Reviewerly/exHarmony huggingface dataset:
A Pragmatic Blockchain Based Solution for Managing Provenance and Characteristics in the Open Data Context

Nowadays, open data is a vital part of the most variety of resource input for many systems. Information originates from different sources and is reused by many various applications under different purposes, thereby exposing several problems about managing data provenance and characteristics. Meanwhile, blockchain is a new raising technology that gets much attention around the world. With its immutability, transparency, distributed mechanisms, and automation capabilities, using blockchain is a possible direction to solve these problems. This paper presents the model design of integrating blockchain into an open data platform to resolve this issue. The research involves some related studies, integration mechanisms, and operating procedures, showing the standard model of communication between two platforms and experimental real model with CKAN and Hyperledger Fabric. The result shows that this combination is logical and a feasible direction of high applicability. Also, the proposed solution is general and scalable for further requirements in the future.
Zuckerli: A New Compressed Representation for Graphs

Zuckerli is a scalable compression system meant for large real-world graphs. Graphs are notoriously challenging structures to store efficiently due to their linked nature, which makes it hard to separate them into smaller, compact components. Therefore, effective compression is crucial when dealing with large graphs, which can have billions of nodes and edges. Furthermore, a good compression system should give the user fast and reasonably flexible access to parts of the compressed data without requiring full decompression, which may be unfeasible on their system. Compared to WebGraph, the de-facto standard in compressing real-world graphs, Zuckerli improves multiple aspects by using advanced compression techniques and novel heuristic graph algorithms. Zuckerli can produce both a compressed representation for storage and one which allows fast direct access to the adjacency lists of the compressed graph without decompressing the entire graph. We validate its effectiveness on real-world graphs with up to a billion nodes and 90 billion edges, conducting an extensive experimental evaluation of both compression density and decompression performance. We show that Zuckerli-compressed graphs are 10% to 29% smaller, and more than 20% in most cases, with a resource usage for decompression comparable to that of WebGraph.
A Study of Expert/Novice Perception in Arthroscopic Shoulder Surgery

Arthroscopic shoulder surgery is an advanced orthopedic surgical procedure, which is particularly challenging due to the complex anatomy of the shoulder, and tight spaces for navigation, which also limits the view from the arthroscope. In carrying out arthroscopy, the ability to quickly and effectively navigate through the joint to reach a desired location is essential. Novices often experience confusion in trying to triangulate the information from arthroscopy output with the background knowledge of anatomy while orienting and navigating the instruments. In this paper, we report on the results of the first cadaveric eye-tracking study of arthroscopic surgery in which we investigate differences in perception between experts and novices. Novices' perception is analyzed with cognitive load analysis throughout the procedure and specifically, during the portions of the procedure in which subjects are observed to be confused. In investigating such portions, the gaze data analysis is supplemented with head rotations and acceleration information from gyroscope and accelerometer sensors from the eye tracker. We also use the gathered eye tracking metrics to construct a model to classify subjects into expert/novice. We find statistically significant relations between head movement as well as pupil diameter and periods of confusion. We identify a subset of the metrics that we use to build a simple classifier that is able to distinguish between novices and experts with accuracy of 84%.
The Boon and Bane of Cross-Signing: Shedding Light on a Common Practice in Public Key Infrastructures

Public Key Infrastructures (PKIs) with their trusted Certificate Authorities (CAs) provide the trust backbone for the Internet: CAs sign certificates which prove the identity of servers, applications, or users. To be trusted by operating systems and browsers, a CA has to undergo lengthy and costly validation processes. Alternatively, trusted CAs can cross-sign other CAs to extend their trust to them. In this paper, we systematically analyze the present and past state of cross-signing in the Web PKI. Our dataset (derived from passive TLS monitors and public CT logs) encompasses more than 7 years and 225 million certificates with 9.3 billion trust paths. We show benefits and risks of cross-signing. We discuss the difficulty of revoking trusted CA certificates where, worrisome, cross-signing can result in valid trust paths to remain after revocation; a problem for non-browser software that often blindly trusts all CA certificates and ignores revocations. However, cross-signing also enables fast bootstrapping of new CAs, e.g., Let's Encrypt, and achieves a non-disruptive user experience by providing backward compatibility. In this paper, we propose new rules and guidance for cross-signing to preserve its positive potential while mitigating its risks.
Game description language and dynamic epistemic logic compared

Several different frameworks have been proposed to model and reason about knowledge in dynamic multi-agent settings, among them the logic-programming-based game description language GDL-III and dynamic epistemic logic (DEL). GDL-III and DEL have complementary strengths and weaknesses in terms of ease of modeling and simplicity of semantics. In this paper, we formally study the expressiveness of GDL-III vs. DEL. We clarify the commonalities and differences between those languages, demonstrate how to bridge the differences where possible, and identify large fragments of GDL-III and DEL that are equivalent in the sense that they can be used to encode games or planning tasks that admit the same legal action sequences. We prove the latter by providing translations between those fragments of GDL-III and DEL.
Monocular Human Pose and Shape Reconstruction using Part Differentiable Rendering

Superior human pose and shape reconstruction from monocular images depends on removing the ambiguities caused by occlusions and shape variance. Recent works succeed in regression-based methods which estimate parametric models directly through a deep neural network supervised by 3D ground truth. However, 3D ground truth is neither in abundance nor can efficiently be obtained. In this paper, we introduce body part segmentation as critical supervision. Part segmentation not only indicates the shape of each body part but helps to infer the occlusions among parts as well. To improve the reconstruction with part segmentation, we propose a part-level differentiable renderer that enables part-based models to be supervised by part segmentation in neural networks or optimization loops. We also introduce a general parametric model engaged in the rendering pipeline as an intermediate representation between skeletons and detailed shapes, which consists of primitive geometries for better interpretability. The proposed approach combines parameter regression, body model optimization, and detailed model registration altogether. Experimental results demonstrate that the proposed method achieves balanced evaluation on pose and shape, and outperforms the state-of-the-art approaches on Human3.6M, UP-3D and LSP datasets.
A cognitive model of digital transformation and IS decision making

Individuals are complex bundles of thoughts, beliefs, emotions, and behaviours. For a digital transformation (DT) to be successful, it is necessary to understand how these bundles impact individuals’ reactions to an impending change to then intervene to increase the likelihood of its success. Cognition is the mental action or process of acquiring understanding through thoughts, beliefs and emotions. Cognitive Models (CMs) describing these thoughts, beliefs, emotions and behaviours are applied and researched in psychology. On the other hand, the Information Systems (IS) domain is dominated by a behaviourist rather than a cognitivist approach. In this paper, we investigate what we can learn from these other sciences. By combining the research from the IS domain with that from the psychology domain, this paper presents a theoretical Cognitive Model for Digital Transformation (CMDT) that is a move towards explaining individual cognitive predispositions to DT and change decision making.
Joint QoS Control and Bitrate Selection for Video Streaming based on Multi-agent Reinforcement Learning

Limited network resource and explosive growth in video services have made the networks resource scheduling and adaptive video streaming control even more important for the multi-users quality of experience (QoE) within the cell. The adaptive streaming technology enables the media player to dynamically select the most suitable encoding bitrate to satisfy the users QoE. To achieve the highest possible quality of service (QoS), network nodes QoS control, aka downstream bandwidth configuration, can be dynamically optimized to improve the utilization of bandwidth resources. In this paper, we propose a joint QoS control and adaptive bitrate (ABR) algorithm based on multi-agent reinforcement learning with asynchronous advantage actor-critic (MARL-A3C). The proposed method can process video streams from multiple user nodes simultaneously and adapt to different indicators of QoS and QoE. Under the predetermined QoE standard, the algorithm dynamically controls the maximum downlink rate for the video streaming delivery links, and dynamically selects the encoding bitrate of the video in an active manner for multiple users. The MARL algorithm learns the reward value function of each user state through continuous interaction with the environment and then learns the optimal strategy through these reward value functions. We evaluate the proposed MARL-A3C algorithm on a simulation platform of a fog radio access point (F-RAN) system and compare it with the state-of-art ABR algorithms. The experiment results show that the MARL-A3C outperforms the existing methods in the multi-user F-RAN scenario.
Improving Sentiment Analysis in Arabic and English Languages by Using Multi-Layer Perceptron Model (MLP)

Sentiment analysis is the process of analyzing people's opinion and feelings toward individual, entities, issues, items or topics. Within the last couple of years, this field has gained increasing research interest varying from simple linear models to more complex neural networks models. Several challenges including the grammar, structures of the language and Morphology which one-word lead to many important meanings. The varieties of dialects and the lack of the appropriate corpora limit the use of sentiment analysis on Arabic content. Throughout this paper, we aim at providing a review on the utilization of the deep learning (DL) approach to analyze sentiments expressed in the Arabic text by using Multilayer perceptron (MLP) model. The results show that the MLP model shows highly effective performance in sentiment analysis for both Arabic and English languages.
MARKOV CHAIN METHOD FOR COMPUTING THE RELIABILITY OF HAMMOCK NETWORKS

In this paper, we develop a new method for evaluating the reliability polynomial of a hammock network. The method is based on a homogeneous absorbing Markov chain and provides the exact reliability for networks of width less than 5 and arbitrary length. Moreover, it produces a lower bound for the reliability polynomial for networks of width greater than or equal to 5. To investigate how sharp this lower bound is, we compare our method with other approximation methods and it proves to be the most accurate in terms of absolute as well as relative error. Using the fundamental matrix, we also calculate the average time to absorption, which provides the mean length of a network that is expected to work.
Color Enhancement Using Global Parameters and Local Features Learning

This paper proposes a neural network to learn global parameters and extract local features for color enhancement. Firstly, the global parameters extractor subnetwork with dilated convolution is used to estimate a global color transformation matrix. The introduction of the dilated convolution enhances the ability to aggregate spatial information. Secondly, the local features extractor subnetwork with a light dense block structure is designed to learn the matrix of local details. Finally, an enhancement map is obtained by multiplying these two matrices. A novel combination of loss functions is formulated to make the color of the generated image more consistent with that of the target. The enhanced image is formed by adding the original image with an enhancement map. Thus, we make it possible to adjust the enhancement intensity by multiplying the enhancement map with a weighting coefficient. We conduct experiments on the MIT-Adobe FiveK benchmark, and our algorithm generates superior performance compared with the state-of-the-art methods on images and videos, both qualitatively and quantitatively.
Electronic Medical Records Management in Health Organizations using a Technology Architecture based on Blockchain

A prime requirement for health organizations is the protection and distribution of Electronic Medical Records (EMR) because they store all the sensitive health information about the patients. However, Peruvian private health organizations have problems in security and interoperability of the EMR because most of them have centralized outsourced systems. New Technology Architectures are develop using Blockchain, offering complete control and availability of their medical record to the patient. However, the architectures found in the literature are not designed for Peruvian private health organization needs. In this paper, the proposal is a technology architecture based on Blockchain for the EMR management in Peruvian private health organizations. It considers Peruvian law and international regulations for the EMR management, providing transaction logs, and off-chain technology to efficiently store medical images. The results obtained in a medical clinic through a proof of concept show that private health organizations have low security and efficiency in their EMR management processes, suggesting an opportunity to develop a technology solution for EMR management.
An Enhanced Multi-Modal Recommendation Based on Alternate Training With Knowledge Graph Representation

Deep network recommendation is a cutting-edge topic in current recommendation system research, which as a combination of recommendation systems and deep learning theory can effectively improve recommendation accuracy.In a real recommendation scenario, all the effective information in a data set should be extracted, both explicit and implicit, because the comprehensive degree of information is proportional to the recommendation performance.This paper proposes an enhanced multi-modal recommendation based on alternate training with knowledge graph representation (SI-MKR) based on the MKR deep learning recommendation model.Our framework is an enhanced recommendation system based on knowledge graph representation, using valuable external knowledge as multi-modal information.The SI-MKR model solves the problem of ignoring the diversity of data types in the multi-modal knowledge-based recommendation system, which adds user and item attribute information from a knowledge graph as an enhancement recommendation multi-tasking training.By analysing the content of the item and user attributes, the SI-MKR model classifies the attributes of the items and users, processes the text type attributes and multi-value type attributes separately for feature extraction, and other types of attributes are used as inputs to the knowledge graph embedding unit.In addition, the knowledge graph data form a triplet unit, thus continuing the knowledge graph data training process.The feature extraction unit of the knowledge graph and the recommended unit are connected through the cross-compression unit for alternate training.During the deep learning framework training process, the recommendation system's item has a potential correlation with the head entity in the knowledge graph which embodies the idea of multi-tasking.Through extensive experiments on real-world datasets, we demonstrate that SI-MKR achieves substantial gains in movie recommendation over advanced model baselines.Even user-item interactions are sparse, SI-MKR maintains better performance than the MKR model.
A Collusion-Resistant Identity-Based Proxy Reencryption Scheme with Ciphertext Evolution for Secure Cloud Sharing

In order to solve the challenges of user data security in the cloud computing (storage) environment, many encryption solutions with different features have been presented. Among them, proxy reencryption (PRE) based on public-key infrastructure (PKI) is a promising technology for secure cloud sharing. And identity-based proxy reencryption (IBPRE), which uses identity as the public key, eliminates burdensome certificate management and is, therefore, more preferable. However, most of the current IBPRE schemes only focus on the processing of data sharing while overlooking the functions of authorization revocation and ciphertext update, which are more closely related to the security of data itself. Moreover, the few existing schemes that involve ciphertext update turn out to be impractical because the length of ciphertext increases with the reencryption of ciphertext. In this paper, an improved IBPRE scheme, which provides improvements on the inadequacies of the scheme proposed by Ateniese et al. especially in terms of collusion safety and ciphertext evolution, is proposed. To the best of our knowledge, this is a practical IBPRE scheme integrating the functions of access authorization, delegation revocation, ciphertext update, reauthorization, and conditional reservation delegation. The proposed technique has high practicability in the scenario where a large number of ciphertexts need to be updated synchronously. Lastly, the comparative analysis and simulation results show that the two reencryption algorithms in the proposed scheme have the shortest computing time than other schemes.
Quantum speed limit time, non-Markovianity and quantum phase transition in Ising spins system

Abstract We study the dynamics of quantum speed limit (QSL) time of a central spin, uniformly interacting with the spins of an Ising spin chain placed in a transverse magnetic field, and the non-Markovianity of the spin environment. Both pure and time dependent mixed initial states of the central spin are taken into account. For a pure initial state, both the quantities work as indicators for quantum phase transition (QPT) and can locate the critical point (CP). For a time dependent mixed initial state of the central spin, QSL time also shows anomalous behavior in the vicinity of the CP, a signature of signaling QPT. The numerical simulations show that the spin environment has potential capacity for speeding up quantum evolution, which can be manipulated through the strength of the applied magnetic field.
A Fuzzy Ensemble Method With Deep Learning for Multi-Robot System

In a multi-robot system, situation assessment evaluates the current situation quantitatively to help decision-makers make the best decision. Conventional situation assessment methods ignore the initiative of each robot, so it often encounters bottlenecks. Collaborative intelligence shows better performance than a single global decision. To address this problem, this work introduces a deep learning-based fuzzy adaptive method (DLFA) to achieve the real-time situation assessment for a multi-robot system. The proposed method employs the shortest path faster algorithm to achieve information sharing between agents. The shortest path faster algorithm ensures that the agent distributes its state information to its teammates in the fastest way. Each agent gets the information from teammates and treats their state as the observation of the scene. Deep neural network maps current observations into a local situation assessment result by combining a large number of nonlinear processing layers. Finally, each local assessment result is regarded as a brick to construct the final situation assessment via a fuzzy ensemble method. Experimental results show that the proposed method outperforms competitors.
Preprocessing for Source Code Similarity Detection in Introductory Programming

It is well documented that some students either work together on programming assessments when required to work individually (collusion) or make unauthorised use of existing code from external sources (plagiarism). One approach used in the detection of these violations of academic integrity is source code similarity detection, the automatic checking of student programs for unduly high levels of similarity. Preprocessing of source code files has the potential to increase the effectiveness, the efficiency, or both, of the source code comparison process. There are many possible steps in the preprocessing, and examination of the literature suggests that these steps are selected and implemented without any empirical evidence as to their value. This paper lists 19 preprocessing steps that have been used in code similarity detection, and assesses the effectiveness and the efficiency of 16 of these steps on data sets of student programs from introductory programming courses. The results should help researchers to decide what preprocessing steps to include when designing source code similarity detection techniques or software. According to the study, identifier removal increases both effectiveness and efficiency. Token renaming and syntax tree linearisation increase effectiveness at a cost of efficiency. Other preprocessing steps are dependent upon characteristics of the data set and should ideally be empirically tested before being applied. The paper should also help alert programming educators to the sorts of disguise that students can apply to copied programs.
Design and Implementation of an Intelligent System to translate Arabic Text into Arabic Sign Language

Sign Language is a widely known medium of communication used by the deaf, hard of hearing individuals, and those who are unable to speak physically because of any reason. There is no common or universal version of sign language. This is especially true for Arabic Sign Language (ArSL) which has various versions depending on the Arab country where it is being used. In this paper, we have designed an intelligent system which can parse Arabic text, keeping in mind the context of the text, and then translate the text into Arabic Sign Language. Preliminary testing of the system has shown an overall accuracy of 85% in correctly translating Arabic words into the Arabic Sign Language.