Reviewerly/exHarmony huggingface dataset:
Error-Driven Chained Multiple-Subnetwork Echo State Network for Time-Series Prediction

Hybrid echo state networks (ESNs), a type of modified ESN, have been developed to improve the prediction accuracy of ESNs. However, they have been criticized for their computational complexity, which makes it difficult to use them directly in industrial applications. In this article, an error-driven chained multiple-subnetwork ESN (CESN) is proposed to build a simple structured hybrid network and improve its prediction accuracy. For this reason, a chain topology is generated to gradually reduce the residual error, while each subnetwork is trained separately. The weight matrix for each subnetwork does not need to be optimized, which reduces the computational cost. Meanwhile, the optimal number of subnetworks is determined on the basis of a given application. The efficiency of the proposed CESN is tested on a Santa Fe Laser and a public building dataset. Compared with ESN, 70% of the test data have been optimized by CESN for the public building dataset.
A 40-nm Cryo-CMOS Quantum Controller IC for Superconducting Qubit

This article presents a cryo-CMOS quantum controller IC for superconducting qubits. The proposed globally synchronized clock system internally generates different local oscillator (LO) frequencies using multiple phase-locked loops (PLLs) driven by a common reference clock. It provides flexibility in spectral management as well as scalability for expansion to a large-scale quantum controller. The test chip includes two PLLs, four pulse modulator channels, and two receiver channels. Implemented chip in 40-nm CMOS shows full functionalities at 3.5 K. The designed pulse modulator circuits are verified with the specifications for expected fidelity of 99.99%.
Problems and Countermeasures of Ideological and Political Education Communication based on Multimedia Network

At this stage, the combination of information technology and education has continuously achieved breakthroughs, and the development of online education has ushered in a good opportunity. Especially after the outbreak of the new crown epidemic, online learning has developed rapidly. High-quality multimedia online education platforms have won a large number of users, among which are NetEase courses, MOCC, and online platforms built by domestic universities such as Peking University and Tsinghua University. After just a few years of development, these open online course platforms have become the representatives of online education platforms. With their high complementarity to offline education, they provide a useful supplement to the ideological and political teaching in schools at all levels in my country. How to make better use of the advantages of the multimedia network education platform so that it can exert a far-reaching influence in the field of ideological and political teaching has become an important subject for further innovation in this field. This paper analyzes the current situation of multimedia online teaching in colleges and universities by means of a questionnaire survey, summarizes the disadvantages, and puts forward suggestions for improvement. In view of the problems existing in multimedia teaching at this stage, we should promote the efficiency of multimedia teaching platform; training is carried out to improve teachers’ ability to use multimedia; resources are coordinated and a unified data resource base is created. The results show that the combination of offline teaching and multimedia network teaching can improve students’ enthusiasm in learning ideological and political courses.
BERT Rankers are Brittle

Contextual ranking models based on BERT are now well established for a wide range of passage and document ranking tasks. However, the robustness of BERT-based ranking models under adversarial inputs is under-explored. In this paper, we argue that BERT-rankers are not immune to adversarial attacks targeting retrieved documents given a query. Firstly, we propose algorithms for adversarial perturbation of both highly relevant and non-relevant documents using gradient-based optimization methods. The aim of our algorithms is to add/replace a small number of tokens to a highly relevant or non-relevant document to cause a large rank demotion or promotion. Our experiments show that a small number of tokens can already result in a large change in the rank of a document. Moreover, we find that BERT-rankers heavily rely on the document start/head for relevance prediction, making the initial part of the document more susceptible to adversarial attacks. More interestingly, we find a small set of recurring adversarial words that when added to documents result in successful rank demotion/promotion of any relevant/non-relevant document respectively. Finally, our adversarial tokens also show particular topic preferences within and across datasets, exposing potential biases from BERT pre-training or downstream datasets.
Deep learning-based Phase Measuring Deflectometry for single-shot 3D shape measurement and defect detection of specular objects

Phase Measuring Deflectometry (PMD) and Structured-Light Modulation Analysis Technique (SMAT) perform effectively in shape and defect measurements of specular objects, but the difficulty of giving consideration to accuracy and speed has also restricted the further development and application of them. Inspired by recent successes of deep learning techniques for computational imaging, we demonstrate for the first time that deep learning techniques can be used to recover high-precision modulation distributions of specular surfaces from a single-frame fringe pattern under SMAT, enabling fast and high-quality defect detection of specular surfaces. This method can also be applied to recover higher-precision phase distributions of specular surfaces from a single-frame fringe pattern under PMD, so as to realize the 3D shape measurement. In this paper, we combine depthwise separable convolution, residual structure and U-Net to build an improved U-Net network. The experimental results prove that the method has excellent performance in the phase and modulation retrieval of specular surfaces, which almost reach the accuracy of the results obtained by ten-step phase-shifting method.
Neural Network Repair with Reachability Analysis

Safety is a critical concern for the next generation of autonomy that is likely to rely heavily on deep neural networks for perception and control. This paper proposes a method to repair unsafe ReLU DNNs in safety-critical systems using reachability analysis. Our repair method uses reachability analysis to calculate the unsafe reachable domain of a DNN, and then uses a novel loss function to construct its distance to the safe domain during the retraining process. Since subtle changes of the DNN parameters can cause unexpected performance degradation, we also present a minimal repair approach where the DNN deviation is minimized. Furthermore, we explore applications of our method to repair DNN agents in deep reinforcement learning (DRL) with seamless integration with learning algorithms. Our method is evaluated on the ACAS Xu benchmark and a rocket lander system against the state-of-the-art method ART. Experimental results show that our repair approach can generate provably safe DNNs on multiple safety specifications with negligible performance degradation, even in the absence of training data (Code is available online at https://github.com/Shaddadi/veritex.git ).
Effects of Frequent Changes in Extended Self-Avatar Movements on Adaptation Performance

Among several perceptive traits of virtual reality, the relationship between the physical body and a self-avatar is unclear. In this study, we investigate a case of hyper-adaptability, i.e., the capability of users to adjust to the movements of an altered self-avatar when such movements abruptly and frequently change. Focusing on movements of the upper limbs, we show experimentally the effect of the frequency of variations in virtual body alterations on adaptability. Moreover, we report a positive evaluation of the sense of embodiment and the overall user experience with virtual reality, and finally underline how these studies can be considered a basis for the design and development of virtual rehabilitation systems.
A Joint Automatic Modulation Classification Scheme in Spatial Cognitive Communication

Automatic modulation discrimination (AMC) is one of the critical technologies in spatial cognitive communication systems. Building a high-performance AMC model in intelligent receivers can help to realize adaptive signal synchronization and demodulation. However, tackling the intra-class diversity problem is challenging to AMC based on deep learning (DL), as 16QAM and 64QAM are not easily distinguished by DL networks. In order to overcome the problem, this paper proposes a joint AMC model that combines DL and expert features. In this model, the former builds a neural network that can extract the time series and phase features of in-phase and quadrature component (IQ) samples, which improves the feature extraction capability of the network in similar models; the latter achieves accurate classification of QAM signals by constructing effective feature parameters. Experimental results demonstrate that our proposed joint AMC model performs better than the benchmark networks. The classification accuracy is increased by 11.5% at a 10 dB signal-to-noise ratio (SNR). At the same time, it also improves the discrimination of QAM signals.
The Jordanian Universities Experience in Integrating Online Learning and its Quality Assurance

University educational institutions in Jordan, in general, have quickly dealt with emergency situations facing education, such as the Corona pandemic, in which they needed to move from traditional learning to online learning (in its fully and blended forms) during the previous period. This led the public and private educational institutions in Jordan to implement the executive action plan that was prepared by the Ministry of Higher Education and Scientific Research (MOHESR) for integrating online learning into the higher education system and reflect it on their programs in order to keep pace with developments at the local, regional and international levels. This study includes a specification of the components relevant to online learning management system, and aims to determine the percentages of achievement at the level of fully online learning and blended learning for all components of the online learning management system in public and private Jordanian universities and also determine the percentages of achievement for each one of these components at the university level, and the most important actions that should be taken by universities to achieve them. The study also aims to benefit the integration of online learning within the Jordanian higher education system in an effective manner that achieves high levels of educational quality for online learning in its both forms and ensure the desired shift in the performance of Jordanian higher education institutions and the quality of its output, keeping pace with global developments in this field. The study concludes with a set of results that will benefit decision-makers in the Accreditation and Quality Assurance Commission for Higher Education Institutions (AQACHEI), the Ministry of Higher Education and Scientific Research (MOHESR), and Jordanian higher education institutions.
An empirical analysis of double reduction education policy based on public psychology

To effectively reduce the homework burden and off-campus training burden of students in the compulsory education stage in China, China adopts the double reduction policy to carry out corresponding governance in the field of education. This study aims to explore the public's psychological cognition of the current education system under the implementation of the double reduction policy. The public's opinion data on education concepts under the implementation of the double reduction policy are collected, then the SPSS software and Amos software to quantitatively analyze the public's educational concept data are used, and a corresponding structural model equation to analyze the public's opinion on the implementation of the double reduction policy is established. The psychological concepts of all aspects of education are observed, the public's psychological views on education under the implementation of the double reduction policy are finally obtained, the ideological connotation of popularizing the double reduction policy for the general public is provided, and the balanced development of the domestic education system for the implementation of the double reduction policy is ensured. Two suggestions are made in order to provide a corresponding reference for the public's psychological cognition research on the current education system under the implementation of the double reduction policy.
Flexible presentations of graded monads

A large class of monads used to model computational effects have natural presentations by operations and equations, for example, the list monad can be presented by a constant and a binary operation subject to unitality and associativity. Graded monads are a generalization of monads that enable us to track quantitative information about the effects being modelled. Correspondingly, a large class of graded monads can be presented using an existing notion of graded presentation. However, the existing notion has some deficiencies, in particular many effects do not have natural graded presentations. We introduce a notion of flexibly graded presentation that does not suffer from these issues, and develop the associated theory. We show that every flexibly graded presentation induces a graded monad equipped with interpretations of the operations of the presentation, and that all graded monads satisfying a particular condition on colimits have a flexibly graded presentation. As part of this, we show that the usual algebra-preserving correspondence between presentations and a class of monads transfers to an algebra-preserving correspondence between flexibly graded presentations and a class of flexibly graded monads.
A Decentralized IIoT Identity Framework based on Self-Sovereign Identity using Blockchain

The fundamental requirement for interaction between digital entities is a secure and privacy-preserving digital identity infrastructure. Traditional approaches rely heavily on centralized architectural components such as Certificate Authorities (CAs) and credential storage databases that have drawbacks like a single point of failure, attack prone honeypot databases and poor scalability. Self-Sovereign Identity (SSI) is a novel decentralized digital identity model that uses Decentralized Identifiers (DIDs) and Verifiable Credentials (VCs). In this work, we propose a novel decentralized identity framework for Industrial Internet-of-Things (IIoT) based on SSI model. The proposed framework is implemented on two blockchain platforms namely Ethereum and Hyperledger Indy to study the underlying overheads.
Integration of chatbots into the system of professional training of Masters

The article presents and describes innovative technologies of training in the professional training of Masters. For high-quality training of students of technical specialties, it becomes necessary to rethink the purpose, results of studying and means of teaching professional disciplines in modern educational conditions. The experience of implementing the chatbot tool in teaching the discipline “Mathematical modeling of socio-economic systems” in the educational and professional program 124 System Analysis is described. The characteristics of the generalized structure of the chatbot information system for investment analysis are presented and given: input information, information processing system, output information, which creates a closed cycle (system) of direct and feedback interaction. The information processing system is represented by accounting and analytical data management blocks. The investment analysis chatbot will help masters of the specialty system analysis to manage the investment process efficiently based on making the right decisions, understanding investment analysis in the extensive structure of financial management and optimizing risks in these systems using a working mobile application. Also, the chatbot will allow you to systematically assess the disadvantages and advantages of investment projects or the direction of activity of a system analyst, while increasing interest in performing practical tasks. A set of software for developing a chatbot integrated into training is installed: Kotlin programming, a library for network interaction Retrofit, receiving and transmitting data, linking processes using the HTTP API. Based on the results of the study, it is noted that the impact of integrating a chatbot into the training of Masters ensures the development of their professional activities, which gives them the opportunity to be competent specialists and contributes to the organization of high-quality training.
Model Predictive Control Strategies for Optimized mHealth Interventions for Physical Activity

Many individuals fail to engage in sufficient physical activity (PA), despite its well-known health benefits. This paper examines Model Predictive Control (MPC) as a means to deliver optimized, personalized behavioral interventions to improve PA, as reflected by the number of steps walked per day. Using a health behavior fluid analogy model representing Social Cognitive Theory, a series of diverse strategies are evaluated in simulated scenarios that provide insights into the most effective means for implementing MPC in PA behavioral interventions. The interplay of measurement, information, and decision is explored, with the results illustrating MPC’s potential to deliver feasible, personalized, and user-friendly behavioral interventions, even under circumstances involving limited measurements. Our analysis demonstrates the effectiveness of sensibly formulated constrained MPC controllers for optimizing PA interventions, which is a preliminary though essential step to experimental evaluation of constrained MPC control strategies under real-life conditions.
Astraea: Anonymous and Secure Auditing Based on Private Smart Contracts for Donation Systems

Many regions are in urgent need of facial masks for slowing down the spread of COVID-19. To fight the pandemic, people are contributing masks through donation systems. Most existing systems are built on a centralized architecture which is prone to the single point of failure and lack of transparency. Blockchain-based solutions neglect fundamental privacy concerns ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">donation privacy</i> ) and security attacks ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">collusion attack, stealing attack</i> ). Moreover, current auditing solutions are not designed to achieve donation privacy, thus not appropriate in our context. In this work, we design a decentralized, anonymous, and secure auditing framework <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Astraea</i> based on private smart contracts for donation systems. Specifically, we integrate a Distribute Smart Contract (DiSC) with an SGX Enclave to distribute donations, prove the integrity of donation number (intention) and donation sum while preserving donation privacy. With DiSC, we design a Donation Smart Contract to refund deposits and defend against the stealing attack the collusion attack from malicious collector and transponder. We formally define and prove the privacy and security of Astraea by using security reduction. We build a prototype of Astraea to conduct extensive performance analysis. Experimental results demonstrate that Astraea is practically efficient in terms of both computation and communication.
Cellular-Assisted, Deep Learning Based COVID-19 Contact Tracing

The Coronavirus disease (COVID-19) pandemic has caused social and economic crisis to the globe. Contact tracing is a proven effective way of containing the spread of COVID-19. In this paper, we propose CAPER, a Cellular-Assisted deeP lEaRning based COVID-19 contact tracing system based on cellular network channel state information (CSI) measurements. CAPER leverages a deep neural network based feature extractor to map cellular CSI to a neural network feature space, within which the Euclidean distance between points strongly correlates with the proximity of devices. By doing so, we maintain user privacy by ensuring that CAPER never propagates one client's CSI data to its server or to other clients. We implement a CAPER prototype using a software defined radio platform, and evaluate its performance in a variety of real-world situations including indoor and outdoor scenarios, crowded and sparse environments, and with differing data traffic patterns and cellular configurations in common use. Microbenchmarks show that our neural network model runs in 12.1 microseconds on the OnePlus 8 smartphone. End-to-end results demonstrate that CAPER achieves an overall accuracy of 93.39%, outperforming the accuracy of BLE based approach by 14.96%, in determining whether two devices are within six feet or not, and only misses 1.21% of close contacts. CAPER is also robust to environment dynamics, maintaining an accuracy of 92.35% after running for ten days.
Exploring the Link Between Cashless Society and Cybercrime in Indonesia

This study examines whether the rise in cybercrime activity in Indonesia is associated with the perceived benefits, convenience, and risk of a cashless society. In doing so, we apply Structural Equation Modelling (SEM) to a total sample of 200 Indonesian respondents who have been victims of cyber fraud. The results indicate that the high cybercrime rate, including phishing, distributed denial-of-service (DDoS), and social engineering, is positively associated with the perceived benefits and risks of transacting online. The rise in cases of DDoS can particularly be linked to consumers’ increasing perceptions of convenience in undertaking cashless transactions. The findings suggest that more stringent cyber law enforcement needs to be implemented. Digital technologies will continue to improve, and more consumers will do transactions digitally. With increasing volumes of cashless transactions, the risk of cyber attacks will likely increase. Stakeholders need to strengthen data privacy and provide a secure environment for customers. International cooperation should be promoted through the establishment of virtual world law as economic activities and cybercrime risks now become borderless.
Noticing the Environment – A Design Ethnography of Urban Farming

Sustainable HCI attempts to shift focus beyond humans, to care for both ourselves and our environment. In this paper, we build on this growing interest and contribute with a design ethnography of urban farming. We focus on practices of observing and gathering data about the environment which we frame as 'noticing'. In our analysis, three approaches to noticing the environment were identified, and design suggestions were developed for each approach: Green Thumbs (control-oriented), Dirty Nails (sensibility-oriented) and BeeNoculars (appreciation-oriented). The design suggestions, presented as posters, focus on ways to improve the alignment of the acquisition and display of data with the identified approaches. We discuss two themes: the noticing and balancing of systemic relations and needs, and sensory-rich experiences of the environment. The paper contributes to a broader discussion in HCI of how technologies could create a different understanding of and relationship to the environment.