Reviewerly/exHarmony huggingface dataset:
Combined Fault and DPA Protection for Lattice-Based Cryptography

The progress on constructing quantum computers and the ongoing standardization of post-quantum cryptography (PQC) have led to the development and refinement of promising new digital signature schemes and key encapsulation mechanisms (KEM). Especially lattice-based schemes have gained some popularity in the research community, presumably due to acceptable key, ciphertext, and signature sizes as well as good performance results and cryptographic strength. However, in some practical applications like smart cards, it is also crucial to secure cryptographic implementations against side-channel and fault attacks. In this work, we analyze the so-called redundant number representation (RNR) that can be used to counter side-channel attacks. We show how to avoid security issues with the RNR due to unexpected de-randomization and we apply it to the Kyber KEM and show that the RNR has a very low overhead. We then verify the RNR methodology by practical experiments, using the non-specific t-test methodology and the ChipWhisperer platform. Furthermore, we present a novel countermeasure against fault attacks based on the Chinese remainder theorem (CRT). On an ARM Cortex-M4, our implementation of the RNR and fault countermeasure offers better performance than masking and redundant calculation. Our methods thus have the potential to expand the toolbox of a defender implementing lattice-based cryptography with protection against two common physical attacks.
Real-Time Light Field Signal Processing Using 4D/5D Linear Digital Filter FPGA Circuits

Light fields (LFs) and light field videos (LFVs) capture both angular and spatial variation of light rays emanating from scenes. This richness of information leads to novel applications such as post-capture refocusing, depth estimation and depth-velocity filtering which are not possible with images and videos. These capabilities come, however, with a significant increase in data to be processed. In order to fully exploit opportunities provided by LFs and LFVs, low-complexity signal processing algorithms that process LF and LFV data in real-time are required. In this brief, we survey such state-of-the-art algorithms, in particular for depth filtering, refocusing and denoising of LFs and depth-velcoty filtering for LFVs, and future directions for these real-time LF an LFV processing algorithms.
Community Preserved Social Graph Publishing with Node Differential Privacy

The goal of privacy-preserving social graph publishing is to protect individual privacy while preserving data utility. Community structure, which is an important global pattern of nodes, is a crucial data utility as it serves as fundamental operations for many graph analysis tasks. Yet, most existing methods with differential privacy (DP) commonly fall in edge-DP to sacrifice security in exchange for utility. Moreover, they reconstruct graphs from the local feature-extraction of nodes, resulting in poor community preservation. Motivated by this, we propose PrivCom, a strict node-DP graph publishing algorithm to maximize the utility on the community structure while maintaining a higher level of privacy. Specifically, to reduce the huge sensitivity, we devise a Katz index-based private graph feature extraction method, which can capture global graph structure features while greatly reducing the global sensitivity via a sensitivity regulation strategy. Yet, with a fixed sensitivity, the feature captured by Katz index, which is presented in matrix form, requires privacy budget splits. As a result, plenty of noise is injected, thereby mitigating global structural utility. To this end, we design a private Oja algorithm approximating eigen-decomposition, which yields the noisy Katz matrix via privately estimating eigenvectors and eigenvalues from extracted low-dimensional vectors. Experimental results confirm our theoretical findings and the efficacy of PrivCom.
Research of Energy Consumption Monitoring System Based on IoT and Blockchain Technology

Abstract As the underlying platform for security applications of the Internet of Things, blockchain can provide reliable storage, data encryption, access control, identity authentication, and other service support for the security of the Internet of Things. Its immutability can ensure the authenticity of the information on the chain and the construction of shared accounts The foundation of trust is to ensure business transparency and traceability. Asymmetric encryption can ensure the information security of the Internet of Things. Smart contracts support automatic transactions of IoT devices to improve the operating efficiency of the system, thereby promoting the flow of IoT data and enabling timely warning and carry out effective control. The application research and industrial application of blockchain in the energy field will bring innovative application models to the Energy Internet and realize the flow of data value. This article focuses on the energy consumption monitoring of the Energy Internet consumption, applies blockchain technology to the construction of an energy consumption monitoring system for key energy-consuming units, analyzes the fit between business needs and blockchain technology, designs a blockchain platform architecture, and discussed the issues of energy consumption monitoring blockchain terminal equipment authentication and collection of trusted data.
Should a Chatbot Disclose Itself? Implications for an Online Conversational Retailer

Today many consumers prefer interactions with companies via chat and instant messaging, however, although in most cases it is now a virtual agent to handle the interactions, many of them feel it would be eerie if a chatbot pretended to be human. The present study aims at disentangling this sort of ambivalence people have for chatbots through an investigation on how the explicit disclosure of the chatbot identity, before the interaction, influences consumers’ perceptions. Specifically, this study compares the effects that the explicit disclosure of the chatbot identity has on social presence trust and users’ attitudes toward the online retailer. Findings from an online experiment with 160 participants show that interacting with the chatbot whose identity has been primed through a disclosure leads to less perceived social presence, trust, and attitude toward the online retailer, compared to interacting with the chatbot whose identity has not been disclosed before the interaction. The study further analyses a causal chain among the variables, proving that social presence and trust mediate the relationship between the chatbot identity disclosure and the attitude toward the online retailer.
Insulator Recognition Method for Distribution Network Overhead Transmission Lines Based on Modified YOLOv3

As an important part of distribution network overhead transmission lines, accurate recognition of insulator is an important prerequisite for intelligent detection and fault diagnosis of insulator. In order to accurately identify insulators, a method of insulator recognition for distribution network overhead transmission lines based on modified YOLOv3 is proposed. Firstly, K-means is used to cluster the target box in the dataset to obtain the appropriate anchor. Then, DenseNet is introduced in DarkNet53 to enhance the reuse and fusion of network characteristic information. At the same time, an additional scale is added to predict the insulator. Finally, combining Cross Entropy function and Focal Loss function is adopted to replace the original loss function. Building insulator of distribution network overhead transmission lines dataset for training and testing, the experimental results show that this method can accurately classify the insulator of distribution network overhead lines, accuracy rate increased by 10% than the original algorithm, which has higher detection accuracy and stronger robustness. It basically meets the inspection task of power network and has high engineering application value.
Entertainment in Era of AI, Big Data &amp; IoT

The Entertainment industry is witnessing the convergence of device, network, and platform. This chapter highlights and discusses services and applications that apply these three technologies. AI has found application in the Entertainment industry in all three areas Creation, Production, and Consumption. In the areas of production, inputs from IoT sensors result in large amounts of data, which is then analyzed using Big Data analytics to arrive at actionable insights. AI with help from Big Data analyses IoT data collected from sensors and presents us with highly accurate data of consumer behavior helping us analyzing trends and patterns of purchasing. Virtual Reality based emotion personification can enhance the audience's experience, by engaging with them through sharing of emotions thus creating an immersive VR experience. As industries enjoy the benefits of AI, Big DATA & IoT they should be equally prepared for its undesirable negative consequences. Increasing AI, Big DATA, & IoT deployments across disciplines will bring in legal, moral, and ethical issues. Appropriate regulatory measures by competent authorities after anticipating its impacts will help in striking the balance. The Management of security risks through adherence and compliance to industry best practices in data security as well as the physical security of the IoT assets will serve as a driver for AI, Big DATA, & IoT adoption. The industry needs to drive consensus on the adoption of interoperable standards and protocols that will provide the much needed end-to-end interoperability of AI management standards, Big Data Analysis, immersive production, distribution, and consumption of media. Lastly, on the human resources front companies in the Entertainment industry need to upskill their employees in a whole new set of multi-disciplinary skill sets and understanding & expertise in IoT hardware and software.
Spectral effects on the energy yield of various photovoltaic technologies in Lima (Peru)

This study presents for the first time the spectral impact on the performance of different photovoltaic (PV) technologies in Lima, Peru. We experimentally monitored the spectral distributions over one year (March 2019–February 2020). The average photon energy (APE) is calculated as a representative parameter to evaluate the spectral distributions. The spectral mismatch factor (MM) enables an estimation of the spectral gains of distinct PV technologies: amorphous silicon (a-Si), perovskite, cadmium telluride (CdTe), multicrystalline silicon (multi-Si), monocrystalline silicon (mono-Si) and copper indium gallium selenide with two distinct band-gaps (CIGS-1 and CIGS-2). We found that the annual APE has a value of 1.923 eV, indicating that the spectrum is shifted to shorter wavelengths. In contrast to studies performed in other locations, the spectral distribution shows relatively small monthly APE value variations. This nearly negligible seasonality could be attributed to the low latitude and the particular climate in Lima. Larger-bandgap PV technologies, such as a-Si, perovskite, and CdTe, resulted in annual spectral gains of +6.8%, +4.8%, and +2.1%, respectively. Lower-bandgap PV technologies (CIGS-2, multi-Si, CIGS-1, and mono-Si), present small annual spectral gains of −0.9%, −1.4%, −1.6% and −2.3%, respectively. Also, a quasi-linear correlation between APE and MM was found for all PV technologies.
Multimodal Sensor Data Analysis for Detection of Risk Situations of Fragile People in @home Environments

Multimedia (MM) nowadays often means "Multimodality". The target application area of MM technologies further extends to healthcare. Health parameters monitoring, context and situational recognition in ambient assisted living - all these applications require tailored solutions. We are interested in development of AI solutions for prevention of risk situations of fragile people living at home. This research requires a tight collaboration of IT researchers with psychologists and kinesiologists. In this paper we present a large collaborative project between such actors for developing future solutions of risk situations detection of fragile people. We report on definition of risk scenarios which have been simulated in the data collected with the developed Android application. Adapted annotation scenarios for sensory and visual data are elaborated. A pilot corpus recorded with healthy volunteers in everyday life situations is presented. Preliminary detection results on LSC dataset show the complexity of real-life recognition tasks.
Empowerment of Women Self Help Groups: Human Centered Design of a Participatory IoT solution

Empowering women, is one of the major challenges experienced in several developing countries. This is majorly due to lack of education, minimal community engagement and less exposure to mobile phones and mobile applications, age difference, and varying skills. To address this issue, several women self-help groups have been formed worldwide to provide the opportunity for them to engage in several livelihood activities. This paper discusses one of the proposed design for the Internet of Things based solution for empowering women in India. This solution is intended to be used by the rural cluster based organizations intended to work for the social and economic well-being of the rural community. This research details the requirements for empowering such rural community based organizations. However, the existing solutions lack the perceived usefulness, and perceived ease of use; contributing to the failure of several technical innovations. This prompted us to dive deeper into insights of human centered and user centered approaches. This persuaded us in designing a human centered approach based mobile IoT solution. This paper details the key design parameters integrated for the success of this innovation and the different functionalities required for creating an effective solution for enhancing the communication, dissemination and empowerment of women self-help groups. This paper details requirements and learning’s from AmritaSREE, one of the women Self Help Groups having more than 300000 members in India. The paper also details the approaches by which this solution can be used by AmritaSREE group for achieving financial stability and self empowerment.
Unsupervised multi-instance learning for protein structure determination

Many regions of the protein universe remain inaccessible by wet-laboratory or computational structure determination methods. A significant challenge in elucidating these dark regions in silico relates to the ability to discriminate relevant structure(s) among many structures/decoys computed for a protein of interest, a problem known as decoy selection. Clustering decoys based on geometric similarity remains popular. However, it is unclear how exactly to exploit the groups of decoys revealed via clustering to select individual structures for prediction. In this paper, we provide an intuitive formulation of the decoy selection problem as an instance of unsupervised multi-instance learning. We address the problem in three stages, first organizing given decoys of a protein molecule into bags, then identifying relevant bags, and finally drawing individual instances from these bags to offer as prediction. We propose both non-parametric and parametric algorithms for drawing individual instances. Our evaluation utilizes two datasets, one benchmark dataset of ensembles of decoys for a varied list of protein molecules, and a dataset of decoy ensembles for targets drawn from recent CASP competitions. A comparative analysis with state-of-the-art methods reveals that the proposed approach outperforms existing methods, thus warranting further investigation of multi-instance learning to advance our treatment of decoy selection.
Semi-Supervised Automated Layer Identification of X-ray Tomography Imaged PCBs

Reverse engineering (RE) for printed circuit boards (PCB) can be achieved through X-ray Computed Tomography (CT) in a non-destructively yet predominantly manual process. For practical RE to be possible in applications such as obsolescence replacement and hardware assurance, it is important that this process be as automated and fast as possible. This paper introduces a framework to identify which slices of an X-ray CT 3-D PCB stack belong to what layer on a physical PCB in an automated and generalizable fashion for any X-rayed PCB. To the best of our knowledge, this is the first method for identifying the correspondence between an X-ray CT slice and layers of a PCB using reference design information, demonstrated on a 6 layered PCB. First, a spatial pyramid bag-of-visual-words technique is leveraged to enable a semi-supervised approach where the user has either digital or X-ray layout information available for heterogeneous image comparison between X-ray CT image slices. Second, a weighting scheme based on entropy of an image region is proposed. Our results show near perfect quantitative and qualitative layer identification. The completion of this step facilitates improved performance in later RE stages, such as feature extraction or analysis of an X-rayed PCB.
Joint stroke classification and text line grouping in online handwritten documents with edge pooling attention networks

Stroke classification and text line grouping are important tasks in online handwritten document segmentation. In the past, the two tasks were usually performed using different models which are trained independently and perform sequentially. This cannot optimize the integration of contextual information and the system may suffer from error accumulation in stroke classification. In this paper, we propose a method for joint text/non-text stroke classification and text line grouping in online handwritten documents using attention based graph neural network. In our framework, the stroke classification and text line grouping problems are formulated as node classification and node clustering problems in a relational graph, which is constructed based on the temporal and spatial relationship between strokes. We propose a new graph network architecture, called edge pooling attention network (EPAT) to efficiently aggregate information between the features of neighboring nodes and edges. The proposed model is trained by multi-task learning with cross entropy loss for node classification and distance metric loss for node clustering. In experiments on two online handwritten document datasets IAMOnDo and Kondate, the proposed method is demonstrated effective, yielding superior performance in both stroke classification and text line grouping.
Finding Subgroups of UAV Swarms Using a Trajectory Clustering Method

Abstract Unmanned Aerial Vehicles(UAV) swarm is a rapidly developing field, and with it comes the need to identify the swarm based on observations. The problem of trajectory clustering is put forward in the identification of UAV swarms, especially modularized UAV swarms. We propose a new method of Network Integrated trajectory clustering(NIT) to solve the trajectory clustering problem in a fast-changing and chaotic environment which requires a quick response, fault tolerance, and accuracy. The experiment results prove the flexibility and adaptability of the NIT method towards various demands and multi-dimensional data. Moreover, the algorithm proposed based on the method shows priority over the other three trajectory clustering methods(DTW, Fréchet distance, GMM) on the accuracy, and fault tolerance in clustering swarm trajectories. The method raised in this paper is an innovation to both multi-agent systems identification and trajectory clustering methods.
Monocular Image-Based 3-D Model Retrieval: A Benchmark

Monocular image-based 3-D model retrieval aims to search for relevant 3-D models from a dataset given one RGB image captured in the real world, which can significantly benefit several applications, such as self-service checkout, online shopping, etc. To help advance this promising yet challenging research topic, we built a novel dataset and organized the first international contest for monocular image-based 3-D model retrieval. Moreover, we conduct a thorough analysis of the state-of-the-art methods. Existing methods can be classified into supervised and unsupervised methods. The supervised methods can be analyzed based on several important aspects, such as the strategies of domain adaptation, view fusion, loss function, and similarity measure. The unsupervised methods focus on solving this problem with unlabeled data and domain adaptation. Seven popular metrics are employed to evaluate the performance, and accordingly, we provide a thorough analysis and guidance for future work. To the best of our knowledge, this is the first benchmark for monocular image-based 3-D model retrieval, which aims to help related research in multiview feature learning, domain adaptation, and information retrieval.
Hierarchical Multi-Classification for Sensor-based Badminton Activity Recognition

Fast development of sensor technology makes sensor equipments more and more smart and wearable. It further boost the need of sensor-based human activity recognition. Due to the lack of large-scale labeled datasets in practical AI applications, it is important to utilize prior information of the categories in sensor-based human activity recognition. In this paper, we propose a Hierarchical Multi-Classificaion (HMC) framework for sensor-based badminton activity recognition with the help of the prior information of badminton activity categories. Specifically, the multi-class sensor-based badminton activity recognition task is performed in two steps: (1). Any input data for a badminton activity are classified into one of the major classes which are based on their characteristic features; (2). They are further classified into one of the specific categories of badminton activity as required. It is demonstrated by the experimental results on BSS-V2 dataset that our proposed method can get up to 83.9% badminton activity recognition accuracy which is 1.7% better than previous state-of-the-art models.
Joint Unsupervised Learning of Optical Flow and Egomotion with Bi-Level optimization

We address the problem of joint optical flow and camera motion estimation in rigid scenes by incorporating geometric constraints into an unsupervised deep learning framework. Unlike existing approaches which rely on brightness constancy and local smoothness for optical flow estimation, we exploit the global relationship between optical flow and camera motion using epipolar geometry. In particular, we formulate the prediction of optical flow and camera motion as a bi-level optimization problem, consisting of an upper-level problem to estimate the flow that conforms to the predicted camera motion, and a lower-level problem to estimate the camera motion given the predicted optical flow. We use implicit differentiation to enable backpropagation through the lower-level geometric optimization layer independent of its implementation, allowing end-toend training of the network. With globally-enforced geometric constraints, we are able to improve the quality of the estimated optical flow in challenging scenarios, and obtain better camera motion estimates compared to other unsupervised learning methods.