Miracle-dz/UniFAR-FTUs huggingface dataset:
Assessing a Technology for Usability and User Experience Evaluation of Conversational Systems: An Exploratory Study

Conversational Systems (CS) are increasingly present in people’s daily lives. CS must provide a good experience and meet the needs of its users. Therefore, the Usability and User Experience (UX) evaluation is an appropriate step before making CSs available to society. To guide developers to identify problems, improvement suggestions, and user perceptions during CSs development, we developed a technology named Usability and User Experience Evaluation of Conversational Systems (U2XECS). U2XECS is a questionnaire-based technology that provides Usability and UX statement specifics to evaluate CSs. We conducted an exploratory study performed to evaluate and evolve U2XECS. Our results evidenced positive points of U2XECS related to ease of use, usefulness, and intentions to use. Moreover, we identified opportunities for improvement in U2XECS, such as ambiguous statements that generated misinterpretations in subjects.
Investigating Information about Software Requirements in Projects That Use Continuous Integration or Not: An Exploratory Study

Continuous Integration (CI) is a development practice that involves the automation of compilation and testing procedures, increasing the frequency of code integration and the delivery of new features and providing improvements in software quality. Open Source Software (OSS) projects are increasingly associated with the use of CI practices. However, the literature has not yet explored how and if this practice can influence the presence and the types of artifacts and information related to requirements. Thus, this study aimed to investigate the presence, types of artifacts, and information related to requirements found in projects on GitHub, in particular projects that use CI. An exploratory methodology was used to identify and classify the requirements artifacts where the result shows that projects that adopt the CI have, in general, a more amount of requirements artifacts, mainly in artifacts of the GitHub platform such as issues, pull requests, and labels.
A Possibility in Algorithmic Fairness: Can Calibration and Equal Error Rates Be Reconciled?

Decision makers increasingly rely on algorithmic risk scores to determine access to binary treatments including bail, loans, and medical interventions. In these settings, we reconcile two fairness criteria that were previously shown to be in conflict: calibration and error rate equality. In particular, we derive necessary and sufficient conditions for the existence of calibrated scores that yield classifications achieving equal error rates at any given group-blind threshold. We then present an algorithm that searches for the most accurate score subject to both calibration and minimal error rate disparity. Applied to the COMPAS criminal risk assessment tool, we show that our method can eliminate error disparities while maintaining calibration. In a separate application to credit lending, we compare our procedure to the omission of sensitive features and show that it raises both profit and the probability that creditworthy individuals receive loans.
On Functional Requirements for Keyword-based Query over Heterogeneous Databases on the Web

Context. A large amount of data is made available daily on the Web, but many databases cannot be accessed by conventional search engines, as they require proper access methods and specialised knowledge through their access languages. Focus. In the scenario of non-expert users to access databases, multiple database categories, and plural idioms, this work analyzes the functional requirements that need to be considered for keyword queries processing over data sources on the Web. The problem is still open and involves challenges such as query interpretation and access to databases. Method. The investigation is centered on the problem itself, which is portrayed by a set of functional issues, which together represent the challenges linked to the research field. Approach. This work introduces and systematically analyzes the functional requirements to the problem scope. Issues reported in the literature are refined and evolved to support the modeling of the problem views: functional responsibilities and their interactions by messaging between problem objects. Conclusions and Results. This paper contributes to characterize the problem, makes clearer its understanding and promotes the development of keyword-based query processing systems. A software engineering artifact is used to model the problem and make it more formal and precise. Further studies will refine such requirements and build (specialise) artifacts tailored to the solution space.
Streetwise: Mapping Citizens' Perceived Spatial Qualities

Streetwise is the first map of spatial quality of urban design of Switzerland. Streetwise measures the human perception of spatial situations and uses crowdsourcing methods for this purpose: a large number of people are shown pairs of street-level images of public space online; by clicking on an image, they each give an evaluation about the place they consider has a better atmosphere, which is the focus of this article. With the gathered data, a machine learning model was trained, which allowed learning features that motivate people to choose one image over another. The trained model was then used to estimate a score representing the perceived atmosphere in a large number of images from different urban areas within the Zurich metropolitan region, which could then be visualized on a map to offer a comprehensive overview of the atmosphere of the analyzed cities. The accuracy obtained from the evaluation of the machine learning model indicates that the method followed can perform as well as a group of humans.
An Effective Intrusion Detection Model based on Random Forest Algorithm with I-SMOTE

With the wide applications of network in our daily lives, network security is becoming increasing prominent. Intrusion detection systems have been widely used to detect various types of malicious network which cannot be detected by a conventional firewall. Therefore, various machine-learning techniques have been proposed to improve the performance of intrusion detection system. However, the balance of different data classes is critical and will affect detection performance. In order to reduce the impact of class imbalance of the intrusion dataset, this paper proposes a scheme that applies the improved synthetic minority oversampling technique (I-SMOTE) to balance the dataset, employs correlation analysis and random forest to reduce features and uses the random forest algorithm to train the classifier for detection. The experimental results based on the NSLKDD dataset show that it achieves a better and more robust performance in terms of accuracy, detection rate, false alarms and training speed.
Novel Supreme Drug-Eluting Stents With Early Synchronized Antiproliferative Drug Delivery to Inhibit Smooth Muscle Cell Proliferation After Drug-Eluting Stents Implantation in Coronary Artery Disease

Supplemental Digital Content is available in the text. Background: Accelerated endothelial healing after targeted antiproliferative drug delivery may limit the long-term inflammatory response of drug-eluting stents (DESs). The novel Supreme DES is designed to synchronize early drug delivery within 4 to 6 weeks of implantation, leaving behind a prohealing permanent base layer. Whether the Supreme DES is safe and effective in the short term and can improve long-term clinical outcomes is not known. Methods: In an international, 2:1 randomized, single-blind trial, we compared treatment with Supreme DES to durable polymer everolimus-eluting stents (DP-EES) in patients with acute and chronic coronary syndromes. The primary end point was target lesion failure—a composite of cardiac death, target vessel myocardial infarction, or clinically driven target lesion revascularization. The trial was designed to demonstrate noninferiority (margin of 3.58%) of the Supreme DES at 12 months compared with DP-EES (URL: https://www.clinicaltrials.gov; Unique identifier: NCT03168776). Results: From October 2017 to July 2019, a total of 1629 patients were randomly assigned (2:1) to the Supreme DES (N=1086) or DP-EES (N=543). At 12 months, target lesion failure occurred in 57 of 1057 patients (5.4%) in the Supreme DES group and in 27 of 532 patients (5.1%) in the DP-EES group (absolute risk difference, 0.32% [95% CI, −1.87 to 2.5]; Pnoninferiority=0.002]. There were no significant differences in rates of device success, clinically driven target lesion revascularization, or stent thrombosis at 12 months, and the safety composite of cardiovascular death and target vessel myocardial infarction was 3.5% versus 4.6% (hazard ratio, 0.76 [95% CI, 0.46–1.25]) with Supreme DES compared with DP-EES, although rates of combined clinically and non–clinically driven target lesion revascularization at 12 months were higher with Supreme DES. Conclusions: Among patients with acute and chronic coronary syndromes undergoing percutaneous coronary intervention, the Supreme DES proved to be noninferior to the standard DP-EES. Registration: URL: https://www.clinicaltrials. gov; Unique identifier: NCT03168776.
The Interleukin 6 (IL-6) gene expression is decreased by probiotic supplementation in young Barbary ducks

The present study was conducted to evaluate the effects of probiotic supplementation on productive performance traits and gene expression level of interleukin 6 (IL-6) that acts as a major proinflammatory cytokine in young duck. A total of 200 one-day-old male Barbary ducks were randomly separated to 4 dietary treatments, and each treatment consisted of 5 replicates with 10 birds. The four dietary treatments included the basal diet (control group), the basal diet supplemented with probiotic at different level such as 0.02%, 0.05% and 0.10% (0.02%group, 0.05% group and 0.10% group, respectively) from 1 to 14 days of age. In addition to growth performance parameters, 6 ducks with a live weight close to the group average were selected from each group and euthanized to collect the spleen tissue for gene expression analysis at 14 days old. Result showed that probiotic supplementation had no significant differences on body weight, weight gain, average daily gain, feed intake and feed conversion ratio. However, the gene expression level of probiotic supplemented group, as measure in the spleen was lower than control group (P < 0.05). This present study suggests that dietary probiotics have some properties to regulate the pro-inflammatory cytokine gene expression such as IL-6, and could be used as the feed additive to promote health and immune system in young duck.
Social Capital of Left-Behind Children: Determinants and Association With School Performance

This study examined the influence of social capital on the school performance of left-behind children who were deprived of the physical presence of migrant parents. Social capital in the family, in school, and the community were examined through quantitative design. A survey was conducted among 384 left-behind children selected through multi-stage probability sampling. Results of regression analysis showed that higher levels of shared values and emotions in the family, social trust in the school, involvement in community life, and age of migrant parents determine a higher level of values learned. Higher levels of supportive relationship, social trust at school, and involvement in community life predict a higher level of right conduct. Higher levels of supportive relationship and supportive norms determine a higher average grade. Left-behind children with mother-present, father migrant draw more social capital in the family; while left-behind children with father-present, mother-migrant generate social capital in the school and the community. It is recommended that fathers should be involved in providing care and in the nurturing of left-behind children. Schools should cultivate an environment that invests in social capital especially for the left-behind children lacking in social capital at home. Government institutions serving for the welfare of migrant parents should educate and assist in the psycho-social needs of left-behind children and migrants’ households.
Assistive Technologies for People with Dementia: Personal Review

1.2 Classifications of memory Baddeley’s Theory Episodic memory Function and Lesion of Episodic Memory Semantic Memory Impairment of Semantic and episodic memory Working Memory Anterograde Amnesia (Bauer, 2008) Prospective memory Retrograde Amnesia Source Amnesia Meta-memory and "Feeling of Knowing" Basal forebrain amnesia Procedural Memory The Semantic Memory Remain in the Elderly Procedural Memory Shows Little Decline Age-related Memory Declines The Types of Memory to be Impaired in Dementia 1.3 Dissociation between Episodic and Semantic Memory Case Reports of episodic and Semantic memory impairments Case Reports with Opposite Pattern Lesion of Semantic Memory Lesion and Sub-categorization of Semantic Memory 1.4 Evaluation for Dementia, Proper Names and Discourse Anomia for Proper Names Why are peoples' names difficult to retrieve
UNIVERSIDADE FEDERAL DE JUIZ DE FORA INSTITUTO DE CIÊNCIAS EXATAS PROGRAMA DE PÓS-GRADUAÇÃO EM CIÊNCIA DA COMPUTAÇÃO

Video lectures are very popular nowadays. Following the new teaching trends, students are increasingly seeking educational videos on the web for the most different purposes: learn something new, review content for exams or just out of curiosity. Unfortunately, finding specific content in this type of video is not an easy task. Many video lectures are extensive and cover several topics, and not all of these topics are relevant to the user who has found the video. The result is that the user spends so much time trying to find a topic of interest in the middle of content irrelevant to him. The temporal segmentation of video lectures in topics can solve this problem allowing users to navigate of a non-linear way through all topics of a video lecture. However, temporal video lecture segmentation is a time-consuming task and must be automatized. For this reason, in this work we propose an optimization framework for the temporal video lecture segmentation problem. Our proposal only uses information from the teacher’s speech, therefore it does not depend on any additional resources such as slides, textbooks or manually generated subtitles. This makes our proposal versatile, as we can apply it to a wide range of different video lectures, as it only requires the teacher’s speech on the video. To do this, we formulate this problem as a linear programming model where we combine prosodic and semantic features from speech that may indicate topic transitions. To optimize this model, we use a elitist genetic algorithm with local search. Through the experiments, we were able to evaluate different aspects of our approach such as sensibility to parameter variation and convergence behavior. Also, we show that our method was capable of overcoming state-of-the-art methods, both in Recall and in F1-Score, in two different datasets of video lectures. Finally, we provide the implementation of our framework so that other researchers can contribute and reproduce our results. Key-words: Video Lectures. Temporal Segmentation of Video Lectures. Linear Programming. Natural Language Processing. Speech Processing.
Comment on “Sovereign Bankruptcy in the EU: A Comparative Perspective” by Leszek Balcerowicz, Lectio

More than three years after the emergence of the financial excesses and regulatory failures that triggered a global economic crisis second only to that of the 1930s, its consequences are still to be fully understood and, especially, adequately addressed. In the aftermath of the Great Recession of 2008-09, we are left with a series of risks linked to the possible re-emergence of global imbalances and associated turmoil in currency markets and to the huge accumulation of public debt in advanced economies, which could lead to higher borrowing costs and further financial turbulence if markets started to worry about its sustainability. The two risks are interrelated and are the subject of discussions within the G20 and in other venues. Serious sovereign debt distress has indeed materialized this year in a number of countries, in particular in the euro area, and issues such as debt resolution mechanisms and the design of fiscal rules have again come to the forefront of the policy debate in the European Union (EU). They are the subject of this most interesting lecture by Leszek Balcerowicz and I will deal with them sequentially.
Implementing Information and Communication Technologies in Educating Knowledge Workers

The possession and access to relevant information, as well as to the newly created knowledge, became the main asset within knowledge-based economies, and, hence, societies. In order to achieve considerable level of innovation and competitiveness in the modern labor market, companies are striving to manage knowledge they dispose with in the most optimal way. One of the basic preconditions for accomplishing this goal is to employ highly skilled knowledge workers. Given that this is the workforce of the future, all levels of education, particularly institutions at the university level, must adapt their curricula in order to form professionals capable of anticipating and solving problems within the business environment. Considering that these professionals must remain experts in their scientific fields during their entire career, the concept of lifelong learning especially applies to them. Hence, universities must pay particular attention not only to educating future knowledge workers, but also to providing constant additional training, primarily through digital means and distance type of learning. Knowledge workers are encouraged to use digital tools and methods as much as possible for the purpose of their professional development. In order to achieve and maintain a high level of competitiveness in the ever-changing labor market, these experts are also required to stay constantly and adequately informed, which they mostly accomplish by means of digital media. Therefore, it became obvious that when educating future knowledge workers universities have to implement ICT to a much greater extent. Digital tools should also be utilized at the university level in order to incite a higher level of creativity of university students, as this is one of the essential features of knowledge professionals. Encouraging inventiveness at the university level is particularly important so that future knowledge workers can acquire the habit of transferring theoretical knowledge in an original manner into new applicable knowledge. Within this paper, we will offer a review of current knowledge workers‟ education at the university level and possible innovations in this regard, particularly with the application of digital tools in lifelong education and training of future workforce based on knowledge.
Overview of Comprehensive Information Quality Management under Mass Customization

—Mass customization is a mode of production for a single customer or a large number or varieties of products in the market. It is based on the mass production of the cost and speed. But due to the presence of the customer diversification and personalized order, make mass customization information quality management more complex and difficult. This paper outlines the necessity of information management under mass customization, and puts forward the concept, basic theory and recursive formula of the total information quality management.
Foods, Sex, and Drugs: Appetitive Desires and Subjective Well-Being

In their everyday environments, people spend a lot of time experiencing and dealing with appetitive desires of all kinds, including those for foods, sex, and drugs. In this chapter, we review the concept of appetitive desire and spell out its multifaceted link with subjective well-being. Our main conclusions are that, oftentimes, appetitive desires are quite unproblematic and adaptive, and enacting them is typically a source for affective and cognitive well-being. However, desires sometimes conflict with important long-term goal or moral values, and their mere experience can be aversive. More importantly, the enactment of such unproblematic desires appears to result in diminished hedonic gains, and is related to reduced cognitive well-being. Having identified motivational conflict as the main source for the sometimes problematic nature of appetitive desire, we conclude by discussing various ways through which individuals faced with the dilemma of problematic desire may be able to optimize their subjective well-being.
Reweighting in Conditional Random Fields using an Expert-Domain Dictionary

For Named Entity Recognition (NER) in specialized fields, it is often the case that one lacks access to gold data. Therefore, many studies have explored techniques such as noise reduction of ‘incomplete data’ where some of the named entities are given false negative labels. In these studies, they assign multiple labels (all possible label candidates) to tokens with incomplete annotations. The model improves the recognition performance by learning probability distributions over the given labels. However, these models still face three main challenges. The first problem is that confirmation bias causes noise propagation. These models perform sequence labeling learning and noise reduction in the same process and with the same resources. Therefore, the noise at the beginning of learning propagates to the latter stages of learning. The second problem is the tradeoff between Precision and Recall. When applying noise reduction using multiple label, Recall improves, but Precision decreases. The third problem is false positives in the model prediction, due to mismatches between the resource(s) used to create the multiple labels for training relative to the evaluation data. For these problems, we propose the following strategies. As a strategy for problem 1, we reduce noise from different processes and resources. Specifically, we improve the weighting process. We use ‘entity linking, knowledge graph embedding and clustering’ as another process, and ‘hierarchical dictionary’ as another resource. As a strategy for problem 2, we reduce false positive and improve Precision without decreasing Recall. As a strategy for problem 3, we recognize word sets targeted by evaluation data from various word sets on the dictionary based on ‘hierarchical structure of dictionary’ and ‘incomplete data’. We describe the contribution of this paper below.
Predictive effects of self-esteem, shyness and loneliness on internet addicton, case of Albania

Aim. This study aim to give an overview about the phenomenon of internet addiction in Albania, gender differences according internet addiction, tend to examine if exist any relationship between internet addiction, self esteem, loneliness and shynnes and also to study if internet addiction differ according to this three variables. Methods. The sample of the study consisted of 652 adolescents, aged 15-18, in the city of Tirana. The data was collected through the application of the Internet Addiction Test, Rozenberg Self Esteem Test, UCLA Loneliness Scale and Shyness Scale. Results. The results of the study show us that adolescents have high levels of internet addiction, and male are more addicted that females. Internet addiction correlates positively with loneliness and shyness, but there is no significant relationship between internet addiction and self – esteem. Shyness best predicts internet addiction.