Reviewerly/exHarmony huggingface dataset:
Identification and assessment of sites for solar farms development using GIS and density based clustering technique- A case of Pakistan

Site selection plays a vital role in the entire life cycle of solar farm and merits further consideration. Current studies for the site selection have several limitations. First, application of criteria related to infrastructure in the initial phase can screen out prospective solar farms or make them less desirable. Second, infrastructure criteria are less significant in circumstances where new cities and town are to be planned. Lastly, clustering is often ignored in site selection problem to classify farms and determine their sizes. To overcome these limitations, this study proposes a methodology that focuses primarily on areas that has maximum energy potential and excludes the infrastructure requirements initially from the analysis. The methodology uses Geographical Information System (GIS) for data acquisition and mapping, whereas a novel density-based clustering approach is employed to identify and group sites with high solar potential. The methodology is applied to the geographic boundaries of Pakistan. However, the methodology can be applied to any spatial context subject to the availability of similar data. The paper concludes with recommendations to energy policy makers by providing a list of potential clusters, with their sizes ranging between 10−289km2 located in the Baluchistan province of Pakistan.
Scene Recognition With Prototype-Agnostic Scene Layout

Exploiting the spatial structure in scene images is a key research direction for scene recognition. Due to the large intra-class structural diversity, building and modeling flexible structural layout to adapt various image characteristics is a challenge. Existing structural modeling methods in scene recognition either focus on predefined grids or rely on learned prototypes, which all have limited representative ability. In this paper, we propose Prototype-agnostic Scene Layout (PaSL) construction method to build the spatial structure for each image without conforming to any prototype. Our PaSL can flexibly capture the diverse spatial characteristic of scene images and have considerable generalization capability. Given a PaSL, we build Layout Graph Network (LGN) where regions in PaSL are defined as nodes and two kinds of independent relations between regions are encoded as edges. The LGN aims to incorporate two topological structures (formed in spatial and semantic similarity dimensions) into image representations through graph convolution. Extensive experiments show that our approach achieves state-of-the-art results on widely recognized MIT67 and SUN397 datasets without multi-model or multi-scale fusion. Moreover, we also conduct the experiments on one of the largest scale datasets, Places365. The results demonstrate the proposed method can be well generalized and obtains competitive performance.
Requirements Engineering in the Days of Artificial Intelligence

Artificial Intelligence (AI) has a long tradition in software and requirements engineering (RE). Over the years, many AI techniques have been employed to represent and analyze requirements, ranging from knowledge representation and reasoning in the 1980s to the use of natural language (NL) processing, machine learning, and deep learning since the 2000s. AI techniques have been successfully applied in practice, for example, to manage large-volume requirements.<sup>1<sup>
Text Messaging-Based Medical Diagnosis Using Natural Language Processing and Fuzzy Logic

The use of natural language processing (NLP) methods and their application to developing conversational systems for health diagnosis increases patients’ access to medical knowledge. In this study, a chatbot service was developed for the Covenant University Doctor (CUDoctor) telehealth system based on fuzzy logic rules and fuzzy inference. The service focuses on assessing the symptoms of tropical diseases in Nigeria. Telegram Bot Application Programming Interface (API) was used to create the interconnection between the chatbot and the system, while Twilio API was used for interconnectivity between the system and a short messaging service (SMS) subscriber. The service uses the knowledge base consisting of known facts on diseases and symptoms acquired from medical ontologies. A fuzzy support vector machine (SVM) is used to effectively predict the disease based on the symptoms inputted. The inputs of the users are recognized by NLP and are forwarded to the CUDoctor for decision support. Finally, a notification message displaying the end of the diagnosis process is sent to the user. The result is a medical diagnosis system which provides a personalized diagnosis utilizing self-input from users to effectively diagnose diseases. The usability of the developed system was evaluated using the system usability scale (SUS), yielding a mean SUS score of 80.4, which indicates the overall positive evaluation.
Progressive Transformers for End-to-End Sign Language Production

The goal of automatic Sign Language Production (SLP) is to translate spoken language to a continuous stream of sign language video at a level comparable to a human translator. If this was achievable, then it would revolutionise Deaf hearing communications. Previous work on predominantly isolated SLP has shown the need for architectures that are better suited to the continuous domain of full sign sequences. In this paper, we propose Progressive Transformers, the first SLP model to translate from discrete spoken language sentences to continuous 3D sign pose sequences in an end-to-end manner. A novel counter decoding technique is introduced, that enables continuous sequence generation at training and inference. We present two model configurations, an end-to-end network that produces sign direct from text and a stacked network that utilises a gloss intermediary. We also provide several data augmentation processes to overcome the problem of drift and drastically improve the performance of SLP models. We propose a back translation evaluation mechanism for SLP, presenting benchmark quantitative results on the challenging RWTH-PHOENIXWeather-2014T (PHOENIX14T) dataset and setting baselines for future research. Code available at https://github.com/BenSaunders27/ProgressiveTransformersSLP .
Multilingual LAMA: Investigating Knowledge in Multilingual Pretrained Language Models

Recently, it has been found that monolingual English language models can be used as knowledge bases.Instead of structural knowledge base queries, masked sentences such as "Paris is the capital of [MASK]" are used as probes.We translate the established benchmarks TREx and GoogleRE into 53 languages.Working with mBERT, we investigate three questions.(i) Can mBERT be used as a multilingual knowledge base?Most prior work only considers English.Extending research to multiple languages is important for diversity and accessibility.(ii) Is mBERT's performance as knowledge base language-independent or does it vary from language to language?(iii) A multilingual model is trained on more text, e.g., mBERT is trained on 104 Wikipedias.Can mBERT leverage this for better performance?We find that using mBERT as a knowledge base yields varying performance across languages and pooling predictions across languages improves performance.Conversely, mBERT exhibits a language bias; e.g., when queried in Italian, it tends to predict Italy as the country of origin.
General Performance Score for classification problems

Abstract Several performance metrics are currently available to evaluate the performance of Machine Learning (ML) models in classification problems. ML models are usually assessed using a single measure because it facilitates the comparison between several models. However, there is no silver bullet since each performance metric emphasizes a different aspect of the classification. Thus, the choice depends on the particular requirements and characteristics of the problem. An additional problem arises in multi-class classification problems, since most of the well-known metrics are only directly applicable to binary classification problems. In this paper, we propose the General Performance Score (GPS) , a methodological approach to build performance metrics for binary and multi-class classification problems. The basic idea behind GPS is to combine a set of individual metrics, penalising low values in any of them. Thus, users can combine several performance metrics that are relevant in the particular problem based on their preferences obtaining a conservative combination. Different GPS -based performance metrics are compared with alternatives in classification problems using real and simulated datasets. The metrics built using the proposed method improve the stability and explainability of the usual performance metrics. Finally, the GPS brings benefits in both new research lines and practical usage, where performance metrics tailored for each particular problem are considered.
A novel optimization algorithm (Lion-AYAD) to find optimal DNA protein synthesis

In this paper, we present a new algorithm to find the optimal proteins generated through DNA synthesis. The algorithm executes in five stages: in the first stage, it takes a DNA sequences and consider it as the initial populations of lions, determined the main positions of each lion and the main distances among lions and goal point then consider this distance as fitness of that lions, after that sort the lions based on their fitness to preparing it to the second stage. The second stage develops lion optimization algorithm (LOA) by adding four new features on it, each feature performance one task, a replacing the kernel of LOA (i.e., searching machnizam) by spirally searching & Bubble net searching to increase the accuracy, at the same time reduce the execution time to reach of the goal achieve by A Smart feature. The main purpose of the third stage is determining lion active or more yauld where each lion in population need update the positions and fitness after each move in searching space to reach of their goal., this achieved through Yauld feature. The fourth stage applies the Cooperative features to convert the active sequence of DNA (i.e., Yauld lion) into mRNA after that built tRNA from it after splitting it into triplet to start to generate the proteins. Synthesis of all triplet of tRNA to generated final proteins result by new optimization algorithm achieved based on deep composite that satisfies the four rules, this feature called Deep feature and represent the final stage of the algorithm. The new algorithm appears as a pragmatic optimization model, it proves their robust to work with dynamic length of DNA sequence. It increases accuracy and reduces execution times.
Raiders of the Lost Kek: 3.5 Years of Augmented 4chan Posts from the Politically Incorrect Board

This paper presents a dataset with over 3.3M threads and 134.5M posts from the Politically Incorrect board (/pol/) of the imageboard forum 4chan, posted over a period of almost 3.5 years (June 2016-November 2019). To the best of our knowledge, this represents the largest publicly available 4chan dataset, providing the community with an archive of posts that have been permanently deleted from 4chan and are otherwise inaccessible. We augment the data with a set of additional labels, including toxicity scores and the named entities mentioned in each post. We also present a statistical analysis of the dataset, providing an overview of what researchers interested in using it can expect, as well as a simple content analysis, shedding light on the most prominent discussion topics, the most popular entities mentioned, and the toxicity level of each post. Overall, we are confident that our work will motivate and assist researchers in studying and understanding 4chan, as well as its role on the greater Web. For instance, we hope this dataset may be used for cross-platform studies of social media, as well as being useful for other types of research like natural language processing. Finally, our dataset can assist qualitative work focusing on in-depth case studies of specific narratives, events, or social theories.
Automating ontology engineering support activities with OnToology

Due to the increasing uptake of semantic technologies, ontologies are now part of a good number of information systems. As a result, software development teams that have to combine ontology engineering activities with software development practices are facing several challenges, since these two areas have evolved, in general, separately. In this paper we present OnToology, an approach to manage ontology engineering support activities (i.e., documentation, evaluation, releasing and versioning). OnToology is a web-based application that builds on top of Git-based environments and integrates existing semantic web technologies. We have validated OnToology against a set of representative requirements for ontology development support activities in distributed environments, and report on a survey of the system to assess its usefulness and usability.
Adaptive multifactorial particle swarm optimisation

Existing multifactorial particle swarm optimisation (MFPSO) algorithms only explore a relatively narrow area between the inter-task particles. Meanwhile, these algorithms use a fixed inter-task learning probability throughout the evolution process. However, the parameter is problem dependent and can be various at different stages of the evolution. In this work, the authors devise an inter-task learning-based information transferring mechanism to replace the corresponding part in MFPSO. This inter-task learning mechanism transfers the searching step by using a differential term and updates the personal best position by employing an inter-task crossover. By this mean, the particles can explore a broad search space when utilising the additional searching experiences of other tasks. In addition, to enhance the performance on problems with different complementarity, they design a self-adaption strategy to adjust the inter-task learning probability according to the performance feedback. They compared the proposed algorithm with the state-of-the-art algorithms on various benchmark problems. Experimental results demonstrate that the proposed algorithm can transfer inter-task knowledge efficiently and perform well on the problems with different complementarity.
MegaParallax: Casual 360° Panoramas with Motion Parallax

The ubiquity of smart mobile devices, such as phones and tablets, enables users to casually capture 360° panoramas with a single camera sweep to share and relive experiences. However, panoramas lack motion parallax as they do not provide different views for different viewpoints. The motion parallax induced by translational head motion is a crucial depth cue in daily life. Alternatives, such as omnidirectional stereo panoramas, provide different views for each eye (binocular disparity), but they also lack motion parallax as the left and right eye panoramas are stitched statically. Methods based on explicit scene geometry reconstruct textured 3D geometry, which provides motion parallax, but suffers from visible reconstruction artefacts. The core of our method is a novel multi-perspective panorama representation, which can be casually captured and rendered with motion parallax for each eye on the fly. This provides a more realistic perception of panoramic environments which is particularly useful for virtual reality applications. Our approach uses a single consumer video camera to acquire 200-400 views of a real 360° environment with a single sweep. By using novel-view synthesis with flow-based blending, we show how to turn these input views into an enriched 360° panoramic experience that can be explored in real time, without relying on potentially unreliable reconstruction of scene geometry. We compare our results with existing omnidirectional stereo and image-based rendering methods to demonstrate the benefit of our approach, which is the first to enable casual consumers to capture and view high-quality 360° panoramas with motion parallax.
The day-of-the-week effect on Bitcoin return and volatility

This study investigates the day-of-the-week effect on both return and volatility of Bitcoin (BTC) from January 2013 to December 2018 using daily data obtained from CoinDesk Bitcoin Price Index. Estimation results suggest that the day-of-the-week effect in return equation varies with sample periods, while significantly high volatilities are observed on Monday and Thursday. Hence, the significantly high mean return of Bitcoin on Monday is found as a response to higher volatility. Besides, the day-of-the-week effect on both return and volatility remains robust after accounting for stock market returns (S&P 500; SSEC; Nikkei 225) and foreign exchange market returns (USD/CNY; USD/JPY; EURO/USD). Finally, no asymmetry effect on volatility is discovered here.
Reachability Analysis for AWS-Based Networks

Cloud services provide the ability to provision virtual networked infrastructure on demand over the Internet. The rapid growth of these virtually provisioned cloud networks has increased the demand for automated reasoning tools capable of identifying misconfigurations or security vulnerabilities. This type of automation gives customers the assurance they need to deploy sensitive workloads. It can also reduce the cost and time-to-market for regulated customers looking to establish compliance certification for cloud-based applications. In this industrial case-study, we describe a new network reachability reasoning tool, called Tiros, that uses off-the-shelf automated theorem proving tools to fill this need. Tiros is the foundation of a recently introduced network security analysis feature in the Amazon Inspector service now available to millions of customers building applications in the cloud. Tiros is also used within Amazon Web Services (AWS) to automate the checking of compliance certification and adherence to security invariants for many AWS services that build on existing AWS networking features.
Medical Word Embeddings for

Word embeddings are representations of words in a dense vector space. Although they are not recent phenomena in Natural Language Processing (NLP), they have gained momentum after the recent developments of neural methods and Word2Vec. Regarding their applications in medical and clinical NLP, they are invaluable resources when training in-domain named entity recognition systems, classifiers or taggers, for instance. Thus, the development of tailored word embeddings for medical NLP is of great interest. However, we identified a gap in the literature which we aim to fill in this paper: the availability of embeddings for medical NLP in Spanish, as well as a standardized form of intrinsic evaluation. Since most work has been done for English, some established datasets for intrinsic evaluation are already available. In this paper, we show the steps we employed to adapt such datasets for the first time to Spanish, of particular relevance due to the considerable volume of EHRs in this language, as well as the creation of in-domain medical word embeddings for the Spanish using the state-of-the-art FastText model. We performed intrinsic evaluation with our adapted datasets, as well as extrinsic evaluation with a named entity recognition systems using a baseline embedding of general-domain. Both experiments proved that our embeddings are suitable for use in medical NLP in the Spanish language, and are more accurate than general-domain ones.
Hybrid multi-objective evolutionary algorithm based on Search Manager framework for big data optimization problems

Big Data optimization (Big-Opt) refers to optimization problems which require to manage the properties of big data analytics. In the present paper, the Search Manager (SM), a recently proposed framework for hybridizing metaheuristics to improve the performance of optimization algorithms, is extended for multi-objective problems (MOSM), and then five configurations of it by combination of different search strategies are proposed to solve the EEG signal analysis problem which is a member of the big data optimization problems class. Experimental results demonstrate that the proposed configurations of MOSM are efficient in this kind of problems. The configurations are also compared with NSGA-III with uniform crossover and adaptive mutation operators (NSGA-III UCAM), which is a recently proposed method for Big-Opt problems.
Photon-Mediated Localization in Two-Level Qubit Arrays

We predict the existence of a novel interaction-induced spatial localization in a periodic array of qubits coupled to a waveguide. This localization can be described as a quantum analogue of a self-induced optical lattice between two indistinguishable photons, where one photon creates a standing wave that traps the other photon. The localization is caused by the interplay between on-site repulsion due to the photon blockade and the waveguide-mediated long-range coupling between the qubits.
Entity Abstraction in Visual Model-Based Reinforcement Learning

This paper tests the hypothesis that modeling a scene in terms of entities and their local interactions, as opposed to modeling the scene globally, provides a significant benefit in generalizing to physical tasks in a combinatorial space the learner has not encountered before. We present object-centric perception, prediction, and planning (OP3), which to the best of our knowledge is the first fully probabilistic entity-centric dynamic latent variable framework for model-based reinforcement learning that acquires entity representations from raw visual observations without supervision and uses them to predict and plan. OP3 enforces entity-abstraction -- symmetric processing of each entity representation with the same locally-scoped function -- which enables it to scale to model different numbers and configurations of objects from those in training. Our approach to solving the key technical challenge of grounding these entity representations to actual objects in the environment is to frame this variable binding problem as an inference problem, and we develop an interactive inference algorithm that uses temporal continuity and interactive feedback to bind information about object properties to the entity variables. On block-stacking tasks, OP3 generalizes to novel block configurations and more objects than observed during training, outperforming an oracle model that assumes access to object supervision and achieving two to three times better accuracy than a state-of-the-art video prediction model that does not exhibit entity abstraction.
Preserving personalized location privacy in ride-hailing service

Ride-hailing service has become a popular means of transportation due to its convenience and low cost. However, it also raises privacy concerns. Since riders' mobility information including the pick-up and drop-off location is tracked, the service provider can infer sensitive information about the riders such as where they live and work. To address these concerns, we propose location privacy preserving techniques that efficiently match riders and drivers while preserving riders' location privacy. We first propose a baseline solution that allows a rider to select the driver who is the closest to his pick-up location. However, with some side information, the service provider can launch location inference attacks. To overcome these attacks, we propose an enhanced scheme that allows a rider to specify his privacy preference. Novel techniques are designed to preserve rider's personalized privacy with limited loss of matching accuracy. Through trace-driven simulations, we compare our enhanced privacy preserving solution to existing work. Evaluation results show that our solution provides much better ride matching results that are close to the optimal solution, while preserving personalized location privacy for riders.