Miracle-dz/UniFAR-FTUs huggingface dataset:
Sports Analytics: Maximizing Precision in Predicting MLB Base Hits

As the world of sports expands to never seen levels, so does the necessity for tools which provided material advantages for organizations and other stakeholders. The main objective of this paper is to build a predictive model capable of predicting what are the odds of a baseball player getting a base hit on a given day, with the intention of both winning the game Beat the Streak and to provide valuable information for the coaching staff. Using baseball statistics, weather forecasts and ballpark characteristics several models were built with the CRISP-DM architecture. The main constraints considered when building the models were balancing, outliers, dimensionality reduction, variable selection and the type of algorithm – Logistic Regression, Multi-layer Perceptron, Random Forest and Stochastic Gradient Descent. The results obtained were positive, in which the best model was a Multi-layer Perceptron with an 85% correct pick ratio.
A Mixed Neural Network and Support Vector Machine Model for Tender Creation in the European Union TED Database

This research article proposes a new method of automatized text generation and subsequent classification of the European Union (EU) Tender Electronic Daily (TED) text documents into predefined technological categories of the dataset. The TED dataset provides information about the respective tenders includes features like Name of project, Title, Description, Types of contract, Common procurement vocabulary (CPV) code, and Additional CPV codes. The dataset is obtained from the SIMAP-Information system for the European public procurement website, which is comprised of tenders described in XML files. The dataset was preprocessed using tokenization, removal of stop words, removal of punctuation marks etc. We implemented a neural machine learning model based on Long Short-Term Memory (LSTM) nodes for text generation and subsequent code classification. Text generation means that given a single line or just two or three words of the title, the model generates the sequence of a whole sentence. After generating the title, the model predicts the main applicable CPV code for that title. The LSTM model reaches an accuracy of 97% for the text generation and 95% for code classification using Support Vector Machine(SVM). This experiment is a first step towards developing a system that based on TED data is able to auto-generate and code classify tender documents, easing the process of creating and disseminating tender information to TED and ultimately relevant vendors. The development and automation of this system will future vision and understand current undergoing projects and the deliveries by a SIMAP-Information system for European public procurement tenders organisation based on the tenders published by it.
Detecting Political Bias Trolls in Twitter Data

Ever since Russian trolls have been brought to light, their interference in the 2016 US Presidential elections has been monitored and studied. These Russian trolls employ fake accounts registered on several major social media sites to influence public opinion in other countries. Our work involves discovering patterns in these tweets and classifying them by training different machine learning models such as Support Vector Machines, Word2vec, Google BERT, and neural network models, and then applying them to several large Twitter datasets to compare the effectiveness of the different models. Two classification tasks are utilized for this purpose. The first one is used to classify any given tweet as either troll or non-troll tweet. The second model classifies specific tweets as coming from left trolls or right trolls, based on apparent extreme political orientations. On the given data sets, Google BERT provides the best results, with an accuracy of 89.4% for the left/right troll detector and 99% for the troll/non-troll detector. Temporal, geographic, and sentiment analyses were also performed and results were visualized.
Microblog Sentiment Prediction based on User Past Content

Analyzing massive, noisy and short microblogs is a very challenging task where traditional sentiment analysis and classification methods are not easily applicable due to inherent characteristics such social media content. Sentiment analysis, also known as opinion mining, is a mechanism for understanding the natural disposition that people possess towards a specific topic. Therefore, it is very important to consider the user context that usually indicates that microblogs posted by the same person tend to have the same sentiment label. One of the main research issue is how to predict twitter sentiment as regards a topic on social media? In this paper, we propose a sentiment mining approach based on sentiment analysis and supervised machine learning principles to the tweets extracted from Twitter. The originality of the suggested approach is that classification does not rely on tweet text to detect polarity, but it depends on users’ past text content. Experimental validation is conducted on a tweet corpus taken from data of SemEval 2016. These tweets talk about several topics, and are annotated in advance at the level of sentiment polarity. We have collected the past tweets of each author of the collection tweets. As an initial experiment in the prediction of user sentiment on a topic, based on his past, the results obtained seem acceptable, and could be improved in future work.
The Political Power of Twitter

In June 2016, the British voted by 52 per cent to leave the EU, a club the UK joined in 1973. This paper examines Twitter public and political party discourse surrounding the BREXIT withdrawal agreement. In particular, we focus on tweets from four different BREXIT exit strategies known as “Norway”, “Article 50”, the “Backstop” and “No Deal” and their effect on the pound and FTSE 100 index from the period of December 10th 2018 to February 24th 2019. Our approach focuses on using a Naive Bayes classification algorithm to assess political party and public Twitter sentiment. A Granger causality analysis is then introduced to investigate the hypothesis that BREXIT public sentiment, as measured by the twitter sentiment time series, is indicative of changes in the GBP/EUR Fx and FTSE 100 Index. Our results from the Twitter public sentiment indicate that the accuracy of the “Article 50” scenario had the single biggest effect on short run dynamics on the FTSE 100 index, additionally the “Norway” BREXIT strategy has a marginal effect on the FTSE 100 index whilst there was no significant causation to the GBP/EUR Fx. The BREXIT Political party sentiment for the “No Deal” was indicative of short-term dynamics on the GBP/EUR Fx at a marginal rate. Our test concluded that there was no causality on the FTSE 100.
Detecting Anomalous Behaviour from Textual Content in Financial Records

Most financial institutions mainly use numerical statistics to detect anomalous (malpractice) activity. The textual content in financial records however contains precious information which to date has not been effectively used for detection of anomalous behaviors by users because these are often unintelligible, cluttered with abbreviations, numbers and symbols, which makes it difficult to build a framework system that can coherently understand and draw conclusions. Rule-based techniques have been proposed but such systems are easy to elude, as they are difficult to generalize and do not scale up. The work presented in this paper differs from previous work in that we exclusively base anomalous activities on text (excluding numerical values) in financial records and treat this as a classification problem for a deep learning network. We propose four solutions using deep learning techniques on textual data to distinguish between normal with anomalous behaviors of the users. The results of our experiments convincingly show that use of the textual content in financial records yields greater accuracy in anomalous behavior detection. They also suggest that deep learning is a viable and effective solution for real time anomaly detection by financial institutions.CCS CONCEPTS• Applied computing → Secure online transactions.
CluStream-GT: Online Clustering for Personalization in the Health Domain

Clustering of users underlies many of the personalisation algorithms that are in use nowadays. Such clustering is mostly performed in an offline fashion. For a health and wellbeing setting, offline clustering might however not be suitable, as limited data is often available and patient states can also quickly evolve over time. Existing online clustering algorithms are not suitable for the health domain due to the type of data that involves multiple time series evolving over time. In this paper we propose a new online clustering algorithm called CluStream-GT that is suitable for health applications. By using both artificial and real datasets, we show that the approach is far more efficient compared to regular clustering, with an average speedup of 93%, while only losing 12% in the accuracy of the clustering with artificial data and 3% with real data.CCS CONCEPTS• Computing methodologies$\rightarrow$ Cluster analysis.
Flow and Optimal Difficulty in the Portable EEG: On the Potentiality of using Personalized Frequency Ranges for State Detection

The experience of flow has been centrally linked to peak task performances and heightened well-being. To more effectively elicit these outcomes, flow is increasingly studied using neurophysiological measures. For example, portable EEG is employed to enable automatic state detection required for adaptive system design. However, so far, there is a lack of highly diagnostic findings, and moderately diagnostic ones relate more strongly to a central flow pre-condition – namely optimal task difficulties. Unfortunately, even these metrics might be infeasible in real-world scenarios and for portable EEG systems without midline electrodes. In this work, we discuss how frequency band personalization and separation could provide options to overcome these problems. Results from an experiment with a task manipulated in difficulty highlight that upper Alpha and Beta ranges show differentiating patterns to their lower frequency counterparts (i.e. within bands). These sub-bands could be used to detect instances of higher flow and optimized difficulty using portable EEG.
Not a Target. A Deep Learning Approach for a Warning and Decision Support System to Improve Safety and Security of Humanitarian Aid Workers

Humanitarian aid workers who try to provide aid to the most vulnerable populations in the Middle East or Africa are risking their own lives and safety to help others. The current lack of a collaborative real-time information system to predict threats prevents responders and local partners from developing a shared understanding of potentially threatening situations, causing increased response times and leading to inadequate protection. To solve this problem, this paper presents a threat detection and decision support system that combines knowledge and information from a network of responders with automated and modular threat detection. The system consists of three parts. It first collects textual information, ranging from social media, and online news reports to reports and text messages from a decentralized network of humanitarian staff. Second, the system uses deep neural network techniques to automatically detects a threat or incident and provide information including location, threat category, and casualties. Third, given the type of threat and the information extracted by the NER, a feedforward network proposes a mitigation plan based on humanitarian standard operating procedures. The classified information is rapidly redistributed to potentially affected humanitarian workers at any level. The system testing results show a high precision of 0.91 and 0.98 as well as an F-measure of 0.87 and 0.88 in detecting the threats and decision support respectively. We thus combine the collaborative intelligence of a decentralized network of aid workers with the power of deep neural networks. CCS CONCEPTS • Computing methodologies → Neural networks.
Emotion Recognition from Speech: A Survey

Emotion recognition from speech signals is an important field in its own right as well as a mainstay of many multimodal sentiment analysis systems. The latter may as well include a broad spectrum of modalities which are strongly associated with consciously or subconsciously communicating human emotional state such as visual cues, gestures, body postures, gait, or facial expressions. Typically, emotion discovery from speech signals not only requires considerably less computational complexity than other modalities, but also at the same time in the overwhelming majority of studies the inclusion of speech modality increases the accuracy of the overall emotion estimation process. The principal algorithmic cornerstones of emotion estimation from speech signals are Hidden Markov Models, time series modeling, cepstrum processing, and deep learning methodologies, the latter two being prime examples of higher order data processing. Additionally, the most known datasets which serve as emotion recognition benchmarks are described.
Analysis of Sensor Locations on Human Body for Wearable Sensor based Activity Classification during Fast Bowling in Cricket

This paper focuses on determining best body sensor position among calf, thigh, upper trunk and forearm when classifying Run Up, Delivery Stride and Follow Through phases during fast bowling in cricket by the usage of a machine learning model. Nine-axis Inertial Measurement Units (IMU) were used to collect data at 350Hz and Madgwick’s quaternion based algorithm was used for orientation estimation. The study also focused on determining best quaternion to be considered for such activity classification requirements in fast bowling. Three fast bowlers with Mixed type bowling action were considered for the study. A sliding window with 200 samples/window with 50% overlap collected eight, time domain statistical features from the sensor data and Principal Component Analysis was used to reduce dimensionality of the feature set. A linear kernel based Support Vector Machine classified the features into the three main phases and five-fold cross validation was used to determine model performance. The results indicate that fourth quaternion on calf or forearm is the best quaternion and body position to be considered for activity classification of fast bowling action in cricket.
What is Real or Fake?-Machine Learning Approaches for Rumor Verification using Stance CIassification

In a recent survey, over half (54%) of a global sample agree or strongly agree that they are concerned about what is real and fake when thinking about online news. Rumors are spreading all the time and affect people’s perceptions and behavior. In this paper, we apply several machine learning approaches, from simple supervised algorithms to deep learning models, for the stance classification and rumor verification tasks; and evaluate the impact of the stance information in the performance of rumor veracity evaluation. According to the results, the traditional machine learning algorithms presented better performance than deep learning models, in both tasks, and the information of stance (deny or query) do not improve the results of the rumor verification task. CCS CONCEPTS • Networks → Social media networks • Human-centered computing → Social media • Computing methodologies → Natural language processing.
Sentence Simplification from Non-Parallel Corpus with Adversarial Learning

In this study, we propose sentence simplification from a non-parallel corpus with adversarial learning. In recent years, sentence simplification based on a statistical machine translation framework and neural networks have been actively studied. However, most methods require a large parallel corpus, which is expensive to build. In this paper, our purpose is sentence simplification with a non-parallel corpus in open data en-Wikipedia and Simple-Wikipedia articles. We use a style transfer framework with adversarial learning for learning by non-parallel corpus and adapted a prior work [by Barzilay et al.] to sentence simplification as a base framework. Furthermore, from the perspective of improving retention of sentence meaning, we add pretraining reconstruction loss and cycle consistency loss to the base framework. We also improve the sentence quality output from the proposed model as a result of the expansion. CCS CONCEPTS • Computing methodologies $\rightarrow$ Natural language generation.
Distributed Data Validation for a Key-value Store in a Decentralized Electric Vehicle Charging Network

The mobility sector experiences a fundamental shift to more connected, autonomous, shared and electric means of transportation. For an electric mobility system to function, an efficient and reliable electric vehicle charging network is required. The Open Charging Network, which is built and curated by the Share&Charge Foundation is a digital, open and decentralized infrastructure for operating and connecting assets of the e-mobility ecosystem like charge points and electric vehicles. In such a network validity and consistency of data are crucial. Since the underlying information system is designed based on distributed ledger technologies and distributed hash tables, also the validation of data for the respective key-value store should be implemented and executed in a distributed manner. In this paper, we contribute to the body of research by analyzing the current situation in distributed systems and presenting the design and development of a mechanism for a distributed data validation. We provide an outlook into the future implementation within the Open Charging Network, where the solution will be demonstrated in a suitable context. Further it will be evaluated regarding the primary requirement of data validity and secondary requirements such as availability, reliability and scalability.
Optimizing moving average control procedures for small-volume laboratories: can it be done?

Introduction Moving average (MA) means calculating the average value from a set of patient results and further using that value for analytical quality control purposes. The aim of this study was to examine whether the selection, optimization and validation of MA procedures can be performed using the already described bias detection simulation method and whether it is possible to select appropriate MA procedures for a laboratory with a small daily testing volume. Materials and methods The study was done on four analytes: creatinine, potassium, sodium and albumin. All patient results of these tests processed during six months were taken from the laboratory information system. Using the MA Generator software, different MA procedures were analysed. Different inclusion criteria, calculation formulas, batch sizes and weighting factors were tested. Selection of optimal MA procedures was based on their ability to detect simulated biases of different sizes. After optimization, the validation of MA procedures was done. The results were presented by bias detection curves and MA validation charts. Results Simple MA procedures for albumin and sodium without truncation limits were selected as optimal. Exponentially weighted MA procedures were found optimal for creatinine and potassium, with the upper truncation limits of 150 μmol/L and 6 mmol/L, respectively. Conclusions It has been experimentally confirmed that it is possible to perform the selection, optimization and validation of MA procedures using the bias detection simulation method. Also, it is possible to define MA procedures optimal for a laboratory with a small daily testing volume.
β-carotene Inhibits Expression of c-Myc and Cyclin E in Helicobacter pylori-infected Gastric Epithelial Cells

Background Helicobacter pylori infection is a major risk factor in the development of gastric cancer. H. pylori infection of gastric epithelial cells increases the levels of reactive oxygen species (ROS), activates oncogenes, and leads to β-catenin-mediated hyper-proliferation. β-Carotene reduces ROS levels, inhibits oxidant-mediated activation of inflammatory signaling and exhibits anticancer properties. The present study was carried out to determine if β-carotene inhibits H. pylori-induced cell proliferation and the expression of oncogenes c-myc and cyclin E by reducing the levels of β-catenin and phosphorylated glycogen synthase kinase 3β (p-GSK3β). Methods Gastric epithelial AGS cells were pre-treated with β-carotene (5 and 10 μM) for 2 hours prior to H. pylori infection and cultured for 6 hours (for determination of the levels of p-GSK3β, GSK3β, and β-catenin) and 24 hours (for determination of cell viability and protein levels of c-myc and cyclin E). Cell viability was determined by the MTT assay and protein levels were determined via western blot-based analysis. Results β-Carotene inhibited H. pylori-induced increases in the percentage of viable cells, phosphorylated GSK3β (p-GSK3β), and the levels of β-catenin, c-myc and cyclin E. Conclusions β-Carotene inhibits H. pylori-induced hyper-proliferation of gastric epithelial cells by suppressing β-catenin signaling and oncogene expression.
Match Running Performance on Three Different Competitive Standards in Norwegian Soccer

Abstract The aim of the study was to compare running performance of three competitive standards and to examine the effects of being promoted to a higher league in Norwegian football. One clubʼs first and second team were included. The first team consisted of professional soccer players playing at Level 2 (2015 season) and Level 1 (2016 season). The second team consisted of amateurs playing at Level 4. A fully automatic tracking system was used to examine running performance, divided into different running-speed categories and playing position. Forty-one matches were included containing 278 observations. Level 1 performed 61 and 51% sprinting compared to Level 2 and Level 4 but similar high-speed running. Similar high-speed running distances were observed only for the different playing positions at Level 1 compared to Level 2 and 4. The sprinting distance was greater for the central defender and attacker, and the number of accelerations was greater for central midfielders and wide midfielders’ playing at Level 1 compared to lower competitive standards. In conclusion, better competitive standards resulted in greater high-intensity actions than lower leagues in Norwegian soccer. Furthermore, only central defenders and attackers increased their high-intensity locomotions when the team was promoted.