Reviewerly/exHarmony huggingface dataset:
Cauruler: Causal Irredundant Association Rule Miner for Clinical Relationships

Discovering causal associations between variables is one of the main goals of most clinical trials, with the ultimate aim of identifying possible causes of a specific health status. Prior knowledge of causal paths could be helpful in ensuring patients do not develop the resultant conditions. Randomized controlled trials (RCTs) have historically been used to identify causal associations in a sub-population representing the associated conditions, based on a clinical hypothesis derived from the experience and observations of physicians. In recent years, thanks to the enormous amount of health data which has been gathered, attempts have been made to employ Machine Learning (ML) and Artificial Intelligence (AI) to identify associations between attributes. Some of these techniques have gone beyond trying to infer causality from these associations and have obtained encouraging results. Such methodologies present a series of benefits over preceding ones such as the deployment of observational data and time and resources savings. Nevertheless, they suffer from certain shortcomings when controlling for confounders in the analysis phase, and in their ability to provide causal rules which are sufficiently general to be useful in healthcare practice. Conversely, this work presents a new approach, CauRuler, to deal with causality by using a new method of reducing the number of rules to be generated, while controlling the confounding variables and generating the most general causal rules. CauRuler has been tested on a complex medical database in order to evaluate its functionality against the state-of-the-art algorithms. The algorithm has already proven its efficiency and effectiveness in targeting causal associations, thus reaching a broad consensus in the medical community.
ALAMNI: Adaptive LookAside Memory based Near-Memory Inference Engine for Eliminating Multiplications in Real-Time

The CNN algorithm seeks high performance and energy efficiency in real-time inference. The costly off-chip memory accesses put additional burdens on CNN's execution. Towards avoiding off-chip accesses, we propose ALAMNI, a novel near-memory architecture that expedites the CNNs in the logic layer of the Hybrid Memory Cube. We exploit intra- and inter-vault parallelism to accelerate the highly parallel CNN operations. The proposed ALAMNI replaces costly multiplications of CNNs with lookaside memory (LAM) based searches. The proposed ALAMNI policy is effective on unseen data as it discards the data pre-profiling overhead by an adaptive LAM update policy. The ALAMNI controller keeps the most frequent triplets of weight (W), activation (A), and multiplication result (M), <W, A, M>, in the LAM to eliminate redundant computations. As an optimization, we incorporate a bitmasking concept to raise the hit rate of LAMs and further amortize computations. We also present a study on the relation between the amount of bitmasking and the loss of classification accuracy of the popular ConvNets. We keep the bitmasking as a reconfigurable feature of the ALAMNI units to achieve desired classification accuracy. Experimental results show substantial improvement in the system's performance and energy efficiency compared to the baseline and state-of-the-art.
Research of an Intelligent System for Face Recognition on Embedded Platforms with Limited Computing Power

Today, computer vision technologies are used in many areas of human activity. There are many methods for extracting faces in the original image, the most promising of which is the use of algorithms based on neural networks. The aim of the study was to design and test an intelligent system for face recognition on embedded platforms with limited computing power. In the course of a preliminary analysis of machine learning methods for solving the problem, it was found that it is most expedient to use machine learning methods based on the analysis of facial micro-motions with the subsequent construction of a map of points. Based on the data obtained, a face identification system was developed using computer vision technology, which was based on a method for creating complex architectures using various features with additional algorithms. A distinctive feature of the developed intelligent system is the ability to analyze several frames that confirm micro-movements of the head or blinking. As a result of testing the resulting system using the gradient boosting algorithm for regression trees, a map of 68 face points was obtained, on the basis of which human faces were identified with objects from the database.
An Antigravity-Based Fuzzy Gravitational Search Algorithm for Economic Dispatch Problems

This paper presents an antigravity-based fuzzy Gravitational Search Algorithm (AFGSA) to solve the economic dispatch problem (EDP) with various constraints. Based on an antigravity mechanism combined with an adaptive fuzzy parameter controller AFGSA is designed. By doing so, it ensures a proper balance between exploration and exploitation of GSA. Furthermore, the simulated annealing (SA) process is introduced to control the application of anti-gravitation forces and guarantee the convergence rate. A fuzzy optimizing system is applied to adaptively control the parameters in GSA. The success of the suggested algorithm is firstly shown in eight various test EDPs including static and dynamic economic load problems as well as hydrothermal scheduling problems by varying the complex constraints. Then, the efficiency of the proposed optimizer is validated through ten CEC2020 test functions. The performance of AFGSA is verified by comparison with numerous state-of-the-art techniques. The experimental results show that AFGSA provides a more competing and robust solution to the EDPs than the compared algorithms.
Modeling the effects of network size in a deception game involving honeypots

Cyberattacks, an intentional effort to capture information or interrupt a network, are growing. Prior research in cybersecurity has investigated the influence of network size on adversarial decisions in a deception game involving honeypots experimentally. However, little is known about the cognitive mechanisms that modulate the influence of network size on adversarial decisions. The primary objective of this research is to investigate how an instance-based learning(IBL) model involving recency, frequency, and cognitive noise would make predictions about adversarial decisions in the presence of networks of different sizes. The experimental study involved the use of a deception game (DG) across three between-subjects conditions of different network sizes: small, medium, and large (N = 20 per condition). The results revealed that the proportion of honeypot and regular probes and attacks were more in the medium-sized and large-sized networks compared to small-sized networks. Similarly, the proportion of no-probe and no-attack actions were more in small-sized networks compared to medium- and large-sized networks. An IBL model was calibrated to the human decisions collected in the above experiment. An IBL model with ACT-R default parameters was also developed as a baseline. Results revealed that the IBL model with calibrated parameters explained adversary's decisions more accurately compared to the IBL model with ACT-R default parameters. Also, participants showed a greater reliance on recency and frequency of outcomes and smaller cognitive noise in their decision choices across three different network sizes. We highlight the main implications of our findings for the cognitive modeling community.
Towards an Understanding of Student Digital Ecosystems for Education

Today, it is still unclear to what degree and in what ways students have embraced information and communication technologies for educational purposes. For this reason, curriculum designers remain uncertain what the impact these technologies are having in the modern classroom. At the same time, educators are faced with a diverse array of available devices, which students seem to effortlessly move between. These shifting and blurred boundaries that determine device use have added to the already complex blending of traditional and nontraditional learning environments. All of this creates a significant challenge to those who recognize the inherent positive affordances offered by today’s technologies but are unsure of how they can be incorporated into the curriculum. The goal of this chapter is to shed light on this issue and take an initial step towards mapping students’ digital ecosystems to enable course design that better fits student learning expectations. Meaningful contextualization of technologies and learning environments requires a clear “map” of the learners’ current technological adoption and the learners’ perceived usefulness of those technologies to specific learning contexts. In this chapter, methods were employed to obtain a better understanding of the participants’ degree of technological adoption and practice in relation to collaborative educational activities. Insight into the structure of Japanese university students’ digital ecosystems were developed by first capturing participant activity in log data. Then Kernel-Based Principal Component Analysis (KPCA) was applied to data on read and post activities over one academic year. There were several key findings that may inform curriculum designers. First, the frequency of read access relative to post access fluctuated significantly after the first few activities. Second, there was a consistent decrease in all online activity over the entire academic year. Third, the number of read accesses was always greater than post assesses over all the activities. Fourth, and of particular interest, students appeared to favor mobile and non-mobile devices at different times during the activities, which suggests they are selectively utilizing different affordances. If this is the case, it would be an insight into how they are shifting between device types depending on the context and purpose. The overall results, as well as implications for education, were discussed, along with suggestions for future research.
Tabu-Adaptive Artificial Bee Colony Metaheuristic for Image Segmentation

This paper proposes to enhance the Artificial Bee Colony (ABC) metaheuristic with a Tabu adaptive memory to optimize the multilevel thresholding for Image Segmentation. This novel method is named Tabu-Adaptive Artificial Bee Colony (TA-ABC). To find the optimal thresholds, two novel versions of the proposed technique named TA-ABC-BCV and TA-ABC-ET are developed using respectively the thresholding functions namely the Between-Class Variance (BCV) and the Entropy Thresholding (ET). To prove the robustness and performance of the proposed methods TA-ABC-BCV and TA-ABC-ET, several benchmark images taken from the USC-SIPI Image Database are used. The experimental results show that TA-ABC-BCV and TA-ABC-ET outperform other existing optimization algorithms in the literature. Besides, compared to TA-ABC-ET and other methods from the literature all experimental results prove the superiority of TA-ABC-BCV.
Online and Real-Time Mask-Guided Multi-Object Tracking and Segmentation

Multi-object tracking and segmentation(MOTS) can be mainly used for autonomous driving and video surveillance systems. For this, the tracking system must be able to track in real-time and online. However, recent studies to improve the tracking performance of MOTS have been actively conducted, but studies that do not consider online and real-time performance have been mainly conducted. This paper proposes a MOTS system that operates in real-time using a deep learning-based model with a light backbone and can be tracked online. Additionally, state-of-the-art trackers use an object's bounding box to extract re-identification(ReID) features, which includes unnecessary background. However, this causes confusion in accurately expressing the appearance feature of an object, which causes difficulty in matching. To solve this difficulty, instead of providing the feature of the bounding box as the input of the ReID branch, we focused on expressing the object except for the background by providing the mask feature of the object as an input. As a result of the mask-based ReID experiment, the association accuracy performance was higher than that of the existing bounding box based ReID model, and among the KITTI MOTS benchmarks, it ranked second among models that can operate online. Our experiments show that background information causes ambiguous ReID matching in MOT systems, and at the same time show that object mask information is important to avoid ambiguous matching.
Ppnnp: A Privacy-Preserving Neural Network Prediction with Separated Data Providers Using Multi-Client Inner-Product Encryption

In a neural network of deep learning, it needs a series of algorithms that endeavor to recognize underlying relationships in a set of data. To protect the privacy of user dataset, traditional schemes can perform the prediction task with setting only single data provider in the system. However, the data may come from multiple separated data providers rather than single data source in real world since each data provider might hold partial features of a complete prediction sample. It requires that multiple data providers cooperate to perform the prediction for the neural networks by sending their own local data to a well-trained prediction model deployed on a remote cloud server to obtain a predictive label. However, the data owned by multiple data providers usually contain a large amount of private information which can lead to serious security problems once leaked. To resolve the security and privacy issues of the data owned by multiple data providers, this paper propose a Privacy-Preserving Neural Network Prediction model (PPNNP) that deploys multi-client inner-product functional encryption to the first layer of prediction model. Multiple data providers encrypt their data and upload it to a well-trained model deployed on cloud server and the server make prediction by calculating inner-products related to them. It can provide sufficient privacy and security for the data while deploying different neural network architectures with activation functions even non-linear on remote server. We evaluated our scheme based on the real datasets and provide a comparison with related scheme. Experimental results demonstrate that our scheme can reduce the computational cost of the whole process while significantly reducing the encryption time. It can obtain the accuracy over 90% in different network architectures with even non-linear activation function. Meanwhile, our solution can reduce communication overhead in whole protocol.
Relating Biometric Sensors with Serious Games Results

With the growing amount of data, it is necessary to create methods that facilitate its processing and that allow a more efficient knowledge extraction from them. The main objective of the project here discussed is to correlate the performance of a patient while playing a serious game with the data collected, at the same time, by an eye tracker and the emotions detected by the Face API of Microsoft Azure suite after processing the photos collected by a webcam. Using analysis algorithms and adequate data visualisations, it is intended to discover new relationships that connect the game results to biometric data and create new knowledge. Through the eye movement and facial expressions, collected while game playing, it is possible to show that children with memory deficit have a slower processing speed, fix more times, make more regressions, and they have more disposal to express an emotion of sadness or surprise.
Are HTTPS Configurations Still a Challenge?: Validating Theories of Administrators’ Difficulties with TLS Configurations

HTTPS has been the standard for securing online communications for over 20 years. Despite the availability of tools to make the configuration process easier (e.g., Let's Encrypt, Certbot), SSL Pulse scans show that still more than 50% of the most popular websites are poorly configured, which emphasizes room for improvement. Although a few recent studies looked at the remaining challenges for administrators in configuring HTTPS from a qualitative perspective, there is little work that produced quantitative results. Therefore, we conducted a survey with 96 experienced administrators (as opposed to a student sample) to investigate to which extent configuration problems revealed in prior studies actually exist in the wild. Our results confirm that Let's Encrypt and ACME clients, such as Certbot, simplify configuration and maintenance for administrators, thus increasing the security of HTTPS configurations. Moreover, we extend the current body of work by examining the trust administrators put into Let's Encrypt and Certbot. We found that trust and usability issues are currently barriers to the widespread adoption of Certbot.
Correlation Power Analysis and Protected Implementation on Block Cipher RainDrop

RainDrop is a lightweight block cipher algorithm proposed in 2019. Even though RainDrop proved to be resistant to classical cipher analysis such as differential analysis and liner analysis, little effort has been made to assess its implementation security of side-channel analysis. In this paper, we first give correlation power analysis on RainDrop and a method to recover the main key by using the round key. And we validate their effect via experiments. Then, we propose a masking scheme against correlation power analysis for RainDrop. The experiment result shows that the protected RainDrop only costs 1.1%, 20.8%, 136.8% of extra time, code and RAM.
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An Approach of Holographic Technology for the Practical Distance Education

In order to face the requirement of distant education after covid-19, this paper has presented a cyber-physical co-existence environment for practical distance learning events, we called it as a "Co-existing Practical Environment, CPE". CPE includes: "Holographic Wearable Device, HWD" for behavior sensing and vision sharing mechanism, cloud "Digital Twin Model, DTM" database for expert correcting variables real time, and collaborative fabrication discussion interface. We built an immersive HoloLens interface to help distance learning participators, understanding the concept of structural mechanics form morphs with mix-reality sensing, communicating the augmented information of parts of models for collaborative fabrication, and sharing the views of operation process for lecturers to solve the technical problems instantly. Furthermore, users are able to make their own custom digital twin project in CPE and optimize their fabricating process by adding more realistic material factors, operation callbacks, or analyzing their practice results in parameters relationships. This paper revised an application of holographic technology with a practical distance learning workshop as example, and discover CPE as a new kind of co-creation, cross-field, and remotely prepared cooperation mechanism. While the epidemic is still ragging, we are looking further researchers of more immersive methods for distance education.
Discovery Of Fosterville Deeps, Victorian Gold Province: Long Term Science

The path to a major discovery is far from uniform, and every discovery is different. Anecdotally it is about the seventh owner of a property who makes the big discovery, and to extend this tale, it is important not to be amongst the first six, nor waiting in line to be number eight. The siting of the so-called discovery hole is greatly over-rated in its importance, and the critical moments may have been much earlier and possibly protracted. The critical moment can even come well after drilling—there are multiple examples of a discovery having been drilled and not recognised until a new owner comes along. In this setting, the challenge of management is to engender conviction well before the big intersection is obvious and announced to the world, and this is best done using all scales of geology. Fosterville Deeps, in the Victorian Gold Province, is a major gold-only discovery in the 21st century, and the input of three decades of external research played a significant role in understanding the opportunities at depth. Transfer of the research findings led to a change of direction of the company and a major discovery.
Reformed QoE-Based Approach in Bitrate-Adaptation for Dynamic Adaptive Streaming Systems

With the COVID-19 pandemic, the demand for popular streaming applications is expected to soar. Ensuring high user-perceived quality correlates with higher profits for content providers and delivery systems. Dynamic adaptive streaming over HTTP (DASH) is a widely adopted video streaming standard, utilized by service providers to provide competitive quality of experience (QoE). DASH is capable of providing seamless streaming even during uncertain network conditions by switching across different video qualities and their corresponding segment bitrates. This paper introduces a non-complex algorithm that allows smooth step-like transition in the streamed video quality. The proposed approach deploys an optimization model to manage key elements that affect the user-perceived experience such as video quality, re-buffering and quality switching events. The objective is to optimize the QoE metric, which is constrained by the network throughput, segment size and buffer occupancy to continuously select the optimum bitrate levels with low complexity.
Misinformation in Machine Translation: Error Categories and Levels of Recognition Difficulty

Extensive research has been done on typologies of machine translation(MT) errors, but one subset of mistranslation—misinformation—is relatively understudied. Unlike other types of mistranslation, misinformation does not necessarily affect the readability or coherence of the translation, but will inhibit target readers from accessing the accurate information presented in the source text. It is unreasonable to expect post-editors(PE) to devote the equivalent levels of time and effort on the MT pre-translated text as in traditional translation projects, given that PE tasks have relatively lower pay but identical, if not tighter, deadlines. To gain more understanding on the concept of misinformation with the aim to improve the efficiency and accuracy of post-editors' work, this study analyzed four English to Chinese MT texts to categorize the misinformation instances, observe distribution patterns of error types, and evaluate their levels of recognition difficulty. It was observed that the highest number of misinformation instances fell into the category of polysemy/named-entity errors, attributing to around half of all misinformation instances. The second most common misinformation category is the non-equivalent rhetoric/idiomatic expression. To help post-editors identify the hard-to-recognize misinformation, we propose three different approaches: (1) use interactive MT platforms or CAT tools that can provide alternative translation suggestions; (2) compare MT results generated by multiple MT tools, as the discrepancies in translations can alert post-editors of potential misinformation; (3) compare the original source text with the back translation result of MT to identify non-equivalent rhetorical/idiomatic expressions.