Reviewerly/exHarmony huggingface dataset:
Role of Data Augmentation Strategies in Knowledge Distillation for Wearable Sensor Data

Deep neural networks are parametrized by several thousands or millions of parameters, and have shown tremendous success in many classification problems. However, the large number of parameters makes it difficult to integrate these models into edge devices such as smartphones and wearable devices. To address this problem, knowledge distillation (KD) has been widely employed, that uses a pre-trained high capacity network to train a much smaller network, suitable for edge devices. In this paper, for the first time, we study the applicability and challenges of using KD for time-series data for wearable devices. Successful application of KD requires specific choices of data augmentation methods during training. However, it is not yet known if there exists a coherent strategy for choosing an augmentation approach during KD. In this paper, we report the results of a detailed study that compares and contrasts various common choices and some hybrid data augmentation strategies in KD based human activity analysis. Research in this area is often limited as there are not many comprehensive databases available in the public domain from wearable devices. Our study considers databases from small scale publicly available to one derived from a large scale interventional study into human activity and sedentary behavior. We find that the choice of data augmentation techniques during KD have a variable level of impact on end performance, and find that the optimal network choice as well as data augmentation strategies are specific to a dataset at hand. However, we also conclude with a general set of recommendations that can provide a strong baseline performance across databases.
Self-observation of a virtual body-double engaged in social interaction reduces persecutory thoughts

The proportion of the population who experience persecutory thoughts is 10-15%. People then engage in safety-seeking behaviours, typically avoiding social interactions, which prevents disconfirmatory experiences and hence paranoia persists. Here we show that persecutory thoughts can be reduced if prior to engaging in social interaction in VR participants first see their virtual body-double doing so. Thirty non-clinical participants were recruited to take part in a study, where they were embodied in a virtual body that closely resembled themselves, and asked to interact with members of a crowd. In the Random condition (n = 15) they observed their body-double wandering around but not engaging with the crowd. In the Targeted condition the body-double correctly interacted with members of the crowd. The Green Paranoid Thoughts Scale was measured 1 week before and 1 week after the exposure and decreased only for those in the Targeted condition. The results suggest that the observation of the body-double correctly carrying out a social interaction task in VR may lead to anxiety-reducing mental rehearsal for interaction thus overcoming safety behaviours. The results also extend knowledge of the effects of vicarious agency, suggesting that identification with the actions of body-double can influence subsequent psychological state.
Development of Unmanned Aerial Vehicle (UAV) Fixed-Wing for Monitoring, Mapping and Dropping applications on agricultural land

Abstract UAV technology is used in various fields, namely the military, to strengthen defence, surveillance, dropping logistics. In addition, UAV applications are also used in agriculture. In this study, a fixed-wing UAV will be developed as a monitoring, mapping and dropping process. Several things were carried out in the development process, namely design, manufacturing, installing electronic components, and test flights. The results showed that the vehicle could carry out the mission well. The monitoring process is carried out to review land security. Besides that, it can monitor if there is a fire in agricultural land. In the mapping process, photo data processing taken in the context of the mapping is carried out into an orthophoto map. Meanwhile, in the dropping stage, the aircraft managed to drop a payload weighing 0.5 kg with a distance of 20m from a predetermined centre point. The dropped payload is also equipped with a parachute for increased safety and reduced speed when the payload is dropped.
DiPETrans: A framework for distributed parallel execution of transactions of blocks in blockchains

Summary Contemporary blockchain such as Bitcoin and Ethereum execute transactions serially by miners and validators and determine the Proof‐of‐Work (PoW). Such serial execution is unable to exploit modern multi‐core resources efficiently, hence limiting the system throughput and increasing the transaction acceptance latency. The objective of this work is to increase the transaction throughput by introducing parallel transaction execution using a static analysis over the transaction dependencies. We propose the DiPETrans framework for distributed execution of transactions in a block. Here, peers in the blockchain network form a community of trusted nodes to execute the transactions and find the PoW in‐parallel, using a leader–follower approach. During mining, the leader statically analyzes the transactions, creates different groups (shards) of independent transactions, and distributes them to followers to execute concurrently. After execution, the community's compute power is utilized to solve the PoW concurrently. When a block is successfully created, the leader broadcasts the proposed block to other peers in the network for validation. On receiving a block, the validators re‐execute the block transactions and accept the block if they reach the same state as shared by the miner. Validation can also be done in parallel, following the same leader–follower approach as mining. We report experiments using over 5 million real transactions from the Ethereum blockchain and execute them using our DiPETrans framework to empirically validate the benefits of our techniques over a traditional sequential execution. We achieve a maximum speedup of 2.2 and 2.0 and an average speedup of 1.6 and 1.5 for the miner and the validator, respectively, with 100–500 transactions per block when using 6 machines in the community. Further, we achieve a peak of 5 end‐to‐end block creation speedup using a parallel miner over a serial miner.
A Reward Population-Based Differential Genetic Harmony Search Algorithm

To overcome the shortcomings of the harmony search algorithm, such as its slow convergence rate and poor global search ability, a reward population-based differential genetic harmony search algorithm is proposed. In this algorithm, a population is divided into four ordinary sub-populations and one reward sub-population, for each of which the evolution strategy of the differential genetic harmony search is used. After the evolution, the population with the optimal average fitness is combined with the reward population to produce a new reward population. During an experiment, tests were conducted first on determining the value of the harmony memory size (HMS) and the harmony memory consideration rate (HMCR), followed by an analysis of the effect of their values on the performance of the proposed algorithm. Then, six benchmark functions were selected for the experiment, and a comparison was made on the calculation results of the standard harmony memory search algorithm, reward population harmony search algorithm, differential genetic harmony algorithm, and reward population-based differential genetic harmony search algorithm. The result suggests that the reward population-based differential genetic harmony search algorithm has the merits of a strong global search ability, high solving accuracy, and satisfactory stability.
Fast Target Recognition Based on Improved ORB Feature

A novel fast target recognition algorithm is proposed under the dynamic scene moving target recognition. Aiming at the poor matching effect of the traditional Oriented Fast and Rotated Brief (ORB) algorithm on underexposed or overexposed images caused by illumination, the idea of combining adaptive histogram equalization with the ORB algorithm is proposed to get better feature point quality and matching efficiency. First, the template image and each frame of the video stream are processed by grayscale. Second, the template image and the image to be input in the video stream are processed by adaptive histogram equalization. Third, the feature point descriptors of the ORB feature are quantized by the Hamming distance. Finally, the K-nearest-neighbor (KNN) matching algorithm is used to match and screen feature points. According to the matching good feature point logarithm, a reasonable threshold is established and the target is classified. The comparison and verification are carried out by experiments. Experimental results show that the algorithm not only maintains the superiority of ORB itself but also significantly improves the performance of ORB under the conditions of underexposure or overexposure. The matching effect of the image is robust to illumination, and the target to be detected can be accurately identified in real time. The target can be accurately classified in the small sample scene, which can meet the actual production requirements.
Hough Transform for Detecting Space Curves in Digital 3D Models

Abstract We present and analyse the Hough transform (HT) to recognise and approximate space curves in digital models, a problem that is not currently addressed by the standard HT. Our method works on meshes and point clouds and applies to models even incomplete or affected by noise, thus being suitable for the analysis of digital models deriving from 3D scans. In our approach we take advantage of a recent HT formulation for algebraic curves to define both parametric and implicit space curve representations. We also provide a comparative analysis of the HT-based method when dealing with both curve representations, discussing the computational performance and the approximation accuracy of both strategies.
Generation of hyperentangled states and two-dimensional quantum walks using <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>J</mml:mi></mml:math> or <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>q</mml:mi></mml:math> plates and polarization beam splitters

A single photon can be made to entangle simultaneously in its different internal degrees of freedom (DoF) -- polarization, orbital angular momentum (OAM), and frequency -- as well as in its external DoF -- path. Such entanglement in multiple DoF is known as hyperentanglement and provide additional advantage for quantum information processing. We propose a passive optical setup using $q$-plates and polarization beamsplitters to hyperentangle an incoming single photon in polarization, OAM, and path DoF. By mapping polarization DoF to a two-dimensional coin state, and path and OAM DoF to two spatial dimensions, $x$ and $y$, we present a scheme for realization of two-dimensional discrete-time quantum walk using only polarization beamsplitters and $q$-plates ensuing the generation of hyperentangled states. The amount of hyperentanglement generated is quantified by measuring the entanglement negativity between any two DoF. We further show the hyperentanglement generation can be controlled by using an additional coin operation or by replacing the $q$-plate with a $J$-plate.
Design Matters! How Visual Gendered Anthropomorphic Design Cues Moderate the Determinants of the Behavioral Intention Towards Using Chatbots

The design cue gendered anthropomorphism plays an important role in tailoring chatbots towards customers’ design preferences. Yet, it still remains unexplored if this design cue affects the determinants of the behavioral intention towards using chatbots as moderating variable. Therefore, this paper investigates the moderating influence of gendered anthropomorphic design cues on the key determinants of the behavioral intention that are defined by the Unified Theory of Acceptance and Use of Technology (UTAUT). For this purpose, three chatbot design groups have been created and compared: a female and a male designed chatbot with gendered anthropomorphic design cues, as well as one chatbot without gendered anthropomorphic design cues. A sample of 209 participants is randomly assigned to each design group. The PLS-SEM multigroup analysis shows gendered anthropomorphic chatbot design cues yield significant positive impact of the performance expectancy towards the behavioral intention to use chatbots. This was not the case for the design group without gendered anthropomorphic design cues. Also, in contrast to the male anthropomorphic chatbot design, the female anthropomorphic chatbot design yields significant positive impact of social influence on the behavioral intention.
An Efficient Deep Learning Framework for Multimedia Big Data Analytics

The rapid growth in the computing and mobile technologies, the Internet, social media, storage mediums and the user-generated e-content have a great propensity towards the worldwide generation of a tremendous amount of multimedia big data of different formats (such as text, audio, video and animation) and this contributes to the ever-booming universal multimedia data sphere. Multimedia big data open ups huge amount of opportunities for multimedia services and multimedia applications. The multimedia analytics have a wide range of applications such as sentiment analysis of social media data, computational health informatics, disaster management systems, multimedia summarization, digital forensics, data journalism, natural sciences and urban computing. Deep learning techniques are the improvised version of the machine learning technique, and have drastically changed the way in which the computing system deal with multimedia data. The system learns more and to do better analysis as it receives more data. Hence, using deep learning techniques for multimedia data analytics is the next logical step for the better utilization of massive data available. Moreover, recent studies show that using deep learning techniques for multimedia data analytics outperforms traditional machine learning approaches. So this chapter focuses on deep learning models and the applications of corresponding models are discussed.
Spatial Contrastive Learning for Anomaly Detection and Localization

With the development of deep learning, abnormal detection methods have been widely presented to improve performances in various applications, including visual inspection systems.However, there remains difficult to be directly applied to real-world applications, which often include the lack of abnormal samples and diversity.This paper proposes contra embedding that adopts progressive autoencoder with contrastive learning to address these difficulties.The autoencoder is trained progressively to reproduce the details of the original images, and modified CutPaste augmentation helps to learn to recover normal images.Especially, contrastive learning based on normal embedding vectors effectively reduces false positives caused by the autoencoder.The proposed method is also helpful when normal data have complex shapes, sizes, and colors.In experiments, MVTec AD dataset is used to show the generalization ability of the proposed method in various real-world applications.It achieves over 98.0% AUROCs in detection and 97.7% AUROCs in the localization, respectively, without using the ImageNet pre-trained model as in previous methods.
Triangle Graph Interest Network for Click-through Rate Prediction

Click-through rate prediction is a critical task in online advertising. Currently, many existing methods attempt to extract user potential interests from historical click behavior sequences. However, it is difficult to handle sparse user behaviors or broaden interest exploration. Recently, some researchers incorporate the item-item co-occurrence graph as an auxiliary. Due to the elusiveness of user interests, those works still fail to determine the real motivation of user click behaviors. Besides, those works are more biased towards popular or similar commodities. They lack an effective mechanism to break the diversity restrictions. In this paper, we point out two special properties of triangles in the item-item graphs for recommendation systems: Intra-triangle homophily and Inter-triangle heterophiy. Based on this, we propose a novel and effective framework named Triangle Graph Interest Network (TGIN). For each clicked item in user behavior sequences, we introduce the triangles in its neighborhood of the item-item graphs as a supplement. TGIN regards these triangles as the basic units of user interests, which provide the clues to capture the real motivation for a user clicking an item. We characterize every click behavior by aggregating the information of several interest units to alleviate the elusive motivation problem. The attention mechanism determines users' preference for different interest units. By selecting diverse and relative triangles, TGIN brings in novel and serendipitous items to expand exploration opportunities of user interests. Then, we aggregate the multi-level interests of historical behavior sequences to improve CTR prediction. Extensive experiments on both public and industrial datasets clearly verify the effectiveness of our framework.
FORT: Right-Proving and Attribute-Blinding Self-Sovereign Authentication

Nowadays, there are a plethora of services that are provided and paid for online, such as video streaming subscriptions, car-share, vehicle parking, purchasing tickets for events, etc. Online services usually issue tokens that are directly related to the identities of their users after they sign up to a platform; users need to authenticate themselves by using the same credentials each time they use the service. Likewise, when using in-person services, such as going to a concert, after paying for this service, the user usually receives a ticket, which proves that he/she has the right to use that service. In both scenarios, the main concerns surround the centralization of these systems and that they do not ensure customers’ privacy. The involved service providers are trusted third parties—authorities that offer services and handle private data about users. In this paper, we designed and implemented FORT, a decentralized system that allows customers to prove their rights to use specific services (either online or in-person) without revealing sensitive information. To achieve decentralization, we proposed a solution where all of the data are handled by a blockchain. We describe and uniquely identify users’ rights using non-fungible tokens (NFTs), and possession of these rights is demonstrated by using zero-knowledge proofs—cryptographic primitives that allow us to guarantee customers’ privacy. Furthermore, we provide benchmarks of FORT, which show that our protocol is efficient enough to be used in devices with low computing resources, such as smartphones or smartwatches, which are devices commonly used in our use case scenario.
Informed Multi-context Entity Alignment

Entity alignment is a crucial step in integrating knowledge graphs (KGs) from multiple sources. Previous attempts at entity alignment have explored different KG structures, such as neighborhood-based and path-based contexts, to learn entity embeddings, but they are limited in capturing the multi-context features. Moreover, most approaches directly utilize the embedding similarity to determine entity alignment without considering the global interaction among entities and relations. In this work, we propose an Informed Multi-context Entity Alignment (IMEA) model to address these issues. In particular, we introduce Transformer to flexibly capture the relation, path, and neighborhood contexts, and design holistic reasoning to estimate alignment probabilities based on both embedding similarity and the relation/entity functionality. The alignment evidence obtained from holistic reasoning is further injected back into the Transformer via the proposed soft label editing to inform embedding learning. Experimental results on several benchmark datasets demonstrate the superiority of our IMEA model compared with existing state-of-the-art entity alignment methods.
From contact tracing to COVID-19 pass holder; the tortured journey of the French TousAntiCovid contact tracing app

Our study aimed to provide an updated overview of the use of the French contact tracing application, TousAntiCovid, and identify evolutions since the beginning of the pandemic.We conducted a survey study on a representative sample of the French adult population.Our data were collected by the Obervatoire Régional de la Santé (ORS) using a self-administered online questionnaire. This was completed by a sample of 2,022 people stratified to match French official census statistics for gender, age, occupation, and area of housing. We conducted statistical analysis using Python (Pandas - Scipy - Statsmodels) with chi-squared and Wilcoxon rank-sum tests to control for statistical significance.A small majority of respondents used TousAntiCovid (55.5%), while 41.0% had never downloaded it. Only one-quarter of the respondents (23.3%) used it for contact tracing with Bluetooth, while a third (32.2%) used it only for storing their health pass. The app's use increased with education level, income, and younger age. A large majority (85%) of non-vaccinated respondents had never downloaded TousAntiCovid.Our results suggest that the role and use of France's official COVID-19 app TousAntiCovid has evolved in line with the government's strategy; while initially focusing on contact tracing, its development has led to the possibility to store test and vaccination documentation. The survey also confirmed previous results pointing to the lasting differences in socio-economic status in terms of adoption of the app. This is problematic because the long-term nature of the pandemic could require the government to keep a range of strategies open, including contact tracing. Public discussion of the current and future roles of the French contact tracing app is therefore needed.
Counterfactual Explanations for Predictive Business Process Monitoring

Predictive business process monitoring increasingly leverages sophisticated prediction models. Although sophisticated models achieve consistently higher prediction accuracy than simple models, one major drawback is their lack of interpretability, which limits their adoption in practice. We thus see growing interest in explainable predictive business process monitoring, which aims to increase the interpretability of prediction models. Existing solutions focus on giving factual explanations.While factual explanations can be helpful, humans typically do not ask why a particular prediction was made, but rather why it was made instead of another prediction, i.e., humans are interested in counterfactual explanations. While research in explainable AI produced several promising techniques to generate counterfactual explanations, directly applying them to predictive process monitoring may deliver unrealistic explanations, because they ignore the underlying process constraints. We propose LORELEY, a counterfactual explanation technique for predictive process monitoring, which extends LORE, a recent explainable AI technique. We impose control flow constraints to the explanation generation process to ensure realistic counterfactual explanations. Moreover, we extend LORE to enable explaining multi-class classification models. Experimental results using a real, public dataset indicate that LORELEY can approximate the prediction models with an average fidelity of 97.69\% and generate realistic counterfactual explanations.