Reviewerly/exHarmony huggingface dataset:
Verified CSAC-Based CP-ABE Access Control of Cloud Storage in SWIM

As a wide-ranging distributed system, System Wide Information Management (SWIM) aims to provide an aviation information sharing platform that carries various information systems in the aviation field, so that the business of each subsystem can safely interact. As the data center of SWIM, the cloud storage platform has many functions and services based on third-party platforms. At the same time, it also brings the problem of a third-party trust crisis. In order to effectively verify the trustworthiness of third parties, this paper designs a new verifiable CSAC attribute encryption access control scheme. It introduces the cloud storage administration center (CSAC), and decentralizes multiple sub-authorization centers based on a subset of user attributes. The client uses the verification information provided by multiple sub-authorization centers to combine with the verification algorithm, which is based on the ciphertext access policy attribute encryption algorithm (CP-ABE) and introduces a secret sharing scheme that can identify fraudsters, and client can use this to verify CSAC. The credibility of and the security of key distribution. Compared with the previous solutions, this solution not only effectively solves the problem of the incomplete credibility of third parties, but also improves the efficiency of encryption and decryption, and ensures the security of data interaction in SWIM.
Robust naval localization using a particle filter on polar amplitude gridmaps

Maritime navigation heavily relies on global navigation satellite systems and related technologies for positioning. Since these technologies are vulnerable to external threats such as signal spoofing, alternatives are needed to increase reliability and ensure safe navigation. Our proposal is to use radar to construct polar amplitude gridmaps tailored for the intended route, and using a particle filter for position estimation. The proposed approach has been successfully demonstrated on data from a surface vessel in the harbor of Helsinki.
Variational Quantum Unsampling on a Programmable Nanophotonic Processor

We introduce the Variational Quantum Unsampling (VQU) protocol, a nonlinear quantum neural network approach for verification and inference of near-term quantum circuits outputs. We experimentally demonstrate this protocol on a quantum photonic processor.
Constant-Time Loading: Modifying CPU Pipeline to Defeat Cache Side-Channel Attacks

Cache side-channel attacks exploit cache state changes to steal confidential information. The emergence of transient execution attacks, a new form of microarchitecture side-channel attack that can access any address, makes cache side-channel attacks more threatening. Most of these attacks infer information by measuring the execution time of load operations. Therefore, from the microarchitecture perspective, we propose a novel countermeasure against cache side-channel attacks by eliminating the access time difference caused by cache hits or misses. We use the constant-time loading mechanism to limit each load instruction's execution to a fixed time and specify this mechanism's wake-up condition to avoid excessive performance loss. We modify the CPU pipeline to simulate this design and run SPEC2006 applications. The results show that the performance loss of our mechanism is negligible.
Study on Load Balancing Scheduling Strategy based on Prediction and ACO Algorithm

It is difficult for cloud computing providers to provide quantitative computing resources for dynamic resource requests of cloud computing tasks in advance. The static scheduling algorithm cannot serve cloud computing tasks well without Pre-sense load status. To make better use of cloud computing resources and decrease the power consumption of datacenter, we proposed a load balancing scheduling strategy based on prediction and ant colony optimization algorithm. The prediction algorithm is used to sense the load of the datacenter, the ACO algorithm reasonably schedules the VMs to the hosts. The results of simulation show that datacenter energy consumption and SLA violation rate can be effectively reduced by using load balancing scheduling strategy based on prediction and ant colony optimization algorithm.
A Novel Machine Learning Algorithm for Cognitive Concept Elicitation by Cognitive Robots

Cognitive knowledge learning (CKL) is a fundamental methodology for cognitive robots and machine learning. Traditional technologies for machine learning deal with object identification, cluster classification, pattern recognition, functional regression and behavior acquisition. A new category of CKL is presented in this paper embodied by the Algorithm of Cognitive Concept Elicitation (ACCE). Formal concepts are autonomously generated based on collective intension (attributes) and extension (objects) elicited from informal descriptions in dictionaries. A system of formal concept generation by cognitive robots is implemented based on the ACCE algorithm. Experiments on machine learning for knowledge acquisition reveal that a cognitive robot is able to learn synergized concepts in human knowledge in order to build its own knowledge base. The machine–generated knowledge base demonstrates that the ACCE algorithm can outperform human knowledge expressions in terms of relevance, accuracy, quantification and cohesiveness.
Deferred Well-Formedness and Validity

This proposal emerges out of conversations about introducting collaborative editing into OpenDocument Format (ODF) applications, as a type of change tracking. Vis-a-vis a document, a lone author is a lesser and included case of collaborative editing. In either case, changes have to be captured, along with their metadata, and reconciled, in the case of conflicting edits. Despite progress on the software side of collaborative editing for a variety of formats, there has been no visible progress on the capturing of changes, or their reconcilation in OpenDocument Format documents. Being habituated, not to say addicted, to markup approaches, it's understandable I find the lack of format discussions disquieting. It's all well and good to have change tracking/collaborative editing, successfully in software, but what the hell am I going to write down in ODF? How to capture changes, from one or many authors, and how to capture reconciliations are the focus of this proposal. That requires unique identification of changes (one or many authors), identifying where changes may be applied, and recording the application of changes (the resulting document).
Digital Accessibility Rights Rights Evaluation Index

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Ensemble Based Model for Privacy in Online Social Network

Online social networks become a more popular and efficient way to connect millions of people around the world."Many social network sites have changed the way people are communicating or connected with each other to share their personal information. However, these sites do not ensure the privacy of users' data from third-party applications. With the abundance amount of information available over the internet, the privacy-preserving data mining is effectively used for pertaining to the privacy of underlying data. In order to do so, this research work proposed an ensemble method to classify the user’s profile based on a set of privacy level. The proposed model has used various machine learning algorithms like a Decision tree, Naïve Bayes, IBK, NB tree, and Bayes Network."
Understanding Cloud Computing in a Higher Education Context

Cloud computing has become prevalent in many sectors today, including higher education. The chapter is premised on the assumption that despite the popularity of cloud computing in higher education, research within this context remains limited. The study, which is qualitative and exploratory in nature, involved an innovative methodological approach, drawing on interviews with three groups of participants: (1) members of a global, Fortune 100 technology company supplying cloud solutions; (2) members of a selected UK university's IT department implementing cloud solutions; and (3) students from the same UK university using cloud solutions. The findings improve understanding around cloud solutions in the higher education context by unpacking—through a qualitative thematic analysis approach—relevant themes that inform the extant information systems literature. Finally, the study provides recommendations for future researchers, cloud suppliers, universities, and students.
Events Automatic Extraction from Arabic Texts

The event extraction task consists in determining and classifying events within an open-domain text. It is very new for the Arabic language, whereas it attained its maturity for some languages such as English and French. Events extraction was also proved to help Natural Language Processing tasks such as Information Retrieval and Question Answering, text mining, machine translation etc… to obtain a higher performance. In this article, we present an ongoing effort to build a system for event extraction from Arabic texts using Gate platform and other tools.
MPEG Audio Compression

In this chapter, we consider the set of tools for audio compression applicable to general audio, such as music, as opposed to specifically speech compression, developed under the aegis of the Motion Picture Experts Group, MPEG. Surprisingly, this subject has much to do with psychology, specifically within the field of aural sense perception—psychoacoustics. The phenomena of frequency masking and temporal masking are exploited in a waveform coding approach that makes use of a psychoacoustic model of hearing, with the result generally referred to as perceptual coding. We look in some detail at audio compression as it benefits from psychoacoustics, and how this plays out in MPEG-1 Audio Compression, Layer 3 (MP3) and later MPEG audio developments: MPEG-2 and 4 including MPEG Advanced Audio Coding (AAC). We begin the study of psychoacoustics as it applies here with the determination of the equal-loudness relations, which leads to a discussion of frequency masking. Critical Bands are introduced as well as the Bark Unit. Temporal Masking is a familiar phenomenon from our own experience. MPEG Audio is introduced to make use of these properties, along with MPEG Audio Layers including MP3. MPEG-2 AAC (Advanced Audio Coding) and MPEG-4 Audio are considered next. Finally, other audio codecs such as Ogg Vorbis and MPEG-7 audio are also discussed.
Mixture-Based Probabilistic Graphical Models for the Partial Label Ranking Problem

The Label Ranking problem consists in learning preference models from training datasets labeled with a ranking of class labels, and the goal is to predict a ranking for a given unlabeled instance. In this work, we focus on the particular case where both, the training dataset and the prediction given as output allow tied labels (i.e., there is no particular preference among them), known as the Partial Label Ranking problem. In particular, we propose probabilistic graphical models to solve this problem. As far as we know, there is no probability distribution to model rankings with ties, so we transform the rankings into discrete variables to represent the precedence relations (precedes, ties and succeeds) among pair of class labels (multinomial distribution). In this proposal, we use a Bayesian network with Naive Bayes structure and a hidden variable as root to collect the interactions among the different variables (predictive and target). The inference works as follows. First, we obtain the posterior-probability for each pair of class labels, and then we input these probabilities to the pair order matrix used to solve the corresponding rank aggregation problem. The experimental evaluation shows that our proposals are competitive (in accuracy) with the state-of-the-art Instance Based Partial Label Ranking (nearest neighbors paradigm) and Partial Label Ranking Trees (decision tree induction) algorithms.
Augmented Reality in Military Applications

The Art of Modern Warfare has seen a significant shift with the onset of the 21st Century as Military Operations have become more diverse and intense in nature. The rise of insurgency and terrorism with the onset of the “War on Terror” led by the USA and its allies had led to a need where soldiers are more specifically trained inMilitary Operations in Urban Terrain ( MOUT), Counter-Insurgency and Counter-Terrorism duties. This has prompted the enlistment of late headways done in the field of Computers and innovation like Augmented Reality (AR) into military applications to improve a soldier’s battle-space knowledge as real-world objects can then be augmented onto the real-world environment to create better training facilities for the soldiers and help the soldier to adapt up to complex conditions and increment communitarian circumstance mindfulness among warriors occupied with high-hazard tasks. AR can limit inadvertent blow-back, increment risk markings like the likelihood of Improvised Explosive Devices (IEDs) and increment war zone observation. AR in coming time would be viewed as a venturing stone to future military applications as an arranged correspondence framework would mean a minimal loss of life and maximum impact during Military Operations.
Adaptive personalized federated learning for heterogeneous data: a method based on parameter decomposition and continual learning

Federated learning allows resource-constrained edge computing devices to cooperatively train machine learning models while keeping data locally. However, federated learning faces the challenge that the global model slowly converges and even deviates from the optimal solution under heterogeneous data. To solve this problem, this paper proposes an adaptive personalized federated learning (APFL) algorithm,considering the federation optimization problem for heterogeneous data under a multi-task learning framework that includes spatial and temporal dimensions.First, a parameter decomposition strategy is adoptedto decompose the model parameters into globally shared parameters and client-specific parameters to achieve model personalization for each client while extracting general knowledge for all clients. Then, APFL models the local optimization as sequential multi-task learning performed on each client. An elastic weight consolidation penalty term is imposed on the update of the globally shared parameters to realize the memory retention of important parameters and the fast learning of unimportant parameters for the globally shared model.Comparative experiments on multiple federated benchmark datasets verify the effectiveness and superiority of the proposed method.
User Click Preference Prediction Using Attention-based LSTM Model

Predicting user clicking preferences is a critical issue in the ranking process reflected by the returned results from search engines. A few studies have explored the extracted sequen- tial information in the queries while this underlying information can be focused on as a valuable source for predicting user click preferences. This paper proposes an attention-based LSTM model to predict user click preferences on a submitted input quey using the previous queries sequence and user click history. In this model, we use the topic distribution of user documents as attention to the LSTM model. The feature information of the content extracted is used as the attention information of the LSTM network during the training process. We compare the model with topic-based ranking models with data from an AOL search engine and Session TREC 2013,2014 to show its performance. The result reveals significant improvement in the attention-based LSTM model using topics in the Mean Reciprocal Rank by 13% compared to the baseline topic-based models.
Pedagogical conditions and methods aimed at the formation of musical-auditory representations of future teachers of Musical Arts

The article is devoted to the phenomenon of auditory representations, their content, compliance with musical content, formation, tuning and implementation in the performance process of the future teacher during piano training. The purpose of the article is to substantiate the pedagogical conditions and develop the methods aimed at forming of the future music teacher’s musical-auditory representations. The goal is realised through the implementation of relevant tasks using the methods of theoretical research: analysis, synthesis, deduction, systematisation, pedagogical observation. In the article musical-auditory representation is considered as a professional skill of a musician, formed on the basis of the understanding of semantics – semantic units of musical language, which facilitates the qualitative reproduction of artistic and figurative content and maximum efficiency of musician’s own performance process in creation of artistic-pedagogical and performing interpretations. The list of the skills acquired by the applicant during the formation of auditory perceptions is specified, namely: analytical (to identify and understand the symbolism of musical language; its genre and style) (compare semantic constructs of different musical directions); reflexive, figurative-auditory and sensorimotor. The pedagogical conditions for the formation of auditory representations are offered: gradual expansion of a musical thesaurus in the course of the profession-centred training; stimulating auditory perception by recognising and understanding the elements of musical language; the direction of musical-perceptual experience on the coordination of auditory representations with the sign-semantic context of the performed works. In accordance with the defined conditions, a number of methods have been developed: comparative textual analysis; figurative and auditory analysis; perceptual-auditory analysis; associative music model; semantic identification; artistic reincarnation; polytonation expressiveness; tactile-auditory method. Further research involves the development of the future Arts teachers’ auditory perceptions in classes on accompaniment and ensemble playing, taking into account the specifics of these subjects.
A 3D Measurement Method Based on Coded Image

The binocular stereo vision system is often used to reconstruct 3D point clouds of an object.However, it is challenging to find effective matching points in two object images with similar color or less texture.This will lead to mismatching by using the stereo matching algorithm to calculate the disparity map.In this context, the object can't be reconstructed precisely.As a countermeasure, this study proposes to combine the Gray code fringe projection with the binocular camera as well as to generate denser point clouds by projecting an active light source to increase the texture of the object, which greatly reduces the reconstruction error caused by the lack of texture.Due to the limitation of the camera viewing angle, a one-perspective binocular camera can only reconstruct the 2.5D model of an object.To obtain the 3D model of an object, point clouds obtained from multiple-view images are processed by coarse registration using the coarse SAC-IA algorithm and fine registration using the ICP algorithm, which is followed by voxel filtering fusion of the point cloud.To improve the reconstruction quality, a polarizer is mounted in front of the cameras to filter out the redundant reflected light.Eventually, the 3D model and the dimension of a vase are obtained after calibration.
The Design and Application of Sports Video Analysis System for Sports Training

The application of sports training video system in modern sports training field can improve the analysis and playback ability of athletes' movements, strengthen the quality of sports training, and improve the movement standards and performance of sports athletes. With the development of computer information technology, sports video in sports training has many advantages in the fields of intelligent discrimination, computer resolution technology, key frame extraction, quality, motion morphology, and so on, which is gradually replacing the traditional naked eye analysis method. To address the current sports video analysis system's shortcomings, through optimization and improvement, we proposed and constructed a new analysis system to improve the comprehensiveness and accuracy of information analysis, and enhance the accuracy and recall of key frame extraction. It can be effectively applied to guide sports training.
Model Based Engineering and Product Line Engineering: Combining Two Powerful Approaches at Raytheon

ABSTRACT Model‐based engineering (MBE) refers to a systems engineering approach that employs models as an integral part of a system's engineering stream, providing a formality and semantic rigor that lends itself to analysis and prediction, thus enabling earlier detection of problems. Product line engineering (PLE) is a way to engineer a portfolio of related products in an efficient manner, taking full advantage of the products’ similarities while respecting and managing their differences. Raytheon, one of the world's largest defense contractors, is applying PLE and MBE together and combining the benefits of each. This paper will show how Raytheon is using the two methodologies to support each other, and the lessons it is learning as it does so.
Literature Survey

Fully homomorphic encryption (FHE) scheme enables computation of arbitrary functions on encrypted data, hence considered as “holy grail” of modern cryptography. This chapter presents the relevance of FHE in present day cloud computing, a brief history of different homomorphic encryption schemes and formally defines fully homomorphic encryption along with basic idea behind Gentry’s construction. Gradually, few recent works will be described in this area those are proposed following Gentry’s scheme but with simpler constructions and better efficiency.
Distribution Characterization Study of the Heavy Metals for a Mining Area of East Tianshan Mountain in Xinjiang Based on the Kriging Interpolation Method

Abstract In order to understand the impact of mining development on the environment, a metal mine at east Tianshan Mountain in Xinjiang province was used as the object of study. The heavy metal content data were obtained from field sampling and indoor measurements, and the data were compared with the heavy metal background values, applying the geostatistic method and Kriging spatial interpolation theory to map the heavy metal distribution in the study area. The results show that the overall heavy metal contamination in the mining area is serious, with significant accumulation of As, Cd and Cr, which is a threat to local human and animal health. And there are obvious correlations and synergies between the distribution of these heavy metals, mostly showing positive correlations, which should be taken into more account in the subsequent environmental rehabilitation of the mine site.