Reviewerly/exHarmony huggingface dataset:
Privacy-Preserving Cloud Auditing for Multiple Users Scheme With Authorization and Traceability

With the widespread application of cloud storage, users could obtain many conveniences such as low-price data remote storage and flexible data sharing.Considering cloud service provider (CSP) is not full-trusted, lots of cloud auditing schemes are proposed to ensure the shared data security and integrity.However, existing cloud auditing schemes have some security risks, such as user identity disclosure, denial of service attack and single-manager abuse of power.To solve the above issues, we use certificateless signature technology to construct a privacy-preserving cloud auditing scheme for multiple users with authorization and traceability in this paper.Unlike the traditional schemes, our scheme realizes user identity anonymity without group signature and ring signature techniques, which guarantees the tag is compact.Meanwhile, our scheme supports that at least d managers could trace the identity of malicious user collaboratively, which avoids the abuse of single-manager power and provides non-frameability.Furthermore, we introduce an identity authentication process between the third-party auditor (TPA) and the CSP to prevent the denial of service attack.That is, our scheme could solve the problem that anyone can challenge the CSP for the proofs, which averts network congestion and waste of cloud resources.In terms of function, the proposed scheme also supports efficient user revocation from a group.Certificateless cryptography ensures that our scheme does not involve certificate management burden and the key escrow problem.The security analysis shows that our scheme is provably secure against two types of adversaries in the environment of certificateless cryptography.The performance analysis demonstrates that our scheme is efficient.
A Cryptocurrency Spectrum Short Analysis

Technological development brings about economic changes that affect most citizens, both in developed and undeveloped countries. The implementation of blockchain technologies that bring cryptocurrencies into the economy and everyday life also induce risks. Authorities are continuously concerned about ensuring balance, which is, among other things, a prudent attitude. Achieving this goal sometimes requires the development of standards and regulations applicable at the national or global level. This paper attempts to dive deeper into the worldwide operations, related to cryptocurrencies, as part of a general phenomenon, and also expose some of the intersections with cybercrime. Without impeding creativity, implementing suggested proposals must comply with the rules in effect and provide sufficient flexibility for adapting and integrating them. Different segments need to align or reposition, as alteration is only allowed in a positive way. Adopting cryptocurrency decisions should be unitary, based on standard policies.
IoT-Inspired Framework of Intruder Detection for Smart Home Security Systems

The proliferation of IoT devices has led to the development of smart appliances, gadgets, and instruments to realize a significant vision of a smart home. Conspicuously, this paper presents an intelligent framework of a foot-mat-based intruder-monitoring and detection system for a home-based security system. The presented approach incorporates fog computing technology for analysis of foot pressure, size, and movement in real time to detect personnel identity. The task of prediction is realized by the predictive learning-based Adaptive Neuro-Fuzzy Inference System (ANFIS) through which the proposed model can estimate the possibility of an intruder. In addition to this, the presented approach is designed to generate a warning and emergency alert signals for real-time indications. The presented framework is validated in a smart home scenario database, obtained from an online repository comprising 49,695 datasets. Enhanced performance was registered for the proposed framework in comparison to different state-of-the-art prediction models. In particular, the presented model outperformed other models by obtaining efficient values of temporal delay, statistical performance, reliability, and stability.
PANDA

In this demonstration, we present a privacy-preserving epidemic surveillance system. Recently, many countries that suffer from COVID-19 crises attempt to access citizen's location data to eliminate the outbreak. However, it raises privacy concerns and may open the doors to more invasive forms of surveillance in the name of public health. It also brings a challenge for privacy protection techniques: how can we leverage people's mobile data to help combat the pandemic without scarifying location privacy. We demonstrate that we can achieve this by implementing policy-based location privacy for epidemic surveillance. Our system has three primary functions for epidemic surveillance: people flow monitoring, epidemic analysis, and contact tracing. We provide an interactive tool allowing the attendees to explore and examine the usability of our system: (1) the utility of location monitor and disease transmission model estimation, (2) the procedure of contact tracing in our systems, and (3) the privacy-utility trade-offs w.r.t. different policy graphs. The attendees will find that we can have the high usability for epidemic surveillance while preserving location privacy.
Optical single-channel cryptosystem based on the discrete wavelet transform and the chaotic standard map for multi-image

In this paper, an optical single-channel asymmetric cryptosystem based on the inverse discrete wavelet transform (IDWT) and chaotic standard map for multi-image in cyan-magenta-yellow-black (CMYK) mode is proposed. To the best of our knowledge, this is the first time that the color image in CMYK format is encoded into a real-valued two-dimensional (2D) format by the IDWT; thus, our scheme can be implemented in a single-channel step. In addition, we propose to generate the random phase mask based on the chaotic standard map. Due to the large key space and high efficiency of the chaotic standard map, the security level of the proposed scheme can be improved. The chaotic standard map is employed to generate the chaotic standard phase mask (CSPM). Then the encoded 2D image can be encrypted in the linear canonical transform domain based on the CSPM. Numerical simulation results are shown to verify the feasibility and effectiveness of our cryptosystem. In addition, our approach outperforms other relevant cryptosystems and can be extended to encrypt multiple color images directly.
A Custom EOG-Based HMI Using Neural Network Modeling to Real-Time for the Trajectory Tracking of a Manipulator Robot

Although different physiological signals such as Electrooculography (EOG) have been widely used in the control of assistance systems for people with disabilities, customizing the signal classification system remains a challenge. In most interfaces, the user must adapt to the classification parameters, although ideally, the systems must adapt to the user parameters. Therefore, in this work is presented the use of a Multilayer Neural Network (MNN) to model the EOG signal as a mathematical function, which is optimized using genetic algorithms, in order to obtain the maximum and minimum amplitude threshold of the EOG signal of each person that uses the designed interface. The problem of the variation of the voltage threshold of the physiological signals is addressed by means of an intelligent calibration, if an assistance system is not calibrated it loses functionality. Artificial intelligence techniques such as machine learning and fuzzy logic are used for classification of the EOG signal, but they need calibration parameters that are obtained through databases generated through prior user training, depending on the effectiveness of the algorithm, the learning curve and the response time of the system. In this work, by optimizing the parameters of the EOG signal, the classification is customized and the domain time of the system is reduced without the need for a database and the training time of the user is minimized, significantly reducing the time of the learning curve. The results are implemented in an HMI for the generation of points in a Cartesian space ( , , ) in order to control a manipulator robot that follows a desired trajectory by means of the movement of the user's eyeball.
Phase Retrieval With Bregman Divergences and Application to Audio Signal Recovery

Phase retrieval (PR) aims to recover a signal from the magnitudes of a set of inner products. This problem arises in many audio signal processing applications which operate on a short-time Fourier transform magnitude or power spectrogram, and discard the phase information. Recovering the missing phase from the resulting modified spectrogram is indeed necessary in order to synthesize time-domain signals. PR is commonly addressed by considering a minimization problem involving a quadratic loss function. In this paper, we adopt a different standpoint. Indeed, the quadratic loss does not properly account for some perceptual properties of audio, and alternative discrepancy measures such as beta-divergences have been preferred in many settings. Therefore, we formulate PR as a new minimization problem involving Bregman divergences. Since these divergences are not symmetric with respect to their two input arguments in general, they lead to two different formulations of the problem. To optimize the resulting objective, we derive two algorithms based on accelerated gradient descent and alternating direction method of multipliers. Experiments conducted on audio signal recovery from spectrograms that are either exact or estimated from noisy observations highlight the potential of our proposed methods for audio restoration. In particular, leveraging some of these Bregman divergences induce better performance than the quadratic loss when performing PR from spectrograms under very noisy conditions.
Face Mask Detection at the Fog Computing Gateway

This work proposes a fog computing-based face mask detection system for controlling the entry of a person into a facility.The proposed system uses fog nodes to process the video streams captured at various entrances into a facility.Haar-cascade-classifiers are used to detect face portions in the video frames.Each fog node deploys two MobileNet models, where the first model deals with the dichotomy between mask and no mask case.The second model deals with the dichotomy between proper mask wear and improper mask wear case and is applied only if the first model detects mask in the facial image.This two-level classification allows the entry of people into a facility, only if they wear the mask properly.The proposed system offers performance benefits such as improved response time and bandwidth consumption, as the processing of video stream is done locally at each fog gateway without relying on the Internet.
A Graph-Based Framework for Structured Prediction Tasks in Sanskrit

We propose a framework using energy-based models for multiple structured prediction tasks in Sanskrit. Ours is an arc-factored model, similar to the graph-based parsing approaches, and we consider the tasks of word segmentation, morphological parsing, dependency parsing, syntactic linearization, and prosodification, a “prosody-level” task we introduce in this work. Ours is a search-based structured prediction framework, which expects a graph as input, where relevant linguistic information is encoded in the nodes, and the edges are then used to indicate the association between these nodes. Typically, the state-of-the-art models for morphosyntactic tasks in morphologically rich languages still rely on hand-crafted features for their performance. But here, we automate the learning of the feature function. The feature function so learned, along with the search space we construct, encode relevant linguistic information for the tasks we consider. This enables us to substantially reduce the training data requirements to as low as 10%, as compared to the data requirements for the neural state-of-the-art models. Our experiments in Czech and Sanskrit show the language-agnostic nature of the framework, where we train highly competitive models for both the languages. Moreover, our framework enables us to incorporate language-specific constraints to prune the search space and to filter the candidates during inference. We obtain significant improvements in morphosyntactic tasks for Sanskrit by incorporating language-specific constraints into the model. In all the tasks we discuss for Sanskrit, we either achieve state-of-the-art results or ours is the only data-driven solution for those tasks.
Efficient Neural Speech Synthesis for Low-Resource Languages Through Multilingual Modeling

Recent advances in neural TTS have led to models that can produce high-quality synthetic speech. However, these models typically require large amounts of training data, which can make it costly to produce a new voice with the desired quality. Although multi-speaker modeling can reduce the data requirements necessary for a new voice, this approach is usually not viable for many low-resource languages for which abundant multi-speaker data is not available. In this paper, we therefore investigated to what extent multilingual multi-speaker modeling can be an alternative to monolingual multi-speaker modeling, and explored how data from foreign languages may best be combined with low-resource language data. We found that multilingual modeling can increase the naturalness of low-resource language speech, showed that multilingual models can produce speech with a naturalness comparable to monolingual multi-speaker models, and saw that the target language naturalness was affected by the strategy used to add foreign language data.
Interactive Fiction Game Playing as Multi-Paragraph Reading Comprehension with Reinforcement Learning

Interactive Fiction (IF) games with real humanwritten natural language texts provide a new natural evaluation for language understanding techniques.In contrast to previous text games with mostly synthetic texts, IF games pose language understanding challenges on the humanwritten textual descriptions of diverse and sophisticated game worlds and language generation challenges on the action command generation from less restricted combinatorial space.We take a novel perspective of IF game solving and re-formulate it as Multi-Passage Reading Comprehension (MPRC) tasks.Our approaches utilize the context-query attention mechanisms and the structured prediction in MPRC to efficiently generate and evaluate action outputs and apply an object-centric historical observation retrieval strategy to mitigate the partial observability of the textual observations.Extensive experiments on the recent IF benchmark (Jericho) demonstrate clear advantages of our approaches achieving high winning rates and low data requirements compared to all previous approaches. 1
An Efficient Numerical Algorithm for Solving Data Driven Feedback Control Problems

The goal of this paper is to solve a class of stochastic optimal control problems numerically, in which the state process is governed by an Itô type stochastic differential equation with control process entering both in the drift and the diffusion, and is observed partially. The optimal control of feedback form is determined based on the available observational data. We call this type of control problems the data driven feedback control. The computational framework that we introduce to solve such type of problems aims to find the best estimate for the optimal control as a conditional expectation given the observational information. To make our method feasible in providing timely feedback to the controlled system from data, we develop an efficient stochastic optimization algorithm to implement our computational framework.
Entropic uncertainty relation of a qubit–qutrit Heisenberg spin model and its steering

Abstract We investigate the quantum-memory-assisted entropic uncertainty relation (QMA-EUR) in a Heisenberg XYZ mixed-spin (1/2, 1) model. Coupling strength, Dzyaloshinskii–Moriya (DM) interaction and inhomogeneous magnetic field, respectively, contributing to QMA-EUR by a thermal entanglement in the hybrid-spin model are studied in detail. Furthermore, we compare the uncertainty of the bipartite hybrid model with those of qubit–qubit and qutrit–qutrit systems. Meanwhile, the effects of local PT-symmetric operation and weak measurement on the steering of entropic uncertainty are analyzed. We find that the local PT-symmetric operation can reduce the entropic uncertainty, and the entropic uncertainty can also be decreased by weak measurement reversal.
Differences between human and machine perception in medical diagnosis

Abstract Deep neural networks (DNNs) show promise in image-based medical diagnosis, but cannot be fully trusted since they can fail for reasons unrelated to underlying pathology. Humans are less likely to make such superficial mistakes, since they use features that are grounded on medical science. It is therefore important to know whether DNNs use different features than humans. Towards this end, we propose a framework for comparing human and machine perception in medical diagnosis. We frame the comparison in terms of perturbation robustness, and mitigate Simpson’s paradox by performing a subgroup analysis. The framework is demonstrated with a case study in breast cancer screening, where we separately analyze microcalcifications and soft tissue lesions. While it is inconclusive whether humans and DNNs use different features to detect microcalcifications, we find that for soft tissue lesions, DNNs rely on high frequency components ignored by radiologists. Moreover, these features are located outside of the region of the images found most suspicious by radiologists. This difference between humans and machines was only visible through subgroup analysis, which highlights the importance of incorporating medical domain knowledge into the comparison.
Model-Based Product Configuration in Augmented Reality Applications

Augmented Reality (AR) has recently found high attention in mobile shopping apps such as in domains like furniture or decoration. Here, the developers of the apps focus on the positioning of atomic 3D objects in the physical environment. With this focus, they neglect the configuration of multi-faceted 3D object composition according to the user needs and environmental constraints. To tackle these challenges, we present a model-based approach to support AR-assisted product configuration based on the concept of Dynamic Software Product Lines. Our approach splits products (e.g. table) into parts (e.g. tabletop, table legs, funnier) with their 3D objects and additional information (e.g. name, price). The possible products, which can be configured out of these parts, are stored in a feature model. At runtime, this feature model can be used to configure 3D object compositions out of the product parts and adapt to user needs and environmental constraints. The benefits of this approach are demonstrated by a case study of configuring modular kitchens with the help of a prototypical mobile-based implementation.
Study of the Digital Teaching Competence of Physical Education Teachers in Primary Schools in One Region of Spain

In today’s society and, in the teaching profession especially, it is demanded that we have a remarkable digital competence and have continuous formation in technological recycling. This study intends to describe and expose the levels of digital competency amongst physical education teachers working in the public school system of Spain, by what was established in the portfolio of teacher digital competence of one region of Spain which was published on the 12th of June 2015 in the Official Journal of Extremadura. The design of our research is of a descriptive type. The instrument used to collect data was the questionnaire published in appendix IV of the previously mentioned portfolio official of a region of Spain. A total of 201 students were tested. The principle obtained results show that primary physical education teachers in public schools in Spain have a basic level of digital teaching competence, more specifically an A2 level. This is in comparison with the guidelines established by the Common Framework for Digital Competence of Teachers 2.0.
On the Systematicity of Probing Contextualized Word Representations: The Case of Hypernymy in BERT

Contextualized word representations have become a driving force in NLP, motivating widespread interest in understanding their capabilities and the mechanisms by which they operate. Particularly intriguing is their ability to identify and encode conceptual abstractions. Past work has probed BERT representations for this competence, finding that BERT can correctly retrieve noun hypernyms in cloze tasks. In this work, we ask the question: do probing studies shed light on systematic knowledge in BERT representations? As a case study, we examine hypernymy knowledge encoded in BERT representations. In particular, we demonstrate through a simple consistency probe that the ability to correctly retrieve hypernyms in cloze tasks, as used in prior work, does not correspond to systematic knowledge in BERT. Our main conclusion is cautionary: even if BERT demonstrates high probing accuracy for a particular competence, it does not necessarily follow that BERT ‘understands’ a concept, and it cannot be expected to systematically generalize across applicable contexts.
The Effectiveness of Mobile-Assisted Language Learning (MALL)

This chapter examines the effectiveness of mobile-assisted language learning (MALL) through investigating the evidence from the extant literature. MALL is widely used by teachers and institutions to support different models of language teaching such as content and language integrated learning (CLIL) and English as a medium instruction (EMI). In particular, the chapter focuses on the determinants of MALL through reviewing a number studies which show that the evidence points to a complex relationship rather than unidimensional one in terms of MALL's place in language learning and its effectiveness in reality. The study once more emphasises the complex nature of language learning and cautions about the promise and risks of adoption of MALL.
Mining Interpretable Rules for Sentiment and Semantic Relation Analysis Using Tsetlin Machines

Tsetlin Machines (TMs) are an interpretable pattern recognition approach that captures patterns with high discriminative power from data. Patterns are represented as conjunctive clauses in propositional logic, produced using bandit-learning in the form of Tsetlin Automata. In this work, we propose a TM-based approach to two common Natural Language Processing (NLP) tasks, viz. Sentiment Analysis and Semantic Relation Categorization. By performing frequent itemset mining on the patterns produced, we show that they follow existing expert-verified rule-sets or lexicons. Further, our comparison with other widely used machine learning techniques indicates that the TM approach helps maintain interpretability without compromising accuracy – a result we believe has far-reaching implications not only for interpretable NLP but also for interpretable AI in general.