Miracle-dz/UniFAR-FTUs huggingface dataset:
Third Workshop on Evaluation of Personalisation in Information Retrieval (WEPIR 2020): In Memoriam Seamus Lawless

The Third WEPIR 2020 workshop builds on the success of the first two WEPIR meetings held at CHIIR 2018 and CHIIR 2019. WEPIR 2020 again brings together researchers from different backgrounds interested in continuing to explore and advance the evaluation of personalisation in information retrieval. Similar to the first two workshops, WEPIR 2020 has a strong emphasis on active participation by workshop attendees. This was very successfully achieved in the first two workshops by the use of workshop breakout groups exploring topics related to personalisation and information retrieval, and the evaluation of personalisation in information retrieval in general, with subsequent report back to the workshop as a whole. However, a key difference for WEPIR 2020 is that while the first two workshops focused on developing and articulating principles and ideas relating general topics relating to these topics, identified as interesting and important by the attendees at the workshops, WEPIR 2020 focuses breakout discussion on a number of relevant specific use cases of the evaluation of personalisation in information retrieval. A use case is assigned to each breakout group with the plan being to have more than one group work on each use case. The task for each group is to identify specific relevant factors relating to the use case in terms of user activities, data to be collected, ethical issues, and evaluation metrics. Groups will make reports of their discussions to the assembled workshop in the final session with discussion of the alternative solutions relating to the same use case, and contrasting the issues raised by the different use cases. The overall goal of the workshop is to work towards developing a general set of principles and guidelines for addressing the evaluation of specific instances of the use of personalisation in information retrieval tasks. This is consistent with the activities and goals of the first two workshops, but represents a significant progression of the activities towards concrete outcomes of benefit to those exploring personalisation in search, the issues arising in its evaluation, and how researchers might go about tackling this in specific situations.
Association Between 4q25 Variants, Risk of Atrial Fibrillation and Echocardiographic Parameters

Abstract Atrial fibrillation (AF), the most common type of arrhythmia, has a heritable component. Variants at locus 4q25 are best associated with the risk of AF development in genome-wide association studies. Left atrial volume is an independent predictor of recurrence of AF after successful sinus rhythm restoration. The aim of our study was to investigate potential association between genetic variants at 4q25 locus and the risk of AF and echocardiographic parameters. We included 241 AF patients and 119 control individuals into the study. Left ventricle ejection fraction (LVEF, %) and left atrial volume index (LAVI, ml/m2) were assessed by transthoracic echocardiography during outpatient visits. We selected five 4q25 genetic variants (rs6825911, rs1126483, rs10004516, rs6838973, rs2200733) for the analysis. Variant rs6838973 was found to be associated with reduced risk of AF in additive (CCTT) and dominant (CC vs. CT+TT) models of inheritance. On the other hand, additive (CC<CT<TT) and dominant (CC vs. CT+TT) models of rs2200733 were associated with greater risk of AF. The same variations were found to be associated with age of AF onset. Median LAVI was 39.0 ml/m2 (IQR = 10.0) and median LVEF was 56.0% (IQR = 13.0). Statistically significant association was observed only between LAVI and variant rs1126483 in the dominant model of inheritance (median LAVI in CC vs. CT+TT – 38 ml/m2 vs. 40 ml/m2, U = 1602.5, p = 0.032). No significant association was found for LVEF and the analysed genotypes.
Hands-free but not Eyes-free: A Usability Evaluation of Siri while Driving

Distractions while driving are a major cause of traffic accidents and chief among these is the use of mobile phones. Driver distractions typically fall into four categories-visual, cognitive, bio-mechanical, and auditory-and different technological solutions have been proposed to address these. Intelligent Personal Assistants (IPAs), such as Siri, is a recent example of such a technological solution that offers the potential for hands-free phone interaction through a voice-controlled interface. IPAs could potentially reduce visual and bio-mechanical distractions if they are usable enough to not increase a driver's cognitive load. We present the results of a controlled experiment with the aim of understanding how the use of Siri while driving compares to manual interaction in terms of usability and distractions. We also tested these two interaction types in the lab in order to understand how the main driving task influences Siri's (perceived) usability. Our study shows that Siri is not ready for every-day use in the car: interacting with Siri while driving is likely to be unsafe for most participants, especially less experienced drivers. Participants were distracted by Siri due to its over-reliance on visual feedback as well as frequent time-outs by Siri when waiting for a response from a driver occupied with the road environment. Speech recognition quality in a noisy car as well as problematic multi-lingual speech recognition in general are other issues that resulted in low usability and more cognitive distractions. While interacting with Siri may be hands-free, it does not provide an eyes-free and distraction-free experience yet.
Artificial intelligence for team sports: a survey

Abstract The sports domain presents a number of significant computational challenges for artificial intelligence (AI) and machine learning (ML). In this paper, we explore the techniques that have been applied to the challenges within team sports thus far. We focus on a number of different areas, namely match outcome prediction, tactical decision making, player investments, fantasy sports, and injury prediction. By assessing the work in these areas, we explore how AI is used to predict match outcomes and to help sports teams improve their strategic and tactical decision making. In particular, we describe the main directions in which research efforts have been focused to date. This highlights not only a number of strengths but also weaknesses of the models and techniques that have been employed. Finally, we discuss the research questions that exist in order to further the use of AI and ML in team sports.
Investigating Expectations for Voice-based and Conversational Argument Search on the Web

Millions of arguments are shared on the web. Future information systems will be able to exploit this valuable knowledge source and to retrieve arguments relevant and convincing to our specific need---all with an interface as intuitive as asking your friend "Why . ". Although recent advancements in argument mining, conversational search, and voice recognition have put such systems within reach, many questions remain open, especially on the interface side. In this regard the paper at hand presents the first study of argument search behavior. We conduct an online-survey and a focused user study, putting emphasis on what people expect argument search to be like, rather than on what current first-generation systems provide. Our participants expected to use voice-based argument search mostly at home, but also together with others. Moreover, they expect such search systems to provide rich information on retrieved arguments, such as the source, supporting evidence, and background knowledge on entities or events mentioned. In observed interactions with a simulated system we found that the participants adapted their search behavior to different types of tasks, and that up-front categorization of the retrieved arguments is perceived as helpful if this is short. Our findings are directly applicable to the design of argument search systems, not only voice-based ones.
Detecting attacks in high-speed networks: Issues and solutions

ABSTRACT Intrusion detection systems are one of the necessities of networks to identify the problem of network attacks. Organizations striving to protect their data from intruders are often challenged by attackers, who find new ways to attack and compromise the security of the network. The detection process becomes quite difficult while dealing with high-speed and distributed attacks that are performed using botnets. These attacks threat both the confidentiality of legitimate users and the infrastructure of the network and to protect them, early discovery of network attacks is important. In this paper, an open source Intrusion Detection System (IDS), Snort is presented as a solution to detect DoS and Port Scan network attacks in a high-speed network. A set of custom rules has been proposed for Snort to detect DoS and Port Scan attacks in high-speed network. The rules are compared and tested using different attack generators like Scapy, Hping3, LOIC and Nmap. Snort’s efficiency in detecting the DoS and Port Scan attacks using the new rules is experimentally proved to be around 99% for all the attacks except for Ping of Death. The proposed system works well for different attack generators in a high-speed network.
Digital image watermarking based on ANN and least significant bit

ABSTRACT The tremendous AI benefits, machine learning and deep learning, have led to the adoption of advanced technology and modern applications. Especially in an image, Video processing, natural language processing, speech recognition. AI algorithms have recently overcome many drawbacks, thanks to the DNN models, that have contributed to delivering state-of-the-art results in computing and other areas. But safety and security are always challenging tasks.Our proposed approach provides a secure and efficient watermarking method based on a neural network for digital images using the least significant method. First, we used the least significant bit (LSB) to insert a watermark for the image pixel. Because only LSB -based methods are not robust; they are not sufficient in an attack-free environment and lossless compression. We used an Artificial Application Neural Network (ANN) to detect the presence of sensitive information and extract information from the source image. It is inherently unstable when the proper machine learning algorithm is trained, re-trained, and adapted to a few new applications. The standard solution would have a digital signature there as there are very simple ways to change the neural network model so that it still does the same thing as before, but the overall presentation will be different. This paper highlights the essential needs of the ANN model in watermarking.
THE NATURAL HISTORY OF CHILDHOOD SPINAL TUBERCULOSIS – A 15 YEAR PROSPECTIVE STUDY

Introduction: The progress of post tubeculous kyphosis in children during ‘growth spurt’ is unpredictable and has not been clearly documented in literature. Methods: The progression of deformity in 63 children treated conservatively and belonging to a controlled clinical trial was studied over 15 years. Results: The average Kyphosis increased from 35.2 degrees to 41.3 degrees in the ‘Active Stage’ (Phase I), there was an increase in all patients. In the ‘Healed stage’ (Phase II), a variable progress continued which was more prominent during the ‘growth spurt’. In Type I, there was a worsening which occured either continuously (Type 1a; n=19.30%), or suddently after a gap of few years (Type 1b;n=6.9%). In Type II, an improvement occured either after an initial increase or a plateau (Type IIa; n=18.29%), or continuously after disease cure (Type IIb; n=9.15%). In Type III, the deformity was static (n=11.17%). The average increase in Type Ia was 24.2 degrees, Type IIb was 20;5 degrees, Type Ib was 58.4 degrees; the decrease in Type IIa was 4.9 degrees, Type IIb was 20.5 degrees and the decrease in Type III was 1.7 degrees. Overall, the growth spurt sresulted in an increase in deformity in 25 (39%) children, a decrease in 27 (44%) and no change in 11 (17%). Conclusion: Post-tuberculous kyphosis in children is a ‘Dynamic deformity’ which changes till skeletal maturity. Children must not be discharged after disease cure and yearly follow up to monitor deformity is mandatory. Surgical intervention to prevent late profress will be needed in one third of children.
Artificial neural network analysis of bone quality DXA parameters response to teriparatide in fractured osteoporotic patients

Teriparatide is a bone-forming therapy for osteoporosis that increases bone quantity and texture, with uncertain action on bone geometry. No data are available regarding its influence on bone strain. To investigate teriparatide action on parameters of bone quantity and quality and on Bone Strain Index (BSI), also derived from DXA lumbar scan, based on the mathematical model finite element method. Forty osteoporotic patients with fractures were studied before and after two years of daily subcutaneous 20 mcg of teriparatide with dual X-ray photon absorptiometry to assess bone mineral density (BMD), hip structural analysis (HSA), trabecular bone score (TBS), BSI. Spine deformity index (SDI) was calculated from spine X-ray. Shapiro-Wilks, Wilcoxon and Student’s t test were used for classical statistical analysis. Auto Contractive Map was used for Artificial Neural Network Analysis (ANNs). In the entire population, the ameliorations after therapy regarded BSI (-13.9%), TBS (5.08%), BMD (8.36%). HSA parameters of femoral shaft showed a worsening. Dividing patients into responders (BMD increase >10%) and non-responders, the first presented TBS and BSI ameliorations (11.87% and -25.46%, respectively). Non-responders presented an amelioration of BSI only, but less than in the other subgroup (-6.57%). ANNs maps reflect the mentioned bone quality improvements. Teriparatide appears to ameliorate not only BMD and TBS, but also BSI, suggesting an increase of bone strength that may explain the known reduction in fracture risk, not simply justified by BMD increase. BSI appears to be a sensitive index of TPD effect. ANNs appears to be a valid tool to investigate complex clinical systems.
Genetic differentiation and phylogenetic potential of Ty3/Gypsy LTR retrotransposon markers in soil and plant pathogenic fungi

Genetic diversity studies are crucial for understanding the genetic structure and evolutionary dynamics of fungal species and communities. Fungal genomes are often reshaped by their repetitive components such as transposable elements. These elements are key players in genomic rearrangements and are ideal targets for genetic diversity and evolutionary studies. Herein, we used three Ty3/Gypsy long terminal repeat retrotransposons, Grasshopper, Maggy, and Pyret, for genetic differentiation and diversity in soil and plant pathogenic fungi, representing diverse species, order, and phyla. Pyret DNA markers showed the highest gene diversity and Shannon's information indices, followed by Maggy and Grasshopper. The observed high levels of multilocus polymorphism indicate the continuous mobility of these elements after their transfer in the new host. In conclusion, this study presents novel markers for genetic differentiation and evolutionary studies of fungi, and sheds light on the prevalence of gene acquisition phenomenon in field fungi.
Reconsidering “Best Practices” for Testing the Ideal Standards Model: A Response to Eastwick, Finkel, and Simpson (2018)

Eastwick, Finkel, and Simpson (2018) advanced recommendations for “best practices” in testing the predictive validity of individual differences in the extent to which perceptions of partners match ideal standards (ideal-partner matching). We respond to their article evaluating the strengths and weaknesses of different tests, presenting new analyses of existing data, and setting out conclusions that differ from Eastwick et al. We (a) argue that correlations between ideal standards for attributes in partners and corresponding partner perceptions are relevant to the ideal standards model (ISM), (b) show that important methodological and statistical issues qualify their interpretations of prior research, (c) illustrate a new analytic approach used in the accuracy literature that tests (and controls for) confounds highlighted by Eastwick et al., and (d) provide evidence that the direct-estimation measure of ideal-partner matching is a valid and useful method. We conclude with a cautionary note on the concept of best practices.
Mediators of socioeconomic inequalities in dietary behaviours among youth: A systematic review

Children and adolescents with a lower socioeconomic position have poorer dietary behaviours compared to their counterparts with a higher socioeconomic position. A better understanding of the mechanisms behind such socioeconomic inequalities is vital to identify targets for interventions aimed at tackling these inequalities. This systematic review aimed to summarize existing evidence regarding the mediators of socioeconomic differences in dietary behaviours among youth. A systematic literature search of MEDLINE, Embase, PsycINFO, and Web of Science databases yielded 20 eligible studies. The dietary behaviours included in the reviewed studies were the intake of fruit and vegetables, sugar‐sweetened beverages, unhealthy snacks/fast food and breakfast. The consistent mediators of the effects of socioeconomic position on dietary behaviours among youth were: self‐efficacy, food preferences and knowledge at the intrapersonal level; and availability and accessibility of food items at home, food rules and parental modelling at the interpersonal level. Few studies including mediators at the organisational, community or policy levels were found. Our review found several modifiable factors at the intrapersonal and interpersonal levels that could be targeted in interventions aimed at combating inequalities in dietary behaviours among youth. Rigorous studies exploring organisational, community and policy level mediators are warranted.
AptaBlocks Online: A Web-Based Toolkit for the In Silico Design of Oligonucleotide Sticky Bridges

The AptaBlocks Web Interface is focused on providing graphical, intuitive, and platform-independent access to AptaBlocks, an experimentally validated algorithmic approach for the in silico design of oligonucleotide sticky bridges. The availability of AptaBlocks online to the nucleic acid research community at large makes this software a highly effective tool for accelerating the design and development of novel oligonucleotide-based drugs and other biotechnologies.
A rare localized giant cell tumor of the tendon sheath originating from the ligamentum mucosum: A case report

Giant cell tumor of the tendon sheath (GCTTS) occurs most often in the hand and rarely in the feet, and as an extremely uncommon presentation in the knee joint. Case reports involving GCTTS in the knee joint generally describe it originating from the nearby anterior cruciate ligament, posterior cruciate ligament, patellar tendon, and medial plica. To the best of our knowledge, there are no previously reported case reports involving GCTTS originating in the ligamentum mucosum. In this article, we describe a 27-year-old male patient who was admitted to the orthopedic emergency room with a painful locked knee. He had severe pain that was worse with activity and a decreased range of motion. Magnetic resonance imaging (MRI) indicated massive swelling and a well- circumscribed lobulated intraarticular mass at the distal one third of the ligamentum mucosum. The mass was removed successfully with arthroscopic-assisted mini-open excision, and histological analysis subsequently diagnosed it as a localized type of GCSTT. The patient remained asymptomatic and a follow-up MRI two years after surgery did not show any recurrence of the lesion.
Infection prevention and chronic disease management in cystic fibrosis and noncystic fibrosis bronchiectasis

Bronchiectasis is a chronic lung disease (CLD) characterized by irreversible bronchial dilatation noted on computed tomography associated with chronic cough, ongoing viscid sputum production, and recurrent pulmonary infections. Patients with bronchiectasis can be classified into two groups: those with cystic fibrosis and those without cystic fibrosis. Individuals with either cystic fibrosis related bronchiectasis (CFRB) or noncystic fibrosis related bronchiectasis (NCFRB) experience continuous airway inflammation and suffer airway architectural changes that foster the acquisition of a unique polymicrobial community. The presence of microorganisms increases airway inflammation, triggers pulmonary exacerbations (PEx), reduces quality of life (QOL), and, in some cases, is an independent risk factor for increased mortality. As there is no cure for either condition, prevention and control of infection is paramount. Such an undertaking incorporates patient/family and healthcare team education, immunoprophylaxis, microorganism source control, antimicrobial chemoprophylaxis, organism eradication, daily pulmonary disease management, and, in some cases, thoracic surgery. This review is a summary of recommendations aimed to thwart patient acquisition of pathologic organisms, and those therapies known to mitigate the effects of chronic airway infection. A thorough discussion of airway clearance techniques and treatment of or screening for nontuberculous mycobacteria (NTM) is beyond the scope of this discussion.
New preparation techniques for molecular and in‐situ analysis of ancient organic micro‐ and nanostructures

Organic microfossils preserved in three dimensions in transparent mineral matrices such as cherts/quartzites, phosphates, or carbonates are best studied in petrographic thin sections. Moreover, microscale mass spectrometry techniques commonly require flat, polished surfaces to minimize analytical bias. However, contamination by epoxy resin in traditional petrographic sections is problematic for the geochemical study of the kerogen in these microfossils and more generally for the in situ analysis of fossil organic matter. Here, we show that epoxy contamination has a molecular signature that is difficult to distinguish from kerogen with time‐of‐flight secondary ion mass spectrometry (ToF‐SIMS). This contamination appears pervasive in organic microstructures embedded in micro‐ to nano‐crystalline carbonate. To solve this problem, a new semi‐thin section preparation protocol without resin medium was developed for micro‐ to nanoscale in situ investigation of insoluble organic matter. We show that these sections are suited for microscopic observation of Proterozoic microfossils in cherts. ToF‐SIMS reveals that these sections are free of pollution after final removal of a <10 nm layer of contamination using low‐dose ion sputtering. ToF‐SIMS maps of fragments from aliphatic and aromatic molecules and organic sulfur are correlated with the spatial distribution of organic microlaminae in a Jurassic stromatolite. Hydrocarbon‐derived ions also appeared correlated with kerogenous microstructures in Archean cherts. These developments in analytical procedures should help future investigations of organic matter and in particular, microfossils, by allowing the spatial correlation of microscopy, spectroscopy, precise isotopic microanalyses, and novel molecular microanalyses such as ToF‐SIMS.
Under his thumb the effect of president Donald Trump’s Twitter messages on the US stock market

Does president Trump’s use of Twitter affect financial markets? The president frequently mentions companies in his tweets and, as such, tries to gain leverage over their behavior. We analyze the effect of president Trump’s Twitter messages that specifically mention a company name on its stock market returns. We find that tweets from the president which reveal strong negative sentiment are followed by reduced market value of the company mentioned, whereas supportive tweets do not render a significant effect. Our methodology does not allow us to conclude about the exact mechanism behind these findings and can only be used to investigate short-term effects.