Reviewerly/exHarmony huggingface dataset:
CanaryTrap: Detecting Data Misuse by Third-Party Apps on Online Social Networks

Abstract Online social networks support a vibrant ecosystem of third-party apps that get access to personal information of a large number of users. Despite several recent high-profile incidents, methods to systematically detect data misuse by third-party apps on online social networks are lacking. We propose CanaryTrap to detect misuse of data shared with third-party apps. CanaryTrap associates a honeytoken to a user account and then monitors its unrecognized use via different channels after sharing it with the third-party app. We design and implement CanaryTrap to investigate misuse of data shared with third-party apps on Facebook. Specifically, we share the email address associated with a Facebook account as a honeytoken by installing a third-party app. We then monitor the received emails and use Facebook’s ad transparency tool to detect any unrecognized use of the shared honeytoken. Our deployment of CanaryTrap to monitor 1,024 Facebook apps has uncovered multiple cases of misuse of data shared with third-party apps on Facebook including ransomware, spam, and targeted advertising.
CLOCs: Camera-LiDAR Object Candidates Fusion for 3D Object Detection

There have been significant advances in neural networks for both 3D object detection using LiDAR and 2D object detection using video. However, it has been surprisingly difficult to train networks to effectively use both modalities in a way that demonstrates gain over single-modality networks. In this paper, we propose a novel Camera-LiDAR Object Candidates (CLOCs) fusion network. CLOCs fusion provides a low-complexity multi-modal fusion framework that significantly improves the performance of single-modality detectors. CLOCs operates on the combined output candidates before Non-Maximum Suppression (NMS) of any 2D and any 3D detector, and is trained to leverage their geometric and semantic consistencies to produce more accurate final 3D and 2D detection results. Our experimental evaluation on the challenging KITTI object detection benchmark, including 3D and bird's eye view metrics, shows significant improvements, especially at long distance, over the state-of-the-art fusion based methods. At time of submission, CLOCs ranks the highest among all the fusion-based methods in the official KITTI leaderboard. We will release our code upon acceptance.
IoT Based Human Activity Recognition System Using Smart Sensors

Internet of Things provides a virtual view of real-life things by guiding challenges faced by persons in daily life. It is reforming our world with trillions of sensors and other IoT enabled devices by creating a smart environment. Effective use of IoT sensors in various smart IoT applications is very important. After analyzing different sensor applications, this paper presents various types of wearable sensors used for monitoring of human activities along with different locations optimal for their placement. This paper enlightens sensors suitable for any particular application. IoT has opened up a new avenue of research in the field of human activity recognition using wearable sensors. It provides an individual's valuable information regarding functional ability and lifestyle. Human activities are monitored automatically to provide personalized support to different individuals. Recently,various researchers presented different human activity recognition systems, a few are cumulated in this paper.Furthermore, a Human Activity Recognition (HAR) system is also proposed in this paper for a smart IoT environment that would be secure enough to use.
Database of virtual objects to be used in psychological research

Although many visual stimulus databases exist, to our knowledge, none includes 3D virtual objects, that can directly be used in virtual reality (VR). We present 121 objects that have been developed for scientific purposes. The objects were built in Maya, and their textures were created in Substance Painter. Then, the objects were exported to an FBX and OBJ format and rendered online using the Unreal Engine 4 application. Our goal was to develop the first set of high-quality virtual objects with standardized names, familiarity, and visual complexity. The objects were normed based on the input of 83 participants. This set of stimuli was created for use in VR settings and will facilitate research using VR methodology, which is increasingly employed in psychological research.
Affordable and Faster Transradial Prosthetic Socket Production Using Photogrammetry and 3D Printing

This study aims to invent a new, low-cost, and faster method of prosthetic socket fabrication, especially in Indonesia. In this paper, the photogrammetry with the 3D printing method is introduced as the new applicative way for transradial prosthetic making. Photogrammetry is used to retrieve a 3D model of the amputated hand stump using a digital camera. A digital camera is used for photogrammetry technique and the resulting 3D model is printed using a circular 3D printer with Polylactic acid (PLA) material. The conventional casting socket fabrication method was also conducted in this study as a comparison. Both prosthetic sockets were analyzed for usability, and sectional area conformities to determine the size deviation using the image processing method. This study concludes that the manufacturing of transradial prosthetic sockets incorporating the photogrammetry technique reduces the total man-hour production. Based on the results, it can be implied that the photogrammetry technique is a more efficient and economical method compared to the conventional casting method. The 3D printed socket resulting from the photogrammetry method has a 5–19% area deviation to the casting socket but it is still preferable and adjustable for the transradial amputee when applied to the stump of the remaining hand.
Demystifying the spectral collapse in two-photon Rabi model

Abstract We have investigated the eigenenergy spectrum of the two-photon Rabi model at the critical coupling, particularly the special feature “spectral collapse”, by means of an elementary quantum mechanics approach. The eigenenergy spectrum is found to consist of both a set of discrete energy levels and a continuous energy spectrum. Each of these eigenenergies has a two-fold degeneracy corresponding to the spin degree of freedom. The discrete eigenenergy spectrum has a one-to-one mapping with that of a particle in a “Lorentzian function” potential well, and the continuous energy spectrum can be derived from the scattering problem associated with a potential barrier. The number of bound states available at the critical coupling is determined by the energy difference between the two atomic levels so that the extent of the “spectral collapse” can be monitored in a straightforward manner.
Cryptanalysis of Two Signature Schemes for IoT-Based Mobile Payments and Healthcare Wireless Medical Sensor Networks

Certificateless cryptography does not require any certificate for the public key authentication and users' public keys are transmitted with ciphertext/signatures or by making them available in the IoTbased public directory in a proper way.Due to these features, certificateless cryptosystems are considered as fundamental cryptographic buildingblocks to provide authenticity, integrity and non-repudiation suitable for IoT applications.Yeh proposed a transaction scheme based on a certificateless signature scheme for IoTbased mobile payments implementing on Android Pay.He showed that the CLS scheme was unforgeable against Type I and Type II adversaries under the intractability of the mathematical problem.Despite the security proofs, we show that Yeh's scheme is still insecure against both Type I and Type II adversaries.Recently, Gayathri et al. constructed a compact certificateless aggregate signature scheme for Healthcare Wireless Medical Sensor Networks.Their aggregate signatures are constant-size independent of the number of signers.In this paper, we show that anyone can forge certificateless aggregate signatures of their scheme on any sets of messages and identities from only publicly known information, i.e. their scheme is entirely broken.We then discuss some improvements.
Cybersecurity is not very important

There is a rising tide of security breaches. There is an even faster rising tide of hysteria over the ostensible reason for these breaches, namely the deficient state of our information infrastructure. Yet the world is doing remarkably well overall, and has not suffered any of the oft-threatened giant digital catastrophes. This continuing general progress of society suggests that cyber security is not very important. Adaptations to cyberspace of techniques that worked to protect the traditional physical world have been the main means of mitigating the problems that occurred. This "chewing gum and baling wire" approach is likely to continue to be the basic method of handling problems that arise, and to provide adequate levels of security.
Covid-19: Scotland launches contact tracing app with England and Wales to follow

Scotland has launched its own contact tracing app for covid-19 based on the same Apple and Google toolkit used for the Northern Ireland and the Republic of Ireland apps.1The day after the Scottish government’s announcement the Department of Health and Social Care said that a covid-19 app will be available across England and Wales on 24 September. Launch of the app has been delayed while NHSX initially tried to develop a centralised system in which anonymised data from people with covid-19 symptoms could be held on an NHS database.The UK government was forced to make a U turn in June after testing in the Isle of Wight found the app only recognised 4% of Apple phones and 75% of Google’s android devices.2 It has since been developing a decentralised model that uses the Apple and Google application programming interface but incorporates extra features. This …
A chemistry laboratory platform enhanced with virtual reality for students’ adaptive learning

In recent years, virtual reality system (VRS) has become more prominent among many researchers due to its capacity of providing as close as possible to real-life experience for users from diverse fields of life, such as tourism, academics, manufacturing and medicine. In this study, we present an VRS for the titration experiment in a chemistry laboratory to enable students to learn the titration experiment in a virtual laboratory environment before proceeding to the chemistry wet lab. The virtual chemistry laboratory environment was developed using the Unity Real-Time Development Platform, and the Microsoft SQL Server was used for the database to enable easy assessment of the student performance after the experiment. To evaluate our VRS, we tested it among 50 students (25 high school and 25 first-year undergraduate chemistry students). We collate their user’s experience through a structured questionnaire, and the responses from the students show that 60% agreed that it was helpful, 66% easy to interact with and 54% strongly agreed that it improved learning. Therefore, it is evidence that the proposed VR-enabled chemistry laboratory platform could be used to improve the understanding of chemistry titration practical process among students.
Research Trends in Mobile Learning

A total of 1023 selected articles published in 2016–2019 related to mobile learning were examined and classified according to the categories in this research: 40% of these articles used quantitative approaches, 18% of them used mixed, and 13% of them were literature reviews. The published studies were analyzed according to research model, sample size, sample level, learning fields, subject-area classification, data collection tool, data analysis technique, dependent variable, independent variable, mobile device, number of authors, and publication year. The findings were analyzed and interpreted as a percentage and frequency. This research will be useful for reviewing current research trends related to mobile learning studies, indicating potential research on the topics, and revealing the needs of the field.
Automatic text-independent speaker verification using convolutional deep belief network

This paper is devoted to the use of the convolutional deep belief network as a speech feature extractor for automatic text-independent speaker verification. The paper describes the scope and problems of automatic speaker verification systems. Types of modern speaker verification systems and types of speech features used in speaker verification systems are considered. The structure and learning algorithm of convolutional deep belief networks is described. The use of speech features extracted from three layers of a trained convolution deep belief network is proposed. Experimental studies of the proposed features were performed on two speech corpora: own speech corpus including audio recordings of 50 speakers and TIMIT speech corpus including audio recordings of 630 speakers. The accuracy of the proposed features was assessed using different types of classifiers. Direct use of these features did not increase the accuracy compared to the use of traditional spectral speech features, such as mel-frequency cepstral coefficients. However, the use of these features in the classifiers ensemble made it possible to achieve a reduction of the equal error rate to 0.21% on 50-speaker speech corpus and to 0.23% on the TIMIT speech corpus.
Multi-Objective Evolutionary Design of Deep Convolutional Neural Networks for Image Classification

Early advancements in convolutional neural networks (CNNs) architectures are primarily driven by human expertise and by elaborate design processes. Recently, neural architecture search was proposed with the aim of automating the network design process and generating task-dependent architectures. While existing approaches have achieved competitive performance in image classification, they are not well suited to problems where the computational budget is limited for two reasons: (1) the obtained architectures are either solely optimized for classification performance, or only for one deployment scenario; (2) the search process requires vast computational resources in most approaches. To overcome these limitations, we propose an evolutionary algorithm for searching neural architectures under multiple objectives, such as classification performance and floating-point operations (FLOPs). The proposed method addresses the first shortcoming by populating a set of architectures to approximate the entire Pareto frontier through genetic operations that recombine and modify architectural components progressively. Our approach improves computational efficiency by carefully down-scaling the architectures during the search as well as reinforcing the patterns commonly shared among past successful architectures through Bayesian model learning. The integration of these two main contributions allows an efficient design of architectures that are competitive and in most cases outperform both manually and automatically designed architectures on benchmark image classification datasets: CIFAR, ImageNet, and human chest X-ray. The flexibility provided from simultaneously obtaining multiple architecture choices for different compute requirements further differentiates our approach from other methods in the literature. Code is available at https://github.com/mikelzc1990/nsganetv1
In Vino Veritas? Blockchain Preliminary Effects on Italian Wine SMEs

Transparency and traceability in the food industry have become two central themes for both consumers and companies. On the one hand, consumer awareness increases with more available in depth information, whilst food manufacturers try to mitigate food safety risks, reduce coordination costs and fraud by improving their presence on the market. In this scenario, innovative technologies and blockchain may have a major impact. The authors investigate the adoption of blockchain in the Italian wine industry and, in particular, the effects of blockchain on the complex inter-organizational supply chain systems SMEs are engaged in. A qualitative approach was chosen to preliminary analyse the motivations driving small Italian wineries to adopt blockchain technologies, and the advantages or drawbacks managers identified during pilot experiments.
Learning Unseen Emotions from Gestures via Semantically-Conditioned Zero-Shot Perception with Adversarial Autoencoders

We present a novel generalized zero-shot algorithm to recognize perceived emotions from gestures. Our task is to map gestures to novel emotion categories not encountered in training. We introduce an adversarial autoencoder-based representation learning that correlates 3D motion-captured gesture sequences with the vectorized representation of the natural-language perceived emotion terms using word2vec embeddings. The language-semantic embedding provides a representation of the emotion label space, and we leverage this underlying distribution to map the gesture sequences to the appropriate categorical emotion labels. We train our method using a combination of gestures annotated with known emotion terms and gestures not annotated with any emotions. We evaluate our method on the MPI Emotional Body Expressions Database (EBEDB) and obtain an accuracy of 58.43%. We see an improvement in performance compared to current state-of-the-art algorithms for generalized zero-shot learning by an absolute 25-27%. We also demonstrate our approach on publicly available online videos and movie scenes, where the actors' pose has been extracted and map to their respective emotive states.
Distribution Matching for Crowd Counting

In crowd counting, each training image contains multiple people, where each person is annotated by a dot. Existing crowd counting methods need to use a Gaussian to smooth each annotated dot or to estimate the likelihood of every pixel given the annotated point. In this paper, we show that imposing Gaussians to annotations hurts generalization performance. Instead, we propose to use Distribution Matching for crowd COUNTing (DM-Count). In DM-Count, we use Optimal Transport (OT) to measure the similarity between the normalized predicted density map and the normalized ground truth density map. To stabilize OT computation, we include a Total Variation loss in our model. We show that the generalization error bound of DM-Count is tighter than that of the Gaussian smoothed methods. In terms of Mean Absolute Error, DM-Count outperforms the previous state-of-the-art methods by a large margin on two large-scale counting datasets, UCF-QNRF and NWPU, and achieves the state-of-the-art results on the ShanghaiTech and UCF-CC50 datasets. DM-Count reduced the error of the state-of-the-art published result by approximately 16%. Code is available at https://github.com/cvlab-stonybrook/DM-Count.
JPEG Steganalysis Detectors Scalable With Respect to Compression Quality

Practical steganalysis inevitably involves the necessity to deal with a diverse cover source. In the JPEG domain, one key element of the diversification is the JPEG quality factor, or, more generally, the JPEG quantization table used for compression. This paper investigates experimentally the scalability of various steganalysis detectors w.r.t. JPEG quality. In particular, we report that CNN detectors as well as older feature-based detectors have the capacity to contain the complexity of multiple JPEG quality factors within a single model when the quality factors are properly grouped based on their quantization tables. Detectors trained on multiple JPEG qualities show no loss of detection accuracy when compared with dedicated detectors trained for a specific JPEG quality factor. We also demonstrate that CNNs (but not so much feature-based classifiers) trained on multiple qualities can generalize to unseen custom quantization tables compared to detectors trained for specific JPEG qualities. Their ability to generalize to very different quantization tables, however, remains a challenging task. A semi-metric comparing quantization tables is introduced and used to interpret our results.
Web-based Teamwork: Distributed Software Development Course under Covid-19

In a increasingly globalized world, it is very important to help students to acquire the hard and soft skills needed to cope with the challenges they will face when working in such environments.However, even though students find this subject interesting and volunteer for it, it is hard, even impossible to teach college students such a distributed fashion of teamwork.In this paper, a case study is provided to illustrate the methodology and experiences of a joint course, distributed software development, of multiple universities from Asia, Europe, North and South Americas.The results show that student can get the spirit, effective communication for such web-based teamwork environment and achieve software project development.The recent developments because of the Covid-19 pandemic are the facts that now students have to cooperate remotely with each other not only across continents but also inside their own city or country.It has increased the awareness of the need for this kind of training and effectively improved the interactions between team elements in different countries and improved the students' feeling of belonging to their team.
Scheduling Sensor Duty Cycling Based on Event Detection Using Bi-Directional Long Short-Term Memory and Reinforcement Learning

A smart home provides a facilitated environment for the detection of human activity with appropriate Deep Learning algorithms to manipulate data collected from numerous sensors attached to various smart things in a smart home environment. Human activities comprise expected and unexpected behavior events; therefore, detecting these events consisting of mutual dependent activities poses a key challenge in the activities detection paradigm. Besides, the battery-powered sensor ubiquitously and extensively monitors activities, disputes, and sensor energy depletion. Therefore, to address these challenges, we propose an Energy and Event Aware-Sensor Duty Cycling scheme. The proposed model predicts the future expected event using the Bi-Directional Long-Short Term Memory model and allocates Predictive Sensors to the predicted event. To detect the unexpected events, the proposed model localizes a Monitor Sensor within a cluster of Hibernate Sensors using the Jaccard Similarity Index. Finally, we optimize the performance of our proposed scheme by employing the Q-Learning algorithm to track the missed or undetected events. The simulation is executed against the conventional Machine Learning algorithms for the sensor duty cycle, scheduling to reduce the sensor energy consumption and improve the activity detection accuracy. The experimental evaluation of our proposed scheme shows significant improvement in activity detection accuracy from 94.12% to 96.12%. Besides, the effective rotation of the Monitor Sensor significantly improves the energy consumption of each sensor with the entire network lifetime.