Reviewerly/exHarmony huggingface dataset:
A Novel Duplication Based Countermeasure to Statistical Ineffective Fault Analysis

The Statistical Ineffective Fault Analysis, SIFA, is a recent addition to the family of fault based cryptanalysis techniques. SIFA based attack is shown to be formidable and is able to bypass virtually all the conventional fault attack countermeasures. Reported countermeasures to SIFA incur overheads of the order of at least thrice the unprotected cipher. We propose a novel countermeasure that reduces the overhead (compared to all existing countermeasures) as we rely on a simple duplication based technique. In essence, our countermeasure eliminates the observation that enables the attacker to perform SIFA. The core idea we use here is to choose the encoding for the state bits randomly. In this way, each bit of the state is free from statistical bias, which renders SIFA unusable. Our approach protects against stuck-at faults and also does not rely on any side channel countermeasure. We show the effectiveness of the countermeasure through an open source gate-level fault attack simulation tool. Our approach is probably the simplest and the most cost effective.
Volumetric human teleportation

Existing volumetric capture systems require many cameras and lengthy post processing. We introduce the first system that can capture a completely clothed human body (including the back) using a single RGB webcam and in real time. Our deep-learning-based approach enables new possibilities for low-cost and consumeraccessible immersive teleportation.
Knowledge Graph Representation Reasoning for Recommendation System

In view of the low interpretability of existing collaborative filtering recommendation algorithms and the difficulty of extracting information from content-based recommendation algorithms, we propose an efficient KGRS mod... | Find, read and cite all the research you need on Tech Science Press
Receding horizon path planning of automated guided vehicles using a time‐space network model

Summary Time‐space network (TSN) models have been widely used for collision‐free path planning of automated guided vehicles. However, existing TSN models are planned globally. The global method suffers from computational complexity and uncertainties cannot be dealt with in the dynamic environment. To address these limitations, this article proposes a new methodology to decompose the global planning problem into smaller local planning problems, which are planned in a receding horizon way. For the local problem, new decision variables and constraints are incorporated into the TSN framework. Extensive simulation experiments are carried out to show the potential of the proposed methodology. Simulation results show that the proposed method obtains competitive performances and computational times are considerably reduced, compared with the global method.
Entity Type Recognition – Dealing with the Diversity of Knowledge

Semantic Heterogeneity is the problem that arises when multiple resources present differences in how they represent the same real-world phenomenon. In KR, an early approach was the development of ontologies and, later on, when ontologies showed at the knowledge level the same semantic heterogeneity that they were meant to fix at the data level, to compute mappings among them. In this paper we acknowledge the impossibility of avoiding semantic heterogeneity, this being a consequence of the more general phenomenon of the diversity of the world and of the world descriptions. In this perspective, the heterogeneity of ontologies is a feature (and not a bug to be fixed by aligning them) which gives the possibility to use the most suitable ontology in any given application context. The main contributions of this paper are: (i) a novel articulation of the problem of semantic heterogeneity, as it appears at the knowledge level, as contextuality, (ii) its qualitative and quantitative formalization in terms of a set of diversity and unity metrics and (iii) an Entity Type Recognition algorithm which selects the contextually most appropriate ontology and exploits it to solve the current problem, e.g., the alignment and integration of a set of input schemas. The experimental results show the validity of the approach.
Logical Separability of Incomplete Data under Ontologies

Finding a logical formula that separates positive and negative examples given in the form of labeled data items is fundamental in applications such as concept learning, reverse engineering of database queries, and generating referring expressions. In this paper, we investigate the existence of a separating formula for incomplete data in the presence of an ontology. Both for the ontology language and the separation language, we concentrate on first-order logic and three important fragments thereof: the description logic ALCI, the guarded fragment, and the two-variable fragment. We consider several forms of separability that differ in the treatment of negative examples and in whether or not they admit the use of additional helper symbols to achieve separation. We characterize separability in a model-theoretic way, compare the separating power of the different languages, and determine the computational complexity of separability as a decision problem.
Update Aware Device Scheduling for Federated Learning at the Wireless Edge

We study federated learning (FL) at the wireless edge, where power-limited devices with local datasets collaboratively train a joint model with the help of a remote parameter server (PS). We assume that the devices are connected to the PS through a bandwidth-limited shared wireless channel. At each iteration of FL, a subset of the devices are scheduled to transmit their local model updates to the PS over orthogonal channel resources, while each participating device must compress its model update to accommodate to its link capacity. We design novel scheduling and resource allocation policies that decide on the subset of the devices to transmit at each round, and how the resources should be allocated among the participating devices, not only based on their channel conditions, but also on the significance of their local model updates. We then establish convergence of a wireless FL algorithm with device scheduling, where devices have limited capacity to convey their messages. The results of numerical experiments show that the proposed scheduling policy, based on both the channel conditions and the significance of the local model updates, provides a better long-term performance than scheduling policies based only on either of the two metrics individually. Furthermore, we observe that when the data is independent and identically distributed (i.i.d.) across devices, selecting a single device at each round provides the best performance, while when the data distribution is non-i.i.d., scheduling multiple devices at each round improves the performance. This observation is verified by the convergence result, which shows that the number of scheduled devices should increase for a less diverse and more biased data distribution.
Deep Learning Approach in Gregg Shorthand Word to English-Word Conversion

Shorthand or Stenography has been used in a variety of fields of practice, particularly by court stenographers. To record every detail of the hearing, a stenographer must write fast and accurate In the Philippines, the stenographers still used the conventional way of writing shorthand, which is by hand. Transcribing shorthand writing is time-consuming and sometimes confusing because of a lot of characters or words to be transcribed. Another problem is that only a stenographer can understand and translate shorthand writing. What if there is no stenographer available to decipher a document? A deep learning approach was used to implement and developed an automated Gregg shorthand word to English-word conversion. The Convolutional Neural Network (CNN) model used was the Inception-v3 in TensorFlow platform, an open-source algorithm used for object classification. The training datasets consist of 135 Legal Terminologies with 120 images per word with a total of 16,200 datasets. The trained model achieved a validation accuracy of 91%. For testing, 10 trials per legal terminology were executed with a total of 1,350 handwritten Gregg Shorthand words tested. The system correctly translated a total of 739 words resulting in 54.74% accuracy.
Improved Building Blocks for Secure Multi-party Computation Based on Secret Sharing with Honest Majority

Secure multi-party computation permits evaluation of any desired functionality on private data without disclosing the data to the participants. It is gaining its popularity due to increasing collection of user, customer, or patient data and the need to analyze data sets distributed across different organizations without disclosing them. Because adoption of secure computation techniques depends on their performance in practice, it is important to continue improving their performance. In this work, we focus on common non-trivial operations used by many types of programs, where any advances in their performance would impact the runtime of programs that rely on them. In particular, we treat the operation of reading or writing an element of an array at a private location and integer multiplication. The focus of this work is on secret sharing setting with honest majority in the semi-honest security model. We demonstrate improvement of the proposed techniques over prior constructions via analytical and empirical evaluation.
QoS Prediction of Web Services Based on Reputation-Aware Network Embedding

As the emergence of numerous services with similar functions, it is very helpful to recommend personalized services for users, and urgent to accurately predict the QoS(Quality-of-Service) values of Web services. Collaborative Filtering (CF) is a commonly-used method to handle above issues. However, it faces two common issues: data sparsity problem and trustworthiness issue, which greatly reduces its prediction accuracy. To address this problem properly and systematically, we introduce the network embedding learning into the QoS prediction process and propose an improved QoS prediction method based on the reputation-aware network embedding learning. Firstly, a two-phase K-means clustering is adopted to filter untrustworthy users. Next, the reputation of trustworthy users is calculated, and an attributed user-service bipartite network is constructed between trustworthy users and services while considering the user reputation. Then the reputation-aware network embedding is adopted to learn the hidden representations of users. Finally, user-based CF is adopted to predict the unknown QoS values. The experimental results show that our method has a significant improvement in accuracy compared with other methods.
A Multi-blockchain Architecture Supporting Cross-Blockchain Communication

Blockchain technology is a distributed ledger technology [1] that can be applied in many fields, but due to factors such as throughput and connectivity, current blockchain projects are not well suited for commercial applications. This paper analyzes the cross-blockchain interaction in the multi-blockchain scenario, and proposes a new multi-blockchain architecture to support cross-blockchain communication. By establishing a relay node for the blockchain network, the specific blockchain information format is converted into a common interactive format. A coordination network composed of coordination nodes is constructed between the multi-blockchains to achieve interconnection between the blockchains. In addition, this paper proposes a cross-blockchain communication protocol to ensure the security and global consistency of cross-blockchain interactions in multi-blockchain scenarios. Experiments show that the proposed multi-blockchain architecture can complete the interconnection between heterogeneous blockchains, and as the number of blockchains increases, the communication throughput between multi-blockchains also increases.
Behavioral and Motivational Patterns of Internet Users: A Logico-Categorial Analysis

The ongoing digitalization is giving rise to new sciences, such as psychoinformatics which studies the links between digital footprint and individual psychological characteristics. According to top-level researchers, the most important problems to be solved in the nearest future are to assess the impact of the new forms of self-perception, self-reflection and self-presentation on social communication; find strategies to facilitate the flow state, i.e. full immersion in whatever someone is doing, in the increasingly fragmented reality; explore the mechanisms of the digital worlds affecting human brain development and the ways of reducing the negative impact of digital technology on human brain; coordinate the design of digital worlds with the society’s emotional and cultural heritage in order to ensure a comfortable existence; and develop social communication rules for digital environments.Internet use behaviors can be prosocial or antisocial, depending on whether the existing social norms are accepted or rejected. Users engaging in the two types of online behavior differ in their online communication strategies and manifest specific cognitive, motivational and emotional characteristics.The goal of this study was to review international findings in order to identify and analyze the logico-categorial characteristics of online antisocial behavior associated with specific motivational patterns of Internet users.As a result, internal and external determinants of online antisocial behavior have been identified. Significant correlations have been found between pathologic Internet use and user’s communicative, emotional, motivational, and cognitive psychological characteristics. A promising direction for building online behavior models and designing initiatives to tackle online antisocial behavior may be to explore the links between users’ behavior on websites of different purpose and content with their personal psychological characteristics.
The Generational Digital Gap within Dual Vocational Education and Training Teachers

&lt;p style="text-align: justify;"&gt;In our current society, Digital Competence is an essential and basic competence for teachers. The students need trained teachers who know how to use Information and communication technologies (ICTs) and develop them into pedagogy. However, some of them do not know how. This phenomena is called “The generational digital divide”, which occurs when teachers do not have sufficient digital or technological skills or do not adapt to them. This article aims to determine whether the age of teachers has an influence on their knowledge about Information and Communication Technologies. The objective is to detect if there really exists a digital gap between generations, specifically in Dual Vocational Education Training, a kind of education which has been growing in the recent years. To do so, a study with a quantitative and descriptive method has been carried out, with the participation of 1.568 teachers of this training modality in the Autonomous Community of Andalusia. The data were extracted by means of a questionnaire to measure the level of digital teaching competence in Dual Vocational Education and Training schools. The results showed that the level of digital competence shown was medium to low across all dimensions. With regard to the age factor, only the problem-solving dimension was found to be age-dependent.&lt;/p&gt;
Quantum search for scaled hash function preimages

Abstract We present the implementation of Grover’s algorithm in a quantum simulator to perform a quantum search for preimages of two scaled hash functions, whose design only uses modular addition, word rotation and bitwise exclusive or. Our implementation provides the means to assess with precision the scaling of the number of gates and depth of a full-fledged quantum circuit designed to find the preimages of a given hash digest. The detailed construction of the quantum oracle shows that the presence of AND gates, OR gates, shifts of bits and the reuse of the initial state along the computation require extra quantum resources as compared with other hash functions based on modular additions, XOR gates and rotations. We also track the entanglement entropy present in the quantum register at every step along the computation, showing that it becomes maximal at the inner core of the first action of the quantum oracle, which implies that no classical simulation based on tensor networks would be of relevance. Finally, we show that strategies that suggest a shortcut based on sampling the quantum register after a few steps of Grover’s algorithm can only provide some marginal practical advantage in terms of error mitigation.
Particle swarm optimization neural network for research on artificial intelligence college English classroom teaching framework

This paper based on the algorithm of particle swarm optimization neural network, the university English classroom training framework with artificial intelligence is researched and designed, and a personalized learning path based on an improved binary particle swarm algorithm based on the non-linear increase of inertial weights and the exploration of unknown space is proposed. The recommendation method improves the algorithm’s convergence speed and convergence accuracy. It is easy to jump out of the local optimum through the improvement of the algorithm, thereby solving the problem of low recommendation accuracy of the personalized learning path and improving the recommendation efficiency. To verify the recommended effect of the model and algorithm, this paper designs a simulation experiment and a learning platform that take the college English course as an example to verify the running performance and practical application effect of the proposed method. The above experiments show that the proposed method can improve the matching degree of the personalized learning path and the needs of learners, and improve the accuracy of application in personalized learning path recommendation.
Capturing Video Frame Rate Variations via Entropic Differencing

High frame rate videos are increasingly getting popular in recent years, driven by the strong requirements of the entertainment and streaming industries to provide high quality of experiences to consumers. To achieve the best trade-offs between the bandwidth requirements and video quality in terms of frame rate adaptation, it is imperative to understand the effects of frame rate on video quality. In this direction, we devise a novel statistical entropic differencing method based on a Generalized Gaussian Distribution model expressed in the spatial and temporal band-pass domains, which measures the difference in quality between reference and distorted videos. The proposed design is highly generalizable and can be employed when the reference and distorted sequences have different frame rates. Our proposed model correlates very well with subjective scores in the recently proposed LIVE-YT-HFR database and achieves state of the art performance when compared with existing methodologies.
A corpus-based versus experimental examination of word- and character-frequency effects in Chinese reading: Theoretical implications for models of reading.

Chinese words consist of a variable number of characters that are normally written in continuous lines, without the blank spaces that are used to separate words in most alphabetic writing systems. These conventions raise questions about the relative roles of character versus whole-word processing in word identification, and how words are segmented from strings of characters for the purpose of their identification and saccade targeting. The present article attempts to address these questions by reporting an eye-movement experiment in which 60 participants read a corpus of sentences containing two-character target words that varied in terms of their overall frequency and the frequency of their initial characters. We examine participants’ eye movements using both corpus-based statistical models and more standard analyses of our target words. In addition to documenting how key lexical variables influence eye movements and highlighting a few discrepancies between the results obtained using our two statistical approaches, our experiment shows that high-frequency initial characters can actually slow word identification. We discuss the theoretical significance of this finding and others for current models of Chinese reading, and then describe a new computational model of eye-movement control during the reading of Chinese. Finally, we report simulations showing that this model can account for our findings. (PsycInfo Database Record (c) 2021 APA, all rights reserved)
Facial Beauty Prediction via Local Feature Fusion and Broad Learning System

Facial beauty prediction (FBP), as a frontier topic in the domain of artificial intelligence regarding anthropology, has witnessed some good results as deep learning technology progressively develops. However, it is still limited by the complexity of the deep structure network in need of a large number of parameters and high dimensions, easily leading to a great consumption of time. To solve this problem, this paper proposes a fast training FBP method based on local feature fusion and broad learning system (BLS). Firstly, two-dimensional principal component analysis (2DPCA) is employed to reduce the dimension of the local texture image so as to lessen its redundancy. Secondly, local feature fusion method is adopted to extract more advanced features through avoiding the effects from unstable illumination, individual differences, and various postures. Finally, extensional feature eigenvectors are input to the broad learning network to train an efficient FBP model, which effectively shortens operational time and improve its preciseness. Extensive experiments with the proposed method on large scale Asian female beauty database (LSAFBD) can be conducted within 13.33s while sustaining an accuracy of 58.97%, impressively outstripping other state-of-the-art methods in training speed.
OWL2Bench: A Benchmark for OWL 2 Reasoners

There are several existing ontology reasoners that span a wide spectrum in terms of their performance and the expressivity that they support. In order to benchmark these reasoners to find and improve the performance bottlenecks, we ideally need several real-world ontologies that span the wide spectrum in terms of their size and expressivity. This is often not the case. One of the potential reasons for the ontology developers to not build ontologies that vary in terms of size and expressivity, is the performance bottleneck of the reasoners. To solve this chicken and egg problem, we need high quality ontology benchmarks that have good coverage of the OWL 2 language constructs, and can test the scalability of the reasoners by generating arbitrarily large ontologies. We propose and describe one such benchmark named OWL2Bench. It is an extension of the well-known University Ontology Benchmark (UOBM). OWL2Bench consists of the following – TBox axioms for each of the four OWL 2 profiles (EL, QL, RL, and DL), a synthetic ABox axiom generator that can generate axioms of arbitrary size, and a set of SPARQL queries that involve reasoning over the OWL 2 language constructs. We evaluate the performance of six ontology reasoners and two SPARQL query engines that support OWL 2 reasoning using our benchmark. We discuss some of the performance bottlenecks, bugs found, and other observations from our evaluation.
Towards a Formally Verified Implementation of the MimbleWimble Cryptocurrency Protocol

MimbleWimble is a privacy-oriented cryptocurrency technology which provides security and scalability properties that distinguish it from other protocols of its kind. We present and briefly discuss those properties and outline the basis of a model-driven verification approach to address the certification of the correctness of an implementation of the protocol.