Reviewerly/exHarmony huggingface dataset:
Monitoring neurological disorders with AI-enabled wearable systems

The age distribution has changed in Europe over the last decade. The group of 45 year-olds and above has increased and the median age in the EU is estimated to increase by 4.5 years during the next 3 decades reaching a median age of approximately 48.2 years according to Eurostat. A similar trend is noticeable in the United States, where the median age increased by 3.3 years from 2000 to 2020 according to Statista. Neurological diseases, such as Huntington disease, have a highly variable onset of 30 - 50 years but they are more prevalent to the older population. One of the first observable physical symptoms is chorea which includes random, uncontrollable and involuntary movements. Internet of Things and wearable systems can assist long-term monitoring of digital biomarkers such as plantar pressure and gait pattern which are associated with the aforementioned neurological disease. Emerging artificial intelligence models can be utilized to monitor the related digital biomarkers and check if these demonstrate a potential pattern denoting the presence or the development of a neurological disease. Enabling long-term monitoring by utilizing a unobtrusive wearable will increase the possibilities of early diagnosis, a longer life expectancy, and an improved quality of life for the patient.
Constructing membership function systems using the middle hedge operator

Here, we present an operator-dependent, analytic membership function family that is derived from two soft inequalities by using the so-called middle hedge operator. This operator is defined over a pair of a strictly increasing and a strictly decreasing parametric membership functions that represent two soft inequalities. The middle hedge operator can also be treated as a membership function of an interval on a bounded or unbounded domain. Owing to its construction, the values of this membership function at the end points of the interval, which it represents, are equal to a given parameter value. This parameter may be interpreted as an intersection parameter. That is, if a fuzzy partition of a bounded or unbounded interval is created by using middle hedge operators with a fixed value of the intersection parameter, then each two successive membership functions in the fuzzy partition intersect at the same level. Here, we demonstrate that this new membership function family is very flexible and it can be readily used for constructing a membership function system in a fuzzy control system.
Recommender systems, ground truth, and preference pollution

Abstract Interactions between individuals and recommender systems can be viewed as a continuous feedback loop, consisting of pre‐consumption and post‐consumption phases. Pre‐consumption, systems provide recommendations that are typically based on predictions of user preferences. They represent a valuable service for both providers and users as decision aids. After item consumption, the user provides post‐consumption feedback (e.g., a preference rating) to the system, often used to improve the system's subsequent recommendations, completing the feedback loop. There is a growing understanding that this feedback loop can be a significant source of unintended consequences, introducing decision‐making biases that can affect the quality of the “ground truth” preference data, which serves as the key input to modern recommender systems. This paper highlights two forms of bias that recommender systems inherently inflict on the “ground truth” preference data collected from users after item consumption: non‐representativeness of such preference data and so‐called “preference pollution,” which denotes an unintended relationship between system recommendations and the user's post‐consumption preference ratings. We provide an overview of these issues and their importance for the design and application of next‐generation recommendation systems, including directions for future research.
We Can Make Mistakes: Fault-tolerant Forward Private Verifiable Dynamic Searchable Symmetric Encryption

Verifiable Dynamic Searchable Symmetric Encryption (VDSSE) enables users to securely outsource databases (document sets) to cloud servers and perform searches and updates. The verifiability property prevents users from accepting incorrect search results returned by a malicious server. However, we discover that the community currently only focuses on preventing malicious behavior from the server but ignores incorrect updates from the client, which are very likely to happen since there is no record on the client to check. Indeed most existing VDSSE schemes are not sufficient to tolerate incorrect updates from the client. For instance, deleting a nonexistent keyword-identifier pair can break their correctness and soundness. In this paper, we demonstrate the vulnerabilities of a type of existing VDSSE schemes that fail them to ensure correctness and soundness properties on incorrect updates. We propose an efficient fault-tolerant solution that can consider any DSSE scheme as a black-box and make them into a fault-tolerant VDSSE in the malicious model. Forward privacy is an important property of DSSE that prevents the server from linking an update operation to previous search queries. Our approach can also make any forward secure DSSE scheme into a fault-tolerant VDSSE without breaking the forward security guarantee. In this work, we take FAST [1] (TDSC 2020), a forward secure DSSE, as an example, implement a prototype of our solution, and evaluate its performance. Even when compared with the previous fastest forward private construction that does not support fault tolerance, the experiments show that our construction saves 9× client storage and has better search and update efficiency.
SVG Vector Font Generation for Chinese Characters with Transformer

Designing fonts for Chinese characters is highly labor-intensive and time-consuming. While the latest methods successfully generate the English alphabet vector font, despite the high demand for automatic font generation, Chinese vector font generation has been an unsolved problem owing to its complex shape and numerous characters. This study addressed the problem of automatically generating Chinese vector fonts from only a single style and content reference. We proposed a novel network architecture with Transformer and loss functions to capture structural features without differentiable rendering. Although the dataset range was still limited to the sans-serif family, we successfully generated the Chinese vector font for the first time using the proposed method.
Walking Position Data in Football Training Based on Embedded Action Recognition System

With the development of image recognition and pattern recognition, action recognition has become a hot research direction in the field of computer actions. Because the embedded platform has the advantages of small size and low power consumption, it is a good choice to use the embedded platform for action recognition. These sensor data are used to overcome the drift error of the gyroscope when the positioning data are collected, and the accuracy and reliability of the data are improved. The system uses two data forwarding nodes to aggregate the data of each sensor and then transmit it to the host computer. The data aggregation node can communicate and upload data by means of Bluetooth or wire. In order to deeply study the relevance of the embedded action recognition system in collecting football training positioning data, this paper uses simulation model establishment method, data collection method, and theory and practice combination method to collect samples, analyzes the embedded action recognition system, and streamlines the algorithm. Using computer technology as a support when building a simulation model, the first thing you need to have is a certain computer technology because when building a simulation model, not all objects can be modeled. At this time, only computer technology can be used as a support to build a simulation model of some objects and create an action recognition system that can record the position data in football training. After establishing the human body simulation recognition model, use MATLAB to extract the K bone information. When collecting, the human body is facing the K device, the number of acquisition frames is 25, 55, 75, 95, 105, and 205, and each frame number is collected 10 times. Take the average. The delay time is 4s. The result shows that the 20 key bone point outputs by K come from the RGB camera on the same side. Further study the actual utility of the compensated model in the presence of occlusion. They are worn on the middle of the thigh and calf, respectively. In a sensor, the measured value of thigh length L1 is 0.6 m, and the value of calf length L2 is 0.4 m. Take the right knee as an example. When the leg is raised, the b-axis coordinate increases by 2%, and the c-axis coordinate decreases by 1.8%. When the leg is lowered, the opposite is true. It can be seen that the compensated coordinate is consistent with the action. It is basically realized that starting from the embedded action recognition system, a model that can support the analysis of football positioning data is designed.
Synwmd: Syntax-Aware Word Mover's Distance for Sentence Similarity Evaluation

Word Mover’s Distance (WMD) computes the distance between words and models text similarity with the moving cost between words in two text sequences. Yet, it does not offer good performance in sentence similarity evaluation since it does not incorporate word importance and fails to take inherent contextual and structural information in a sentence into account. An improved WMD method using the syntactic parse tree, called Syntax-aware Word Mover’s Distance (SynWMD), is proposed to address these two shortcomings in this work. First, a weighted graph is built upon the word co-occurrence statistics extracted from the syntactic parse trees of sentences. The importance of each word is inferred from graph connectivities. Second, the local syntactic parsing structure of words is considered in computing the distance between words. To demonstrate the effectiveness of the proposed SynWMD, we conduct experiments on 6 textual semantic similarity (STS) datasets and 4 sentence classification datasets. Experimental results show that SynWMD achieves state-of-the-art performance on STS tasks. It also outperforms other WMD-based methods on sentence classification tasks.
Trainable Communication Systems Based on the Binary Neural Network

End-to-end learning of the communication system regards the transmitter, channel, and receiver as a neural network-based autoencoder. This approach enables joint optimization of both the transmitter and receiver and can learn to communicate more efficiently than model-based ones. Despite the achieved success, high complexity is the major disadvantage that hinders its further development, while low-precision compression such as one-bit quantization is an effective solution. This study proposed an autoencoder communication system composed of binary neural networks (BNNs), which is based on bit operations and has a great potential to be applied to hardware platforms with very limited computing resources such as FPGAs. Several modifications are explored to further improve the performance. Experiments showed that the proposed BNN-based system can achieve a performance similar to that of the existing neural network-based autoencoder systems while largely reducing the storage and computation complexities.
CS:NO – an Extended Reality Experience for Cyber Security Education

This work-in-progress presents the design of an XR prototype for the purpose of educating basic cybersecurity concepts. We have designed an experimental virtual reality cyberspace to visualise data traffic over network, enabling the user to interact with VR representations of data packets. Our objective was to help the user better conceptualise abstract cybersecurity topics such as encryption and decryption, firewall and malicious data. Additionally, to better stimuli the sense of immersion we have used Peltier thermoelectric modules and Arduino Uno to experiment with multisensory XR. Furthermore, we reflect on early evaluation of this experimental prototype and present potential paths for future improvements.
Data Verification of Logical Pk-Anonymization with Big Data Application and Key Generation in Cloud Computing

Background. As more data becomes available about how frequently the cloud can be updated, a more comprehensive picture of its safety is emerging. The suggested artworks use a cloud-based gradual clustering device to cluster and refresh a large number of informational indexes in a useful manner. Purpose. Anonymization of data is done at the point of collection in order to safeguard the data. More secure than K-Anonymization, Pk-Anonymization is the area’s first randomization method. A cloud service provider (CSP) is an independent company that provides a cloud-based network and computing resources. Customers’ security and connection protection must be verified by an authority before facts may be transferred to cloud servers for storing information. Method. Logical Pk-Anonymization and key era techniques are proposed in this proposed artwork in order to verify the cloud records, as well as to store sensitive information in the cloud. Cloud-based informational indexes are used in the proposed framework, which is effective at handling large amounts of data through MapReduce; a parallel data preparation form is obtained; to get all information as new facts that joins after a while, information anonymization techniques to carry out each protection and immoderate information utilization while updating take place; information loss and clean time is reduced for substantial amounts of data. As a result, the safety and records software might be in sync.
Rectified Wasserstein Generative Adversarial Networks for Perceptual Image Restoration

Wasserstein generative adversarial network (WGAN) has attracted great attention due to its solid mathematical background, i.e., to minimize the Wasserstein distance between the generated distribution and the distribution of interest. In WGAN, the Wasserstein distance is quantitatively evaluated by the discriminator, also known as the critic. The vanilla WGAN trained the critic with the simple Lipschitz condition, which was later shown less effective for modeling complex distributions, like the distribution of natural images. We try to improve the WGAN training by introducing pairwise constraint on the critic, oriented to image restoration tasks. In principle, pairwise constraint is to suggest the critic assign a higher rating to the original (real) image than to the restored (generated) image, as long as such a pair of images are available. We show that such pairwise constraint may be implemented by rectifying the gradients in WGAN training, which leads to the proposed rectified Wasserstein generative adversarial network (ReWaGAN). In addition, we build interesting connections between ReWaGAN and the perception-distortion tradeoff. We verify ReWaGAN on two representative image restoration tasks: single image super-resolution (4× and 8×) and compression artifact reduction, where our ReWaGAN not only beats the vanilla WGAN consistently, but also outperforms the state-of-the-art perceptual quality-oriented methods significantly. Our code and models are publicly available at https://github.com/mahaichuan/ReWaGAN.
Differentially Private Real-Time Release of Sequential Data

Many data analytics applications rely on temporal data, generated (and possibly acquired) sequentially for online analysis. How to release this type of data in a privacy-preserving manner is of great interest and more challenging than releasing one-time, static data. Because of the (potentially strong) temporal correlation within the data sequence, the overall privacy loss can accumulate significantly over time; an attacker with statistical knowledge of the correlation can be particularly hard to defend against. An idea that has been explored in the literature to mitigate this problem is to factor this correlation into the perturbation/noise mechanism. Existing work, however, either focuses on the offline setting (where perturbation is designed and introduced after the entire sequence has become available), or requires a priori information on the correlation in generating perturbation. In this study we propose an approach where the correlation is learned as the sequence is generated, and is used for estimating future data in the sequence. This estimate then drives the generation of the noisy released data. This method allows us to design better perturbation and is suitable for real-time operations. Using the notion of differential privacy, we show this approach achieves high accuracy with lower privacy loss compared to existing methods.
Virtual Ornament Room Using Haar Cascade Algorithm During Pandemic

Generally, purchaser wishes to attempt all jewels on them and share those photos on social media to get a recommendation. But now, in the present situation, all are avoiding touching activities unnecessarily. It is not so safe due to COVID-19. So without stopping purchaser jewellery attempts, a solution is provided by using an application called virtual ornament room. The proposed scheme is based on creating a Web application that identifies where the human face is located in the frame and superimposes the chosen jewellery on the face using the HAAR Cascade Algorithm. By using the concepts of augmented reality, the digital objects are estimated and placed onto the frame in real time. The system is implemented by using Flask framework and OpenCV, a Python module. This application works with an attached camera, Internet, and a Web browser. From the results, it can be seen all the jewellery can be easily selected by the customers from their home itself during the pandemic situation.
Smartwatch activity recognition feature comparison using ML.net

Smart environments are more accessible than ever and are created by a synergy of different devices that provide smart capabilities and can work together. Activity recognition is an important part of the smart environment, as it allows the system to know what activities are the occupants doing. One of the simplest options to do activity recognition is to use body-worn sensors, and the smartwatch, in this case, proves to be a good candidate. Our objective is to compare the precision of the activity recognition process using accelerometer or gyroscope extracted features data. For this, we use the ML.net machine learning framework to analyze 18 different activities.
Scene Construction and Application of Panoramic Virtual Simulation in Interactive Dance Teaching Based on Artificial Intelligence Technology

In recent years, with the rapid development of human motion simulation technology and virtual simulation technology, natural human-computer interaction has become the main form of research in the computer industry. Due to the research capabilities and practical value of mobile video technology, it is widely used in advanced fields, such as video animation production, rehabilitation medicine, sports training, and game software, effectively realizing the connection between the three-dimensional world and the simulation world. However, the application of motion imaging technology in teaching activities is not perfect. This article is based on dance teaching, adopts a dance movement analysis method based on vector matching, and uses artificial intelligence technology to create a virtual panoramic scene. Experimental data show that the average recognition rate of gestures is 89.8%, which can meet the gesture switching interaction of working conditions in virtual simulation of equipment operation.
Hand Sanitizers in the Wild: A Large-scale Study of Custom JavaScript Sanitizer Functions

Despite the considerable amounts of resources invested into securing the Web, Cross-Site Scripting (XSS) is still widespread. This is especially true for Client-Side XSS as, unlike server-side application frameworks, Web browsers do not ship with standard protection routines, so-called sanitizers. Web developers, therefore, have to either resort to third-party libraries or write their own sanitizers to stop XSS in its tracks. Such custom sanitizer routines – dubbed hand sanitizers in the following – are notoriously difficult to implement securely. In this paper, we present a technique to automatically detect, extract, analyze, and validate JavaScript sanitizer functions using a combination of taint tracking and symbolic string analysis. While existing work evaluates server-side sanitizers using a small number of applications, we present the first large-scale study of client-side JavaScript sanitizers. Of the most popular 20,000 websites, our method detects 705 unique sanitizers across 1,415 domains, of which 12.5% are insecure. Of the vulnerable sanitizers, we were able to automatically generate circumventing exploits for 51.3% of them, highlighting the dangers of manual sanitization attempts. Interestingly, vulnerable sanitizers are present across the entire range of website rankings considered, and we find that most sanitizers are not generic enough to thwart XSS if used in just a slightly different context. Finally, we explore the origins of vulnerable sanitizers to motivate adopting a standardized sanitization API available directly in the browser.
Kintsugi VR: Designing with Fractured Objects

This paper presents Fractured Objects for the design of virtual and mixed-reality experiences. Drawing on the qualitative analysis of three weeks of artistic activities within a residency program, we present six types of Fractured Objects that were used in sketching a mixed-reality performance. Building on these Fractured Objects, as they were articulated by the artists, we present speculative designs for their use in scenarios inspired by research within the IMX community. In discussion, we look to expand the concept of Fractured Objects by relating it to other design concepts such as Seamful Design and Wabi-Sabi, and explore the relationship to the temporality of interaction. We introduce Kintsugi VR with Fractured Objects, drawing on the concept of 'golden repair' in which the act of reconnecting fractured parts improves the resulting whole object.