Reviewerly/exHarmony huggingface dataset:
Using Personal Data to Support Authentication: User Attitudes and Suitability

Dynamic personal data based on a user's activity, such as recent visited physical locations, browsing history, and call logs, update frequently, making it a promising token for user authentication. However, it is not clear how users perceive this use of personal data and which data types are most suitable for authentication. To investigate this, we conducted an online survey with N=100 participants. For 10 personal data types we asked participants about their comfort with this data for authentication, its perceived security, its impact on behaviour, who has access to it, how frequently it updates, and how memorable they perceive it to be. We found that participants were generally uncomfortable with personal data being used for authentication and, knowing their personal data is used, they may intentionally change their behaviour due to privacy concerns. We discuss the benefits and drawbacks of using personal data as a source of dynamic tokens to complement authentication and conclude with three learned lessons.
Use of Virtual Reality in Psychiatric Diagnostic Assessments: A Systematic Review

Technological developments such as Virtual reality (VR) provide new opportunities to extend and innovate mental healthcare. VR as a tool for clinical assessment has been described as promising, as it can enable real-time assessment within real-like environments or contexts as opposed to self-report and behavioral tasks in laboratory settings.With this systematic review we aimed to provide an overview of recent studies using VR in the assessment of psychiatric disorders.A systematic search was performed using Pubmed, Embase, PsycInfo, and Web of Science between 2016 and 2020. Studies were included if they used immersive VR, concerned assessment of psychiatric symptoms/disorders, and included adult patients with psychiatric disorders.The search resulted in 3,163 potentially eligible articles, from which a total of 27 studies fulfilled inclusion criteria. Most studies considered anxiety (n = 7), addictive, (n = 7), or psychotic disorders (n = 5). Regarding ADHD (n = 3), PTSD (n = 3), and pedophilic disorder (n = 1), a few studies had been performed since 2016. The majority of the included studies compared patient groups to healthy control groups.Recent studies on VR-assisted psychiatric assessments have been conducted to validate VR environments, to assess symptoms for diagnostics or therapy goals, search for biomarkers, and to gain knowledge on psychiatric disorders. VR tasks were able to detect some difference between patient and healthy control groups, mainly with regard to self-report measures. Despite previous, promising prospects, the use of VR as a tool in clinical assessments must still be considered as a field in need of continued developments and evaluations.www.crd.york.ac.uk/prospero, identifier: CRD42021233772.
Learn to Cluster Faces via Pairwise Classification

Face clustering plays an essential role in exploiting massive unlabeled face data. Recently, graph-based face clustering methods are getting popular for their satisfying performances. However, they usually suffer from excessive memory consumption especially on large-scale graphs, and rely on empirical thresholds to determine the connectivities between samples in inference, which restricts their applications in various real-world scenes. To address such problems, in this paper, we explore face clustering from the pairwise angle. Specifically, we formulate the face clustering task as a pairwise relationship classification task, avoiding the memory-consuming learning on large-scale graphs. The classifier can directly determine the relationship between samples and is enhanced by taking advantage of the contextual information. Moreover, to further facilitate the efficiency of our method, we propose a rank-weighted density to guide the selection of pairs sent to the classifier. Experimental results demonstrate that our method achieves state-of-the-art performances on several public clustering benchmarks at the fastest speed and shows a great advantage in comparison with graph-based clustering methods on memory consumption.
Field-Guide-Inspired Zero-Shot Learning

Modern recognition systems require large amounts of supervision to achieve accuracy. Adapting to new domains requires significant data from experts, which is onerous and can become too expensive. Zero-shot learning requires an annotated set of attributes for a novel category. Annotating the full set of attributes for a novel category proves to be a tedious and expensive task in deployment. This is especially the case when the recognition domain is an expert domain. We introduce a new field-guide-inspired approach to zero-shot annotation where the learner model interactively asks for the most useful attributes that define a class. We evaluate our method on classification benchmarks with attribute annotations like CUB, SUN, and AWA2 and show that our model achieves the performance of a model with full annotations at the cost of significantly fewer number of annotations. Since the time of experts is precious, decreasing annotation cost can be very valuable for real-world deployment.
Non-Markovian Quantum Dynamics in a Squeezed Reservoir

We study non-Markovian dynamics of an open quantum system system interacting with a nonstationary squeezed bosonic reservoir. We derive exact and approximate descriptions for the open system dynamics. Focusing on the spin boson model, we compare exact dynamics with Redfield theory and a quantum optical master equation for both short and long time dynamics and in non-Markovian and Markov regimes. The squeezing of the bath results in asymptotic oscillations in the stationary state, which are captured faithfully by the Redfield master equation in the case of weak coupling. Furthermore, we find that the bath squeezing direction modifies the effective system-environment coupling strength and, thus, the strength of the dissipation.
HaT5: Hate Language Identification using Text-to-Text Transfer Transformer

We investigate the performance of a state-of-the-art (SoTA) architecture T5 (available on the SuperGLUE) and compare it with 3 other previous SoTA architectures across 5 different tasks from 2 relatively diverse datasets. The datasets are diverse in terms of the number and types of tasks they have. To improve performance, we augment the training data by using a new autoregressive conversational AI model checkpoint. We achieve near-SoTA results on a couple of the tasks - macro F1 scores of 81.66% for task A of the OLID 2019 dataset and 82.54% for task A of the hate speech and offensive content (HASOC) 2021 dataset, where SoTA are 82.9% and 83.05%, respectively. We perform error analysis and explain why one of the models (Bi-LSTM) makes the predictions it does by using a publicly available algorithm: Integrated Gradient (IG). This is because explainable artificial intelligence (XAI) is essential for earning the trust of users. The main contributions of this work are the implementation method of T5, which is discussed; the data augmentation, which brought performance improvements; and the revelation on the shortcomings of the HASOC 2021 dataset. The revelation shows the difficulties of poor data annotation by using a small set of examples where the T5 model made the correct predictions, even when the ground truth of the test set were incorrect (in our opinion). We also provide our model checkpoints on the HuggingFace hub <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> https://huggingface.co/sana-ngu/HaT5_augmentation https://huggingface.co/sana-ngu/HaT5.
A novel video recommendation system for algebra: An effectiveness evaluation study

This study presents a novel video recommendation system for an algebra virtual learning environment (VLE) that leverages ideas and methods from engagement measurement, item response theory, and reinforcement learning. Following Vygotsky's Zone of Proximal Development (ZPD) theory, but considering low affect and high affect students separately, we developed a system of five categories of video recommendations: 1) Watch new video; 2) Review current topic video with a new tutor; 3) Review segment of current video with current tutor; 4) Review segment of current video with a new tutor; 5) Watch next video in curriculum sequence. The category of recommendation was determined by student scores on a quiz and a sensor-free engagement detection model. New video recommendations (i.e., category 1) were selected based on a novel reinforcement learning algorithm that takes input from an item response theory model. The recommendation system was evaluated in a large field experiment, both before and after school closures due to the COVID-19 pandemic. The results show evidence of effectiveness of the video recommendation algorithm during the period of normal school operations, but the effect disappears after school closures. Implications for teacher orchestration of technology for normal classroom use and periods of school closure are discussed.
Does Cryptocurrency Hurt African Firms?

This paper aimed to assess the effect of the cryptocurrency market on firms’ market value, especially on the sectoral level, in Africa. To reach the study’s main goal, the authors adopted the Panel-Corrected Standard Errors (PCSEs) and Panel Double-Clustered Standard Errors (PDCSEs). Using firm-level data, the results of this study can be summarized as follows: (a) The cryptocurrency market hurts the firm market value in Africa. (b) The firms operating across different sectors respond disproportionally to the cryptocurrency market. For instance, the sectors that offer low returns in Africa (industrial, energy, financial) negatively respond to the cryptocurrency market, while the sectors that offer high returns (real estate and information technology) are not significantly affected. (c) The cryptocurrency market has a perverse effect on less experienced and highly indebted firms. (d) The consistent policies of governments to ban cryptocurrency do not work efficiently.
Image Registration Techniques and Applications: Comparative Study on Remote Sensing Imagery

Image registration is a crucial task in many computer vision applications. It is the process of matching and aligning two or more images of a scene. These images can be captured from different viewpoints, different sensors, or different times. Feature based image registration has four main steps: feature detection and description, feature matching, outliers rejection and computing homography and image re-sampling. Computational cost and registration accuracy of feature-based image registration mainly depend on the robustness of feature detection and description methods. Therefore, choosing an optimal feature detection and description method is vital in image registration applications. This research illustrates a comparison between popular image registration algorithms; Scale-invariant feature transform (SIFT), Speeded Up Robust Features (SURF), Oriented FAST and Rotated BRIEF (ORB), KAZE, Binary Robust Invariant Scalable Keypoints (BRISK) and Accelerated-KAZE (AKAZE) in different scenarios: rotation (0 to 360 degrees), scaling (25% to 600%) and multitemporal. The remote sensing images that are used in the experiments are Radar images, Aerial images, and Unmanned Aerial Vehicle (UAV) images. Nearest Neighbour Distance Ratio (NNDR) is performed in the feature matching, whereas RANSAC is applied to reject the outliers matching. The results of the experiments show that SIFT outperforms other algorithms, showing strong stability and high precision in all scenarios. As for real-time application, ORB performs well, and it is the fastest algorithm for all scenarios and then AKAZE as the second fastest one.
Usability Measurement in User Interface Design Using Heuristic Evaluation &amp; Severity Rating (Case Study: Mobile TA Application based on MVVM)

Each User interface design needs to be tested, one of which is by using heuristic evaluation. Heuristic evaluation is used to test ten usability aspects of the application and a severity rating to determine the magnitude of the user interface problems of the application. There are problems with usability in the TA mobile application. It was starting from the current user interface display, which is unresponsive (88%). Furthermore, some icons do not match the icon name and information (83%). There is an accumulation of information on one page, especially on the home page, where users need excessive scrolling (72%). So it is necessary to redesign the user interface design in order to meet user needs. This study aims to determine the suitability of the TA trial mobile application with user requirements through requirements elicitation (observation and questionnaires) and testing with heuristic evaluation & severity rating to measure the usability of the TA mobile application. This research has produced seven Functional Requirements, four Non-Functional Requirements, and Use Case diagrams involving three actors and eleven Use Cases. In addition, for the MVVM implementation, we have implemented interface design, for example, applied to the LoginAction class to avoid using ENUM. The implementation of View on ‘SignInButton,’ ‘usernameView,’ and ‘passwordView’ to create actions and states, and implementation of RxViewModel as the basis of all ViewMode. In the final stage of this research, usability and severity measurements have been successfully carried out, which are included in the category with a rating = 1 (cosmetic problem).
Driving information communication technology for tax revenue mobilization in Sub-Saharan Africa

This study explores whether increasing Information and Technology Communication (ICT) boosts government revenue mobilization for sustainable development in 48 Sub-Saharan African countries from 2004 to 2020. While total tax revenue non-resource as a percentage of GDP and tax revenue as a percentage of GDP are used to proxy for tax revenue mobilization, three ICT measures are used, namely; the telephone penetration rate, the mobile phone penetration rate and internet penetration rate. To perform the analysis, we adopt the Generalized Method of Moments (GMM). The empirical findings are as follows. First, while the calculated net impacts are substantially positive, the corresponding marginal ICT effects utilized for calculating net effects are extremely negative. Second, an extensive study is carried out to determine complementing policy thresholds. These thresholds include: 21.959 (per 100 people) telephone penetration for total income from tax revenue; 16.333 (per 100 people) internet penetration for total income from tax; 21.125 internet penetration (per 100 people) for the income from the tax on non-resource income. This study has policy relevance, and implications as the penetration of the ICT rate can be influenced by policies to mobilize government revenue effectively.
Inner Knowledge-based Img2Doc Scheme for Visual Question Answering

Visual Question Answering (VQA) is a research topic of significant interest at the intersection of computer vision and natural language understanding. Recent research indicates that attributes and knowledge can effectively improve performance for both image captioning and VQA. In this article, an inner knowledge-based Img2Doc algorithm for VQA is presented. The inner knowledge is characterized as the inner attribute relationship in visual images. In addition to using an attribute network for inner knowledge-based image representation, VQA scheme is associated with a question-guided Doc2Vec method for question–answering. The attribute network generates inner knowledge-based features for visual images, while a novel question-guided Doc2Vec method aims at converting natural language text to vector features. After the vector features are extracted, they are combined with visual image features into a classifier to provide an answer. Based on our model, the VQA problem is resolved by textual question answering. The experimental results demonstrate that the proposed method achieves superior performance on multiple benchmark datasets.
Bangla Food Review Sentimental Analysis using Machine Learning

In this modern age, people are dependent on the internet. They prefer to order food online or Food App rather than the restaurant. They are giving various reviews online about the foods. In this project, we aim to build a machine learning model to analyze the sentiment of that reviews. In Bangladesh, internet users are increasing day by day. So we have decided to build the model for the Bangla language. We have found no Bangla dataset for food reviews that we can use for our project. Then we have collected more than one thousand Bangla food reviews from various online platforms like Foodpanda, Hungrynaki, Shohoz food, Pathao food, etc., and labeled them. After some necessary preprocessing, we have extracted various features from cleaned data and used them to train and test for machine learning and deep learning models. We have come to the result that Long Term Short Term (LSTM), a deep learning model giving the best accuracy, that is 90.89%, where we have used word2sequence as feature extraction. Our research contribution will help the food industry by using this model. This model can help them to understand the Bangla food review sentiment.
Inertial Navigation System Based Vehicle Temporal Relative Localization With Split Covariance Intersection Filter

In autonomous vehicle navigation, it usually involves a basic process of estimating the vehicle pose at one instant relative to the vehicle pose at a previous instant, and we refer to this process as temporal relative localization (TRL). Accurate and reliable TRL is valuable for vehicle localization and mapping which plays a fundamental role in autonomous vehicle navigation. A common way for realizing TRL is dead reckoning based on motion sensor such as inertial navigation system (INS). To augment the performance of INS based TRL, a nine-degree-of-freedom (9-DOF) vehicle dynamics model (VDM) is proposed as the system model for the filtering process of TRL; it can effectively characterize vehicle pitch and roll motion, especially in slope road scenarios. For better fault tolerance, a distributed fusion framework which consists of a master filter and two local filters for multi-source data fusion is introduced. The split covariance intersection filter (Split CIF) is applied to handle unknown temporal and spatial correlation that inevitably exists in such distributed fusion mechanism. A comparative experimental study demonstrates the advantage of the proposed method in terms of accuracy and robustness.
Comparative Study of Human Skin Detection Using Object Detection Based on Transfer Learning

With the increasing aging of the population, the design of automatic bath robot has the forward-looking significance. The robot needs to detect the skin position, so as to perform the bathing task. The perception of skin is the key technology to achieve the bathing task. In this paper, object detection is used to identify the skin, which provides reference information for the pose of the robot. According to the classification of the object detection algorithms, this paper selects four typical object detection algorithms, namely, Faster R-CNN, YOLOv3, YOLOv4 and CenterNet. Due to the limitation of the self-built data set, this paper adopts the transfer learning to promote the completion of new tasks, which takes the pre-trained model as the starting point. The experimental results show that the detection results of YOLOv4 is the best, with mAP of 78%. This paper proves the feasibility and effectiveness of object detection completing the human skin detection in the bathing task.
Model-based identification of eating behavioral patterns in populations with type 1 diabetes

In the paper, a probabilistic model of the eating behavior of subjects with type 1 diabetes is proposed and validated against two extensive datasets collected during experiments in free-living conditions in Padova and Amsterdam clinical centers. Meals are modeled as a discrete-time marked point process. The random meal events are associated to a Markov Chain whose state is the fasting period and the transition probabilities depend on variables such as daytime and carbohydrate intake of the previous meal. The random marks associated to each meal represent the carbohydrate intake and their distribution possibly depends on daytime, fasting period and carbohydrate intake of the previous meal. Logistic and Gaussian Mixture models are used to identify two models from the Padova and Amsterdam datasets, respectively. In order to validate it, the proposed model is used to simulate synthetic datasets, whose statistical properties appear to be in good agreement with those of the experimental datasets. The availability of a probabilistic model of eating behavior is expected to be valuable for several purposes ranging from the optimization and customization of automatic insulin dosing systems to decision supporting tools for insulin dosing and the alert management for missing meal announcements.
An Improved Quantum Private Set Intersection Protocol Based on Hadamard Gates

Recently, Liu and Yin (Int. J. Theor. Phys. 60, 2074-2083 (2021)) proposed a two-party private set intersection protocol based on quantum Fourier transform. We find the participant can deduce the other party’s private information, which violates the security requirement of private set computation. In order to solve this problem, an improved private set intersection protocol based on Hadamard gate is proposed. Firstly, the more feasible Hadamard gates are used to perform on the original n qubits instead of the quantum Fourier transform, which may reduce the difficulty of implementation. In addition, through the exclusive OR calculation, the participant’s private information is randomly chosen and encoded on the additional n qubits, which prevents participants from obtaining the result of the difference set Sdiff, and then avoids the internal leakage of private information. Finally, the correctness and security analysis are conducted to show the proposed protocol can guarantee the correctness of computation result as well as resist outside attacks and participant internal attacks.