Reviewerly/exHarmony huggingface dataset:
GSS: A group similarity system based on unsupervised outlier detection for big data computing

Outlier detection has always received research attention in the data mining community and is a challenging problem in various domains. Existing unsupervised outlier detection approaches evaluate the level of outlierness of the data instances and treat the instances with high scores as outliers. However, almost all unsupervised methods only identify outliers based on the score of individual outliers, ignoring the similarity among instances. Highly similar samples within a neighborhood region have the same behavior. Based on this, we propose an outlier detection system based on group similarity. The proposed system groups similar instances within a neighborhood region together, then evaluates the resulting groups by using the underlying measure, and finally makes a decision based on the outlierness of groups to detect outliers. Experiments on synthetic and real data demonstrate that the proposed scheme brings performance improvements for unsupervised outlier detection.
The Mixed-Integer Linear Programming for Cost-Constrained Decision Trees with Multiple Condition Attributes

In many real-world applications, cost factors play a significant role. Costs have been taken into consideration in numerous previous studies in machine learning, especially, in building decision trees. This research also considers a cost-sensitive decision tree construction problem with an assumption that test costs must be paid to obtain the values of the decision attribute and a record must be classified without exceeding the spending cost threshold. Moreover, our problem considers records with multiple condition attributes. We construct a cost-constrained decision tree using a Mixed-Integer formulation, which enables us to identify the optimal trees. The experimental results demonstrate that our formulation satisfactorily handles small data sets with multiple condition attributes under different cost constraints.
Ensemble Learning Methods for Legal Processing Tasks in ALQAC 2022

Automated Legal Question Answering Competition is an annual competition to find the best solution to automatically answer legal questions based on well-known statute laws in the Vietnamese Language. In this paper, we will demonstrate how to solve the problems posed by ALQAC 2022, using BERT and its variants as a backbone network. In addition, we also study using tf-idf and BM-25 to rank the relevance of legal documents. At the same time, this publication also show how to enhance training data to solve the problem of limited training data.
Miko Team: Deep Learning Approach for Legal Question Answering in ALQAC 2022

We introduce efficient deep learning-based methods for legal document processing including Legal Document Retrieval and Legal Question Answering tasks in the Automated Legal Question Answering Competition (ALQAC 2022). In this competition, we achieve 1 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">st</sup> place in the first task and 3 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">rd</sup> place in the second task. Our method is based on the XLM-RoBERTa model that is pre-trained from a large amount of unlabeled corpus before fine-tuning to the specific tasks. The experimental results showed that our method works well in legal retrieval information tasks with limited labeled data. Besides, this method can be applied to other information retrieval tasks in low-resource languages.
Research of a Foreign Objects Detection Fusion Algorithm Using Video and LIDAR in FAO System

This paper aims to research a fusion algorithm for subway screen doors to make future fully automatic operation (FAO) systems safer and more efficient. This paper adopts video with light detection and ranging (LIDAR) fusion algorithm technology, and proposes a dual-criteria artificial intelligence (AI) strategy using video image recognition and radar point cloud fusion to detect foreign objects in the gap between subway platform doors. A novel approach of cross-stacking and layered installation of sensors is innovatively proposed to realize the function of redundant detection of foreign objects in the gap. Cross-checking improves the reliability of the detection device while 2D sensors are used to achieve 3D detection effects. The developed system provides safety interlocking signals for the subway signal system, delivering alarm information to the integrated monitoring system, then pushes the smart hand ring alarm information to the on-site operators.
Defect Identification of Power Line Insulator Based on an Improved yolov4-tiny Algorithm

With the rapid development of artificial intelligence technology, the collection of power line images through unmanned aerial vehicles (UAV) and the further use of deep learning algorithms to automatically detect power insulator defects is gradually replacing the traditional manual inspection and identification methods. The image capture and machine recognition and learning has gradually become a kind of emerging automatic inspection methods of power line insulators. Aiming at the problems of small target size, complex background, and low defect recognition rate of insulator defects, this paper proposes an insulator defect detection method based on an improved yolov4-tiny deep learning algorithm (IYTDLA). Compared to the traditional yolov4-tiny deep learning algorithm (TYTDLA), the key improvement lies that a coordinate attention (CA) module is introduced after the major feature extraction network to enhance the network feature representation ability. First, based on the original UAV-collected image data sets, the improved algorithm is used to randomly scale captured images, and Gaussian noise is mixed to further enhance the data sets. Then, IYTDLA is applied to discern the two different defects “missing” and “broken” from the power line insulator images captured by UAVs. The experimental results show that the IYTDLA has a higher recognition accuracy than the TYTDLA. Compared to the mAP of TYTDLA, the mean average precision (mAP) of IYTDLA is increased by 0.94%, the average precision (AP) of missing insulator defects of IYTDLA is increased by 1.19%, the AP of insulator damage defects of IYTDLA is increased by 2.99%. At the same time, the performances of IYTDLA are also higher than those of traditional faster-rcnn (FRCNN) and efficientdet (EDET) in terms of recognition accuracy and processing speed. However, IYTDLA also has a processing speed comparable to the TYTDLA. That verifies that both of IYTDLA and TYTDLA are suitable for the deploy applications on mobile devices or embedded devices to implement device-side edge computing.
Raven finch optimized deep convolutional neural network model for intra‐frame video forgery detection

SUMMARY Due to the tremendous growth of video editing software, it has become extremely simple to introduce malicious content by manipulating multimedia data. This may include modification of videos either by adding or deleting selective frames with malicious intentions. Hence, it is essential to find the forged frames of the videos by introducing efficient and reliable video forensic methods. This article presents an automatic intra‐frame video forgery detection strategy based on a hybrid optimization tuned deep‐convolutional neural network (deep‐CNN) classifier. The significance of the proposed method lies in developing the proposed raven‐finch optimization algorithm that tunes the weights of the deep‐CNN to exhibit enhanced detection accuracy. The proposed raven‐finch optimization algorithm combines raven search agents and finches search agents, possessing the benefits of both search agents. The features of the input video frames act as the input to the deep‐CNN classifier that detects forgery. The performance of the proposed raven‐finch‐based deep CNN method is analyzed in terms of the performance indices, such as accuracy, sensitivity, and specificity. It is attained to be 97.56%, 95.48%, and 96.38%, respectively, which shows the superiority of the proposed method for intra‐frame forgery detection.
Algorithmic decision-making in financial services: economic and normative outcomes in consumer credit

Abstract Consider how much data is created and used based on our online behaviours and choices. Converging foundational technologies now enable analytics of the vast data required for machine learning. As a result, businesses now use algorithmic technologies to inform their processes, pricing and decisions. This article examines the implications of algorithmic decision-making in consumer credit markets from economic and normative perspectives. This article fills a gap in the literature to explore a multi-disciplinary approach to framing economic and normative issues for algorithmic decision-making in the private sector. This article identifies optimal and suboptimal outcomes in the relationships between companies and consumers. The economic approach of this article demonstrates that more data allows for more information which may result in better contracting outcomes. However, it also identifies potential risks of inaccuracy, bias and discrimination, and ‘gaming’ of algorithmic systems for personal benefit. Then, this article argues that these economic costs have normative implications. Connecting economic outcomes to a normative analysis contextualises the challenges in designing and regulating ML fairly. In particular, it identifies the normative implications of the process, as much as the outcome, concerning trust, privacy and autonomy and potential bias and discrimination in ML systems. Credit scoring, as a case study, elucidates the issues relating to private companies. Legal norms tend to mirror economic theory. Therefore, this article frames the critical economic and normative issues required for further regulatory work.
On the learnability of quantum state fidelity

Abstract Current quantum processing technology is generally noisy with a limited number of qubits, stressing the importance of quantum state fidelity estimation. The complexity of this problem is mainly due to not only accounting for single gates and readout errors but also for interactions among which. Existing methods generally rely on either reconstructing the given circuit state, ideal state, and computing the distance of which; or forcing the system to be on a specific state. Both rely on conducting circuit measurements, in which computational efficiency is traded off with obtained fidelity details, requiring an exponential number of experiments for full information. This paper poses the question: Is the mapping between a given quantum circuit and its state fidelity learnable? If learnable, this would be a step towards an alternative approach that relies on machine learning, providing much more efficient computation. To answer this question, we propose three deep learning models for 1-, 3-, and 5-qubit circuits and experiment on the following real-quantum processors: ibmq_armonk (1-qubit), ibmq_lima (5-qubit) and ibmq_quito (5-qubit) backends, respectively. Our models achieved a mean correlation factor of 0.74, 0.67 and 0.66 for 1-, 3-, and 5-qubit random circuits, respectively, with the exponential state tomography method. Additionally, our 5-qubit model outperforms simple baseline state fidelity estimation method on three quantum benchmarks. Our method, trained on random circuits only, achieved a mean correlation factor of 0.968 while the baseline method achieved 0.738. Furthermore, we investigate the effect of dynamic noise on state fidelity estimation. The correlation factor substantially improved to 0.82 and 0.74 for the 3- and 5-qubit models, respectively. The results show that machine learning is promising for predicting state fidelity from circuit representation and this work may be considered a step towards efficient end-to-end learning.
NEARS-Hub, a Lightweight Edge Computing for Real-Time Monitoring in Smart Environments

iNnovative Easy Assistance System (NEARS) is a Canadian transdisciplinary research project that aims to create a platform and standards that will make the development of Ambient Assisted Living (AAL) solutions technically feasible and clinically viable. It built and operationalized a hardware and software infrastructure for smart environments called NEARS-Hub. The key function of the NEARS-Hub is to deliver data generated by The Internet of Things (IoT) devices near the edge. The processing of the collected data in homes and its storage in the event of break-in services are carried out locally at the level of the edge node. The goal is to provide high-quality services and a quick response time. Therefore, edge nodes must be capable of flexibility, interoperability, and scalability to adapt their services in case of unforeseen situations. A common modeling approach is to create services without separating responsibilities between system layers. However, very few solutions allow the administration of services and the provisioning of resources in a flexible way and as close as possible to the equipment, at the edge of the network, or, ultimately, at the places where the data has been generated. This article proposes NEARS-Hub, a lightweight edge computing platform for AAL solutions, which revolves around three main notions: interoperability, flexibility, and scalability. The design of the current version of the NEARS-Hub is based on knowledge from several home experiments. The proposed model is validated by comparing the performances of the NEARS-Hub with a version based on a classic AAL solution.
Dynamic Difficulty Adjustment of Video Games Using Biofeedback

Video games are typically designed to challenge the player, aiming to increase their engagement and enjoyment. However, this sense of challenge must be carefully balanced to provide a successful experience. For instance, if the player is unable to progress due to a high difficulty, they will probably feel frustrated and will possibly leave the game session; conversely, if the perceived difficulty is low, the player will feel bored and will also likely leave the session. In this paper we report the design of an algorithm, based in biofeedback, that dynamically adjusts the difficulty of a video game: if the heart rate of the player decreases, then the difficulty of the game increases; if the heart rate of the player increases, then the difficulty of the game decreases. Through a controlled user study, we evaluated the effectiveness of the algorithm and its implementation in a prototype video game, in terms of performance, perceived gaming experience, and player satisfaction. The obtained results show that users who were affected by the algorithm effectively reported an improved player experience, completed the game levels in a lower time with a fewer number of tries, and displayed a better subjective impression. The proposed algorithm highlights the feasibility of dynamically adjusting the difficulty of a video game using biofeedback, hence providing an improved and personalized player experience. These results can be useful to designers and researchers in the video game industry, as a way to conceive novel—and more engaging—experiences.
Hand Gesture Preferences Analysis for a Virtual Reality Scenario

Virtual Reality has been successfully used to implement highly motivating learning environments where learners interact with the represented environment using hand gestures. However, little has been done in order to study which are the most convenient gestures for implementing this interaction in order to discover good practices for HCI. This investigation proposes a guideline for gesture evaluation based on user experience, in the scenario of small children learning words of a foreign language. Particularly, it provides a starting point by contrasting two gestures per action for selecting, moving and inspecting objects in a virtual environment. This study found out that these gestures were not indistinguishable. Instead, children have a clear preference for each action according to several indicators, which enabled us to establish a ranking of gesture preference for each action. Further research may expand the experiment by including additional gestures and actions.
Gesture Coding: Easing the Introduction to Block-Based Programming Languages with Motion Controls

Current approaches that introduce young learners to programming depend heavily on an exposure to block-based languages. Most, if not all of these, rely on traditional inputs, such as pointing and clicking with a mouse, and typing commands with a keyboard. While these input mechanics usually do not pose major usability concerns in their adoption, we see an opportunity in exploring novel interaction paradigms for sparking interest in programming within this population. In this paper we present Gesture Coding, an interactive block-based language, inspired by Scratch, that is controlled using Nintendo Switch Joy-Cons. We expect that interactive, playful, and non-traditional input controls would simplify user onboarding, hence easing the acceptance and appropriation of the language. We discuss the usability and perceived usefulness of the natural interaction metaphors built in Gesture Coding for developing short programs, covering sequential and conditional commands. Our results show that, despite requiring more effort to interact with motion-based controls, young learners show a more positive attitude for exploring and connecting blocks when compared to doing the same tasks with a mouse and keyboard. The obtained results are highly promising, highlighting the potential adoption of the tool.
A Phonetics and Semantics-Based Chinese Short Text Fusion Algorithm

With the rapid development of the Internet, short text has become more and more popular on the Internet and many short texts with a length of a few words to dozens of words have exploded, such as chats, text messages and comments. Among them, the extraction and analysis of such short texts relies on accurately text similarity calculations. Due to the ambiguity and data sparsity of short text, improving the accuracy of calculations for the similarity of short text remains an important and challenging task. To solve this problem, this paper conducts an in-depth study on the unsupervised Chinese short text similarity algorithm and proposes a fusion algorithm based on phonetics and semantics. The algorithm takes the sound, character, and word-meaning of the short text as the features, through these features to construct the feature vectors and calculate its own similarity for each vector, and then calculate the comprehensive semantic similarity of the text through the fusion algorithm and finally complete the similarity calculation method that integrates the three features of phonetic, character and semantics. In this paper, our algorithm was experimentally verified by LCQMC, a Large-scale Chinese Question Matching Corpus and the accuracy was improved by up to 29.2% compared with the traditional text similarity algorithm.
On the Capacity Region of Bipartite and Tripartite Entanglement Switching

We study a quantum entanglement distribution switch serving a set of users in a star topology with equal-length links. The quantum switch, much like a quantum repeater, can perform entanglement swapping to extend entanglement across longer distances. Additionally, the switch is equipped with entanglement switching logic, enabling it to implement switching policies to better serve the needs of the network. In this work, the function of the switch is to create bipartite or tripartite entangled states among users at the highest possible rates at a fixed ratio. Using Markov chains, we model a set of randomized switching policies. Discovering that some are better than others, we present analytical results for the case where the switch stores one qubit per user, and find that the best policies outperform a time division multiplexing policy for sharing the switch between bipartite and tripartite state generation. This performance improvement decreases as the number of users grows. The model is easily augmented to study the capacity region in the presence of quantum state decoherence and associated cut-off times for qubit storage, obtaining similar results. Moreover, decoherence-associated quantum storage cut-off times appear to have little effect on capacity in our identical-link system. We also study a smaller class of policies when the switch stores two qubits per user.
An Efficient Searchable Symmetric Encryption Scheme for Smart Grid Data

In recent years, the combination of power systems and IoT (Internet of things) has become a new generation of smart grid. Cloud computing is used to provide storage and computing services for smart grid data due to its convenience. Generally, searchable encryption technology is considered a feasible solution to guarantee data security and support search function. Li proposed a searchable symmetric encryption scheme based on pseudo-random function for smart grid data in 2019. In this paper, we propose a more efficient and more secure searchable symmetric encryption scheme for smart grid data. The scheme improves the search efficiency by introducing the Bloom filter and changing the structure of the index. Specifically, we first narrow the search scope and then perform a second search, which eliminates the false positive caused by the introduction of the Bloom filter. At the same time, we assign an ID to each piece of data (a row of data) in a tabular dataset, store the hash value of the ID (adding salt) in the index, and the search results are returned by the server contain none of the ID information, which improves security. Experiments on real data show that our scheme is 52% more efficient than the previous scheme.
Revisiting the Security of Biometric Authentication Systems Against Statistical Attacks

The uniqueness of behavioral biometrics (e.g., voice or keystroke patterns) has been challenged by recent works. Statistical attacks have been proposed that infer general population statistics and target behavioral biometrics against a particular victim. We show that despite their success, these approaches require several attempts for successful attacks against different biometrics due to the different nature of overlap in users’ behavior for these biometrics. Furthermore, no mechanism has been proposed to date that detects statistical attacks. In this work, we propose a new hypervolumes-based statistical attack and show that unlike existing methods, it (1) is successful against a variety of biometrics, (2) is successful against more users, and (3) requires fewest attempts for successful attacks. More specifically, across five diverse biometrics, for the first attempt, on average our attack is 18 percentage points more successful than the second best (37% vs. 19%). Similarly, for the fifth attack attempt, on average our attack is 18 percentage points more successful than the second best (67% vs. 49%). We propose and evaluate a mechanism that can detect the more devastating statistical attacks. False rejects in biometric systems are common, and by distinguishing statistical attacks from false rejects, our defense improves usability and security. The evaluation of the proposed detection mechanism shows its ability to detect on average 94% of the tested statistical attacks with an average probability of 3% to detect false rejects as a statistical attack. Given the serious threat posed by statistical attacks to biometrics that are used today (e.g., voice), our work highlights the need for defending against these attacks.