Reviewerly/exHarmony huggingface dataset:
Author Gender Identification for Urdu Articles

In recent years, author gender identification has gained considerable attention in the fields of computational linguistics and artificial intelligence. This task has been extensively investigated for resource-rich languages such as English and Spanish. However, researchers have not paid enough attention to perform this task for Urdu articles. Firstly, I created a new Urdu corpus to perform the author gender identification task. I then extracted two types of features from each article including the most frequent 600 multi-word expressions and the most frequent 300 words. After I completed the corpus creation and features extraction processes, I performed the features concatenation process. As a result each article was represented in a 900D feature space. Finally, I applied 10 different well-known classifiers to these features to perform the author gender identification task and compared their performances against state-of-the-art pre-trained multilingual language models, such as mBERT, DistilBERT, XLM-RoBERTa and multilingual DeBERTa, as well as Convolutional Neural Networks (CNN). I conducted extensive experimental studies which show that (i) using the most frequent 600 multi-word expressions as features and concatenating them with the most frequent 300 words as features improves the accuracy of the author gender identification task, and (ii) support vector machine outperforms other classifiers, as well as fine-tuned pre-trained language models and CNN. The code base and the corpus can be found at: https://github.com/raheem23/Gender_Identification_Urdu .
Developing a Student Monitoring System for Online Classrooms Based on Face Recognition Approaches

One of the primary activities that lectures usually do is to take a roll call. This activity not only helps lecturers determine the participation of students but also detect strangers in the classroom. When the number of students increases, lectures take more time to monitor and check students’ attendance. We propose a student monitoring system based on facial recognition approaches to tackle that problem. With the recent development of deep learning techniques, many new approaches have made remarkable progress in face recognition. However, most of those approaches only focus on improving accuracy, while a practical end-to-end face recognition system demands good accuracy and reasonable runtime. We make adjustments and apply CenterFace for the face detection task and ArcFace for extracting embedding features from images to achieve high efficiency in both accuracy and speed. In addition, our proposed system is designed to be lightweight and scalable, capable of running in various environments, especially in a web browser. The results show that the system takes an average of 0.22 s to register a new face and 4.3 s for identifying a face in a database of 500 samples. Experiments also indicate that the system was less likely to misrecognize faces in most of our tests.
Temporal Segmentation of Basketball Continuous Videos Based on the Analysis of the Camera and Player Movements

Sports video segmentation is a very useful process in video indexing, video summarization, or video highlight detections. For continuous videos when the full game is recorded the segmentation cannot be based on the detection of cuts or cross-dissolve transitions as it is the case of edited videos like sports news or other short sports coverages. One basketball game usually lasts even for around two hours including play parts as well as breaks. In the paper the approach of a basketball game segmentation is presented and examined. The proposed method is based on the analysis of the camera and player movements. The tests performed in the AVI Indexer showed that the analysis of the camera and player movements leads to the correct detection of segments with game breaks but also segments with the interesting highlights in continuous basketball videos.
Medical Prescription Recognition Using Heuristic Clustering and Similarity Search

The necessity to convert printed documents to facilitate the storage and retrieval of information is growing, particularly in the medical and healthcare industries. In our last work, we presented a method to extract prescriptions from images using CRAFT and TESSERACT so that patients could quickly save and check up on their pharmaceutical use information. However, the slow processing speed and the limited number of medication names lead to it being impractical. Based on this model structure, a new system is introduced, using bounding box clustering heuristics to detect the featured text areas, before employing VietOCR tool to identify the texts in prescription images. Simultaneously, a fast and accurate technique for extracting prescriptions is developed, utilizing word embedding and the vector search algorithm. The experiment results reveal that the proposed model significantly reduces the error of the retrieved data on the two standard measures, WER and CER, prominently with CER lowered to 26.95. Furthermore, the execution time decreases from 17.81 s to an average of 3.64 s, demonstrating the great effectiveness of our effort to improve the prior system.
Context-free Transformer-based Generative Lemmatiser for Polish

In this paper we present a new approach to the problem of lemmatisation in inflectional languages on the example of Polish. We made an introduction to the problem domain, described the solution used – the Transformer architecture and learning process on lexical data – and presented experimental results showing a high degree of generalization of the new solution. At the very end, we presented conclusions and plans for future research.
Querying and Reasoning in Paraconsistent Rule-Object Languages with Inheritance Expressions

Inheritance has intensively been investigated during the past decades in object-oriented programming and knowledge representation and reasoning areas. In the paper we focus on recently introduced inheritance expressions that allow one to represent dynamic concept hierarchies as well as fuse and disambiguate beliefs acquired by the objects involved. We focus on querying and reasoning about inheritance expressions using a four-valued paraconsistent formalism that has been developed over the last ten years. In particular, we show that querying inheritance expressions and formulas can be efficiently implemented. In addition, we provide tableaux for general reasoning purposes. Complexity of the investigated tools is also analyzed.
Stochastic Expectation Propagation Learning for Unsupervised Feature Selection

We introduce a statistical procedure for the simultaneous clustering and feature selection of positive vectors. The proposed method is based on well-principled infinite generalized inverted Dirichlet (GID) mixture models. Unlike most currently existing learning algorithms, based on frequentist or Bayesian approaches, that have been proposed for GID mixtures, our framework is based on stochastic expectation propagation that offers a good compromise. The performance of the proposed algorithm is examined using both simulated and real world data sets.
Smart Contracts and Payment in the UK Construction: The Legal Framework

This chapter critically evaluates the way in which the existing United Kingdom (UK) construction payment regime will function with—and assist—payment mechanisms which utilise smart contracts. Blockchain is one of several new developments in the increasingly technologically developing UK construction industry. Whilst the law translates real-world actions into legal obligations to pay and then assists in turning those obligations into payment, the blockchain with smart contract mechanisms will automate that process, providing security and removing any intermediation which could stop or slow the process down illegitimately. Coupled with the use of smart contracts, therefore, blockchain technology has the potential to facilitate a solution to the payment and cash flow issues in the UK construction industry. To achieve the added functionality described and thereby make it a useful tool for payment in construction, however, these developments would need to coexist with the existing legal framework. There are important points in the detail that should be more fully understood by users of the blockchain/smart contract systems, and which are explored in this chapter.
Concatenated Permutation Codes under Chebyshev Distance

Permutation codes are error-correcting codes over symmetric groups. We focus on permutation codes under Chebyshev (ℓ∞) distance. A permutation code invented by Kløve et al. is of length n, size 2n-d and, minimum distance d. We denote the code by φn,d. This code is the largest known code of length n and minimum Chebyshev distance d > n/2 so far, to the best of the authors knowledge. They also devised efficient encoding and hard-decision decoding (HDD) algorithms that outperform the bounded distance decoding. In this paper, we derive a tight upper bound of decoding error probability of HDD. By factor graph formalization, we derive an efficient maximum a-posterior probability decoding algorithm for φn,d. We explore concatenating permutation codes of φn,d=0 with binary outer codes for more robust error correction. A naturally induced pseudo distance over binary outer codes successfully characterizes Chebyshev distance of concatenated permutation codes. Using this distance, we upper-bound the minimum Chebyshev distance of concatenated codes. We discover how to concatenate binary linear codes to achieve the upper bound. We derive the distance distribution of concatenated permutation codes with random outer codes. We demonstrate that the sum-product decoding performance of concatenated codes with outer low-density parity-check codes outperforms conventional schemes.
Reinforcement Learning

This chapter briefly reviews some fundamental concepts, standard problem formulations, and classical algorithms of reinforcement learning (RL). Specifically, we first review Markov decision processes (MDPs) and dynamic programming (DP), which provide mathematical foundations for both the problem formulation and algorithm design for RL. Then we review some classical RL algorithms, such as Q-learning, Sarsa, policy gradient, and Thompson sampling. Finally, we provide a high-level review of the exploration schemes in RL and approximate solution methods for large-scale RL problems. At the end of this chapter, we also provide some pointers for further reading.
Time Series Analysis and Prediction

In this chapter, we present essential parts of time series analysis, with the objective of predicting or forecasting its future development. Predicting future behavior is generally more successful for stationary series, which do not change their stochastic characteristics as time proceeds. We develop and illustrate time series which are of both types, namely, covariance stationary and non-stationary.
Conceptual Model Utilizing Blockchain to Automate Project Bank Account (PBA) Payments in the Construction Industry

The UK (United Kingdom) government published a guidance document in 2012 stipulating the use of project bank accounts (PBA) to promote fair and prompt payment practices in the construction industry. This article provides a high-level conceptual model utilising blockchain to automate project bank account payments. In PBA, project funds are partitioned in a separate bank account, like an escrow. Traditionally, before PBA, the main contractor would use the client’s project payments to reinvest in new work, or strategically withhold supply chain payments to sustain positive cash flow. PBA revokes the main contractor as the sole recipient of the project budget and provides the client with transparency over project expenditures. The proposed conceptual model allows project participants to approve and execute automated payments through user dashboards. Part of the security of smart contracts is their unchangeable properties once deployed; however, this is problematic, as construction projects regularly undergo change orders and programme alterations. Furthermore, Ethereum-based smart contracts in the current environment are limited due to the costs associated with auditing and on-chain hosting fees. To mitigate this, transactions in the PBA blockchain model are instantiated through an off-chain application, which stores pre-executed transactions in the form of signed messages. These messages are pushed to the blockchain and converted to transactions once they are approved by validating authorities. The result is a strategy to achieve payment automation at a more economical cost. The proposed model illustrates a high-level amalgamation of PBA, blockchain, off-chain, and asset tokenization. A limitation of this article is that it does not include any programming and the ideas are presented in the form of a flowchart. Future work includes programming the solution. Blockchain Project bank accounts Payments Construction industry Off-chain Smart contracts
Deep Label Feature Fusion Hashing for Cross-Modal Retrieval

The rapid growth of multi-modal data in recent years has driven the strong demand for retrieving semantic related data within different modalities.Therefore, cross-modal hashing has attracted extensive interest and studies due to its fast retrieval speed and good accuracy.Most of the existing crossmodal hashing models simply apply neural networks to extract the features of the original data, ignoring the unique semantic information attached to each data by the labels.In order to better capture the semantic correlation between different modal data, a novel cross-modal hashing model called deep label feature fusion hashing (DLFFH) is proposed in this article.We can effectively embed semantic label information into data features by building label networks in different modal networks for feature fusion.The fused features can more accurately capture the semantic correlation between data and bridge the semantic gap, thus improving the performance of cross-modal retrieval.In addition, we construct feature label branch and corresponding feature label loss to ensure that the generated hash codes are discriminative.Extensive experiments have been conducted on three general datasets and the results demonstrate the superiority of the proposed DLFFH which performs better than most cross-modal hashing models.
Using Agile Principles to Efficiently Execute FMEAs with a Distributed Workforce

The Failure Modes Effects Analysis (FMEA) holds incredible potential for improving products and processes prior to their ultimate manifestation. The FMEA team typically participates in group working meetings in the same meeting room. These meetings can be expensive depending on the size of the FMEA team and how large or detailed the analysis is. With the ever present desire to reduce product budget and the emergence of distributed teams (i.e. a remote workforce), a fresh approach to FMEA facilitation and execution is desired.The Agile project management approach pioneered in the software industry has a promising track record and suite of well-developed concepts and tools. This paper highlights how the values and principles of the Agile manifesto can be interpreted in the FMEA context. Additionally, Agile methods and tools (i.e. Agile practices) which have shown to be useful in FMEA facilitation are examined.The benefits observed from adopting an Agile mindset and implementing Agile methodologies include increased frequency of customer feedback, reduced labor hours for FMEA completion in comparison to historical results, better subject matter expert (SME) engagement, and improved recognition of FMEA progress. The shortcomings observed thus far stem from the Agile core values not being fully embraced and a lack of familiarity with the Agile tools
«THE MAGNETOSPHERE»: A POSTER AT THE EXHIBITION«EARTH IN THE UNIVERSE» IN THE EARTH SCIENCE MUSEUM

The poster at the exhibition «Earth in the Universe» in the Earth Science Museum demonstrates specific features of the structure and dynamics of the complex system of Earth’s magnetosphere. Processes occurring in the magnetosphere are considered.
Reinforcement Learning using Reward Expectations in Scenarios with Aleatoric Uncertainties

In scenarios with aleatoric uncertainties, the reward got by an agent when executing the same action in the same state is random, which can reduce the stability and convergence speed of the reinforcement algorithms. However, in most scenarios, reward functions have regularity, and their expectations are determined, which can be got through models or sample statistics. This paper discusses the distribution relationship between reward functions and value functions in scenarios with aleatoric uncertainties and proves the feasibility of using reward expectations for reinforcement learning. Finally, experiments show that algorithms have better stability and convergence speed when using reward expectations than random rewards.
Improved Action Prediction through Multiple Model Processing of Player Trajectories

Action prediction in video games is the process of extracting useful information in order to predict the future actions of a player. Long-range dependencies and the dynamic nature of video games make it difficult for most algorithms to accurately predict the future actions of players. We propose a novel machine learning approach to improving future action prediction from video game trajectories. This method requires having first clustered player trajectories based on behaviour similarities. Our model consists of a set of LSTM based prediction modules each trained on a subset of data based upon a respective cluster. The effectiveness of our model is analysed on both a synthetic and natural dataset. We find that our future action prediction approach of leveraging multiple models trained on individual data subsets results in greater accuracy over a single model on a complete dataset.
LILAC: Learning a Leader for Cooperative Reinforcement Learning

In cooperative multi-agent reinforcement learning,role-based learning promises to reach satisfactory policy learning through the decomposition of complicated tasks using roles. Different roles are responsible for different aspects of the task. However, how this group of roles can be quickly identified is not clear. To address this problem, we propose a novel framework, LearnIng a LeAder for Cooperative reinforcement learning (LILAC), which introduces a leader to integrate information to assign roles. Leaders take a broad view of the whole task and feed the integrated information into a Gaussian mixture model to sample role embedding distribution. It enables LILAC to assign appropriate roles to different agents and improves cooperative performance. In order to evaluate the cooperation of multiple agents, a mixing network, inputted by individual local utility networks, is constructed to estimate the global action value. Two loss functions, temporal difference loss and mean divergence loss, are adopted by LILAC to learn network parameters and to encourage diversity of policies for different roles. By virtue of the leader module, LILAC outperforms the StarCraft II micromanagement benchmark in our experiments, especially on challenging tasks.
Quickly Detecting Skill Trace in Games

This short paper introduces the notion of the skill trace of a game, which indicates its potential for tactical and/or strategic interest. A simple method is presented for quickly detecting the skill trace of a given game.
Let’s Make Games Together: Explainability in Mixed-initiative Co-creative Game Design

There has been growing development of co-creative systems for game design, where both humans and computers work as colleagues, proactively contributing with creative input. However, the collaborative process is still not as seamless as in human-human co-creativity. A key element still underdeveloped in these approaches is the communication between the human and the machine, which can be facilitated by providing the computational agent with explanatory capabilities. Based on principles of explainability for co-creative systems from previous literature, we propose a framework of explainability specifically applied to mixed-initiative scenarios in game design. We illustrate the applications of the framework by suggesting possible solutions adapted to different use cases of existing approaches and, additionally, of our own proposed approach.
Hero featured learning algorithm for winning rate prediction of Honor of Kings

Recent years have witnessed much research effort on automatically predicting game results (win predictions), which has great potential in esports live streaming and game commentator AI systems. The exsiting works on win prediction largely overlook the interpretability and the predication accuracies are hardly satisfactory. To address this issue, we collected a large-scale dataset that contains real-time game records with rich input features of the popular game Honor of Kings. For interpretable and more accurate predictions, we proposed a Hero Featured Network (HFN) by learning from real Honor of Kings combat data and heros’ mutual attributes and interactions. For each time stage of the game, the prediction accuracy can archive 84.7%. The results show that HFN model can not only provide accurate real-time win predictions but also attribute the ultimate prediction results to the contributions of heros’ mutual attributes and interactions for interpretability.
Stirring the Pot - Teaching Reinforcement Learning Agents a ”Push-Your-Luck” board game

Recent successes in AI research concerning traditional games like GO, have led to increased interest in the field of reinforcement learning. Modern board game design, however, has risen in complexity. This paper introduces a novel task for reinforcement learning: “Quacks of Quedlinburg”. A modern board game with risk management, deck building, and the option to choose a specific rule set out of thousands of possible combinations for every game. We provide an environment based on the game and perform initial experiments. In these, we found that Deep Q-Learning agents can significantly outperform simple heuristics.
Open-Source Hardware Memory Protection Engine Integrated With NVMM Simulator

With growing on-device IoT processing, security on edge devices becomes increasingly important. Among Trusted Execution Environment (TEE), an open-source RISC-V Keystone TEE is the expected one. However, some issues remain when applying it to various devices: untrusted DRAM, and untrusted path to non-volatile storage. These issues can be resolved by Memory Protection Engine (MPE) based on an integrity tree, and Non-Volatile Main Memory (NVMM), respectively. TEE, MPE, and NVMM must be cooperatively optimized to exploit performance. Despite this demand, there is no widely available platform which enables fast, reliable, and system-wide evaluation. In the paper, we provide an open-source hardware simulator for secure edge devices. We implemented an MPE using SGX-style Integrity Tree on the Keystone compatible RISC-V SoC. Then, we ported the NVMM simulation technique to it. Its whole design was publicized to widely provide a baseline hardware design. The MPE behavior was validated by using micro benchmarks. It revealed that the MPE read/write overhead is <inline-formula><tex-math notation="LaTeX">$2.55\times /4.16\times$</tex-math></inline-formula> on DRAM, and <inline-formula><tex-math notation="LaTeX">$3.05\times /5.40\times$</tex-math></inline-formula> on NVMM, respectively. Besides, we discuss our work's role by comparing with the gem5 considering TEE evaluation time and impact of the protected NVMM.