Reviewerly/exHarmony huggingface dataset:
Matching-Based Term Semantics Pre-Training for Spoken Patient Query Understanding

Medical Slot Filling (MSF) task aims to convert medical queries into structured information, playing an essential role in diagnosis dialogue systems. However, the lack of sufficient term semantics learning makes existing approaches hard to capture semantically identical but colloquial expressions of terms in medical conversations. In this work, we formalize MSF into a matching problem and propose a Term Semantics Pre-trained Matching Network (TSPMN) that takes both terms and queries as input to model their semantic inter-action. To learn term semantics better, we further design two self-supervised objectives, including Contrastive Term Discrimination (CTD) and Matching-based Mask Term Modeling (MMTM). CTD determines whether it is the masked term in the dialogue for each given term, while MMTM directly predicts the masked ones. Experimental results on two Chinese benchmarks show that TSPMN outperforms strong baselines, especially in few-shot settings <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> .
A hybrid model for text classification using part-of-speech features

In the field of text classification, current research ignores the role of part-of-speech features, and the multi-channel model that can learn richer text information compared to a single model. Moreover, the method based on neural network models to achieve final classification, using fully connected layer and Softmax layer can be further improved and optimized. This paper proposes a hybrid model for text classification using part-of-speech features, namely PAGNN-Stacking1. In the text representation stage of the model, introducing part-of-speech features facilitates a more accurate representation of text information. In the feature extraction stage of the model, using the multi-channel attention gated neural network model can fully learn the text information. In the text final classification stage of the model, this paper innovatively adopts Stacking algorithm to improve the fully connected layer and Softmax layer, which fuses five machine learning algorithms as base classifier and uses fully connected layer Softmax layer as meta classifier. The experiments on the IMDB, SST-2, and AG_News datasets show that the accuracy of the PAGNN-Stacking model is significantly improved compared to the benchmark models.
Easily applicable social virtual reality and social presence in online higher education during the covid-19 pandemic: A qualitative study

Social virtual reality (social VR) platforms gained popularity among users over the last few years. They provide users with a sense of social presence, potentially stimulating social interaction in distance education. Insights on how VR enhances the social aspects of learning are scarce, however, and past examinations often focused on the effects of short usage times of self-developed VR learning applications. To analyze the potential of a simple-to-apply off-the-shelf social VR environment in distance education, in this qualitative study, a sample of 35 master's students employed the AltspaceVR platform for university seminars and group work over a six-week while campus access was restricted due to the covid-19 pandemic, and 23 of them were interviewed to examine their perception of the technology. The results show that social VR facilitated feelings of social presence, a more natural and spontaneous way of communicating with peers and teachers, and an increased sense of community in the classroom, compared to the alternative of videoconferencing. Participants reported high acceptance of the technology for teaching and learning tasks, particularly for interactive activities (e.g., workgroups) in small groups, but also identified limitations related to aspects such as image resolution, lack of note-taking tools, or the feeling of dizziness and fatigue after prolonged use. These results highlight the potential advantages of social VR for stimulating the social dimensions of online education, as well as the remaining challenges that need to be addressed to enable its use in higher education.
Datafying diplomacy: How to enable the computational analysis and support of international negotiations

Computational diplomacy, the application of digital and computational methods to the study and practice of international negotiations, presents unique challenges compared to most other fields in the computational humanities and social sciences. Among them are the necessity of responsiveness when handling crises, the need to anticipate and respond to adversarial behavior, or the need for secrecy in dealing with sensitive information. In this paper, we propose an agenda to address these challenges, and evaluate the feasibility of the various tasks that could be assigned to computational diplomacy. While most analysis tools seem almost ready to use, the availability and reliability of diplomacy-related data remains a major concern.
Using AI to enhance collective intelligence in virtual teams: augmenting cognition with technology to help teams adapt to complexity

The internet has enabled an increasing amount of collaboration to occur via virtual teamwork, including more complex forms where individuals are working on multiple teams simultaneously. We argue that the environmental complexity teams face requires they be designed for collective intelligence, a capability enabling groups to accomplish goals across a wide range of environments. We describe the transactive systems model of collective intelligence, which articulates how individual memory, attention and reasoning give rise to the emergence and mutual adaptation of the transactive memory, attention, and reasoning processes underlying collective intelligence. Furthermore, as artificial intelligence develops more capabilities to facilitate human interaction, we see how it might augment human cognition in ways that will enhance collective intelligence. Developing trust in AI will be essential for enabling higher levels of collective intelligence with tremendous benefits for organizations and society.
Solar irradiance prediction in the tropics using a weather forecasting model

Abstract Prediction of photovoltaic (PV) system generations is a powerful tool for managing the electric grids with multiple PV systems for reducing the instability of their electricity supply. For the prediction, solar irradiance, which is directly related to PV generation is forecasting using a weather forecasting model in this study. The target country, Thailand, is in the tropical zone. In the tropics, cumulus and cumulonimbus appear frequently, and their behavior makes weather forecasting difficult. The correlations of the forecasted irradiances with the observations are about 0.8 or more in the intra-day, next-day, and 2-day ahead forecastings. It shows that the validity of solar irradiance forecasting in the tropics for the prediction of PV output. On cloudy days, the tendency of the fluctuation of solar irradiance is reproduced well in the forecasting, but a phase of the fluctuation is shifted in the forecasting.
Adjustable Pheromone Reinforcement Strategies for Problems with Efficient Heuristic Information

Ant colony optimization (ACO) is a well-known class of swarm intelligence algorithms suitable for solving many NP-hard problems. An important component of such algorithms is a record of pheromone trails that reflect colonies’ experiences with previously constructed solutions of the problem instance that is being solved. By using pheromones, the algorithm builds a probabilistic model that is exploited for constructing new and, hopefully, better solutions. Traditionally, there are two different strategies for updating pheromone trails. The best-so-far strategy (global best) is rather greedy and can cause a too-fast convergence of the algorithm toward some suboptimal solutions. The other strategy is named iteration best and it promotes exploration and slower convergence, which is sometimes too slow and lacks focus. To allow better adaptability of ant colony optimization algorithms we use κ-best, max-κ-best, and 1/λ-best strategies that form the entire spectrum of strategies between best-so-far and iteration best and go beyond. Selecting a suitable strategy depends on the type of problem, parameters, heuristic information, and conditions in which the ACO is used. In this research, we use two representative combinatorial NP-hard problems, the symmetric traveling salesman problem (TSP) and the asymmetric traveling salesman problem (ATSP), for which very effective heuristic information is widely known, to empirically analyze the influence of strategies on the algorithmic performance. The experiments are carried out on 45 TSP and 47 ATSP instances by using the MAX-MIN ant system variant of ACO with and without local optimizations, with each problem instance repeated 101 times for 24 different pheromone reinforcement strategies. The results show that, by using adjustable pheromone reinforcement strategies, the MMAS outperformed in a large majority of cases the MMAS with classical strategies.
PQSCT: Pseudo-Siamese BERT for Concept Tagging With Both Questions and Solutions

The global outbreak of the new coronavirus epidemic has promoted the development of intelligent education and the utilization of online learning systems. In order to provide students with intelligent services, such as cognitive diagnosis and personalized exercises recommendation, a fundamental task is the concept tagging for exercises, which extracts knowledge index structures and knowledge representations for exercises. Unfortunately, to the best of our knowledge, existing tagging approaches based on exercise content either ignore multiple components of exercises or ignore that exercises may contain multiple concepts. To this end, in this article, we present a study of concept tagging. First, we propose an improved pretrained bidirectional encoder representations from transformers (BERT) for concept tagging with both questions and solutions (QSCT). Specifically, we design a question–solution prediction task and apply the BERT encoder to combine questions and solutions, ultimately obtaining the final exercise representation through feature augmentation. Then, to further explore the relationship between questions and solutions, we extend the QSCT to a pseudo-siamese BERT for concept tagging with both questions and solutions. We optimize the feature fusion strategy, which integrates five different vector features from local and global into the final exercise representation. Finally, we conduct extensive experiments on real-world datasets, which clearly demonstrate the effectiveness of our proposed models for concept tagging.
Detection of test smells with basic language analysis methods and its evaluation

Similar to the concept of "code smells", so-called "test smells" describe situations in test code that possibly indicate a deeper problem and require refactoring measures. The elimination of test smells ensures, among other things, that tests are easy to read, easier to maintain and less prone to errors. To automate the detection of test smells, we created the tool "SniffTest" (STest), which detects five types of test smells in JUnit tests, namely Anonymous Test, Long Test, Conditional Test Logic, Assertion Roulette, and Rotten Green Test. STest applies basic natural language processing methods, namely regular expressions and part-of-speech tagging. These methods are mostly programming language independent and can be easily adapted to other languages and frameworks. To measure the accuracy of the tool, we created a labelled dataset containing 854 JUnit test methods and 106 helper methods from eight large open source GitHub repositories, where we manually annotated the test smells. The tool showed an accuracy of 87 to 100% in detecting the different test smells. Both the tool and the dataset are made available as open source.
A Study on Product Recommendation System based on Deep Learning and Collaborative Filtering

This essay analyses and contrasts the literature that has already been written about various product recommendation systems and the mechanisms that underlie them. Although there are many research contributions in the literature, in this article I have critically and thoroughly examined recent research and review papers that are relevant to AI-based product recommendation systems. The present techniques are divided into groups based on the fundamental ideas included into their processes. The idea put out by the concerned writers, the experimental technique, and the performance assessment criteria are highlighted. Also noted are the researchers' assertions. The flaws that have been found are highlighted together with our results from the thorough literature study. This work is crucial for the comparison of different AI-based product recommendation systems, which is a necessary step before dealing with related problems. Following a review of the literature, I have put up a system for recommending products to users, in which the tool suggests products that a particular user would want to buy. I have gathered information about items and users by using ML algorithms. The suggested system establishes a link between the people and the products.
Secured file transmission in knowledge management-cloud

The victory of an organisation is primarily depending on continuous investment learning as well as obtaining some or more new knowledge which creates new business and improve and enhances new performance and techniques. So, this knowledge management should be resulting to more succeeding, or even greater than that of organisations objectives. As per modern methodologies and also paradigms came and emerged, new techniques and efforts should be made in businesses to clearly in line with them, specifically in the area of knowledge management. Nowadays, more popular and efficient one is cloud computing paradigm, because of its less cost, time and efforts to fulfil the need for software development. Also, it gives an excellent measures and ways to collect and reallocate knowledge. Let us discuss the risk factors in utilising cloud computing in the field of knowledge management systems and their solutions.
Secured Transfer and Storage Image Data for Cloud Communications

In cloud computing, resources are used to communicate instead of local servers or individual devices. However, sharing resources among several users is a difficult issue in cloud communication. Cryptography and steganography techniques are used for cloud storage to address data security challenges. This paper presents a novel method for securely encrypting image data for transmission and link exchange with a cloud storage service. There are two phases to accomplish the encryption process, the first phase encrypts the image file by XORing it with a random key that is generated by a new hybrid of the chaotic map. The second phase converts the encrypted image format to audio format to add another layer of security and improve secure image data transfer. The random key is generated using a hybrid chaotic map and has the benefit of having more than 10256 key spaces and the necessary level of security. Based on a statistical analysis of the encryption, the quality of the image is evaluated using several criteria, and the results demonstrate the algorithm's ability to accomplish resist encryption
Toward 3D Face Reconstruction in Perspective Projection: Estimating 6DoF Face Pose From Monocular Image

In 3D face reconstruction, orthogonal projection has been widely employed to substitute perspective projection to simplify the fitting process. This approximation performs well when the distance between camera and face is far enough. However, in some scenarios that the face is very close to camera or moving along the camera axis, the methods suffer from the inaccurate reconstruction and unstable temporal fitting due to the distortion under the perspective projection. In this paper, we aim to address the problem of single-image 3D face reconstruction under perspective projection. Specifically, a deep neural network, Perspective Network (PerspNet), is proposed to simultaneously reconstruct 3D face shape in canonical space and learn the correspondence between 2D pixels and 3D points, by which the 6DoF (6 Degrees of Freedom) face pose can be estimated to represent perspective projection. Besides, we contribute a large ARKitFace dataset to enable the training and evaluation of 3D face reconstruction solutions under the scenarios of perspective projection, which has 902,724 2D facial images with ground-truth 3D face mesh and annotated 6DoF pose parameters. Experimental results show that our approach outperforms current state-of-the-art methods by a significant margin. The code and data are available at https://github.com/cbsropenproject/6dof_face.
A SAT Approach to the Initial Mapping Problem in SWAP Gate Insertion for Commuting Gates

Most quantum circuits require SWAP gate insertion to run on quantum hardware with limited qubit connectivity. A promising SWAP gate insertion method for blocks of commuting two-qubit gates is a predetermined swap strategy which applies layers of SWAP gates simultaneously executable on the coupling map. A good initial mapping for the swap strategy reduces the number of required swap gates. However, even when a circuit consists of commuting gates, e.g., as in the Quantum Approximate Optimization Algorithm (QAOA) or trotterized simulations of Ising Hamiltonians, finding a good initial mapping is a hard problem. We present a SAT-based approach to find good initial mappings for circuits with commuting gates transpiled to the hardware with swap strategies. Our method achieves a 65% reduction in gate count for random three-regular graphs with 500 nodes. In addition, we present a heuristic approach that combines the SAT formulation with a clustering algorithm to reduce large problems to a manageable size. This approach reduces the number of swap layers by 25% compared to both a trivial and random initial mapping for a random three-regular graph with 1000 nodes. Good initial mappings will therefore enable the study of quantum algorithms, such as QAOA and Ising Hamiltonian simulation applied to sparse problems, on noisy quantum hardware with several hundreds of qubits.
Artificial neural network analysis of the day of the week anomaly in cryptocurrencies

Abstract Anomalies, which are incompatible with the efficient market hypothesis and mean a deviation from normality, have attracted the attention of both financial investors and researchers. A salient research topic is the existence of anomalies in cryptocurrencies, which have a different financial structure from that of traditional financial markets. This study expands the literature by focusing on artificial neural networks to compare different currencies of the cryptocurrency market, which is hard to predict. It aims to investigate the existence of the day-of-the-week anomaly in cryptocurrencies with feedforward artificial neural networks as an alternative to traditional methods. An artificial neural network is an effective approach that can model the nonlinear and complex behavior of cryptocurrencies. On October 6, 2021, Bitcoin (BTC), Ethereum (ETH), and Cardano (ADA), which are the top three cryptocurrencies in terms of market value, were selected for this study. The data for the analysis, consisting of the daily closing prices for BTC, ETH, and ADA, were obtained from the Coinmarket.com website from January 1, 2018 to May 31, 2022. The effectiveness of the established models was tested with mean squared error, root mean squared error, mean absolute error, and Theil’s U1, and $${R}_{OOS}^{2}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msubsup> <mml:mi>R</mml:mi> <mml:mrow> <mml:mi>OOS</mml:mi> </mml:mrow> <mml:mn>2</mml:mn> </mml:msubsup> </mml:math> was used for out-of-sample. The Diebold–Mariano test was used to statistically reveal the difference between the out-of-sample prediction accuracies of the models. When the models created with feedforward artificial neural networks are examined, the existence of the day-of-the-week anomaly is established for BTC, but no day-of-the-week anomaly for ETH and ADA was found.
Real-Time Streaming Analytics using Big Data Paradigm and Predictive Modelling based on Deep Learning

With the evolution of distributed streaming platforms analysing humongous time series data, which is streamed continuously from IoT devices become lot easier. In most of the IoT networks the data are in motion or in data centre/cloud. It is possible to process this data in real time similar to edge devices using the big data framework. In data intensive applications predictive analytics require more resources to perform complex computations. Apache Flink framework is capable of performing real time streaming of schema less data and scales very high in distributed environment with low latency, it is used to collect and store the data in the cloud. This work suggests a suitable environment to collect, transport, preprocess and aggregate the data stream to perform predictive analytics using deep learning models. Deep learning automatically extracts features and builds models after training, it has the potential to solve problems that can't be solved by conventional machine learning models. Therefore, the use of algorithms based on deep learning is recommended for forecasting temporal data. Also, we discuss a number of different deep learning forecasting models and analyse the performance of different deep learning forecasting models in order to determine which one is the effective model for single step, multi step and multi variant methods based on error functions with respect to streamed sensor data.
Large Language Model as Unsupervised Health Information Retriever

Retrieving health information is a task of search for health-related information from a variety of sources. Gathering self-reported health information may help enrich the knowledge body of the disease and its symptoms. We investigated retrieving symptom mentions in COVID-19-related Twitter posts with a pretrained large language model (GPT-3) without providing any examples (zero-shot learning). We introduced a new performance measure of total match (TM) to include exact, partial and semantic matches. Our results show that the zero-shot approach is a powerful method without the need to annotate any data, and it can assist in generating instances for few-shot learning which may achieve better performance.
Outsmarting Pac-Man with Artificial Intelligence, or Why AI-Driven Cartel Screening Is Not a Silver Bullet

Journal Article Outsmarting Pac-Man with Artificial Intelligence, or Why AI-Driven Cartel Screening Is Not a Silver Bullet Get access Jerome De Cooman Jerome De Cooman Search for other works by this author on: Oxford Academic Google Scholar Journal of European Competition Law & Practice, lpad017, https://doi.org/10.1093/jeclap/lpad017 Published: 18 May 2023 Article history Received: 16 September 2022 Revision received: 02 March 2023 Published: 18 May 2023