The typical algorithms Kmeans algorithm, CLARANS algorithm, mean shift algorithm and DBSCAN algorithm of these two methods are introduced in detail. The advantages and limitations of these four methods are analyzed. Finally, it points out the future research trend of clustering algorithms and gives conclusions.
High-Performance Computing: A Deep Learning Perspective

High-performance computing (HPC) is achieved from parallel processing and nowadays, it can also be achieved from distributed processing techniques for solving complex compute-intensive and data-intensive applications. HPC tends to solve complex problems in less time and efficiently with parallel processing techniques. Computer modeling and research activities can be carried out with the help of HPC. It is used for solving contemporary issues. HPC makes efficient utilization of the available resources to provide unremitting performance. HPC can be combined with deep learning techniques to get superior performance. This chapter discusses distributed and parallel deep learning and their applications in the real world.