Reviewerly/exHarmony huggingface dataset:
Investigating Influencer VPN Ads on YouTube

One widespread, but frequently overlooked, source of security information is influencer marketing ads on YouTube for security and privacy products such as VPNs. This paper examines how widespread these ads are, where on YouTube they are found, and what kind of information they convey. Starting from a random sample of 1.4% of YouTube, we identify 243 videos containing VPN ads with a total of 63 million views. Using qualitative analysis, we find that these ads commonly discuss broad security guarantees as well as specific technical features, frequently focus on internet threats, and sometimes emphasize accessing otherwise unavailable content. Different VPN companies tend to advertise in different categories of channels and emphasize different messages. We find a number of potentially misleading claims, including overpromises and exaggerations that could negatively influence viewers’ mental models of internet safety.
ShorTor: Improving Tor Network Latency via Multi-hop Overlay Routing

We present ShorTor, a protocol for reducing latency on the Tor network. ShorTor uses multi-hop overlay routing, a technique typically employed by content delivery networks, to influence the route Tor traffic takes across the internet. In this way, ShorTor avoids slow paths and improves the experience for end users by reducing the latency of their connections while imposing minimal bandwidth overhead. ShorTor functions as an overlay on top of onion routing—Tor’s existing routing protocol—and is run by Tor relays, making it independent of the path selection performed by Tor clients. As such, ShorTor reduces latency while preserving Tor’s existing security properties. Specifically, the routes taken in ShorTor are in no way correlated to either the Tor user or their destination, including the geographic location of either party. We analyze the security of ShorTor using the AnoA framework, showing that ShorTor maintains all of Tor’s anonymity guarantees. We augment our theoretical claims with an empirical analysis. To evaluate ShorTor’s performance, we collect a real-world dataset of over 400,000 latency measurements between the 1,000 most popular Tor relays, which collectively see the vast majority of Tor traffic. With this data, we identify pairs of relays that could benefit from ShorTor: that is, two relays where introducing an additional intermediate network hop results in lower latency than the direct route between them. We use our measurement dataset to simulate the impact on end users by applying ShorTor to two million Tor circuits chosen according to Tor’s specification. ShorTor reduces the latency for the 99 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">th</sup> percentile of relay pairs in Tor by 148ms. Similarly, ShorTor reduces the latency of Tor circuits by 122ms at the 99 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">th</sup> percentile. In practice, this translates to ShorTor truncating tail latencies for Tor which has a direct impact on page load times and, consequently, user experience on the Tor browser.
Making Human-Like Moral Decisions

Many real-life scenarios require humans to make difficult trade-offs: do we always follow all the traffic rules or do we violate the speed limit in an emergency? In general, how should we account for and balance the ethical values, safety recommendations, and societal norms, when we are trying to achieve a certain objective? To enable effective AI-human collaboration, we must equip AI agents with a model of how humans make such trade-offs in environments where there is not only a goal to be reached, but there are also ethical constraints to be considered and to possibly align with. These ethical constraints could be both deontological rules on actions that should not be performed, or also consequentialist policies that recommend avoiding reaching certain states of the world. Our purpose is to build AI agents that can mimic human behavior in these ethically constrained decision environments, with a long term research goal to use AI to help humans in making better moral judgments and actions. To this end, we propose a computational approach where competing objectives and ethical constraints are orchestrated through a method that leverages a cognitive model of human decision making, called multi-alternative decision field theory (MDFT). Using MDFT, we build an orchestrator, called MDFT-Orchestrator (MDFT-O), that is both general and flexible. We also show experimentally that MDFT-O both generates better decisions than using a heuristic that takes a weighted average of competing policies (WA-O), but also performs better in terms of mimicking human decisions as collected through Amazon Mechanical Turk (AMT). Our methodology is therefore able to faithfully model human decision in ethically constrained decision environments.
Plausibility and Faithfulness of Feature Attribution-Based Explanations in Automated Short Answer Scoring

Automated short answer scoring (SAS) is the task of automatically assigning a score as output to a given input answer. In this work, we tackle the challenging task of outputting the basis (i.e., justification cues) as an explanation for scoring given a defined rubric for assigning a score. In previous studies, researchers explored the performance of scoring via justification identification and constructed their own datasets, but their studies focused solely on limited experiments. On the basis of previous studies, we consider justification identification as an explanation by feature attribution methods for explainable artificial intelligence research. We conduct a comprehensive experiment consisting of multiple explanation generation methods, supervised learning of explanations, and evaluating on two axes, plausibility and faithfulness, which are important for automatic SAS. Our results indicate that we can improve the plausibility of gradient-based methods by supervised learning. However, methods with high plausibility and high faithfulness are still different methods, so it is crucial to select an appropriate method depending on which perspective of explainability is essential.
Two-Stage Uniform Adaptive Testing to Balance Measurement Accuracy and Item Exposure

Computerized adaptive testing (CAT) presents a tradeoff problem involving increasing measurement accuracy vs. decreasing item exposure in an item pool. To address this difficulty, we propose two-stage uniform adaptive testing. In the first stage, the proposed method partitions an item pool into numerous uniform item groups using a state-of-the-art uniform test assembly technique based on the Random Integer Programming Maximum Clique Problem. Then the method selects the optimum item from a uniform item group. In the second stage, when the standard error of an examinee's ability estimate becomes less than a certain value, it switches to selecting and to presenting an optimum item from the whole item pool. Results of numerical experiments underscore the effectiveness of the proposed method.
Balancing Fined-Tuned Machine Learning Models Between Continuous and Discrete Variables - A Comprehensive Analysis Using Educational Data

Along with the exponential increase of students enrolling in MOOCs [26] arises the problem of a high student dropout rate. Researchers worldwide are interested in predicting whether students will drop out of MOOCs to prevent it. This study explores and improves ways of handling notoriously challenging continuous variables datasets, to predict dropout. Importantly, we propose a fair comparison methodology: unlike prior studies and, for the first time, when comparing various models, we use algorithms with the dataset they are intended for, thus 'like for like.' We use a time-series dataset with algorithms suited for time-series, and a converted discrete-variables dataset, through feature engineering, with algorithms known to handle discrete variables well. Moreover, in terms of predictive ability, we examine the importance of finding the optimal hyperparameters for our algorithms, in combination with the most effective pre-processing techniques for the data. We show that these much lighter discrete models outperform the time-series models, enabling faster training and testing. This result also holds over fine-tuning of pre-processing and hyperparameter optimisation.
A Distributed Attack-Resistant Trust Model for Automatic Modulation Classification

Recently, the performance of automatic modulation recognition (AMC) has been dramatically improved with the assistance of federated learning (FL). However, FL-based AMC still faces the issue of secure sharing of local model parameters, resulting in poor anti-attack capacity. Motivated by this, a Blockchain-federated learning (BFL) framework is proposed for AMC in this letter, where the AMC model is cooperatively trained by the sharing of local model parameters with Blockchain. In addition, a parameter validity evaluation method is designed therein for the aggregation process, which greatly weakens the influence of malicious nodes. On the basis of enriching training samples, the anti-attack ability of FL-based AMC schemes is significantly improved for proposed BFL framework. Simulation results show that the recognition accuracy of the proposed framework is increased by more than 10% when malicious nodes exist, on the premise of acceptable recognition accuracy.
Usability Heuristics for Evaluating Arabic Mobile Games

The use of Arabic mobile games has increased in the last decade. The Arabic mobile game market is rapidly growing as well, where more games are being developed or localized to target Arabic game players and attract newcomers. Little existing work, however, has been done to ensure the delivery of effective, efficient, and satisfying Arabic mobile games. Previous studies have shown that most developers of Arabic mobile games do not evaluate these games because of the lack of usability evaluation methods that are specifically designed for such games. In this paper, we present Arabic Mobile Game Heuristics, which is a list of usability heuristics that were explicitly adapted for evaluating Arabic mobile games. The list was developed by revising and modifying Nielsen’s list of heuristics and the game usability principles. Validating the proposed heuristics was achieved by comparing the outcomes of evaluating two Arabic mobile games using the three lists of usability heuristics. Results indicate that the proposed heuristics allowed evaluators to find additional usability issues and identify more severe problems than using the other two lists.
Comparison of Human Intestinal Parasite Ova Segmentation Using Machine Learning and Deep Learning Techniques

Helminthiasis disease is one of the most serious health problems in the world and frequently occurs in children, especially in unhygienic conditions. The manual diagnosis method is time consuming and challenging, especially when there are a large number of samples. An automated system is acknowledged as a quick and easy technique to assess helminth sample images by offering direct visibility on the computer monitor without the requirement for examination under a microscope. Thus, this paper aims to compare the human intestinal parasite ova segmentation performance between machine learning segmentation and deep learning segmentation. Four types of helminth ova are tested, which are Ascaris Lumbricoides Ova (ALO), Enterobious Vermicularis Ova (EVO), Hookworm Ova (HWO), and Trichuris Trichiura Ova (TTO). In this paper, fuzzy c-Mean (FCM) segmentation technique is used in machine learning segmentation, while convolutional neural network (CNN) segmentation technique is used for deep learning. The performance of segmentation algorithms based on FCM and CNN segmentation techniques is investigated and compared to select the best segmentation procedure for helminth ova detection. The results reveal that the accuracy obtained for each helminth species is in the range of 97% to 100% for both techniques. However, IoU analysis showed that CNN based on ResNet technique performed better than FCM for ALO, EVO, and TTO with values of 75.80%, 55.48%, and 77.06%, respectively. Therefore, segmentation through deep learning is more suitable for segmenting the human intestinal parasite ova.
Machine Learning Feature Based Job Scheduling for Distributed Machine Learning Clusters

With the rapid proliferation of Machine Learning (ML) and Deep learning (DL) applications running on modern platforms, it is crucial to satisfy application performance requirements such as meeting deadline and ensuring accuracy. To this end, researchers have proposed several job schedulers for ML clusters. However, none of the previously proposed schedulers consider ML model parallelism, though it has been proposed as an approach to increase the efficiency of running large-scale ML and DL jobs. Thus, in this paper, we propose an ML job Feature based job Scheduling system (MLFS) for ML clusters running both data parallelism and model parallelism ML jobs. MLFS first uses a heuristic scheduling method that considers an ML job’s spatial and temporal features to determine task priority for job queue ordering in order to improve job completion time (JCT) and accuracy performance. It uses the data from the heuristic scheduling method for training a deep reinforcement learning (RL) model. After the RL model is well trained, it then switches to the RL method to automatically make decisions on job scheduling. In addition, MLFS has a system load control method that selects tasks from overloaded servers to move to underloaded servers based on task priority, and also intelligently removes the tasks that generate little or no improvement on the desired accuracy performance when the system is overloaded to improve JCT and accuracy by job deadline. Furthermore, we propose Optimal ML iteration stopping method that determines the proper time to stop training ML model when this model reaches the minimum loss value. Our real experiments and large-scale simulation based on real trace show that MLFS reduces JCT by up to 53% and makespan by up to 52%, and improves accuracy by up to 64% when compared with existing ML job schedulers. We also open sourced our code.
Collaboration in Participant-Centric Federated Learning: A Game-Theoretical Perspective

Federated learning (FL) is a promising distributed framework for collaborative artificial intelligence model training while protecting user privacy. A bootstrapping component that has attracted significant research attention is the design of incentive mechanism to stimulate user collaboration in FL. The majority of works adopt a broker-centric approach to help the central operator to attract participants and further obtain a well-trained model. Few works consider forging participant-centric collaboration among participants to pursue an FL model for their common interests, which induces dramatic differences in incentive mechanism design from the broker-centric FL. To coordinate the selfish and heterogeneous participants, we propose a novel analytic framework for incentivizing effective and efficient collaborations for participant-centric FL. Specifically, we respectively propose two novel game models for contribution-oblivious FL (COFL) and contribution-aware FL (CAFL), where the latter one implements a minimum contribution threshold mechanism. We further analyze the uniqueness and existence for Nash equilibrium of both COFL and CAFL games and design efficient algorithms to achieve equilibrium solutions. Extensive performance evaluations show that there exists free-riding phenomenon in COFL, which can be greatly alleviated through the adoption of CAFL model with the optimized minimum threshold.
Nighttime Pedestrian and Vehicle Detection Based on a Fast Saliency and Multifeature Fusion Algorithm for Infrared Images

In recent years, pedestrian and vehicle detection at night has become an important subject of computer vision applications. Because the environment light is weak at night, the common traditional camera-input algorithms often performs poorly. Pedestrians and vehicles in infrared images are usually brighter than the surrounding environment and have salient features. In this paper, a fast saliency map of pedestrian and vehicle targets in infrared images at night is used to achieve rapid acquisition of areas of interest for pedestrians and vehicles. Based on this, we propose a method to refine and separate the target area to obtain accurate pedestrian and vehicle candidate bounding boxes. Specifically, this paper proposed a multi-feature fusion algorithm of pedestrian and vehicle feature extraction combined with support vector machine (SVM) to determine whether the extracted target area really includes pedestrians and vehicles. Our experimental results show that the proposed method can achieve the expected effect of the classification and detection for pedestrians and vehicles at night, and can meet the real-time requirements of actual road scenarios.
Dynamic Waypoint Navigation: Model-Based Adaptive Trajectory Planner for Human-Symbiotic Mobile Robots

Path planning in dynamic environments is still a challenging issue with autonomous mobile robots. Current methods lack adaptability to various passing scenarios, a variety of passing trajectories including an acceleration path, or immediacy in planning time, which require human-aware navigation. In this study, we propose Dynamic Waypoint Navigation (DWN), which is a model-based adaptive real-time trajectory planning method. DWN first predicts human-robot path interference and the time and position of the interference on the basis of the measured velocity of humans. It then dynamically designates several waypoints considering the time delay of both calculation time and robot travel time. Then, DWN generates several trajectories by combining different speeds (default, acceleration, and deceleration) and paths (default, right, and left) and selects the best trajectory in terms of an interference-avoidance energy cost based on the degree of velocity-vector change. DWN can also output a trajectory within 0.5 s to immediately adapt to changes in human behavior and adopt a simple mathematical model and algorithm to enable easy expansion. Simulation and experimental results reveal that the DWN can adequately select a time-efficient trajectory in real-time and adaptively change a trajectory depending on human movement.
Continual BatchNorm Adaptation (CBNA) for Semantic Segmentation

Environment perception in autonomous driving vehicles often heavily relies on deep neural networks (DNNs), which are subject to domain shifts, leading to a significantly decreased performance during DNN deployment. Usually, this problem is addressed by unsupervised domain adaptation (UDA) approaches trained either simultaneously on source and target domain datasets or even source-free only on target data in an offline fashion. In this work, we further expand a source-free UDA approach to a continual and therefore online-capable UDA on a single-image basis for semantic segmentation. Accordingly, our method only requires the pre-trained model from the supplier (trained in the source domain) and the current (unlabeled target domain) camera image. Our method Continual BatchNorm Adaptation (CBNA) modifies the source domain statistics in the batch normalization layers, using target domain images in an unsupervised fashion, which yields consistent performance improvements during inference. Thereby, in contrast to existing works, our approach can be applied to improve a DNN continuously on a single-image basis during deployment without access to source data, without algorithmic delay, and nearly without computational overhead. We show the consistent effectiveness of our method across a wide variety of source/target domain settings for semantic segmentation. Code is available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/ifnspaml/CBNA</uri>
Deep Learning Based Document Layout Analysis on Historical Documents

Mining historical documents provide important information that assists scientists and researchers to gain new insights into their domain. Document Layout Analysis is the first step of the automatic capturing of information from documents. The layout recognition supports establishing relationships between components in the document, which allows extraction of information. A method to visually segment critical regions of Historical handwritten documents using an object detection technique augmented with contextual features are presented in this paper. Layout analysis of Historical documents is complicated due to complex document layout and degradation of documents due to age. The object-detection technique YOLOv3 has been adopted for document layout analysis by extracting the features at different levels by down-sampling the input and combining it using Feature Pyramid Network to detect smaller regions to improve the region detection performance. The experimental results on two distinct datasets that demonstrate the method’s potential and competitive results with respect to state-of-the-art approaches have been presented.
An alternative C++-based HPC system for Hadoop MapReduce

Abstract MapReduce (MR) is a technique used to improve distributed data processing vastly and can massively speed up computation. Hadoop and MR rely on memory-intensive JVM and Java. A MR framework based on High-Performance Computing (HPC) could be used, which is both memory-efficient and faster than standard MR. This article explores a C++-based approach to MR and its feasibility on multiple factors like developer friendliness, deployment interface, efficiency, and scalability. This article also introduces Eager Reduction and Delayed Reduction techniques to speed up MR.
Automatic Detection of Sensitive Data Using Transformer- Based Classifiers

The General Data Protection Regulation (GDPR) has allowed EU citizens and residents to have more control over their personal data, simplifying the regulatory environment affecting international business and unifying and homogenising privacy legislation within the EU. This regulation affects all companies that process data of European residents regardless of the place in which they are processed and their registered office, providing for a strict discipline of data protection. These companies must comply with the GDPR and be aware of the content of the data they manage; this is especially important if they are holding sensitive data, that is, any information regarding racial or ethnic origin, political opinions, religious or philosophical beliefs, trade union membership, data relating to the sexual life or sexual orientation of the person, as well as data on physical and mental health. These classes of data are hardly structured, and most frequently they appear within a document such as an email message, a review or a post. It is extremely difficult to know if a company is in possession of sensitive data at the risk of not protecting them properly. The goal of the study described in this paper is to use Machine Learning, in particular the Transformer deep-learning model, to develop classifiers capable of detecting documents that are likely to include sensitive data. Additionally, we want the classifiers to recognize the particular type of sensitive topic with which they deal, in order for a company to have a better knowledge of the data they own. We expect to make the model described in this paper available as a web service, customized to private data of possible customers, or even in a free-to-use version based on the freely available data set we have built to train the classifiers.