Reviewerly/exHarmony huggingface dataset:
A survey on legal question–answering systems

Many legal professionals think that the explosion of information about local, regional, national, and international legislation makes their practice more costly, time-consuming, and even error-prone. The two main reasons for this are that most legislation is usually unstructured, and the tremendous amount and pace with which laws are released causes information overload in their daily tasks. In the case of the legal domain, the research community agrees that a system allowing to generate automatic responses to legal questions could substantially impact many practical implications in daily activities. The degree of usefulness is such that even a semi-automatic solution could significantly help to reduce the workload to be faced. This is mainly because a Question Answering system could be able to automatically process a massive amount of legal resources to answer a question or doubt in seconds, which means that it could save resources in the form of effort, money, and time to many professionals in the legal sector. In this work, we quantitatively and qualitatively survey the solutions that currently exist to meet this challenge.
OFPI: Optical Flow Pose Image for Action Recognition

Most approaches to action recognition based on pseudo-images involve encoding skeletal data into RGB-like image representations. This approach cannot fully exploit the kinematic features and structural information of human poses, and convolutional neural network (CNN) models that process pseudo-images lack a global field of view and cannot completely extract action features from pseudo-images. In this paper, we propose a novel pose-based action representation method called Optical Flow Pose Image (OFPI) in order to fully capitalize on the spatial and temporal information of skeletal data. Specifically, in the proposed method, an advanced pose estimator collects skeletal data before locating the target person and then extracts skeletal data utilizing a human tracking algorithm. The OFPI representation is obtained by aggregating these skeletal data over time. To test the superiority of OFPI and investigate the significance of the model having a global field of view, we trained a simple CNN model and a transformer-based model, respectively. Both models achieved superior outcomes. Because of the global field of view, especially in the transformer-based model, the OFPI-based representation achieved 98.3% and 94.2% accuracy on the KTH and JHMDB datasets, respectively. Compared with other advanced pose representation methods and multi-stream methods, OFPI achieved state-of-the-art performance on the JHMDB dataset, indicating the utility and potential of this algorithm for skeleton-based action recognition research.
Optical analog computing enabled broadband structured light

Mathematically, any function can be expressed as the operation form of another function. Here, the idea is introduced into an optical system to generate structured light. In the optical system, a mathematical function is represented by an optical field distribution, and any structured light field can be generated by performing different optical analog computations for any input optical field. In particular, optical analog computing has a good broadband performance, as it can be achieved based on the Pancharatnam-Berry phase. Therefore, our scheme can provide a flexible way to generate broadband structured light, and this is theoretically and experimentally demonstrated. It is envisioned that our work may inspire potential applications in high-resolution microscopy and quantum computation.
CWSXLNet: A Sentiment Analysis Model Based on Chinese Word Segmentation Information Enhancement

This paper proposed a method for improving the XLNet model to address the shortcomings of segmentation algorithm for processing Chinese language, such as long sub-word lengths, long word lists and incomplete word list coverage. To address these issues, we proposed the CWSXLNet (Chinese Word Segmentation XLNet) model based on Chinese word segmentation information enhancement. The model first pre-processed Chinese pretrained text by Chinese word segmentation tool, and proposed a Chinese word segmentation attention mask mechanism by combining PLM (Permuted Language Model) and two-stream self-attention mechanism of XLNet. While performing natural language processing at word granularity, it can reduce the degree of masking between masked and non-masked words for two words belonging to the same word. For the Chinese sentiment analysis task, proposed the CWSXLNet-BiGRU-Attention model, which introduces bi-directional GRU as well as self-attention mechanism in the downstream task. Experiments show that CWSXLNet has achieved 89.91% precision, 91.53% recall rate and 90.71% F1-score, and CWSXLNet-BiGRU-Attention has achieved 92.61% precision, 93.19% recall rate and 92.90% F1-score on ChnSentiCorp dataset, which indicates that CWSXLNet has better performance than other models in Chinese sentiment analysis.
Factors Influencing Engagement in Hybrid Virtual and Augmented Reality

Hybridity in immersive technologies has not been studied for factors that are likely to influence engagement. A noticeable factor is the spatial enclosure that defines where users meet. This involves a mutual object of interest, contents that the users may generate around the object, and the proximity between users. This study examines these factors, namely how object interactivity, user-generated contents (UGC) and avatar proximity influence engagement. We designed a Hybrid Virtual and Augmented Reality (HVAR) environment that supports paired users to experience cultural heritage in both Virtual Reality (VR) and Augmented Reality (AR) . A user study was conducted with 60 participants, providing assessments of engagement and presence via questionnaires, together with mobile electroencephalogram (mEEG) and user activity data that measures VR user engagement in real-time. Our findings provide insights into how engagement between users can occur in HVAR environments for the future hybrid reality with multi-device connectivity.
VLSM-Net: A Fusion Architecture for CT Image Segmentation

Region of interest (ROI) segmentation is a key step in computer-aided diagnosis (CAD). With the problems of blurred tissue edges and imprecise boundaries of ROI in medical images, it is hard to extract satisfactory ROIs from medical images. In order to overcome the shortcomings in segmentation from the V-Net model or the level set method (LSM), we propose in this paper a new image segmentation method, the VLSM-Net model, combining these two methods. Specifically, we first use the V-Net model to segment the ROIs, and set the segmentation result as the initial contour. It is then fed through the hybrid LSM for further fine segmentation. That is, the complete segmentation of the V-Net model can be obtained by successively combining the V-Net model and the hybrid LSM. The experimental results conducted in the public datasets LiTS and LUNA show that, compared with the V-Net model or LSM alone, our VLSM-Net model greatly improves the sensitivity, precision and dice coefficient values (DCV) in 3D image segmentation, thus validating our model’s effectiveness.
A comprehensive survey on blockchain-based healthcare industry: applications and challenges

Blockchain has attracted a lot of interest since its publication because to its unique characteristics of immutability, decentralization, smart contract, and consensus mechanism. Today’s healthcare systems are facing many issues in the era of digital health transformation and the growth of electronic health records. Blockchain has the potential to provide solutions to a variety of electronic health record (EHR)-related challenges, including data management, security, data sharing and patient privacy. This paper represents a blockchain-based healthcare industry survey; it includes many research publications in high-ranking scientific journals in recent years from 2016 to 2022. We adopt the preferred reporting items for systematic reviews and meta-analyses (PRISMA) approach and aspects. This work is based on five databases: Elsevier, Springer, IEEE Xplore, PubMed and MDPI. The number of studies included in this study was 56. We found that researchers attempted to use blockchain technology to assist patients and healthcare providers in diagnosis and data processing, as well as including multiple entities. Our study discusses the potential of blockchain technology, its roles and benefits in healthcare, aiming to solve several problems such as data management, data sharing, access control, data security and privacy, which are missed in the conventional healthcare system.
<scp>TestSGD</scp> : Interpretable Testing of Neural Networks against Subtle Group Discrimination

Discrimination has been shown in many machine learning applications, which calls for sufficient fairness testing before their deployment in ethic-relevant domains. One widely concerning type of discrimination, testing against group discrimination, mostly hidden , is much less studied, compared with identifying individual discrimination . In this work, we propose TestSGD , an interpretable testing approach that systematically identifies and measures hidden (which we call “subtle”) group discrimination of a neural network characterized by conditions over combinations of the sensitive attributes . Specifically, given a neural network, TestSGD first automatically generates an interpretable rule set that categorizes the input space into two groups. Alongside, TestSGD also provides an estimated group discrimination score based on sampling the input space to measure the degree of the identified subtle group discrimination, which is guaranteed to be accurate up to an error bound. We evaluate TestSGD on multiple neural network models trained on popular datasets including both structured data and text data. The experiment results show that TestSGD is effective and efficient in identifying and measuring such subtle group discrimination that has never been revealed before. Furthermore, we show that the testing results of TestSGD can be used to mitigate such discrimination through retraining with negligible accuracy drop.
Predicting Stellar Mass Accretion: An Optimized Echo State Network Approach in Time Series Modeling

Modeling the dynamics of the formation and evolution of protostellar disks as well as the history of stellar mass accretion typically involve the numerical solution of complex systems of coupled differential equations.The resulting mass accretion history of protostars is known to be highly episodic due to recurrent instabilities and also exhibits short timescale flickering.By leveraging the strong predictive abilities of neural networks, we extract some of the critical temporal dynamics experienced during the mass accretion including periods of instability.Particularly, we utilize a novel form of the echo state neural network (ESN), which has been shown to deal efficiently with data having inherent nonlinearity.We introduce the use of optimized-ESN (Opt-ESN) to make model-independent time series forecasting of mass accretion rate in the evolution of protostellar disks.We apply the network to multiple hydrodynamic simulations with different initial conditions and exhibiting a variety of temporal dynamics to demonstrate the predictability of the Opt-ESN model.The model is trained on simulation data of ∼ 1 -2 Myr, and achieves predictions with a low normalized mean square error (∼ 10 -5 to 10 -3 ) for forecasts ranging between 100 and 3800 yr.This result shows the promise of the application of machine learning based models to time-domain astronomy.
An Overview of Bengali Speech Recognition: Methods, Challenges, and Future Direction

In the subject of human-computer interactions, speech recognition is an appealing technique that gives users the opportunity to interact with and control the machine. Currently, automatic speech recognition (ASR) systems are being utilized to flawlessly convert speech to text. The implementation of ASR systems in Bengali has not yet achieved an acceptable standard, despite the fact that they are being used extensively in other languages. So far, various ASR models have been implemented for speech recognition like LSTM (Long Short-Term Memory), Transformer-based models like RNN (Recurrent Neural Network) and CNN (Convolutional Neural Network) are also quite popular for speech recognition. The Bengali language is more grammatically and structurally diverse than English. Therefore, it is difficult for researchers to use the same language model as English or any other language. So, the Bengali language is difficult to work with. Different studies have been carried out on Bengali speech recognition. We want to enlist the numerous models that have been used for Bengali speech recognition from 2009 to 2022. In this paper, we will discuss the challenges that were faced and the scope of future research in this field. This survey paper also provides datasets utilized in numerous research studies.
Simulating a Digital Factory and Improving Production Efficiency by Using Virtual Reality Technology

The main goal of every production is an optimally set and stable production process with the lowest possible costs. Such settings can only be achieved through many years of experience or very specific research, which focuses on several critical factors. An example of such factors can be the size and use of available space or the location of the production line and the logistical location of individual production sites, which is individual for each production process. Specific research can be carried out, for example, by means of the TX Plant simulation application, which was used in the present article for the production process of making fiber from pellets. The output of this research is the effective use of the so-called “Digital factory” to make the process in the already created conditions more efficient. This was achieved by the TX Plant simulation application, resulting in a reduced production time and increasing overall productivity. An intuitive interaction with factory equipment is possible with this approach, which allows users to immerse themselves in the virtual factory environment. As a result, a layout’s efficiency of surface use, flow of martial, and ergonomics can be assessed in real time. This paper aims to demonstrate how virtual reality (VR) can be used to simulate a digital factory to aid in decision making and enhance factory efficiency.
The power of typed affine decision structures: a case study

Abstract TADS are a novel, concise white-box representation of neural networks. In this paper, we apply TADS to the problem of neural network verification, using them to generate either proofs or concise error characterizations for desirable neural network properties. In a case study, we consider the robustness of neural networks to adversarial attacks, i.e., small changes to an input that drastically change a neural networks perception, and show that TADS can be used to provide precise diagnostics on how and where robustness errors a occur. We achieve these results by introducing Precondition Projection , a technique that yields a TADS describing network behavior precisely on a given subset of its input space, and combining it with PCA, a traditional, well-understood dimensionality reduction technique. We show that PCA is easily compatible with TADS. All analyses can be implemented in a straightforward fashion using the rich algebraic properties of TADS, demonstrating the utility of the TADS framework for neural network explainability and verification. While TADS do not yet scale as efficiently as state-of-the-art neural network verifiers, we show that, using PCA-based simplifications, they can still scale to medium-sized problems and yield concise explanations for potential errors that can be used for other purposes such as debugging a network or generating new training samples.
Quantum coherence as asymmetry from complex weak values

Abstract Quantum coherence as an asymmetry relative to a translation group generated by a Hermitian operator, is a necessary resource for the quantum parameter estimation. On the other hand, the sensitivity of the parameter estimation is known to be related to the imaginary part of the weak value of the Hermitian operator generating the unitary imprinting of the parameter being estimated. This naturally suggests a question if one can use the imaginary part of the weak value to characterize the coherence as asymmetry. In this work, we show that the average absolute imaginary part of the weak value of the generator of the translation group, maximized over all possible projective measurement bases, can be used to quantify the coherence as asymmetry relative to the translation group, satisfying certain desirable requirements. We argue that the quantifier of coherence so defined, called translationally-covariant (TC) w-coherence, can be obtained experimentally using a hybrid quantum–classical circuit via the estimation of weak value combined with a classical optimization procedure. We obtain upper bounds of the TC w-coherence in terms of the quantum standard deviation, quantum Fisher information, and the imaginary part of the Kirkwood–Dirac quasiprobability. We further obtain a lower bound and derive a relation between the TC w-coherences relative to two generators of translation group taking a form analogous to the Kennard–Weyl–Robertson uncertainty relation.
Practices and Challenges of Using GitHub Copilot: An Empirical Study

With the advances in machine learning, there is a growing interest in AI-enabled tools for autocompleting source code.GitHub Copilot, also referred to as the "AI Pair Programmer", has been trained on billions of lines of open source GitHub code, and is one of such tools that has been increasingly used since its launch in June 2021.However, little effort has been devoted to understanding the practices and challenges of using Copilot in programming with auto-completed source code.To this end, we conducted an empirical study by collecting and analyzing the data from Stack Overflow (SO) and GitHub Discussions.More specifically, we searched and manually collected 169 SO posts and 655 GitHub discussions related to the usage of Copilot.We identified the programming languages, IDEs, technologies used with Copilot, functions implemented, benefits, limitations, and challenges when using Copilot.The results show that when practitioners use Copilot: (1) The major programming languages used with Copilot are JavaScript and Python, (2) the main IDE used with Copilot is Visual Studio Code, (3) the most common used technology with Copilot is Node.js,(4) the leading function implemented by Copilot is data processing, (5) the significant benefit of using Copilot is useful code generation, and (6) the main limitation encountered by practitioners when using Copilot is difficulty of integration.Our results suggest that using Copilot is like a double-edged sword, which requires developers to carefully consider various aspects when deciding whether or not to use it.Our study provides empirically grounded foundations and basis for future research on the role of Copilot as an AI pair programmer in software development.
Characterization of fast ion transport via position-dependent optical deshelving

Ion transport is an essential operation in some models of quantum information processing, where fast ion shuttling with minimal motional excitation is necessary for efficient high-fidelity quantum logic. While fast and cold ion shuttling has been demonstrated, the dynamics and specific trajectory of an ion during diabatic transport have not been studied in detail. Here we describe a position-dependent optical deshelving technique useful for sampling an ion's position throughout its trajectory and we demonstrate the technique on fast linear transport of a ${}^{40}{\text{Ca}}^{+}$ ion in a surface-electrode ion trap. At high speed, the trap's electrode filters strongly distort the transport potential waveform. With this technique, we observe deviations from the intended constant-velocity (100-m/s) transport: We measure an average speed of 82(2) m/s and a peak speed of 251(6) m/s over a distance of 120 $\textmu{}\mathrm{m}$.
Quantum Image Processing Algorithm Using Line Detection Mask Based on NEQR

Line detection is a fundamental technique in image processing. It can extract the required information, while the information that does not need attention can be ignored, thus reducing the amount of data. At the same time, line detection is also the basis of image segmentation and plays an important role in this process. In this paper, we implement a quantum algorithm based on a line detection mask for novel enhanced quantum representation (NEQR). We build a quantum algorithm for line detection in different directions and design a quantum circuit for line detection. The detailed module designed is also provided. On a classical computer, we simulate the quantum method, and the simulation results prove the feasibility of the quantum method. By analyzing the complexity of quantum line detection, we find that the computation complexity of the proposed method is improved compared to some similar edge detection algorithms.
Log-Linear-Based Logic Mining with Multi-Discrete Hopfield Neural Network

Choosing the best attribute from a dataset is a crucial step in effective logic mining since it has the greatest impact on improving the performance of the induced logic. This can be achieved by removing any irrelevant attributes that could become a logical rule. Numerous strategies are available in the literature to address this issue. However, these approaches only consider low-order logical rules, which limit the logical connection in the clause. Even though some methods produce excellent performance metrics, incorporating optimal higher-order logical rules into logic mining is challenging due to the large number of attributes involved. Furthermore, suboptimal logical rules are trained on an ineffective discrete Hopfield neural network, which leads to suboptimal induced logic. In this paper, we propose higher-order logic mining incorporating a log-linear analysis during the pre-processing phase, the multi-unit 3-satisfiability-based reverse analysis with a log-linear approach. The proposed logic mining also integrates a multi-unit discrete Hopfield neural network to ensure that each 3-satisfiability logic is learned separately. In this context, our proposed logic mining employs three unique optimization layers to improve the final induced logic. Extensive experiments are conducted on 15 real-life datasets from various fields of study. The experimental results demonstrated that our proposed logic mining method outperforms state-of-the-art methods in terms of widely used performance metrics.