Reviewerly/exHarmony huggingface dataset:
Crafting animated parables: an embodied approach to representing lifestyle behaviours for reflection

Latest work regarding personal informatics, gamification, and feedback has suggested that visualizing behavioural data for daily reflection should consider dynamic representations in metaphors and narratives with positively and negatively valued outcomes. Grounded in behavioural models from social psychology and embodied cognition theories, this article proposes a framework guiding the generation of animated parables wherein causality between a behaviour and its virtual outcomes is easy to understand. The guidelines include metaphorical mapping and blending the behaviour with a direct cause-effect scenario with similarities in embodied experiences. Application of the guidelines in a series of design workshops generates 47 parables. To evaluate the parables, metrics include naturalness of integrating with life routines, aesthetics of blending into environments, and ease of interpreting the causality. Evaluation results show that more embodied mapping and blending are more likely to result in understandable behaviour-outcome links. Designers can follow the guidelines and metrics to generate and compare ideas. Lastly, recommendations for crafting more embodied parables are provided.
Spatial segment-aware clustering based dynamic reliability threshold determination (SSC-DRTD) for unsupervised person re-identification

Person Re-Identification (re-ID) in a crowded multi-camera surveillance environment is a highly challenging task. The traditional benchmark datasets contain less number of occluded images due to the pre-planned setup and limited duration of the videos recorded. Unlike the traditional benchmark person re-ID datasets, real-world surveillance environment possess high static and dynamic occlusions. The analysis of different image segments captured in diverse environments by using a static reliability threshold leads to a poor matching accuracy. To resolve this issue of poor reliability threshold determination and to handle the occluded person re-ID images efficiently, we propose an unsupervised spatial segmented clustering model (SSC-DRTD) which determines a dynamic segment-wise reliability threshold. The unlabeled person re-ID images are segmented into k-parts to determine the segment-wise reliability threshold and the optimal number of segments for a given dataset. A cluster refinement strategy is proposed by incorporating the determined dynamic reliability threshold values to match the occluded noisy images with its appropriate ground truth identities for robust cluster formation. An improved rank evaluation has been performed on the benchmark person re-ID datasets such as DukeMTMC re-ID, Market1501, CUHK03, and MSMT17. The experimental results show the improved performance of our proposed SSC-DRTD model in handling occluded person re-ID images over the state-of-the-art unsupervised person re-ID methods. To further prove the efficiency of our proposed model, an exploratory analysis is performed by increasing the number of occluded query images to simulate the real-world surveillance scenario.
Monocular 3D Object Detection using Disjoint Generalized Intersection-over-Union Loss

Three-dimensional object estimation from monocular imagery is a difficult task due to the geometric information loss induced by the perspective projection. Currently, most methods try to deal with this limitation by using deep learning models augmented with some form of 3D reasoning, which often leads to complicated inference pipelines. In this work, we argue that explicit 3D reasoning is not mandatory for good monocular 3D detection performance. To this end, we extend a 2D object detection framework with a small subnetwork responsible for 3D bounding box estimation. To train this module, we introduce a loss function based on the Generalized Intersection-over Union in which each degree of freedom is optimized separately. The resulting approach is simple, modular and straightforward to integrate into existing monocular 2D detection frameworks. Experiments on the challenging KITTI dataset show that our method achieves state-of-the-art performance on the 3D detection task.
RETRACTED: Design of embedded system of volleyball training assistant decision support based on association rules

Volleyball generally determined by two judges and two lines hosted. In the first volleyball net official referees in the table is positioned at the opposite side of the raised platform. The first referee does not move around, but they need to stand for long periods, usually 45 min to two hours, depending on the length of the game, while maintaining a high level of focus and alertness. The second referee assists and stood first referee make a call, primarily focused on network play across the floor. Volleyball is a fast-moving, the area contains. Therefore, it is possible to follow the action visually, look at the small details are very important. Volleyball officials must always stay alert and focused during playback, so as not to miss a beat. Ref 12 feet along a second transfer region and a first edge opposite the lateral movement of the referee and the ball game. The judge of the production line stood on the opposite corner throughout the game, and the contact with the in and out call to assist the first referee and search for the ball on the net blocked. Judges need excellent eyesight, good judgment and agility, move up and down the sideline to get the ball and avoid collisions with the best perspective of the player.
Cybersecurity Threats and Organisational Response: Textual Analysis and Panel Regression

This study examines the relationship between cybersecurity threats faced and cybersecurity response planned by organisations. Classifying cybersecurity threats into four types – physical threats, personnel threats, communication and data threats, and operational threats – this study examines organisational responses to such threats. Using textual data on cybersecurity threats and response gathered from the 10-K reports published by 87 organisations, topic modelling was conducted to assess the threats and response. A cross-sectional time-series regression model fitted on the topic weights showed that cybersecurity response was influenced by cybersecurity threats, beyond the time-invariant control and period variables. Specifically, physical threats and operational threats influenced the technical response; physical threats, communication and data threats, and operational threats influenced the non-technical response; and personnel threats influenced the overall response. Implications for research and practice are discussed.
3D Optical Encryption System using Merging Reconstruction Method

In this paper, we propose a photon-counting double random phase encryption with merging reconstruction. Double random phase encryption (DRPE) is a simple and secure optical encryption technique using phase random masks. To enhance the security level, a three-dimensional photon counting technique has been applied to DRPE. However, we cannot recognize the original image information in a photon-limited scene. To solve this problem, we propose a merging reconstruction method, which can detect the photons using several reconstruction layers. Through this method, we can obtain a decrypted image of enhanced visual quality. This paper presents a simulation test for the proposed method.
Heterogeneous Edge CNN Hardware Accelerator

We describe a programmable and scalable Convolutional Neural Network (CNN) hardware accelerator optimized for mobile and edge inference computing. The accelerator is comprised of 4 heterogeneous engines - input engine, filter engine, post processing engine, and output engine. The specialized engines execute independently and concurrently. All engines have a core set of common instructions with each engine further specialized for specific functions. We describe the operation of each engine and provide silicon validated results for a number of CNN networks including LeNet-5, TinySSD, and SqueezeNet. We describe a blind modulation detection application using SqueezeNet. The accelerator has been fabricated in 28nm CMOS and runs at 1GHz. The logic consumes 0.6 mm <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> and the fully hardened core with 2MB of SRAM including built-in self-test consumes 9.36mm <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> . The accelerator's filter engine implements 288 f16 multipliers thereby achieving 288 GFLOPS at 1GHz. Two TOPS of peak performance is achieved with all engines running in parallel. The accelerator including SRAM dissipates 193mW running LeNet-5 at room temperature.
Iterative Soft Decoding of Reed-Solomon Tail-Biting Convolutional Concatenated Codes

Reed-Solomon tail-biting convolutional concatenated (RS-TBCC) codes are investigated in this paper, aiming to eliminate the rate loss caused by the tail bits of the traditional RS convolutional concatenated (RS-CC) codes. The iterative soft decoding (ISD) for the RS-TBCC code is proposed, which integrates the tail-biting maximum a posteriori (TB-MAP) decoding algorithm for the inner tail-biting convolutional (TBC) code and the adaptive belief propagation (ABP) decoding algorithm for the outer RS codes. The proposed decoding is able to obtain significant performance gains through iterations. The outer decoding output will be validated by the maximum likelihood (ML) criterion, which alters the feedback to the inner decoding. However, the effectiveness of ML assessment degrades as the outer codeword length decreases. Therefore, a modified ISD is also proposed for shorter RS-TBCC codes in which smaller RS codes are employed.
Adversarial Domain Adaptation for Crisis Data Classification on Social Media

Smart cities are cyber-physical-social systems, where city data from different sources could be collected, processed and analyzed to extract useful knowledge. As the volume of data from the social world is exploding, social media data analysis has become an emerging area in many different disciplines. During crisis events, users may post informative tweets about affected individuals, utility damage or cautions on social media platforms. If such tweets are efficiently and effectively processed and analyzed, city organizations may gain a better situational awareness of the affected citizens and provide better response actions. Advances in deep neural networks have significantly improved the performance in many social media analyzing tasks, e.g., sentiment analysis, fake news detection, crisis data classification, etc. However, deep learning models require a large amount of labeled data for model training, which is impractical to collect, especially at the early stage of a crisis event. To address this limitation, we proposed a BERT-based Adversarial Domain Adaptation model (BERT-ADA) for crisis tweet classification. Our experiments with three real-world crisis datasets demonstrate the advantages of the proposed model over several baselines.
Social network sites and requirements engineering: A systematic literature review

Abstract A large volume of highly diverse and real‐time data provided by social network sites has revolutionized several data analytical sciences such as stock exchange fluctuation prediction, sentiment analysis, and political affiliations prediction. From a software engineering viewpoint, social network sites have produced an unprecedented opportunity for software development organizations to monitor the opinion of the large population of users of their systems. The extracted data from social network sites can be used to extract information about newly launched systems, and it enable software development organizations to obtain rapid technical and social feedback about their systems. This paper presents on a systematic literature review of the published research articles on how requirements engineering (RE) activities are conducted using social network sites, elaborated in studies published between 2011 and 2019. From the resulting studies, we extracted data to answer our four questions related to social network site (SNS)‐based RE approaches focusing on requirements elicitation, prioritization, and negotiation activities. Thirty‐one relevant studies were identified and analyzed to answer our research questions. Among 31 studies, 62% of the studies reportedly support requirements elicitation activity, and 14% of the studies reportedly support requirements elicitation, prioritization, and negotiation activities. Ten percent studies were about requirements elicitation and prioritization activities, 7% of the studies support both requirements elicitation and negotiation activities, and prioritization activity was supported by 7% of the studies. It was also observed that 58% of the selected studies adopted semi‐automatic approaches to conduct RE activities while 21% of the selected studies proposed automatic tools, and 21% of the selected studies adopted manual approaches to conduct RE activities. The outcomes of this systematic literature review indicate that social network sites can be a major source that can be used successfully to extract and identify user requirements. Moreover, this systematic literature review also presents implications of this study for practitioners and researchers including challenges, unresolved issues, and future research directions.
Adaptive Data Replication Optimization Based on Reinforcement Learning

Data replication plays an important role in enterprise IT landscapes, where data is shared among multiple IT systems. IT administrators need to tune the replicating software's configuration setting for it to perform at its optimum level. It is a challenge to continue optimizing the software's configuration to keep up with the fluctuating workload in a dynamic business environment. We propose a novel approach of using reinforcement learning with meta-heuristics to create an adaptive optimization method for data replication software. The experimental results show the replicating software managed by the proposed approach can perform at an optimum level despite consistently working under changing workloads.
A participatory framework proposal for guiding researchers through an educational mobile app development

The research work reported in this article is part of a wider study aimed at developing a mobile application (app) for Science Education in primary school. For that, we designed a participatory framework proposal nested within the larger framework of&nbsp;Educational Design Research. This framework emerged from the authors’ need to organise the different phases of the mobile apps development and to arrange the expected products that arise from them. Our framework suggests a grounded, participatory and user-centred approach, relating literature contributions with data collected among future end-users. In this study, we exemplify the implementation of the proposed framework by presenting the outcomes of a specific moment of the preliminary research: students’ stories and drawing analysis, to define the mobile app concept. For that, we (1) present and describe the participatory framework proposal, (2) identify and characterise the research method adopted to define the mobile app concept, (3) reveal and analyse data collected from the implementation of a creative writing and drawing activity performed by fourth grade primary-school students, and (4) describe the implications of data analysis in the mobile app concept definition. Our intention is to share with other educational researchers an approach that can be used to develop educational mobile apps grounded on future end-users’ perceptions and ideas. We also aim to contribute to deepen the&nbsp;Educational Design Research&nbsp;apps by proposing and exemplifying the implementation of a framework that brings together researchers, students, teachers and experts.
Forensic Analysis of the August Smart Device Ecosystem

Smart Homes are becoming increasingly popular with homeowners investing in new internet-connected technologies, from smart plugs to smart appliances. One particular area where homeowners use smart devices is at the front door, as they install smart doorbells and smart locks. In this paper, we embark on an investigative journey into the August smart device ecosystem, specifically looking at the interaction between the August Smart Doorbell Pro and the August Smart Lock Pro, with their controlling app, August Home. We aim to determine what type of data forensic investigators may be able to recover about these devices and their use. We also explore how these devices interact with each other, noting any privacy issues that may be discovered. We found that a wealth of forensic data could be recovered, particularly from rooted Android devices and jailbroken iPhone devices. Information about the owner of the smart device, guests who were given access to the devices, device interactions, and pictures taken by the doorbell camera, were all recoverable in plaintext. Our results also show that user's GPS coordinates associated with the lock's Auto-Unlock feature were recovered only from a rooted Android device.
Twisted Hermitian Codes

We define a family of codes called twisted Hermitian codes, which are based on Hermitian codes and inspired by the twisted Reed–Solomon codes described by Beelen, Puchinger, and Nielsen. We demonstrate that these new codes can have high-dimensional Schur squares, and we identify a subfamily of twisted Hermitian codes that achieves a Schur square dimension close to that of a random linear code. Twisted Hermitian codes allow one to work over smaller alphabets than those based on Reed–Solomon codes of similar lengths.
WITHDRAWN: QABA: A privacy model to reduce adversary attacks for cloud storage

The evolution of data storage paradigms from centralized systems to hire and use model based cloud computing are the recent transition and developments in business, retail, government and healthcare organizations. The cloud is deceptive and vulnerable to data leakage attacks during transit or at passive state. The recent malware attacks are live examples to describe data breach instances. To protect the privacy and enhance data hiding for sensitive data pertain to healthcare data is at jeopardize. The proposed QABA algorithm enhances privacy and reduces the uniqueness of records to camouflage sensitive data from re-identity disclosure attacks. The experiments conducted on standard dataset and evaluated the privacy. The proposed algorithm exhibit efficiency and robustness in implementing pseudonymity.
Frame-validity Games and Lower Bounds on the Complexity of Modal Axioms

Abstract We introduce frame-equivalence games tailored for reasoning about the size, modal depth, number of occurrences of symbols and number of different propositional variables of modal formulae defining a given frame property. Using these games, we prove lower bounds on the above measures for a number of well-known modal axioms; what is more, for some of the axioms, we show that they are optimal among the formulae defining the respective class of frames.
Evaluating machine translation systems for Brazilian sign language in the treatment of critical grammatical aspects

Machine translation tools for sign languages are developed with the aim of reducing barriers to access to information for deaf people. However, these technologies have some limitations related to the difficulty of dealing with some specific grammatical aspects of sign languages, which can negatively influence the experience of deaf users. In order to analyze this problem, in this study, we assess the translation of Brazilian Portuguese (Pt-br) content into Brazilian Sign Language (Libras) by three machine translators: HandTalk, Rybená and VLibras. More specifically, we conducted an experiment with 30 Brazilian human interpreters that evaluate the treatment of 7 specific grammatical aspects in the applications. As a result, we observed significant important limitation in the treatment of homonyms, adverbs (negation, mode and intensity), directional verbs, and phrases (interrogative and exclamatory) in the translations performed by these applications, indicating the need for them to improve the treatment of these grammatical aspects.
Converging Artificial Intelligence and Blockchain Technology using Oracle Contract in Ethereum Blockchain Platform

Artificial Intelligence (AI) and blockchain are two emerging concepts that explored exponentially in recent years. In software development, artificial Intelligence offers a sophisticated enhancement on the programming logic, while blockchain technology provides transparency and relief the obligation to trust in a central party. Both are two different technologies but considered a disruptive technology in recent years. Artificial Intelligence faces several issues related to the validity of data, algorithms, and policies, while blockchain is called so “friendly” with transparency, trust, and immutability. Both technologies are still in the early stages of their development age. Always, the opportunity for both technologies to converge in some ways would be an exciting area to be explored. This research elaborates on the technical possibilities of application development on the blockchain platform that communicate with AI to do prediction task. Oracle contract allows the data inside Ethereum blockchain to interact with the external data source.
Improved Ant Colony Genetic Algorithm for Solving Traveling Salesman Problem

A new improved ant colony genetic algorithm(IACGA) is proposed to solve the problems of the basic ant colony optimization algorithm(ACO), such as the slow convergence rate and the local optimal solution. Based on the ACO model, local pheromone increment is updated differently according to the path quality constructed by ants, the current optimal path pheromone residual factor is adjusted adaptively, pheromone updating rules are improved, genetic operator is embedded and genetic algorithm(GA) is dynamically fused. It makes full use of the positive feedback mechanism of ACO and the searching ability of GA to realize the dynamic fusion of ACO and GA. The experimental results show that compared with the ACO and the GA, the improved algorithm can find the global optimal solution quickly, and the solution quality is relatively good, which improves the efficiency of the ant colony algorithm in solving the traveling salesman problem(TSP).
On the Performance of Generative Adversarial Network (GAN) Variants: A Clinical Data Study

Generative Adversarial Network (GAN) is a useful type of Neural Networks in various types of applications including generative models and feature extraction. Various types of GANs are being researched with different insights, resulting in a diverse family of GANs with a better performance in each generation. This review focuses on various GANs categorized by their common traits.
Curriculum Learning for Face Recognition

We present a novel curriculum learning (CL) algorithm for face recognition using convolutional neural networks. Curriculum learning is inspired by the fact that humans learn better, when the presented information is organized in a way that covers the easy concepts first, followed by more complex ones. It has been shown in the literature that that CL is also beneficial for machine learning tasks by enabling convergence to a better local minimum. In the proposed CL algorithm for face recognition, we divide the training set of face images into subsets of increasing difficulty based on the head pose angle obtained from the absolute sum of yaw, pitch and roll angles. These subsets are introduced to the deep CNN in order of increasing difficulty. Experimental results on the large-scale CASIA-WebFace-Sub dataset show that the increase in face recognition accuracy is statistically significant when CL is used, as compared to organizing the training data in random batches.