Reviewerly/exHarmony huggingface dataset:
AraSenCorpus: A Semi-Supervised Approach for Sentiment Annotation of a Large Arabic Text Corpus

At a time when research in the field of sentiment analysis tends to study advanced topics in languages, such as English, other languages such as Arabic still suffer from basic problems and challenges, most notably the availability of large corpora. Furthermore, manual annotation is time-consuming and difficult when the corpus is too large. This paper presents a semi-supervised self-learning technique, to extend an Arabic sentiment annotated corpus with unlabeled data, named AraSenCorpus. We use a neural network to train a set of models on a manually labeled dataset containing 15,000 tweets. We used these models to extend the corpus to a large Arabic sentiment corpus called “AraSenCorpus”. AraSenCorpus contains 4.5 million tweets and covers both modern standard Arabic and some of the Arabic dialects. The long-short term memory (LSTM) deep learning classifier is used to train and test the final corpus. We evaluate our proposed framework on two external benchmark datasets to ensure the improvement of the Arabic sentiment classification. The experimental results show that our corpus outperforms the existing state-of-the-art systems.
Experimental implementation of non-Clifford interleaved randomized benchmarking with a controlled- <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>S</mml:mi></mml:math> gate

The authors demonstrate calibration and benchmarking of a non-Clifford two-qubit controlled pi/2 phase gate with a gate error of 0.59% on an IBM cloud quantum computer using an open source pulse language.
A wrapper methodology to learn interval-valued fuzzy rule-based classification systems

Learning an interval-valued fuzzy rule-based classification system is a challenge as its success directly depends on the interval-valued fuzzy partition used. In fact, the learning of an interval-valued fuzzy system usually starts by creating a partition composed of numerical fuzzy sets, which are used to build an initial fuzzy classifier. Then, it is augmented with interval-valued fuzzy sets whose shape is subsequently optimized to improve the system's performance. However, as in this methodology the fuzzy rules are learned using numerical fuzzy sets, the benefits of the interval-valued fuzzy sets may not be fully exploited. In this paper we define a new learning methodology that avoids building the initial fuzzy classifier but directly learns interval-valued fuzzy rules. To do so, we define a wrapper methodology to learn the interval-valued fuzzy partitions such that they lead to an interval-valued fuzzy rule-based classification system as accurate as possible. Moreover, our new method allows one to represent each membership function using the most proper type of fuzzy set for the sake of modeling the uncertainty in the best possible manner. Consequently, the antecedents of the rules can be formed of only numerical fuzzy sets, only interval-valued fuzzy sets or a mixture of both. The quality of the proposal is compared versus four state-of-the-art fuzzy classifiers like FARC-HD, IVTURS, FURIA and FARC-HD using an inference based on a generalization of the Choquet integral. We also compare our new approach besides its numerical fuzzy counterpart to clearly show the benefits of the usage of interval-valued fuzzy sets. Specifically, the average accuracy rate of our new method is 81.17%, which is at least 0.66% better than the remainder state-of-the-art fuzzy classifiers.
Revealing the multi-stage ore-forming history of a mineral deposit using pyrite geochemistry and machine learning-based data interpretation

Classification algorithms were constructed based on pyrite trace elements using two machine learning methods, support vector machine (SVM) and artificial neural network (ANN), to discriminate the genesis of pyrites sampled from sedimentary rock, orogenic and volcanic hosted massive sulfide (VHMS) deposits. The classifiers were trained with a dataset including published trace element compositions of 2104 pyrite samples from 92 mineral deposits or stratigraphic units. Cross validations were conducted to evaluate the performances of the classifiers on unknown samples, using a variant of the k-fold method. Each time, all pyrite samples from one of the 92 deposits/strata were set aside as the testing set, and the classifiers were trained with samples of the remaining 91. Then, the performances of the classifiers were evaluated based on whether it can correctly determine the genesis of testing deposit/stratum. The circulation was repeated 92 times for each one of the deposits/strata in the dataset, and 91 of them were correctly classified by the SVM-based classifiers, and 90 by the ANN-based. The trained classifiers were then applied to reveal the genesis of the Jiashengpan Zn-Pb deposit, which is characterized by two stages of pyrite formation: early-stage fine-grained massive and late-stage coarse-grained hydrothermal pyrite. The trained algorithms show that the early-stage is compositionally similar to sedimentary pyrite, while the late-stage has affinity to those from orogenic deposits, consistent with geological and geochemical features revealed by previous studies. This study sheds light on the power of machine learning in decoding the geochemical data of pyrite, which can well record the history of ore formation.
Personalization in Practice: Methods and Applications

Personalization is one of the key applications in machine learning with widespread usage across e-commerce, entertainment, production, healthcare and many other industries. While various machine learning techniques present novel state-of-the-art advances and super-human performance year-over-year, personalization and recommender-systems applications are often late-adopters of novel solutions due to problem hardness and implementation complexity. This tutorial presents recent advances across the personalization industry and demonstrates their practical applications in real case-studies of world-leading online platforms. Key trends such as deep learning, causality and active exploration with bandits are depicted with real examples and demonstrated alongside their business considerations and implementation challenges.Rising topics like explainability, fairness, natural interfaces and content generation are covered, touching on aspects of both technology and user experience. Our tutorial relies on recent advances in the field and on work conducted at Booking.com, where we implement personalization models on one of the world's leading online travel platform.
Drone detection using YOLOv3 with transfer learning on NVIDIA Jetson TX2

The rise of drones in the recent years largely due to the advancements of drone technology which provide drones the ability to perform many more complex tasks autonomously with the incorporation of technologies such as computer vision, object avoidance and artificial intelligence. However, the misuse of drones such as the Gatwick Airport drone incident resulted in major disruptions which affected approximately 140,000 passengers. To deter this from happening in the future, drone surveillance are extremely crucial. With this, it will be achieved firstly by detection and followed by tracking of drones. This paper presents and investigates the use of a deep learning object detector, YOLOv3 with pretrained weights and transfer learning to train YOLOv3 to specifically detect drones. We demonstrated that the detection results from YOLOv3 after machine learning had an average accuracy of 88.9% at input image size of 416×416. Finally, we integrated into NVIDIA Jetson TX2 for real-time drone detection.
Temporal Pyramid Network With Spatial-Temporal Attention for Pedestrian Trajectory Prediction

Understanding and predicting human motion behavior with social interactions have become an increasingly crucial problem for a vast number of applications, ranging from visual navigation of autonomous vehicles to activity prediction of intelligent video surveillance. Accurately forecasting crowd motion behavior is challenging due to the multimodal nature of trajectories and complex social interactions between humans. Recent algorithms model and predict the trajectory with a single resolution, making them difficult to exploit the long-range information and the short-range information of the motion behavior simultaneously. In this paper, we propose a temporal pyramid network for pedestrian trajectory prediction through a squeeze modulation and a dilation modulation. The hierarchical design of our framework allows to model the trajectory with multi-resolution, then can better capture the motion behavior at various tempos. By progressively combining the global context with the local one, we finally construct a coarse-to-fine hierarchical pedestrian trajectory prediction framework with multi-supervision. Further, we introduce a unified spatial-temporal attention mechanism to adaptively select important information of persons around in both spatial and temporal domains. We show that our attention strategy is intuitive and effective to encode the influence of social interactions. Experimental results on two benchmarks demonstrate the superiority of our proposed scheme.
Beyond contact-tracing: The public value of eHealth application in a pandemic

This study adopts a public value perspective to examine the eHealth services deployed by national and regional governments to contain the coronavirus (Covid-19) pandemic, including symptoms checkers, information portals and contact-tracing applications. We analyse 50 cases of eHealth applications adopted in 25 European Economic Area (EEA) and outline how these systems and technologies map against four dimensions of public value: user orientation, participation, legality and equity. Our findings reveal that the public value of the eHealth applications adopted in the context of the current pandemic is affected by both endogenous and exogenous factors that undermine their ability to improve the quality of healthcare services and social wellbeing. We conclude by suggesting areas for further research to address such factors and the trade-offs emerging between different dimensions of public value.
A Tutoring System to Learn Code Refactoring

In the last few decades, numerous tutoring systems and assessment tools have been developed to support students with learning programming, giving hints on correcting errors, showing which test cases do not succeed, and grading their overall solutions. The focus has been less on helping students write code with good style and quality. There are several professional tools that can help, but they are not targeted at novice programmers.
An efficient two-phase approach for reliable collaboration-aware service composition in cloud manufacturing

Composing existing services into one value-added composite service for a cooperative business process is especially attended by both academia and industry in the cloud manufacturing (CMF) environment from the perspective of industrial information integration. Various service composition methods based on the QoS have been proposed to satisfy the user's requirement. However, the traditional methods fail to consider interrelation among various services and change in QoS. The unstable QoS may affect the reliability of service composition, which may also affect reliable collaboration as cloud services need to cooperate with each other to accomplish the business process efficiently. In this paper, we propose an efficient two-phase approach by integrating clustering and Chaos-Gauss-based PSO to solve the above problem. In phase one, the K-means clustering algorithm is adopted to improve the quality of candidate services with the consideration of QoS stability for reducing the search space. In phase two, a novel multi-objective PSO algorithm based on Chaos-Gauss named CGPSO is proposed to find the optimal service composition. The Two-Phase approach considers both the QoS stability and service collaboration ability to reduce the probability of service composition failure. Further, we conduct a comprehensive analytical and experimental study to show that our approach has better performance and effectiveness for service composition than other approaches in the CMF environment.
Investigation and assessment of blockchain technology adoption in the pharmaceutical supply chain

The global pharmaceutical industry has gone through several changes in the past decade related to the adoption of new technologies. The challenges that are faced by this industry mostly involve counterfeit drugs and operational issues. The involvement of cold chain for vaccines, medicine etc. is also impacting the secure logistics and transportation. Newly introduce blockchain technologies have capabilities to address these issues. The challenge which needs to address is the requirement of tracking the product authenticity from start production to consumption point to prevent further financial losses. The present study has proposed the investigation and assessment of blockchain for integration in the supply chain model of the pharmaceutical to securely record transactions between parties enhancing trust and transparency. The blockchain adaptation and implementation to address the supply chain challenges are discussed.
An Analytical Appraisal for Supervised Classifiers’ Performance on Facial Expression Recognition Based on Relief-F Feature Selection

Abstract Face expression recognition technology is one of the most recently developed fields in machine learning and has profoundly helped its users through forensic, security, and biometric applications. Many researchers and program developers have allocated their time and energy to figure out various techniques which would add to the technology’s functionality and accuracy. Face expression recognition is a complicated computational process in which is implemented via analyzing changes in facial traits that follow different emotional reactions. This paper endeavors to inspect accuracy ratio of six classifiers based on Relief-F feature selection method, relying on the utilization of the minimum quantity of attributes. The classifiers in which the paper attempts to inspect are Multi-Layer Perceptron, Random Forest, Decision Tree, Support Vector Machine, K-Nearest Neighbor, and Radial Basis Function. The experiment illustrates that K-Nearest Neighbor is the most accurate classifier with the total accuracy ratio of 94.93% amongst the rest when applied on CK+ Dataset.
MCHT: A maximal clique and hash table-based maximal prevalent co-location pattern mining algorithm

Co-location patterns refer to subsets of Boolean spatial features with instances of these features frequently appear in nearby geographic space. Maximal co-location patterns are a compact representation of these patterns that lead users more easily to absorb results and make meaningful inferences. The current algorithms for maximal co-location pattern mining are based on a generate-test candidate model. The main execution time of this model is occupied by collecting co-location instances of candidates, which makes discovering maximal co-location patterns is still very challenging when data is big and/or dense. To take up the challenge, a novel maximal co-location pattern mining framework based on maximal cliques and hash tables (MCHT) is developed in this study. First, all maximal cliques that can compactly represent neighbor relationships between instances of a spatial data set are enumerated. The advantages of bit string operations are fully utilized to speed up the process of enumerating maximal cliques. Next, a participating instance hash table structure is constructed based on these maximal cliques. Then information about the co-location instances of maximal patterns can be queried and collected efficiently from the hash table. After that, by calculating participation indexes of these patterns to measure their prevalence, maximal prevalent co-location patterns can be filtered efficiently. Finally, a series of experiments is conducted on both synthetic and real-facility data sets to demonstrate that the proposed algorithm can efficiently reduce both the computational time and the memory consumption compared with the existing algorithms.
Issues, challenges, and a proposed theoretical core of agile software development research

Abstract Over the last two decades, agile software development (ASD) has garnered much attention in both research and practice. Several ASD methods and techniques have been developed and studied. In particular, researchers have provided several theoretical perspectives on ASD and contributed rich insights to the ASD practice. Still, despite calls for a more unified theoretical understanding of ASD, a theoretical core of ASD has not been identified. This paper offers a theoretical core of ASD research, clarifying what is essential and what is less essential for IS agility, hoping to spark a scholarly discussion, and provides implications of such a core for understanding method tailoring.
Natural language understanding for argumentative dialogue systems in the opinion building domain

This paper introduces a natural language understanding (NLU) framework for argumentative dialogue systems in the information-seeking and opinion building domain. The proposed framework consists of two sub-models, namely intent classifier and argument similarity. Intent classifier model stack BiLSTM with attention mechanism on top of pre-trained BERT model and fine-tune the model for recognizing the user intent, whereas argument similarity model employs BERT+BiLSTM for identifying system arguments the user refers to in his or her natural language utterances. Our model is evaluated in an argumentative dialogue system that engages the user to inform him-/herself about a controversial topic by exploring pro and con arguments and build his/her opinion towards the topic. In order to evaluate the proposed approach, we collect user utterances for the interaction with the respective system labelling intent and referenced argument in an extensive online study. The data collection includes multiple topics and two different user types (native English speakers from the UK and non-native English speakers from China). Additionally, we evaluate the proposed intent classifier and argument similarity models separately on the publicly available Banking77 and STS benchmark datasets. The evaluation indicates a clear advantage of the utilized techniques over baseline approaches on several datasets, as well as the robustness of the proposed approach against new topics and different language proficiency as well as the cultural background of the user. Furthermore, results show that our intent classifier model outperforms DIET, DistillBERT, and BERT fine-tuned models in few-shot setups (i.e., with 10, 20, or 30 labelled examples per intent) and full data setup.
Impossible Explanations?

Can we achieve an adequate level of explanation for complex machine learning models in high-risk AI applications when applying the EU data protection framework? In this article, we address this question, analysing from a multidisciplinary point of view the connection between existing legal requirements for the explainability of AI systems and the current state of the art in the field of explainable AI.
TSMGWO: Optimizing Task Schedule Using Multi-Objectives Grey Wolf Optimizer for Cloud Data Centers

Cloud computing is considered to be the best solution for addressing the increasing computing requirements of high-performance applications. The efficient performance of the system requires optimal mapping of cloud tasks to resources. However, it is challenging to address computing and storage requirements of high-performance applications while achieving conflicting scheduling objectives like throughput, makespan, resource utilization. This work proposes a metaheuristic approach called task schedule using a multi-objective grey wolf optimizer (TSMGWO) to find near-optimal task scheduling solutions while handling conflicting objectives. The TSMGWO approach has been evaluated using three benchmark datasets, namely, GoCJ, HCSP and Synthetic dataset. The results are compared with heuristic FCFS and MT methods, and metaheuristic methods PSO, GA and WOA. The TSMGWO approach reduces makespan upto 67.52% over FCFS method, 60.93%, over PSO, 38.05% over GA, and 23.22% over WOA methods for 100 tasked cloud workload using GoCJ dataset. It reduces makespan upto 60.95% over FCFS method, 55.79% over MT method, 47.04% over PSO, 33.38% over GA and 19.91% over WOA method using synthetic dataset. Similarly, TSMGWO reduces makespan upto 27.03% over MT method, 18.95% over PSO, 11.90% over GA and 7.5% over WOA method using HSCP workload. The comparative analysis demonstrates that TSMGWO approach outperforms the earlier heuristic and metaheuristic methods using benchmark datasets in the cloud environment.
Optimized recommendations by user profiling using apriori algorithm

Collaborative filtering has been the most straightforward and most preferable approach in the recommender systems. This technique recommends an item to a target user from the preferences of top-k similar neighbors. In a sparse data scenario, the recommendation accuracy of the collaborative filtering degrades significantly due to the limitations of existing various similarity measures. Such constraints offer an open scope for enhancing the accuracy of optimized user-specific recommendations. Many techniques have been utilized for this, like Particle Swarm Optimization and other evolutionary collaborative filtering algorithms. The proposed approach utilizes the Apriori algorithm to form users’ profiles from the items’ ratings and categorical attributes. The user profile creation is performed using the apriori algorithm. The profile of each user involves the likes and disliking of categorical characteristics of objects by users. In the collected MovieLens dataset, the efficiency of the proposed recommendation approach is tested. The comparative results show proof that the proposed novel algorithm outperforms other prominent collaborative filtering algorithms on the MovieLens datasets based on rating prediction accuracy.
Multi-Language Spam/Phishing Classification by Email Body Text: Toward Automated Security Incident Investigation

Spamming and phishing are two types of emailing that are annoying and unwanted, differing by the potential threat and impact to the user. Automated classification of these categories can increase the users’ awareness as well as to be used for incident investigation prioritization or automated fact gathering. However, currently there are no scientific papers focusing on email classification concerning these two categories of spam and phishing emails. Therefore this paper presents a solution, based on email message body text automated classification into spam and phishing emails. We apply the proposed solution for email classification, written in three languages: English, Russian, and Lithuanian. As most public email datasets almost exclusively collect English emails, we investigate the suitability of automated dataset translation to adapt it to email classification, written in other languages. Experiments on public dataset usage limitations for a specific organization are executed in this paper to evaluate the need of dataset updates for more accurate classification results.