Reviewerly/exHarmony huggingface dataset:
Cloud Computing: Security Issues and Challenges

Cloud computing has become a vital piece of suggestions; nowadays, as a result of the advantages it offers to the reasonably connected world. Cloud computing has expedited suggestions in additional than one approach; from inserting the in-depth tools to upscaling and downscaling at no matter purpose needed, transferral concerning in-depth assets of the suggestions. Cloud computing market has become exponentially throughout the years and is trusty upon to develop at a lot of faster pace. One different purpose for the accomplishment of cloud computing is the flexibility it proposals like infrastructure as associate degree support.
Contextual Policy Search for Micro-Data Robot Motion Learning through Covariate Gaussian Process Latent Variable Models

In the next few years, the amount and variety of context-aware robotic manipulator applications is expected to increase significantly, especially in household environments. In such spaces, thanks to programming by demonstration, non-expert people will be able to teach robots how to perform specific tasks, for which the adaptation to the environment is imperative, for the sake of effectiveness and users safety. These robot motion learning procedures allow the encoding of such tasks by means of parameterized trajectory generators, usually a Movement Primitive (MP) conditioned on contextual variables. However, naively sampled solutions from these MPs are generally suboptimal/inefficient, according to a given reward function. Hence, Policy Search (PS) algorithms leverage the information of the experienced rewards to improve the robot performance over executions, even for new context configurations. Given the complexity of the aforementioned tasks, PS methods face the challenge of exploring in high-dimensional parameter search spaces. In this work, a solution combining Bayesian Optimization, a data-efficient PS algorithm, with covariate Gaussian Process Latent Variable Models, a recent Dimensionality Reduction technique, is presented. It enables reducing dimensionality and exploiting prior demonstrations to converge in few iterations, while also being compliant with context requirements. Thus, contextual variables are considered in the latent search space, from which a surrogate model for the reward function is built. Then, samples are generated in a low-dimensional latent space, and mapped to a context-dependent trajectory. This allows us to drastically reduce the search space with the covariate GPLVM, e.g. from 105 to 2 parameters, plus a few contextual features. Experimentation in two different scenarios proves the data-efficiency and the power of dimensionality reduction of our approach.
An Improved Sensor Pattern Noise Estimation Method Based on Edge Guided Weighted Averaging

Sensor Pattern Noise (SPN) has proven to be an effective fingerprint for source camera identification. However, its estimation accuracy is still greatly affected by image contents. In this work, considering the confidence difference in varying image regions, an image edge guided weighted averaging scheme for robust SPN estimation is proposed. Firstly, the edge and non-edge regions are estimated by a Laplacian operator-based detector, based on which different weights are assigned to. Then, the improved SPN estimation is obtained by weighted averaging of image residuals. Finally, an edge guided weighted normalized cross-correlation measurement is proposed as similarity metric in source camera identification (SCI) applications. The effectiveness of the proposed method is verified by SCI experiments conducted on six models from the Dresden data set. Comparison results on different denoising algorithms and varying patch sizes reveal that performance improvement is more prominent for small image patches, which is demanding in real forensic applications.
High-Accuracy Reliability Prediction Approach for Blockchain Services Under BaaS

With the continuous evolution of service-oriented computing paradigm, block- chain as a service (BaaS) has emerged, which is crucial in the development of blockchain-based applications. To build high-quality blockchain-based system, users must select highly reliable blockchain services (peers) with excellent quality of service (QoS). However, owing to the large number of services and the sparsity of personalized QoS data, it is difficult to select the optimal services. Hence, we propose a QoS-based blockchain service reliability prediction framework (BSRPF) under BaaS. In this framework, we employ a matrix factorization-based method to perform accurate QoS prediction. To validate BSPRF, we conducted experiments based on large-scale real-world data, and the results show that BSPRF achieves high prediction accuracy and outperforms other popular methods.
Training the Image Classifier with and without Data Augmentation

The main objective of this paper is to train the image classifier using Convolutional Neural Networks with tensorflow architecture. The proposed paper focus on systematic approaches in classifying the sample set of images using Convolutional Neural Networks. The CNN model with activation function thus classifies the dataset into two categories exactly like human. Thus, the paper highlights the importance of augmentation by comparing their accuracies.
Short Lattice Signatures in the Standard Model with Efficient Tag Generation

We propose new short signature schemes under the ring-SIS assumption in the standard model. Specifically, by revisiting an existing construction in [Ducas and Micciancio, CRYPTO 2014], we demonstrate efficient lattice-based signatures with improved tag generation. We firstly construct a scheme under mild security condition that is existentially unforgeable against random message attack with auxiliary information. We then convert the mildly secure scheme to a fully secure scheme by applying a trapdoor commitment scheme. Our schemes enable the generation of tags from messages and the collision of multiple tags, which improves reduction loss. Our schemes have short signature sizes of O(1) and achieves tighter reduction loss than that of Ducas et al.’s scheme. In accordance with two kinds of parameter set for tag generation, we get two signature schemes with different properties of reduction loss and verification key size. One of our schemes has tighter reduction and as the same size verification key of $$O(\log n)$$ as that of Ducas et al.’s scheme, where n is the security parameter. Another scheme achieves much tighter reduction loss of $$O(\frac{Q}{n})$$ for the sake of verification size of O(n), where Q is the number of signing queries.
Cluster States-based Quantum Networks

We propose to implement multipartite quantum communication network (QCN) by employing the cluster- state-based concept. The proposed QCN can be used to: (i) perform distributed quantum computing, (ii) teleport quantum states between any two nodes in QCN, and (iii) enable next generation of cyber security systems.
A Hybrid Metaheuristic Algorithm for Truss Structure Domain’s Optimization Problem

For calculating constrained optimization problem various socio/bio-inspired algorithms have adopted a penalty function approach to handle linear and nonlinear constraints. In a general sense, the approach is quite easy to understand, but a precise choice of penalty parameter is very much important. It requires a bunch number of primer preliminaries. So as to beat this restriction another self-adaptive penalty function (SAPF) approach will be proposed and incorporated into Particle Swarm Optimization (PSO) algorithm. This approach is referred to as PSO-SAPF. Besides, PSO-SAPF approach will be hybridized with Colliding Bodies Optimization (CBO) referred to as PSO-SAPF-CBO algorithm. The performance of PSO-SAPF and PSO-SAPF-CBO algorithm will be distinctly validated by solving discrete and mixed variable problems from truss structure domain and linear and nonlinear domain. The effect of behavior of penalty parameter, penalty function and constrained violation will be analyzed and discussed with the advantages over other algorithms.
Genetic Algorithm Augmented Convolutional Neural Network for Image Recognition Applications

In this paper a Genetic Algorithm (GA) augmented Convolutional Neural Network (CNN) procedure has been developed. The proposed approach was implemented in Python and applied on a simple three layer convolutional neural network to explore its effects on training the process. The global searching capability of the GA is exploited to initiate the training process of the conventional Back Propagation (BP) based CNN. The weights of the network are optimally initiated using the GA genetic algorithm rather than using random initializers before training. The proposed method of GA-BP based CNN shows better performance in terms of the training time and accuracy with respect to the conventional BP based CNN.
Clock Glitch versus SIFA

Fault-injection attacks are among the most powerful physical threats to the security of cryptographic implementations. Trivially, protection against such attacks is of main concerns of hardware designers, particularly for the products which need to go through a common-criteria evaluation process. After the introduction of Statistical Ineffective Fault Attack (SIFA), several countermeasures have been introduced aiming at defeating its frightening power of disabling several protection schemes. Here in this work, we consider a set of such recent countermeasures and practically show their susceptibility to SIFA when the faults are injected by clock glitch. We indeed show that when the faults are injected by clock glitch, SIFA can be seen as a generalized form of Fault Sensitivity Analysis (FSA) attack. Although it is usually seen as the most convenient way to inject faults, we argue that countermeasures devoted to SIFA should not be practically examined by clock glitch.
On Preprocessing the Data for Improving Sexual Predator Detection : Anonymous for review

Sexual predator detection and predatory message identification are critical to avoid under-aged children from being abused online. In this paper, we investigate different feature extraction approaches for predatory detection. While the previous results indicate good accuracy on predatory conversation detection, there is a missing investigation on the robustness of feature space. Further, we also show the impact of preprocessing on data to improve the performance of predator identification and predatory message classification. Various types of the bag of words features, including binary, term frequency, and TF-IDF representation are investigated on the publicly available PAN 2012 competition dataset for predator identification. Further, to cover the relationship between the words in the text analysis, the GloVe feature set is also investigated for word embedding features. With the set of preprocessing of data, we illustrate the improvement in detecting predatory conversation with an accuracy of 0.994 and F <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> -score of 0.964.
Direct User Behavior Data Leads to Better User Centric Thinking than Role Playing: An Experimental Study on HCI Design Thinking

This paper explores the difference in the effects of role-playing and usability testing on the design thinking process of novice designers. While usability testing helps in understanding user mental models by observing user behavior as the user interacts with a system, role-playing relies on a designer’s own perception about the user where the designer him/herself acts as a user. The difference in user research methods that leads to a different understanding of the user might reflect in the design thinking process of a designer while ideating for solutions of an HCI design problem. This paper presents findings from an experimental study done with novice designers to understand the differences in design thinking processes when designers themselves conducted usability tests versus when they designed using role-playing on persona data given to them. The findings suggest that direct observation of user behavior leads to more consideration for users and therefore better user-centered thinking process than the imaginative role-playing.
Patent Text Classification Study Based on Bi-LSTM-A Model

At present, the classification of patent text is still processed manually, but in the classification of patent IPC level, the similarity of text is getting higher and higher, and the difficulty of subjective classification and categorization is increasing one layer at a time, which is time-consuming and laborious. [Method] According to the characteristics of the patent hierarchy with different text similarity and difficult to distinguish, the model improves the classification effect by encoding the input text and introducing an attention mechanism to assign different weight values to different text. Finally, the validity of the model is verified by experimenting with the corresponding data set from the smart bud and comparing it with the traditional classification methods: decision tree, SVM. [Results] The classification accuracy rate of all levels in the Bi-LSTM-A model is good, and most of the results are above 94%, which is effective for automatic classification of patent texts, and the best performance is obtained in experiments with high text similarity. [Limitations] In this paper, only part of the data of the section, class and group were collected for the experiment, and not enough data were available for the experiment. If the classification effect is judged simply on the basis of the correct rate, the traditional classification algorithm can also perform automatic classification better. However, considering the data volume, data balance and different text similarity, the Bi-LSTM-A model proposed in this paper has a higher classification accuracy under the data imbalance and high text similarity, and the robust model is better.
A Delegated Proof of Proximity Scheme for Industrial Internet of Things Consensus

Recently, work with Distributed Ledger Technologies (DLTs) has focussed on leveraging the decentralised, immutable ledger for use outside of cryptocurrency. One industry poised to benefit from DLTs is the Industrial Internet of Things (IIoT); as the inherent cryptographic mechanisms and alternative trust model make DLTs an attractive solution for distributed networks. Existing DLTs are unsuitable for the IIoT, owing to the large computational and energy requirements for consensus operations and the slow throughput of validated blocks. With limited processing, energy and storage resources and a deadline sensitive operational environment, DLTs in their current state could serve to introduce intolerable latency into IIoT processes and deplete constrained, device resources. Designed for the IIoT context, and based off Delegated Proof of Stake, this work serves to introduce a new consensus mechanism called Delegated Proof of Proximity (DPoP). Using existing location discovery processes, nodes in close proximity to a sensor event are elected as delegates; whose role is to handle consensus and block generation. In using information already known to IIoT devices, DPoP aims to reduce wasted effort, improve throughput by limiting the number of nodes required for consensus operations and improve scalability and flexibility of DLT solutions as the IIoT network continues to grow.
Sex, lies, and videotape: A content and textual analysis of media coverage of the HIV criminal prosecution of Michael Johnson

Media coverage of HIV criminal cases has deployed sexualized and racialized tropes, and portrayed defendants as villains. Several media analyses of HIV criminal trials have been conducted, though only one was in the US, and none involved young Black gay men, who are disproportionately impacted by HIV. We conducted content and textual analyses of 91 media stories focused on the 2015 case of Michael Johnson, a Black gay college student living with HIV who was convicted under Missouri’s HIV non-disclosure laws. To capture both dominant and resistant meanings in news stories, we included mainstream news outlets, as well as HIV-related, African American and GLBT community-oriented outlets. Open and axial coding were carried out by a team of four researchers and thematic analysis was used to explore how media framed Johnson, his sexual activities, condom use, and alleged nondisclosure. Results showed that media stories portrayed Johnson as dishonest and sexually predatory by drawing on narratives of deception and framing him as the sole agentic actor in his sexual encounters. We argue that these media frames, coupled with what is included and excluded in media coverage negatively impacts public opinion of PLWH, reinforces HIV criminalization ideologies, and performs a disciplinary function in the lives of PLWH.
Human Action Recognition using ConvLSTM with GAN and transfer learning

Human Action Recognition (HAR)is a challenging time series classification problem that has received significant attention from computer vision researchers. In this paper, different techniques used for human activities are investigated, and a human action recognition approach using a Convolutional Neural Network (CNN) with Long Short Term Memory (LSTM) and generative adversarial network is proposed. The proposed research evaluates the performance of cross-entropy and adversarial loss function for HAR analysis. Two different datasets UFC101 and the classic KTH dataset, are used for experimental purposes. The UFC101 dataset contains 13k videos in which 101 human actions are included i.e., playing instrument, makeup, etc. In contrast, KTH dataset contains 600 videos containing six human activities, including walking, running, jogging, hand clapping and hand waving, performed by 25 different persons. Also, demonstrates the process of HAR by mixing both datasets and evaluate the performance. The GAN enhances the model robustness by applying adversarial training which fully discovers the underlying connections in both intra-view and cross-view aspects.
Language Learning with Artificial Entities: Effects of an Artificial Tutor’s Embodiment and Behavior on Users’ Alignment and Evaluation

Based on the assumption that humans align linguistically to their interlocutor, the present research investigates if linguistic alignment towards an artificial tutor can enhance language skills and which factors might drive this effect. A 2 $$\times $$ 2 between-subjects design study examined the effect of an artificial tutor’s embodiment (robot vs. virtual agent) and behavior (meaningful nonverbal behavior vs. idle behavior) on linguistic alignment, learning outcome and interaction perception. While embodiment and nonverbal behavior affects the perception of the tutor and the interaction with it, no effect on users’ linguistic alignment was found nor an effect on users learning outcomes.
Cloud-based Protection and Performance Improvement in the E-Health Management Framework

Cloud provides several benefits in e-health care. Based on current trends, the E-healthcare database could be linked to cloud services. This work provides an idea of combining a structure for monitoring electronic-health services based on the cloud environment. With this, we are planning to adapt it to distributed computing. This framework has been enhanced in order to provide a variety of health services. The whole framework of the E-Healthcare Database Maintenance System covers the consumer side and the application side and Cloud. In order to improve safety in the e-health system, the biometric confirmation system is added in order to avoid non-criminal situations and to forget about the password situation. In addition to this framework, we add a security module to enhance the safety and privacy of patients. Our proposed framework improves the security, privacy, time, and cost of access to health information.
A Wearable Body Motion Capture System and Its Application in Assistive Exoskeleton Control

Wearable intelligent device has become a hotspot in the research of human-computer interaction system. A representative example is exoskeleton robot, which has already been applied in rehabilitation, assistance, and even in military field. In order to guarantee man-machine coordination, and to achieve a reasonable performance, proper control has to be implemented according to the wearer's body motions, and body motion capture system is necessary in this process. In this paper, a wearable body motion capture (WBMC) system is designed based on inertial measurement unit (IMU) and IEEE 802.11b wireless communication, and demonstration application in assistive exoskeleton control validate the proposed WBMC system.
Would You Lie To Me Bot? Supporting Decision-Making Processes with Deceiving Virtual Agents

Although we learn as children that "you shall not lie", it is widely accepted that deceptions and lies are necessary, yet unloved, parts of human communication. They are part of the clay that holds together the bricks of our society. In human-computer interaction, however, deceiving machines are seen as a taboo. In this paper, we want to challenge this perception by describing a virtual agent that improves decision-making processes by deceiving decision-makers in a controlled manner. A first survey we conducted shows that such a bot could indeed lead to a more informed decision-making process.
Weighted models for higher-order computation

We study a class of quantitative models for higher-order computation: Lafont categories with (infinite) biproducts. Each of these has a complete “internal semiring” and can be enriched over its modules. We describe a semantics of nondeterministic PCF weighted over this semiring in which fixed points are obtained from the bifree algebra over its exponential structure. By characterizing them concretely as infinite sums of approximants indexed over nested finite multisets, we prove computational adequacy. We can construct examples of our semantics by weighting existing models such as categories of games over a complete semiring. This transition from qualitative to quantitative semantics is characterized as a “change of base” of enriched categories arising from a monoidal functor from coherence spaces to modules over a complete semiring. For example, the game semantics of Idealized Algol is coherence space enriched and thus gives rise to to a weighted model, which is fully abstract.
Human Detection System using Different Depths of the Resnet-50 in Faster R-CNN

Locating people in an image or video sequence automatically is a challenging task due to different environmental conditions and wide variety of features. On the other hand, because of its variety of applications, automated human detection is a very popular area. This study proposes a deep learning model for automated human detection. In order to apply this task, the Pascal VOC database has been used for training and testing stages. The feature extraction step is performed using different depths of the Resnet-50 in a faster R-CNN model. Then, the obtained features are used for the classification step. When the results are compared for different depths of the Resnet-50, it is observed that the first 40 depth of the Resnet-50 showed the better performance than the first 46 depth. Also, the faster RCNN model used in this study outperforms the other studies used the same Pascal VOC database in the literature. The proposed system can detect humans indoor or outdoor environments. Also, it produces promising results in human detection task even in occlusion situations. It can be used for different applications such as monitoring people in public areas with security, health, or energy efficiency proposes, etc.