Reviewerly/exHarmony huggingface dataset:
A Low-Power BLS12-381 Pairing Cryptoprocessor for Internet-of-Things Security Applications

We present the first BLS12-381 elliptic curve pairing crypto-processor for Internet-of-Things (IoT) security applications. Efficient finite field arithmetic and algorithm-architecture co-optimizations together enable two orders of magnitude energy savings. We implement several countermeasures against timing and power side-channel attacks. Our crypto-processor is programmable to provide the flexibility to accelerate various elliptic curve and pairing-based protocols such as signature aggregation and functional encryption.
Dirac Spatial Search with Electric Fields

Electric Dirac quantum walks, which are a discretisation of the Dirac equation for a spinor coupled to an electric field, are revisited in order to perform spatial searches. The Coulomb electric field of a point charge is used as a non local oracle to perform a spatial search on a 2D grid of N points. As other quantum walks proposed for spatial search, these walks localise partially on the charge after a finite period of time. However, contrary to other walks, this localisation time scales as N for small values of N and tends asymptotically to a constant for larger Ns, thus offering a speed-up over conventional methods.
Towards an Excitable Microwave Spike Generator for Future Neuromorphic Computing

This paper describes the systematic approach to develop low power consumption excitable neuromorphic spike generators using nano-sized resonant tunnelling diode (RTD), including fabrication, characterization and device modelling and spike circuit simulation. The fabrication process of nano sized RTDs has been developed and devices exhibit peak currents of up to 100 μA. The energy efficiency of the RTD spike generator can reach as low as 0.09 fJ per spike. An accurate small signal model of nano RTD has also been developed and is described. This nano-RTD technology could underpin the development of energy efficient neuromorphic computing in the very near future.
Experimental demonstration of optimal probabilistic enhancement of quantum coherence

We theoretically and experimentally investigate conditional enhancement of overall coherence of quantum states by probabilistic quantum operations that apply to the input state a quantum filter diagonal in the basis of incoherent states. We identify the optimal filters that for a given probability of successful filtering maximize the output coherence. We verify the performance of the studied quantum filters in a proof-of-principle experiment with linear optics, where a pair of two-level quantum systems is represented by polarization states of two photons. We comprehensively characterize the implemented two-qubit linear optical quantum filters by full quantum process tomography and we experimentally observe the optimal quantum coherence enhancement by quantum filtering.
Enhance gated graph neural network with syntactic for sentiment analysis

Sentiment Analysis is of great application value to business, politics and research. Recent years, Graphic Neural Network (GNN) has attracted wide attention in the field of Natural Language Processing (NLP). Constructing textual representations of graph structure is an important step for sentiment analysis using GNN. Existing construction methods mainly include the method based on word co-occurrence and the method based on syntactic structure. The method based on word co-occurrence fully considers the global co-occurrence information of words, the syntactic-based approach considers the syntactic information. Combining the above two methods, in order to extract more information contained in the text, this paper proposes a syntactic edge-enhanced graph neural network model. this model fully considers the global word Co-occurrence information and overall sentence structure information by uses the word co-occurrence method to construct the text graph structure, combines the syntactic structure information. Then extract text representations through gated graph neural network, and learn key text information in combination with attention mechanism. Experimental results on multiple data sets show that the proposed model can achieve best performance in sentiment classification compared with the existing models.
Behaviour Recognition with Kinodynamic Planning Over Continuous Domains

We investigate the application of state-of-the-art goal recognition techniques for behaviour recognition over complex continuous domains using model predictive control (MPC) for trajectory generation. We formally define the problem of kinodynamic behaviour recognition and establish a set of baseline behaviours and performance measures in the complex domain of unmanned aerial maneuvers. We evaluate how well our approach performs over a range of standard aerial maneuvers and representative initial configurations of varying complexity. The work also highlights future research directions in compound model-based behaviour recognition and team behaviour recognition where multiple agents may be acting simultaneously.
Development of the modern educational environment of the institution of professional (vocational-technical) education in the digital space

The purpose of this article is to substantiate and analyze the problem of information development and educational environment of IP(VT)E in the digital space and identify current issues, it inhibits this process, outlining the author's vision of solutions. A set of theoretical and empirical methods was used to study the issue. Using these methods the components were identified and they have became key in determining of the main problems. The current problem of development of information and educational environment in the conditions of digitalization of society is researched in the article. The current state of the digitalization process in IP(VT)E and its impact on the development of information and educational environment are studied. Teachers and students faced with some problems in the training and production process, including in the conditions of distance learning, which is caused by certain quarantine restrictions in the conditions of a COVID-19 pandemic. These main problem issues were determined. The analysis of the activities of the IP(VT) in Ukrainian regions was made as for the creation and development of information and educational environment, by developing models and implementing individual components of the digitalization process; development of electronic educational literature and introduction of 3d-modeling technologies.
Recommendation System Using Machine Learning

Due to increase of data on internet ,there is an increased dependency on internet by people Thus, recommendation systems help people by suggesting products where is overload of information on ecommerce websites. There are various methods for recommendation. This paper study about various techniques used in designing of recommendation system with machine learning algorithm.
Uncertainty regions of observables and state-independent uncertainty relations

The optimal state-independent lower bounds for the sum of variances or deviations of observables are of significance for the growing number of experiments that reach the uncertainty limited regime. We present a framework for computing the tight uncertainty relations of variance or deviation via determining the uncertainty regions, which are formed by the tuples of two or more of quantum observables in random quantum states induced from the uniform Haar measure on the purified states. From the analytical formulae of these uncertainty regions, we present state-independent uncertainty inequalities satisfied by the sum of variances or deviations of two, three and arbitrary many observables, from which experimentally friend entanglement detection criteria are derived for bipartite and tripartite systems.
Quantum Measurements in the Light of Quantum State Estimation

Starting from a simple estimation problem, here we propose a general approach for decoding quantum measurements from the perspective of information extraction. By virtue of the estimation fidelity only, we provide surprisingly simple characterizations of rank-1 projective measurements, mutually unbiased measurements, and symmetric informationally complete measurements. Notably, our conclusions do not rely on any assumption on the rank, purity, or the number of measurement outcomes, and we do not need bases to start with. Our work demonstrates that all these elementary quantum measurements are uniquely determined by their information-extraction capabilities, which are not even anticipated before. In addition, we offer a new perspective for understanding noncommutativity and incompatibility from tomographic performances, which also leads to a universal criterion for detecting quantum incompatibility. Furthermore, we show that the estimation fidelity can be used to distinguish inequivalent mutually unbiased bases and symmetric informationally complete measurements. In the course of study, we introduce the concept of (weighted complex projective) 1/2-designs and show that all 1/2-designs are tied to symmetric informationally complete measurements, and vice versa.1 MoreReceived 20 September 2021Revised 2 May 2022Accepted 9 June 2022Corrected 22 August 2022DOI:https://doi.org/10.1103/PRXQuantum.3.030306Published by the American Physical Society under the terms of the Creative Commons Attribution 4.0 International license. Further distribution of this work must maintain attribution to the author(s) and the published article's title, journal citation, and DOI.Published by the American Physical SocietyPhysics Subject Headings (PhySH)Research AreasQuantum benchmarkingQuantum measurementsQuantum tomographyQuantum Information
Internet of Things Based Contact Tracing Systems

The COVID-19 pandemic has significantly threatened the health and well-being of humanity. Contact tracing (CT) as an important non-pharmaceutical intervention is essential to containing the spread of such an infectious disease. However, current CT solutions are fragmented with limited use of sensing and computing technologies in a scalable framework. These issues can be well addressed with the use of the Internet of Things (IoT) technologies. Therefore, we need to overview the principle, motivation, and architecture for a generic IoT-based CT system (IoT-CTS). A novel architecture for IoT-CTS solutions is proposed with the consideration of peer-to-peer and object-to-peer contact events, as well as the discussion on key topics, such as an overview of applicable sensors for CT needs arising from the COVID-19 transmission methods. The proposed IoT-CTS architecture aims to holistically utilize essential sensing mechanisms with the analysis of widely adopted privacy-preserving techniques. With the use of generic peer-to-peer and object-to-peer sensors based on proximity and environment sensing mechanisms, the infectious cases with self-directed strategies can be effectively reduced. Some open research directions are presented in the end.
Duals of linearized Reed–Solomon codes

We give a description of the duals of linearized Reed–Solomon codes in terms of codes obtained by taking residues of Ore rational functions. Our construction shows in particular that, under some assumptions on the base field, the class of linearized Reed–Solomon codes is stable under duality. As a byproduct of our work, we develop a theory of residues in the Ore setting, extending the results of [7].
A Fast Iterative Method Python package

The anisotropic eikonal equation is a non-linear partial differential equation, given byon Γ ⊂ Ω .
Memetic EDA-Based Approaches to QoS-Aware Fully Automated Semantic Web Service Composition

Quality-of-service (QoS)-aware automated semantic Web service composition aims to find a composite service with optimized or near-optimized QoS and quality of semantic matchmaking within polynomial time. To cope with this NP-hard problem with high complexity, a variety of evolutionary computation (EC) techniques has been developed. To improve the effectiveness and efficiency of these techniques, in this article, we proposed a novel memetic estimation of the distribution algorithm-based approach, namely, MEEDA, to tackle this problem. In particular, MEEDA explores four different domain-dependent local search methods that search for effective composite services by utilizing several neighborhood structures. Apart from that, to significantly reduce the computational time of MEEDA, an efficient local search strategy is introduced by combining a uniform fitness distribution scheme for selecting suitable solutions and stochastic local search operators for effectively and efficiently exploiting neighbors. To better demonstrate MEEDA’s effectiveness and scalability, we create a more challenging, augmented version of the service composition benchmark dataset. Experimental results on this benchmark show that MEEDA with newly developed domain-dependent local search operator, i.e., layer-based constrained one-point swaps, significantly outperforms existing state-of-the-art algorithms in finding high-quality composite services.
Uncertain Local-to-Global Networks for Document-Level Event Factuality Identification

Event factuality indicates the degree of certainty about whether an event occurs in the real world.Existing studies mainly focus on identifying event factuality at sentence level, which easily leads to conflicts between different mentions of the same event.To this end, we study the problem of document-level event factuality identification, which determines the event factuality from the view of a document.For this task, we need to consider two important characteristics: Local Uncertainty and Global Structure, which can be utilized to improve performance.In this paper, we propose an Uncertain Local-to-Global Network (ULGN) to make use of these two characteristics.Specifically, we devise a Local Uncertainty Estimation module to model the uncertainty of local information.Moreover, we propose an Uncertain Information Aggregation module to leverage the global structure for integrating the local information.Experimental results demonstrate the effectiveness of our proposed method, outperforming the previous state-of-the-art model by 8.4% and 11.45% of F1 score on two widely used datasets.
Toward Taming the Overhead Monster for Data-flow Integrity

Data-Flow Integrity (DFI) is a well-known approach to effectively detecting a wide range of software attacks. However, its real-world application has been quite limited so far because of the prohibitive performance overhead it incurs. Moreover, the overhead is enormously difficult to overcome without substantially lowering the DFI criterion. In this work, an analysis is performed to understand the main factors contributing to the overhead. Accordingly, a hardware-assisted parallel approach is proposed to tackle the overhead challenge. Simulations on SPEC CPU 2006 benchmark show that the proposed approach can completely enforce the DFI defined in the original seminal work while reducing performance overhead by 4x, on average.
Graph-based Neural Architecture Search with Operation Embeddings

Neural Architecture Search (NAS) has recently gained increased attention, as a class of approaches that automatically searches in an input space of network architectures. A crucial part of the NAS pipeline is the encoding of the architecture that consists of the applied computational blocks, namely the operations and the links between them. Most of the existing approaches either fail to capture the structural properties of the architectures or use hand-engineered vector to encode the operator information. In this paper, we propose the replacement of fixed operator encoding with learnable representations in the optimization process. This approach, which effectively captures the relations of different operations, leads to smoother and more accurate representations of the architectures and consequently to improved performance of the end task. Our extensive evaluation in ENAS benchmark demonstrates the effectiveness of the proposed operation embeddings to the generation of highly accurate models, achieving state-of-the-art performance. Finally, our method produces top-performing architectures that share similar operation and graph pat-terns, highlighting a strong correlation between the structural properties of the architecture and its performance.
Training Data Reduction for Performance Models of Data Analytics Jobs in the Cloud

Distributed dataflow systems like Apache Flink and Apache Spark simplify processing large amounts of data on clusters in a data-parallel manner. However, choosing suitable cluster resources for distributed dataflow jobs in both type and number is difficult, especially for users who do not have access to previous performance metrics. One approach to overcoming this issue is to have users share runtime metrics to train context-aware performance models that help find a suitable configuration for the job at hand. A problem when sharing runtime data instead of trained models or model parameters is that the data size can grow substantially over time. This paper examines several clustering techniques to minimize training data size while keeping the associated performance models accurate. Our results indicate that efficiency gains in data transfer, storage, and model training can be achieved through training data reduction. In the evaluation of our solution on a dataset of runtime data from 930 unique distributed dataflow jobs, we observed that, on average, a 75% data reduction only increases prediction errors by one percentage point.
Adaptive Blocksize for IoT Payload Data on Fabric Blockchain

The ongoing development of IoT applications led to the integration of Blockchain technology. Hyperledger Fabric is such a blockchain that operates with identifiable participants in a permissioned environment. Although many performance issues of hyperledger fabric have been mitigated to some extent, still there is a trade-off with performance. Blocksize is a critical configuration parameter that has a significant impact on both throughput and latency performance [2] [3]. In this work, A blockchain network for IoT data access is proposed with A) Firstly, the Impact of the network workload on performance is evaluated. B) Secondly, the bottleneck areas are analyzed. C) Finally, an adaptive blocksize algorithm is proposed to optimize the network performance. Results of the workload analysis revealed: (1) The network is capable of 200(tps) send rate, but the throughput is at 40(tps). (2) Static Blocksize, and batch count are major bottlenecks. After implementing an adaptive blocksize mechanism, performance optimization results revealed: It optimized the performance, throughput from 40(tps) to 110(tps), similarly latency form 14(ms) to 3(ms), that states blocksize has a major impact on performance.
How to Select One Among All? An Extensive Empirical Study Towards the Robustness of Knowledge Distillation in Natural Language Understanding.

Knowledge Distillation (KD) is a model compression algorithm that helps transfer the knowledge in a large neural network into a smaller one. Even though KD has shown promise on a wide range of Natural Language Processing (NLP) applications, little is understood about how one KD algorithm compares to another and whether these approaches can be complimentary to each other. In this work, we evaluate various KD algorithms on in-domain, out-of-domain and adversarial testing. We propose a framework to assess adversarial robustness of multiple KD algorithms. Moreover, we introduce a new KD algorithm, Combined-KD, which takes advantage of two promising approaches (better training scheme and more efficient data augmentation). Our extensive experimental results show that Combined-KD achieves state-of-the-art results on the GLUE benchmark, out-of-domain generalization, and adversarial robustness compared to competitive methods.
Weakly Supervised Interaction Discovery Network for Image Sentiment Analysis

Visual sentiment is subjective and abstract, and it is very challenging to locate the sentiment features from images accurately. Some researchers devote themselves to extracting visual features but ignore the relation features. However, sentiment reaction is a comprehensive action of visual content, and regions may express different emotions and contribute to the image sentiment. This paper takes the abstract sentiment relation as the starting point and proposes the Weakly Supervised Interaction Discovery Network that couples detection and classification branch. Specifically, the first branch detects sentiment maps with the cross-spatial pooling strategy, which generates the representations of emotions. Then, we employ a stacked Graph Convolution Network to extract the interaction feature from the above features. The second branch utilizes both interaction and visual features for robust sentiment classification. Extensive experiments on six benchmark datasets demonstrate that the proposed method exceeds the state-of-the-art methods for image sentiment analysis.
Video Desnowing and Deraining via Saliency and Dual Adaptive Spatiotemporal Filtering

Outdoor vision sensing systems often struggle with poor weather conditions, such as snow and rain, which poses a great challenge to existing video desnowing and deraining methods. In this paper, we propose a novel video desnowing and deraining model that utilizes the salience information of moving objects to address this problem. First, we remove the snow and rain from the video by low-rank tensor decomposition, which makes full use of the spatial location information and the correlation between the three channels of the color video. Second, because existing algorithms often regard sparse snowflakes and rain streaks as moving objects, this paper injects salience information into moving object detection, which reduces the false alarms and missed alarms of moving objects. At the same time, feature point matching is used to mine the redundant information of moving objects in continuous frames, and a dual adaptive minimum filtering algorithm in the spatiotemporal domain is proposed by us to remove snow and rain in front of moving objects. Both qualitative and quantitative experimental results show that the proposed algorithm is more competitive than other state-of-the-art snow and rain removal methods.
Integration of Sensor Fusion for Enhancing GPS Navigation

Sensor fusion is a process that indirectly finds its way into various aspects of engineering without being a relevant observatory mark. This same feature of sensor fusion also applies to the GPS navigation sector. The pivotal aim of this research paper is to analyze and demonstrate various ingenious advantages of incorporating sensor fusion with various existing systems regardless of their respective field of operations. Further, paper focuses on the importance of giving more priority to the algorithms and software that ensure smooth working of the sensors rather than heavily investing on more expensive hardware resources. Algorithms like the Kalman filter play a major role in the field of sensor fusion and can be easily integrated with the existing algorithm’s used in modern day GPS navigation systems. Mainly, the explanations provided in this paper are revolving around the aircraft industry with important focus given to Military-based Aircrafts. Finally, the paper includes various simulations that focuses on replicating the movement of aircrafts in real-world scenarios and study various advantages and side effects of using a system that is the resultant of the fusion between an IMU and GPS sensor thereby overcoming the limitations of the existing GPS-based Navigation.