Reviewerly/exHarmony huggingface dataset:
Hybrid Trust Multi-party Computation with Trusted Execution Environment

Trusted execution environment (TEE) such as Intel SGX relies on hardware protection and can perform secure multi-party computation (MPC) much more efficiently than pure software solutions.However, multiple side-channel attacks have been discovered in current implementations, leading to various levels of trust among different parties.For instance, a party might assume that an adversary is unable to compromise TEE, while another might only have a partial trust in TEE or even does not trust it at all.In an MPC scenario consisting of parties with different levels of trust, one could fall back to pure software solutions.While satisfying the security concerns of all parties, those who accept TEE would not be able to enjoy the benefit brought by it.In this paper, we study the above-mentioned scenario by proposing HYBRTC, a generic framework for evaluating a function in the hybrid trust manner.We give a security formalization in universal composability (UC) and introduce a new cryptographic model for the TEEs-like hardware, named multifaceted trusted hardware FT H , that captures various levels of trust perceived by different parties.To demonstrate the relevance of the hybrid setting, we give a distributed database scenario where a user completely or partially trusts different TEEs in protecting her distributed query, whereas multiple servers refuse to use TEE in protecting their sensitive databases.We propose a maliciouslysecure protocol for a typical select-and-join query in the multiparty setting.Experimental result has shown that on two servers with 2 20 records in datasets, and with a quarter of records being selected, only 165.82s is incurred which achieves more than 18, 752.58× speedups compared to cryptographic solutions.
Fine-grained Attributed Graph Clustering

Graph clustering is a prevalent issue associated with social networks, data mining, and machine learning; its objective is to detect communities or groups in networks.Inspired by the recent success of deep learning (DL), new DL-based graph clustering methods have achieved promising results.However, a deep neural network involves a large number of training parameters.Moreover, existing methods typically select the similarity metric by an ad hoc approach, which considerably affects the resulting output.In this study, we propose a principled graph learning perspective, fine-grained attributed graph clustering.Based on a shallow approach, the proposed method sufficiently exploits both node features and structure information by benefiting from graph convolution.Consequently, a fine-grained graph encoded higherorder relations is automatically learned.Comprehensive experiments on benchmark datasets demonstrate the superiority of the proposed method over state-of-the-art algorithms, including several DL methods.
Proxy Re-Encryption-Based Traceability and Sharing Mechanism of the Power Material Data in Blockchain Environment

The need to accelerate the innovation and application of the supply chain has been suggested by the State Council of China. To solve the problem of data isolation caused by privacy protection in the power material supply chain, a data traceability and sharing mechanism based on blockchain is designed in this paper. Firstly, the existing problems of the power material supply chain are introduced, and the applicability of blockchain in the power material supply chain in view of these problems is analyzed. Secondly, blockchain-based power material supply deployment and application structures are proposed. Then, considering the problem of data isolation in the material inspection and distribution links between suppliers and the material company, a data traceability mechanism based on blockchain is designed to provide evidence for the data authenticity and a proxy re-encryption method is used to ensure security and privacy in data sharing. Finally, the effectiveness of the proposed data traceability and sharing mechanism is verified using the Hyperledger Fabric platform for power material case studies. The simulation results show that the combination of proxy re-encryption and blockchain technology in the power material supply chain can confirm the validity of the historical data and keep the private data of the material company confidential, so as to realize the traceability and sharing of the power material supply data.
LinSyn: Synthesizing Tight Linear Bounds for Arbitrary Neural Network Activation Functions

Abstract The most scalable approaches to certifying neural network robustness depend on computing sound linear lower and upper bounds for the network’s activation functions. Current approaches are limited in that the linear bounds must be handcrafted by an expert, and can be sub-optimal, especially when the network’s architecture composes operations using, for example, multiplication such as in LSTMs and the recently popular Swish activation. The dependence on an expert prevents the application of robustness certification to developments in the state-of-the-art of activation functions, and furthermore the lack of tightness guarantees may give a false sense of insecurity about a particular model. To the best of our knowledge, we are the first to consider the problem of automatically synthesizing tight linear bounds for arbitrary n-dimensional activation functions. We propose the first fully automated method that achieves tight linear bounds while only leveraging the mathematical definition of the activation function itself. Our method leverages an efficient heuristic technique to synthesize bounds that are tight and usually sound , and then verifies the soundness (and adjusts the bounds if necessary) using the highly optimized branch-and-bound SMT solver, dReal . Even though our method depends on an SMT solver, we show that the runtime is reasonable in practice, and, compared with state of the art, our method often achieves 2-5X tighter final output bounds and more than quadruple certified robustness.
Deep learning-based multiple moving vehicle detection and tracking using a nonlinear fourth-order reaction-diffusion based multi-scale video object analysis

A novel automatic vehicle detection and tracking framework is proposed in this research work. First, the moving vehicles are detected in the frames of the video sequence by combining some deep learning and Gaussian Mixture Model-based object detection techniques. Then, the correspondence between the video objects detected in successive frames is determined using a multi-scale analysis of those objects. A scale-space representation is created by applying the numerical approximation algorithm that solves a nonlinear fourth-order reaction-diffusion based model whose mathematical validity is rigorously investigated here. A color image feature extraction is then performed at each scale using SURF and HOG-based features and the feature vectors determined at multiple scales are next concatenated into a final descriptor. A novel instance matching-based vehicle tracking technique using the distances between these feature vectors is then proposed. The results of the performed detection and tracking simulations are finally discussed.
Semantic Analysis of Multimodal Sports Video Based on the Support Vector Machine and Mobile Edge Computing

Even though sports video analysis and research have yielded some results, the competition video itself contains a large number of images, video, audio, text, and other elements. Video semantic analysis has always been a hot and difficult topic in video studies. Multimodal sports video data contain a wealth of data. Second, it has a complex structure with an ambiguous relationship between each video unit. A typical algorithm based on kernel function learning is a support vector machine. It has shown promising results in the prediction of landslide displacement time series. The semantic analysis of multimodal sports video using support vector machines and mobile edge computing is the subject of this study. The theoretical prediction results based on the monitoring data of multimodal sports video semantic analysis show that they are comparable to neural network prediction results using genetic algorithm. The semantic analysis of multimodal sports video based on support vector machine method proposed in this study has a better prediction ability, and its theoretical prediction results are close to the actual monitoring values.
The effects of facilitating conditions, customer experience and brand loyalty on customer-based brand equity through social media marketing

The prime concern of the current research effort is to assess the role of social media marketing efforts to explain the consumer-based brand equity among the restaurant industry of the United Arab Emirates. The study incorporated the facilitating conditions, customer experience and brand loyalty that influence the utilization of social media marketing that further develop the consumer-based brand equity. The restaurants are an important element of the hospitality sector in UAE and important due to the huge number of visitors. Therefore, the importance of social media marketing becomes more evident due to effective approaches in attracting the customers remotely. The collected data was analyzed on Smart-PLS and results depict that facilitating conditions, customer experience and brand loyalty influence social media marketing and consumer-based brand equity. However, the mediating role of social media marketing remained insignificant.
Mitigating Popularity Bias in Recommendation via Counterfactual Inference

Popularity bias is a common problem in recommender systems. Existing research mainly tracks this problem by re-weighting training samples or leveraging a small fraction of unbiased data. However, the effect of popularity bias in user behavior data may lead to sacrifices in recommendation. In this paper, we exploit data bias from click behavior to derive popularity bias representation, and investigate how to mitigate its negative impact from a causal perspective. Motivated by causal effects, we propose a novel counterfactual inference framework named Mitigating Popularity Bias in Recommendation via Counterfactual Inference (MPCI), which enables us to capture the popularity bias as the direct causal effect of the prediction score, and we eliminate popularity bias by subtracting the direct popularity bias effect from the total causal effect. In this way, MPCI reduces popularity bias by decreasing the influence of popular items on model training. Extensive experiments on two real-world datasets demonstrate the superiority of our methods over some strong baselines and prove the effectiveness of mitigating popularity bias in recommender systems.
Energy-efficient non-volatile ferroelectric based electrostatic doping multilevel optical readout memory

Non-volatile multilevel optical memory is an urgent needed artificial component in neuromorphic computing. In this paper, based on ferroelectric based electrostatic doping (Fe-ED) and optical readout due to plasma dispersion effect, we propose an electrically programmable, multi-level non-volatile photonics memory cell, which can be fabricated by standard complementary-metal-oxide-semiconductor (CMOS) compatible processes. Hf0.5Zr0.5O2 (HZO) film is chosen as the ferroelectric ED layer and combines with polysilicon layers for an enhanced amplitude modulation between the carrier accumulation and the confined optical field. Insertion loss below 0.4 dB in erasing state and the maximum recording depth of 9.8 dB are obtained, meanwhile maintaining an extremely low dynamic energy consumption as 1.0-8.4 pJ/level. Those features make this memory a promising candidate for artificial optical synapse in neuromorphic photonics and parallel computing.
Reproducing Personalised Session Search Over the AOL Query Log

Despite its troubled past, the AOL Query Log continues to be an important resource to the research community—particularly for tasks like search personalisation. When using the query log these ranking experiments, little attention is usually paid to the document corpus. Recent work typically uses a corpus containing versions of the documents collected long after the log was produced. Given that web documents are prone to change over time, we study the differences present between a version of the corpus containing documents as they appeared in 2017 (which has been used by several recent works) and a new version we construct that includes documents close to as they appeared at the time the query log was produced (2006). We demonstrate that this new version of the corpus has a far higher coverage of documents present in the original log (93%) than the 2017 version (55%). Among the overlapping documents, the content often differs substantially. Given these differences, we re-conduct session search experiments that originally used the 2017 corpus and find that when using our corpus for training or evaluation, system performance improves. We place the results in context by introducing recent adhoc ranking baselines. We also confirm the navigational nature of the queries in the AOL corpus by showing that including the URL substantially improves performance across a variety of models. Our version of the corpus can be easily reconstructed by other researchers and is included in the ir-datasets package.
Violence Detection in Videos Using Transfer Learning and LSTM

Violence recognition in videos with high accuracy can really help law enforcement agencies to effectively cope up with violent activities and save lives. Researchers are working actively on recognizing violence in video footage. This study proposes a deep learning-based model for detecting the presence of violence in video footage. This model uses several variants of pre-trained EfficientNet convolutional neural network (CNN) architecture, which was introduced by GoogleAI, for extracting spatial features from frames of videos. The frame-level features are then fed into a long short-term memory (LSTM) layer to extract spaciotemporal features. Several combinations of fine-tuning layers and dropout layers are used to increase the accuracy. The proposed model can produce an accuracy of 98% on the benchmark hockey dataset. The comparison of the results produced by this model with state of the art indicates a promising future for this model.
In-situ imaging of advanced materials subjected to in-plane biaxial loading using X-ray micro-computed tomography

This study presents development of a novel in-situ biaxial test that enables microscale observation of deformation and failure in advanced aerospace materials using synchrotron-based X-ray micro-computed tomography (μCT). As a proof of concept, the biaxial test is used to study deformation of a Ripstop Nylon fabric intended for parachute decelerators, and to visualize damage formation in a carbon/epoxy tape-laminate intended for cryogenic propellant tanks. The fabric and laminate specimens are subjected to 1:1 and 1:2 biaxial tension-tension loading, respectively. The X-ray μCT images obtained during in-situ testing of the fabric specimen are used to measure the increase in fabric porosity and straightening of individual fibers. The X-ray μCT images of a biaxially-stressed tape-laminate specimen are used to document initiation and through-thickness evolution of closely-spaced intralaminar stitch cracks. Results from both experiments demonstrate that the new biaxial test enables observations of deformation and damage in advanced aerospace materials that have not been possible using more traditional surface-based observations.
Nonlinear Error Compensation Method for 3D Sensing System Based on Grating Image Projection

Correcting the sensing error caused by the gamma nonlinear response of the three-dimensional (3D) sensing system (SS) is a key factor in improving the accuracy of sensing. To deal with the nonlinear error of the SS, we propose a sub-area Hilbert transform (SA-HT) method. First, we employ Hilbert transform (HT) on the grating image captured by the camera sensor. Then the coefficient of the nonlinear error model can be fitted in the reference area. Second, the phase disturbance (PD) area is detected. We calculate the phase maps in spatial domain and in HT domain. Then the PD area can be detected according to the absolute phase difference in the two domains. Finally, the Newton iteration method is adopted to obtain the truth-phase in the PD area. In addition, the phase values in spatial domain and in HT domain are averaged outside of the PD area. The results of experiments show that the proposed method can avoid the influence of non-smooth reflectively (NSR) when performing nonlinear error compensation on 3D sensing.
Event-driven proto-object based saliency in 3D space to attract a robot’s attention

To interact with its environment, a robot working in 3D space needs to organise its visual input in terms of objects or their perceptual precursors, proto-objects. Among other visual cues, depth is a submodality used to direct attention to visual features and objects. Current depth-based proto-object attention models have been implemented for standard RGB-D cameras that produce synchronous frames. In contrast, event cameras are neuromorphic sensors that loosely mimic the function of the human retina by asynchronously encoding per-pixel brightness changes at very high temporal resolution, thereby providing advantages like high dynamic range, efficiency (thanks to their high degree of signal compression), and low latency. We propose a bio-inspired bottom-up attention model that exploits event-driven sensing to generate depth-based saliency maps that allow a robot to interact with complex visual input. We use event-cameras mounted in the eyes of the iCub humanoid robot to directly extract edge, disparity and motion information. Real-world experiments demonstrate that our system robustly selects salient objects near the robot in the presence of clutter and dynamic scene changes, for the benefit of downstream applications like object segmentation, tracking and robot interaction with external objects.
Facial expression recognition using local binary pattern and modified hidden Markov model

Facial expression is the non-verbal communication used to send and receive their inner emotions state. It plays a key role in person's behaviour and communication. Facial expression recognition can be applied in various areas like human-computer interaction, surveillance systems, medicines, home security and credit card verification. An efficient and robust face descriptor is very important in FER systems. This paper is able to analyse the local binary pattern (LBP) to represent the facial features in face images. LBP can be calculated in all eight directions of each pixel to obtain the binary coded number. Each expression is represented using binary code. We incorporated this LBP with our new modified HMM, which acts as a classification method. New modified HMM classifier is made up of two layers: upper layer and bottom layer. Bottom layer consists of atomic expressions and upper layer consists of combinations of atomic expressions. There are seven classes of known expression, i.e., anger, disgust, fear, joy, sadness, surprise, neutral are recognised with this approach. We have also tested our new framework using confusion matrix, ROC curve, recognition performance, processing time, error rates and found the overall accuracy of 85%.
Internet of things devices: digital forensic process and data reduction

The rapid increase in the pervasiveness of digital devices, combined with their heterogeneous nature, has culminated in increasing volumes of diverse data, a.k.a. big data that can become subject to criminal or civil investigations. This growth in big digital forensic data (DFD) has forced digital forensic practitioners (DFPs) to consider seizing a wider range of devices and acquiring larger volumes of data that can be pertinent to the case being investigated. This, in turn, has created an immense backlog of cases for law enforcement agencies worldwide. The method of data reduction by targeted imaging, combined with a robust process model, however, can assist with speeding up the processes of data acquisition and data analysis in IoT device forensic investigations. To this end, we propose an IoT forensic investigation process model, IoT-FIPM, that can facilitate not only the reduction of the evidentiary IoT data, but also a timely acquisition and analysis of this data.
EXTENDING STUDENTS’ MASTERY OF VOCABULARY LEARNED THROUGH PICTIONARY GAME

This study is aimed to help the students learn new vocabularies, meanings, and their function in sentences through Pictionary game at the first grade of SMP Negeri 14 Ambon. It also directly extended the new vocabularies learned into practice writing simple sentences. It is a Classroom Action Research design which stands under the mix method, quantitative- qualitative. The data was collected through test and classroom observation and it is directed to answer the three research questions; 1) how Pictionary Game helps the students in learning vocabularies, the meanings and functions of words, 2) how students apply the vocabularies learned into simple sentences in the extended Pictionary game activities, 3) how is the students’ attitude changes during the teaching and learning process. The result of the study exposed that students successfully acquired the vocabularies through the game. The extended activities of pictionary game helped the students to practice writing simple sentences using the vocabularies learned. It also exhibited that the students’ attitude change both during the game and the learning process.
Application of Project Tasks for the Development of Intellectual Abilities of Students in Primary School

The article reveals the essence of the concepts of "intelligence", "abilities", "intellectual abilities", "intellectual operations" from psychological and pedagogical positions. Based on the analysis of psychological and pedagogical research and practical experience, the effectiveness of the widespread use of design tasks in the educational process of primary school is proved, which stimulates the successful formation of intellectual operations in children of primary school age and the development of intellectual abilities. The peculiarities of conducting classes in which junior schoolchildren solve project problems are characterized. As an example, a description of a lesson on the implementation by fourth grade students of the final annual work aimed at implementing the previously mastered skills to solve design problems is given. In turn, this ensures the readiness of schoolchildren in the future to think and act not according to a template both in a non-standard educational situation and in a problematic life situation.