Reviewerly/exHarmony huggingface dataset:
Minimal Satisfiable Product: A Novel Concept in Agile Project Management for better adherence to Customer Requirements

Digitization of manual tasks and processes leads to the introduction of business scenarios that are addressed through software products and ERP solutions. To co-innovate with a business to produce the next generation business application is a challenge. Understanding the end-user requirements is often considered daunting as it is chaotic. The real needs are often hidden, and adoption rates of such applications are always under question if the quality and usability aspects are not considered. There is a difference between creating a Minimal Viable Product and a Minimal Marketable Product which leads to a balanced initial solution that can be a barebone implementation of a customer’s requirements that fulfills the minimum wishes of a customer. In this paper, we take a look at Usability principles, Agile methodologies, and Design Thinking to help development organizations realize the minimal features from a customer’s perspective without working towards fulfilling all the requirements given by the customer. We also take a look at different scenarios where this approach can help realize requirements and name this version of the product, a Minimal Satisfiable Product (MSP).
Beyond the disenchanted university: A pharmacology of the British university in the age of Coronavirus

In the teeth of the coronavirus crisis the British HE system has been thrown into chaos and the severe limitations of the market model have been cruelly exposed. After thirty years of expansion and increasing neoliberalization, the contradictions of the marketized system have been realized by the virus, pushing the entire sector to the edge of bankruptcy. On this basis it is easy to see why the British HE system is haunted by the spectres of commodification, proletarianization, devaluation, alienation, disenchantment and, what Bernard Stiegler calls, disbelief and discredit. From this point of view the future of the British university looks bleak and yet we must consider the pharmacological possibilities that might arise from this dark situation. After explaining the history of the neoliberalization of British HE through the lens of Stiegler’s work in the first part of the article, and then exploring the history of the university in the second part of the paper, in the concluding section I seek to consider ways in which it might be possible to use the crisis to escape the grip of ever-increasing technological nihilism and invent a new model of HE beyond the disenchanted university.
Methods of Formation of Computational Skills Using Information Technologies

The article is based on the main key ideas of the concept of the development of Russian mathematical education, one of which is the idea of using information technologies in mathematical education as a basis for advancing at the world level. The article introduces mathematics teachers to the possibilities of using interactive simulators for the formation of students ' computational skills in the process of organizing oral and written counting in a playful form. Methodological recommendations on the topic "Actions with decimals" are given. The article is addressed to teachers and students of pedagogical universities, methodologists, mathematics teachers.
Movie Scene Argument Extraction with Trigger Action Information

Movie scene argument is an essential part of the movie scene. Movie scene argument extraction can help to understand the movie plot. In this paper we propose a movie scene argument extraction model, which utilizes the trigger action paraphrase as extra information to help improve the argument extraction. Specifically, we obtain the paraphrase of trigger from the dictionary and employ attention mechanism to encode them into an argument oriented embedding vector. Then we use the argument oriented embedding vector and the instance embedding for argument extraction. Experimental results on a movie scene event extraction dataset and a widely used open domain event extraction dataset prove effectiveness of our model.
Soft clustering and interval type-2 fuzzy set based inference strategy for I.T. personnel selection

Impreciseness and uncertainty are the fabrics that make life interesting. For decades, human beings have developed strategies to cope with uncertainties and automate them. In personnel selection for the I.T. field, selectors often find it very difficult to select candidates by going through a set of resumes containing similar kinds of skills. Hence the selection task becomes a fuzzy decision making with the uncertainty involved. A combination of fuzzy clustering and Interval Type-2 fuzzy sets (IT2FS) is proposed in such scenarios. An experiment is conducted over a resume dataset containing fifteen hundred resumes for a particular job description. Firstly, Fuzzy C-means clustering (FCM) is applied for selective clustering, while decision-making under uncertainty is carried through IT2FS. The candidates in the selected cluster are given a score for ranking as per the skillset criteria. The final decision for shortlisting the resumes is carried through IT2FS. The model shows an average accuracy of 88.2% with an F1-score of 0.76 compared to (K-means + IT2FS) model with an F1-score of 0.72. Thus, the proposed model performs better while decision-making under uncertainty.
richYoga: An Interactive Yoga Recognition System Based on Rich Skeletal Joints

In this demo paper, a yoga action recognition system is proposed. Based on the detected 3D positions of the rich skeletal joints provided by the face API, pose API, and hands API from MediaPipe, 543 skeletal joints are used train an LSTM neural network model to classify four yoga actions, including Half Wind Blown, Warrior II, Triangle, and Default. According to the generated yoga action classifier with 85% accuracy, a VR yoga tutor is implemented in the Unity environment to guide a user.
Deep Learning-Aided Receiver Against Nonlinear Distortion of HPA in OFDM Systems

In this paper, we focus on the design of a novel receiver for wireless systems against the channel and the nonlinear distortion caused by high-power amplifiers. With the aid of deep learning, a channel estimator and a signal detector with robustness and efficiency are designed under the framework of the traditional system. In particular, the channel matrix derived from the pilot is converted into a two-dimensional image, and the residual feature aggregation network is applied to reconstruct the channel state information (CSI). In addition, based on the outcome of the estimator and the flexible denoising convolutional neural network, a detection network is derived to denoise and detect signals. The proposed scheme is evaluated in orthogonal frequency division multiplexing (OFDM) systems with fading channels and nonlinear amplifiers, and the experimental results confirmed its superiority in terms of estimation and detection performance.
Automatic Assessment of Buildings Location Fitness for Solar Panels Installation Using Drones and Neural Network

Solar panel location assessment is usually a time-consuming manual process, and many criteria should be taken into consideration before deciding. One of the most significant criteria is the building location and surrounding environment. This research project aims to propose a model to automatically identify potential roof spaces for solar panels using drones and convolutional neural networks (CNN). Convolutional neural networks (CNNs) are used to identify buildings’ roofs from drone imagery. Transfer learning on the CNN is used to classify roofs of buildings into two categories of shaded and unshaded. The CNN is trained and tested on separate imagery databases to improve classification accuracy. Results of the current project demonstrate successful segmentation of buildings and identification of shaded roofs. The model presented in this paper can be used to prioritize the buildings based on the likelihood of getting benefits from switching to solar energy. To illustrate an implementation of the presented model, it has been applied to a selected neighborhood in the city of Hurricane in West Virginia. The research results show that the proposed model can assist investors in the energy and building sectors to make better and more informed decisions.
Hekate a tool for gauging Data Deduplication Performance

In this paper, we introduce Hekate a tool for gauging the performance of data deduplication. In particular, Hekate allows to test classic, chunk-based deduplication as well as the emerging concept of generalized deduplication, which leverages different data transformations prior to deduplication. We show that Hekate can be used to evaluate performance with different system configurations and deduplication schemes. In fact, we use six real-world data sets to showcase Hekate's ability to test performance over a wide range of conditions. We also use this evaluation to showcase how Hekate can provide insights into data deduplication performance in real systems and optimal configurations given specific data characteristics of an expected workload. Hekate can also be used to determine the suitability of various deduplication approaches for each data set.
A Novel Auction Blockchain System with Price Recommendation and Trusted Execution Environment

Online auctions are now widely used, with all the convenience and efficiency brought by internet technology. Despite the advantages over traditional auction methods, some challenges still remain in online auctions. According to the World Business Environment Survey (WBES) conducted by the World Bank, about 60% of companies have admitted to bribery and manipulation of the auction results. In addition, buyers are prone to the winner’s curse in an online auction environment. Since the increase in information availability can reduce uncertainty, easy access to relevant auction information is essential for buyers to avoid the winner’s curse. In this study, we propose an Online Auction Price Suggestion System (OAPSS) to protect the data from being interfered with by third-party programs based on Intel’s Software Guard Extensions (SGX) technology and the characteristics of the blockchain. Our proposed system provides a smart contract by using α-Sutte indicator in the final transaction price prediction as a bidding price recommendation, which helps buyers to reduce the information uncertainty on the value of the product. The amount spent on the smart contract in this study, excluding deployed contracts, plus the rest of the fees is less than US$1. Experimental results of the simulation show that there is a significant difference (p &lt; 0.05) between the recommended price group and the actual price group in the highest bid. Therefore, we may conclude that our proposed bidder’s price recommendation function in the smart contract may mitigate the loss of buyers caused by the winner’s curse.
Time-Variant and Time-Invariant Matrix Inequations of Zhang Equivalency Besides Matrix Equations

Via the paper, the ZE (Zhang equivalency) concept is firstly revisited, and then, on the basis of ZE, equation-type ZE and inequation-type ZE are further showed and discussed. By utilizing ZND (i.e., Zhang neural dynamics) method, we present the general expressions of equation-type ZE, and apply them in seven types of time-variant matrix situations. Then, we further discuss the inequation-type ZE and present the order-l (i.e, 01), order-2 (i.e, 02), and order-3 (i.e, 03) general expressions of inequation-type ZE. By utilizing these expressions, we hence propose the 01, 02, and 03 expressions of inequation-type ZE in six types of matrix situations for handling corresponding time-variant matrix inequation problems.
Improved Discoverability of Digital Objects in Institutional Repositories Using Controlled Vocabularies

Higher Education Institutions (HEIs) utilise Institutional Repositories (IRs) to electronically store and make available scholarly research output produced by faculty staff and students. With the continued increase of scholarly research output produced, accurate and comprehensive association of subject headings to digital objects, during ingestion into IRs is crucial for effective discoverability of the objects and, additionally facilitating the discovery of related content. This paper outlines a case study conducted at an HEI-The University of Zambia-in order to demonstrate the effectiveness of integrating controlled subject vocabularies during the ingestion of digital objects in to IRs. A situational analysis was conducted to understand how subject headings are associated with digital objects and to analyse subject headings associated with already ingested digital objects. In addition, an exploratory study was conducted to determine domain-specific subject headings to be integrated with the IR. Furthermore, a usability study was conducted in order to comparatively determine the usefulness of using controlled vocabularies during the ingestion of digital objects into IRs. Finally, multi-label classification experiments were carried out where digital objects were assigned with more than one class. The results of the study revealed that the majority of digital objects are currently associated with two or less subject headings (71.2 %), with a significant number of subject headings (92.1 % being associated with a single publication, The comparative study suggests that IRs integrated with controlled vocabularies are perceived to be more usable (SUS Score = 68.9) when compared with IRs without controlled vocabularies (SUS Score = 66.2). The effectiveness of the multi-label arXiv subjects classifier demonstrates the viability of integrating automated techniques for subject classification.
An Effective and Efficient Method for Word-Level Textual Adversarial Attack

Adversarial examples are used to reveal the vulnerability of deep neural networks (DNNs) and improve their robustness. The word-level attack is a well-studied class of textual adversarial attack methods. However, existing word-level attacks have unstable success rates in different application scenarios. And the attacks under black-box setting suffer from low efficiency because they need to query the target DNN model with a great quantity. In this paper, we present SynonymPSO, a word-level attack method for generating adversarial texts. Specifically, we use a variety of means to find and filter synonyms to construct a comprehensive candidate pool. Besides, we design a kind of modification record strategy to improve the efficiency of the particle swarm optimization algorithm. Compared with prior works, SynonymPSO has the following features: (1) effective - it outperforms the state-of-art attacks in terms of attack success rate on most occasions; (2) efficient - it generates adversarial examples with fewer queries and less time. We evaluate SynonymPSO on five datasets that belong to different text classification tasks, including sentiment analysis, natural language inference and spam detection. The experimental results demonstrate its effectiveness and efficiency. For instance, when attacking BiLSTM over Enron dataset, the attack success rate of our method is 20% higher than the baseline while the query number is reduced by 94%.
Adversarial Multi-Agent Path Finding is Intractable

Adversarial Multi-Agent Path Finding (AMAPF) extends the standard discrete Multi-Agent Path Finding with an adversarial element. Agents of two competing teams are deployed in a shared environment represented by an undirected graph. The first team aims to navigate all its agents from their initial locations to given goal locations, while the second team aims to prevent agents of the first team from fulfilling their goal. We prove that the problem of finding a winning strategy is EXPTIME-complete.
A Novel Cultural Algorithm based on Particle Swarm Optimization and Whale Optimization Algorithm

Abstract An ameliorative cultural algorithm (CA) based on particle swarm optimization (PSO) and whale optimization algorithm (WOA) is raised (CA-PSOWOA), so as to conquer the defects of WOA and PSO, such as poor global exploration ability and easy fall into local optimal solution. Firstly, a nonlinear inertia weight strategy is leaded to optimize the PSO and WOA, then CA is introduced to regulate the ability of global exploration and local exploitation of PSO and WOA. By testing on benchmark functions, it is proved that CA-PSOWOA improves the global exploration ability and solution accuracy, and its performance is better than the traditional PSO and WOA, and other algorithms.
A QIM-based Watermarking Method for 3D Mesh Integrity Protection

In this paper, a new QIM-based watermarking method for 3D meshes is proposed. The watermark is embedded into the distance ratio between three points to ensure the method invariance to geometrical transformations. The method is blind and operates efficiently without knowing any information about the watermarked model structure. The experimental study has shown that the method is efficient in terms of fidelity, capacity and robustness to affine transformations, and thus can be applied for integrity protection of 3D models.
ConTEXT Explorer: a web-based text analysis tool for exploring and visualizing concepts across time

ConTEXTExplorer is an open Web-based system that assists in exploring the context of concepts (combinations of co-occurring words and phrases) over time in text documents.It provides a user-friendly interface for the analysis of user-provided text data and integrates functionalities of the Whoosh search engine, Spacy, Gensim, and Plotly Python libraries.By providing suggestions for query expansion and producing interactive plots, ConTEXT Explo rer facilitates exploratory data analysis, which can serve as the basis for subsequent text classification.
Soft Law for Unbiased and Nondiscriminatory Artificial Intelligence

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DRFM Jamming Mode Identification Leveraging Deep Neural Networks

In modern information warfare, radar must apply some anti-jamming technologies. Digital radio frequency memory (DRFM) and its technology can adapt to the complex threat signal environment and have an excellent jamming effect that other technical means in the past cannot match. Currently, traditional DRFM jamming identification is mainly based on the method of extracting and modeling radar signal features. It is unable to identify various types of jamming and lacks a large amount of radar data. In this work, we propose a three-phase framework to identify DRFM jamming. First, we design a hardware system based on PXI module to generate jamming signal data. Second, we perform range-doppler processing on the raw radar signal data to generate range-doppler map (RD-map) which can accurately represent jamming signals. Finally, we use deep learning to identify DRFM jamming and use pre-trained models to improve identification accuracy. We evaluate our proposed framework using real DRFM jamming data collected from PXI system. The valuation results confirm that our approach effectively identifies DRFM jamming, achieving an average accuracy of 84.4%.
Adaptive Ladder Loss for Learning Coherent Visual-Semantic Embedding

For visual-semantic embedding, the existing methods normally treat the relevance between queries and candidates in a bipolar way – relevant or irrelevant, and all “irrelevant” candidates are uniformly pushed away from the query by an equal margin in the embedding space, regardless of their various proximity to the query. This practice disregards relatively discriminative information and could lead to suboptimal ranking in the retrieval results and poorer user experience, especially in the long-tail query scenario where a matching candidate may not necessarily exist. In this paper, we introduce a continuous variable to model the relevance degree between queries and multiple candidates, and propose to learn a coherent embedding space, where candidates with higher relevance degrees are mapped closer to the query than those with lower relevance degrees. In particular, the new ladder loss is proposed by extending the triplet loss inequality to a more general inequality chain, which implements variable push-away margins according to respective relevance degrees. To adapt to the varying mini-batch statistics and improve the efficiency of the ladder loss, we also propose a Silhouette score-based method to adaptively decide the ladder level and hence the underlying inequality chain. In addition, a proper Coherent Score metric is proposed to better measure the ranking results including those “irrelevant” candidates. Extensive experiments on multiple datasets validate the efficacy of our proposed method, which achieves significant improvement over existing state-of-the-art methods.
A Motion-based Seq-bbox Matching Method for Video Object Detection

Actual video object detection performance should be improved because of the blurring and occlusion defor-unation of objects. Current algorithms, such as FGFA and Seq-NMS, cannot achieve a tradeoff between speed and accuracy in practical applications simultaneously. This study aims to propose a practical video object detection algorithm to improve the accuracy of detection and guarantee real-time detection. In this work, a post-processing method that is based on a single-frame detection algorithm and is called motion-based Seq-Bbox matching is proposed, and an inter-frame motion information is introduced to enhance the detection results. Distance Intersection over Union is utilized to represent the inter-frame motion information, and the idea that the same object between adjacent frames should have similar motion information is proposed. Moreover, the dynamic confidence averaging method is combined to complete the enhancement of the prediction results jointly. Based on YOLOv5, experimental re-sults show that the proposed algorithm achieves 72.7% mean average precision (mAP) and obtains a 5.5% mAP (67.2%-72.7%) improvement, while detection speed reaches 40.4 frames per second. Thus, the proposed algorithm achieves excellent results in terms of balancing speed and accuracy.
High- Performance Printed Arabic Optical Character Recognition System Using ANN Classifier

Optical Character Recognition (OCR) systems were developed with high accuracy to facilitate transactions and increase human-computer interaction in most levels of government and commerce sectors. OCR has been adopted for diverse languages, but a few efforts have mainly been conducted in Arabic characters, and it has suffered weakness in the Arabic language. Therefore, a new Arabic Optical Character Recognition (AOCR) system is proposed to achieve a highperformance recognition system in printed images. Several steps are conducted to achieve the proposed AOCR system, such as image preprocessing, segmentation (line, words, and character), feature extraction, and classification. After evaluated the recognition system with multi-criteria, the AOCR results have shown accuracy with 95% with different quality document images (spatial resolution); besides, the system was adequate to resist the degradation of the documents, compared to other commercial systems in the literature.