Reviewerly/exHarmony huggingface dataset:
Security and Performance Evaluation of Master Node Protocol in the Bitcoin Peer-to-Peer Network

This paper proposes a proximity-aware extensions to the current Bitcoin protocol, named Master Node Based Clustering (MNBC). The ultimate purpose of the proposed protocol is to evaluate the security and performance of grouping nodes based on physical proximity. In MNBC protocol, physical internet connectivity increases as well as the number of hops between nodes decreases through assigning nodes to be responsible for propagating based on physical internet proximity.
Graph-Adaptive Activation Functions for Graph Neural Networks

Activation functions are crucial in graph neural networks (GNNs) as they allow defining a nonlinear family of functions to capture the relationship between the input graph data and their representations. This paper proposes activation functions for GNNs that not only adapt to the graph into the nonlinearity, but are also distributable. To incorporate the feature-topology coupling into all GNN components, nodal features are nonlinearized and combined with a set of trainable parameters in a form akin to graph convolutions. The latter leads to a graph-adaptive trainable nonlinear component of the GNN that can be implemented directly or via kernel transformations, therefore, enriching the class of functions to represent the network data. Whether in the direct or kernel form, we show permutation equivariance is always preserved. We also prove the subclass of graph-adaptive max activation functions are Lipschitz stable to input perturbations. Numerical experiments with distributed source localization, finite-time consensus, distributed regression, and recommender systems corroborate our findings and show improved performance compared with pointwise as well as state-of-the-art localized nonlinearities.
Witnessing nonseparability of bipartite quantum operations

We provide a method for witnessing nonseparability of quantum processes on shared systems, which uses the channel-state duality. The method uses a maximally entangled state as a resource. We show that using the resource provides significant advantage over the corresponding protocol for nonseparability detection without the resource.
Designing Transactional Hypervisor for Improving the Performance of Qcow2-Based Virtual Disks

Virtualization has become one of the backbone technologies for constructing cloud servers and data centers, in that the technology supports to partition the physical computing resources of the system into multiple logical ones for concurrently running multiple VMs (virtual machines) in a single physical server. In constructing multiple VM environments, the qcow2 format is widely used as a virtual disk layout due to its attractive features such as high storage space efficiency and high reliability. Unfortunately, according to the policy of the qcow2 format, the hypervisor calls file sync operations excessively during allocating data clusters. Moreover, significant performance degradation may occur due to the CoW (Copy-on-Write) operations that are unconditionally performed by the hypervisor whenever new data clusters are allocated on the virtual disk. In this paper, we introduce a transactional hypervisor that mitigates the performance overhead caused by the data cluster allocation onto the qcow2-based virtual disk. To achieve this, we adopt transactional support to the existing hypervisor and propose a new type of file sync operation, called gsync, which flushes the modified metadata in a bundle. Moreover, we introduce an optimization technique on the CoW mechanism that performs CoW operations selectively according to the amount of data that will be updated on each data cluster. The experimental results clearly show that the proposed hypervisor outperforms the conventional hypervisor in terms of write IOPS (Input/Output Per Second) by up to 78.4%, by reducing the number of file sync calls and that of CoW operations by up to 50.6% and 52.6%, respectively.
Automatic Story Generation Based on Graph Model Using Godot Engine

This paper deals with storytelling aspects of computer games. A new approach to automatic story generation based on graph modelling is proposed. Godot Engine for visualization of output stories is used. In our research the plot is represented by a specific graph called the layered graph composed of story building blocks, namely layer of locations, layer of characters, layer of items, layer of narration and plot dependencies. An appropriate set of graph rules (productions) stored in JSON format is a generative tool for the building blocks. Graph rules are internal representation of actions which can be taken by the hero of the story. A sequence of the rules from start to end can describe the whole gameplay of one player or can be generated in automatic way as a possible gameplay. All such sequences of the rules create the space of all potential gameplays. Every rule corresponds to a certain animation made in Godot Engine. It allows to visualize every possible story, represented by sequence of rules, by seamless animation. Given a rule, animation is based on graphic assets corresponding to the rule nodes of the layered graph.
Side channel analysis of SPECK

SPECK is a family of lightweight block ciphers developed by Beaulieu et al. of the US National Security Agency (NSA) for the Internet of Things (IoT). It is an ARX-based design with a Feistel-like structure which supports keys of size ranging from 64 bits to 256 bits. SPECK has been standardised by ISO/IEC for radio frequency identification (RFID) devices. It has drawn the attention of many cryptanalysts and several cryptanalysis results have been published. In this paper, carry flag attacks on the full SPECK ciphers are presented. Depending on the key size and block size, the complexities of our attacks, to nearly ensure success, vary from 2 59 time and 2 14 data to 2 227 time and 2 62 data.
A New Density Peak Clustering Algorithm for Automatically Determining Clustering Centers

Density Peaks Clustering (DPC) tries to use two objectives: density and peaks, to automatically determine the number of clusters. It is claimed to be applicable to data sets with non-spherical clusters. However, the cutoff distance dc in DPC should be determined based on the experience of decision maker and the cluster centers should be selected manually. But it is very difficult to do so and improper selection of these will result in incorrect results. In order to overcome these shortcomings, an adaptive cutoff distance computing method based on Gini index is proposed firstly, and then the possibility (i.e., multiplication of the local density and the relative distance y=ρiδi) of each point xi as a cluster center is calculated, moreover, the point with the maximal change of possibility is determined as the critical point. Each point whose possibility is larger than that of the critical point will be a cluster center. In this way, both the number of clusters and cluster centers can be automatically determined, and the manually selecting the cluster centers through the decision graph in DPC can be avoided. Based on these, a new density peak clustering algorithm by automatically determining both the number of clusters and cluster centers is proposed. Finally, experiments are conducted and the results show that the new algorithm can not only automatically determine the cluster center, but also has higher accuracy than DPC.
Anomaly Detection and Processing in Artificial Intelligence for IT Operations of Power System

In recent years anomaly detection has been wildly applied in many fields, from zero day attack detection to insider threat detection, from situational awareness to intrusion detection. For power system, secure and stable operation is indispensable and it takes electric utility staff huge amount of time. Naturally, artificial intelligence for IT operations (AIOps) especially anomaly detection can also be used to find out unusual behavior discord with expected pattern in this field. In this paper, we propose an intelligent system that first conducts a joint time series detection to identify outliers or anomalies on the basis of statistical judgment and machine learning, and then automatically discovers those anomalous functions in the method of statistical analysis. The result indicates that the implementation of our system is able to largely reduce labor costs, improve automation and efficiency of power system operations and maintenance.
Incorporating User Feedback into Sequence to Sequence Model Training

As the largest professional network, LinkedIn hosts millions of user profiles and job postings. Users effectively find what they need by entering search queries. However, finding what they are looking for can be a challenge, especially if they are unfamiliar with specific keywords from their industry. Query Suggestion is a popular feature where a search engine can suggest alternate, related queries. At LinkedIn, we have productionized a deep learning Seq2Seq model to transform an input query into several alternatives. This model is trained by examining search history directly typed by users. Once online, we can determine whether or not users clicked on suggested queries. This new feedback data indicates which suggestions caught the user's attention. In this work, we propose training a model with both the search history and user feedback datasets. We examine several ways to incorporate feedback without any architectural change, including adding a novel pairwise ranking loss term during training. The proposed new training technique produces the best combined score out of several alternatives in offline metrics. Deployed in the LinkedIn search engine, it significantly outperforms the control model with respect to key business metrics.
A Block-wise Histogram Shifting based Reversible Data Hiding Scheme with Overflow Handling

Design and development of reversible data hiding schemes are widely studied topic due to its wide scope in cloud computing and medical image transmission. This paper introduces a new reversible data hiding algorithm based on the histogram of the blocks of the cover images with an efficient overflow management technique. In the new scheme, the peak intensity value from each block is used for data hiding, and to make sure the correct recovery of the original image, the grayscale value used for data hiding from each block is embedded in the same block itself by replacing the least significant bits of eight selected pixels. The lossless recovery is ensured by embedding those least significant bits in the same block itself along with the secret message. Detailed theoretical analysis and experimental study of the scheme are carried out and discussed in this paper. The images from the standard image dataset of the University of Southern California (USC-SIPI) are used in our study.
Development of An Augmented Reality Equipped Composites Bonded Assembly and Repair for Aerospace Applications

The prosperity of aircraft transportation together with revolutionary promotion of composite components used in commercial aircraft pose enormous challenges to the aircraft composite assembly and repair (especially uncertainties associated with polymer process parameters control, barely visible defects and non-destructive inspection capability to inspect zero-thickness defects). Therefore, an industry solution owning merits on reliability and repeatability of assembly process is at high demand. Augmented Reality (AR), a human computer interaction technology, possesses its exclusive superiority on its capability of inflicting digital mock-up into physical environment. The above property of AR provides colossal opportunities to be utilised into industrial applications to contribute the realisation of automated, efficient, streamlined and reliable process and assembly. The current ongoing research aims at developing an AR System integrated into aircraft composite bonded assembly and repair as a guidance tool to instruct technicians’ repairing operation, mitigate human errors, and reduce duration of repair and assembly. Upon the accomplishment of the System, the researchers would aim to investigate the incorporation of machine learning and deep network algorithms to enable and significantly improve the interactions between the multitude of process parameters involved in the composites assembly control procedures, solely relying upon the AR geometric data. This will ultimately lead to dramatic reduction of sensors in aircraft assembly, mitigation of in-process analysis time, reduction of process and post-process inspection time, and a higher quality assembly. Stepped scarf composite repair embedded with soft composite patches was selected as the archetype to be brought into effect though hard patches were partially examined as well. The AR System has focused on composite patches assembly and vacuum bagging process to address the predicament of miscellaneous steps and fibre directions.
Secure Key Storage and Access Delegation Through Cloud Storage

Cloud storage is gaining popularity to store and share files. To secure the files, cloud storage providers supply client interfaces with the facility to encrypt the files and upload them into the cloud. When client-side encryption is done, the onus of key management lies with the cloud user. Public key proxy re-encryption mechanisms can be used to distribute the key among stakeholders of the file. However, clients use low powered devices like mobile phones to share their files. Lightweight cryptography operations are needed to carry out the encryption operations. Ring-LWE-based encryption scheme meets this criterion. In this work, a proxy re-encryption scheme is proposed to distribute the file key. The scheme is proved CCA secure under Ring-LWE assumption in the random oracle model. The performance of the scheme is compared with the existing proxy re-encryption schemes which are observed to show better performance for re-encryption and re-key generation.
Analysis of Smartphone Sensor Bias from an Activity Recognition Experiment in the Wild

A multitude of sensor models is being embedded in a variety of smartphone brands. Notably, the accelerometer sensor has been widely used to recognize a variety of human-induced activities leading to the growth of human activity recognition (HAR) research. However, diversities in sensor models create heterogeneity in the data which could result in inaccurate recognition accuracies. Existing empirical work handling a diverse set of mobile devices do not pay attention to this issue. In this paper, we report on the bias observed across 13 different accelerometer models spanning 26 different phones. A continuous sensing application was deployed on all phones over a period of 45 days to empirically assess students’ behavioral patterns (physical activity patterns). Sensor bias analysis revealed One Plus 6, Poco F1 to have the lowest bias and Samsung S4 the largest bias. Except for Samsung S4(11%), Honor 7(6%) and Nexus 5(6%), all other phones showed less than 3% avg deviation from the actual value which was 9.8m/s <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> . Using the bias ranges observed, we classify the entire unlabeled data with accuracy close to 98%. We believe our results will provide a baseline information before using the phones for continuous sensing applications in general and human activity monitoring in particular.
Feature indexing and search optimization for enhancing the forensic analysis of mobile cloud environment

The increased utilization of Mobile Cloud Computing (MCC) technology creates an opportunity for cybercrimes. Modeling the suitable methods for mobile cloud forensic examination and analysis is essential to improve the investigation performance. This paper incorporates data mining and optimization methods to enforce precise handling of the mobile cloud evidence in examination and analysis to improve the investigation performance. It enhances the analysis of the mobile cloud forensics with the incorporation of the evidence indexing, cross-referencing, and keyword searching as the sub-processes. The proposed Forensic Examination and analysis methodology using the Data mining and Optimization (FEDO) approach examines the key features of the evidence and indexes the pieces of evidence with key features to facilitate the investigation over the massive cloud evidence. By analyzing the temporal and geo-information, it applies cross-referencing to alleviate the evidence toward the case-specific evidence. The proposed methodology improves the searching capability of the investigation through the Linearly Decreasing Weight (LDW) strategy based Particle Swarm Optimization (PSO) algorithm. Thus, the experimental results demonstrate that the proposed forensic methodology yields better investigation performance in terms of accuracy of evidence detection.
Statistical Proving of Enhanced Interaction and Augmentative Discourse for BYOD Supported Classroom

Today, mobile devices are cheap, affordable and are a part of our day to day life. It has paved its way into learning inside and outside the class. These devices have become a part of our body; it is with us 24 x 7. These smart devices provide us with learning beyond the classroom and textbooks. It has not only enhanced teacher-student interaction but has also solved many classroom related issues. With the advent of mobile devices MOOCs, m-learning, e-learning have grown exponentially in last few years. These pervasive devices are not only used for communication, but also for teaching, learning and development purpose. In this work we try to find the modalities which will be regulated by systematic inclusion of Bring Your Own Device (BYOD) based classroom interaction. The system used task technology fit (TTF) model to find if this technology is fit for the educational aid. This study includes survey to find the cognitive behaviour of students towards mobile devices including laptop, tablet, and smart phones. The parametric data collected over survey have been analysed for finding out the variation in treatment group and control group using Bayesian paired sample t-test, which clearly shows the effective use of BYOD based interaction in classroom for enhancing teaching learning process.
A surface electromyography and inertial measurement unit dataset for the Italian Sign Language alphabet

Surface Electromyography (EMG) and Inertial Measurement Unit (IMU) sensors are gaining the attention of the research community as data sources for automatic sign language recognition. In this regard, we provide a dataset of EMG and IMU data collected using the Myo Gesture Control Armband, during the execution of the 26 gestures of the Italian Sign Language alphabet. For each gesture, 30 data acquisitions were executed, composing a total of 780 samples included in the dataset. The gestures were performed by the same subject (male, 24 years old) in lab settings. EMG and IMU data were collected in a 2 seconds time window, at a sampling frequency of 200 Hz.
Diffusion Field Estimation Using Decentralized Kernel Kalman Filter with Parameter Learning Over Hierarchical Sensor Networks

In this paper, a task is addressed to track a nonlinear time-varying diffusion field based on data collected by sensor networks. By exploiting kernel methods, the nonlinear field function is approximated by a linear combination of kernel functions in a reproducing kernel Hilbert space (RKHS). To capture the dynamical property of a diffusion field and the relation of system input and output data, a state-space model on weights of these kernel functions is constructed with unknown process noise. Thus, the nonlinear tracking problem is transformed into a linear state estimation solved by Kalman filter. Further, this kernel Kalman filter (KKF) is decomposed into a decentralized fashion in a way to collect sensor data efficiently over a hierarchical network structure with different clusters. To adapt the algorithm to unknown process noise, a decentralized variational Bayesian KKF is proposed to learn the distributions of system unknown variables.
3D Imaging Techniques for Forensic Identification of Human Images

Traditional techniques on forensic identification of human images are more focused on the inspection and analysis of two-dimensional images, while 3D imaging has been around in some form for decades. In this paper, we use the 3D imaging techniques as a new solution for forensic human image identification. The 3D imaging will provide one more dimension information for forensic examination of human images. Firstly, we introduce a dimension classification theory to forensic identification of human images for the first time, namely, the two-dimension, quasi-3D, and three dimension based forensic human image identification techniques. We describe 3D imaging techniques for human being and the new 3D imaging based forensic human identification techniques are discussed, which will provide more intuitive human identity information perception experience in the visual examination for experts, and offer more dimensional individual information and visual angles for feature comparison. Moreover, the more detailed and precise visual features will be available for forensic identification of human images. With the increasingly maturity of the 3D imaging techniques, the dimensional innovation of techniques in the field of forensic identification of human images will be increasingly improved, and the accuracy of forensic identification of human images will be more comprehensively guaranteed.
Novel Hidden Markov Scoring Algorithm for Fraudulent Impression Classification in Mobile Advertising

Excessive usage of smartphones and tablets have led to drastic increase of mobile ad fraud in recent years. The fraudulent users can be either human or automated scripts with the intention of making illegal revenue or exhausting the advertiser budget are being engaged with this multimillion industry. The ad fraud referred to any kind of activities that are generated by a fraudulent user is a huge threat to the existence of the online advertising ecosystem. The researchers have proposed various kinds of methodologies and tools in the context of ad fraud detection and prevention. However, the fraudulent users are smart enough to bypass the significant number of existing detection and prevention systems. The combat between fraud users and researchers or solution designers in this field never ends. Thus, the novel solution of ad fraud detection and prevention techniques is needed. The proposed approach of this study to address this problem is called hidden Markov scoring model—HMSM. The model calculates scores for each observe/emission variable of experimental data set towards the hidden states of target variable based on hidden Markov model so that fraud impression can be classified. The experimental results show that the significance of the proposed approach to classify the fraud and non-fraud impression.
Towards a methodology for online VR application testing

Performing online testing for applications is a common practice in business, but for research without much control over the participants it is unknown as to what this process should look like and what could be possible scenarios. Furthermore, making use of VR testing is reliant on users owning their own devices which could influence the process and level of criticism greatly but also foster new thoughts on best practice for this type of research. This paper explains the steps that were taken for the gathering of the data and reports on the feedback from participants regarding their perception of the process.