nils-herrmann/clean_scirepeval huggingface dataset:
A direct method for measuring user experience in E-government portals (May 2016)

The transition from an informative to a service oriented interactive governmental portals has become a necessity due to the time and cost saving benefits for both governments and users. User experience is a key factor in maintaining these benefits. In this study we propose an E-government Portal Assessment Method (EGPAM), which is a direct method for measuring user experience in e-government portals. We present a case study assessing the portal of the Ministry of Public Works (MOW) in Kuwait. Results showed that having a direct measurement to user experience enabled easier identification of the current level of user satisfaction and provided a guidance on ways to improve user experience and addressing identified issues.
Ultraminiaturized High-Speed Permanent-Magnet Generators for Milliwatt-Level Power Generation

This paper presents the design, fabrication, and characterization of millimeter-scale rotary electromagnetic generators. The axial-flux synchronous machines consist of a three-phase microfabricated surface-wound copper coil and a multipole permanent-magnet (PM) rotor measuring 2 mm in diameter. Several machines with various geometries and numbers of magnetic poles and turns per pole are designed and compared. Moreover, the use of different PM materials is investigated. Multipole magnetic rotors are modeled using finite element analysis to analyze magnetic field distributions. In operation, the rotor is spun above the microfabricated stator coils using an off-the-shelf air-driven turbine. As a result of design choices, the generators present different levels of operating frequency and electrical output power. The four-pole six-turn/pole NdFeB generator exhibits up to 6.6 mWrms of ac electrical power across a resistive load at a rotational speed of 392 000 r/min. This milliwatt-scale power generation indicates the feasibility of such ultrasmall machines for low-power applications.
SURVEY ON VANISHING POINT DETECTION METHOD FOR GENERAL ROAD REGION IDENTIFICATION

In today’s world automobile industries are coming out with so many new features in cars. Road infrastructures are also getting much better in all over the world. Due to this, the number of road accidents has increased and more number of people are dying in it. Researchers have tried to solve this problem by using virtual driver feature in the car. A computer vision based road detection and navigation system is put in the car that can help the driver about upcoming accident and rough driving. But the major problem in development of this type of system is identification of road using computer. And also detection of the unstructured roads or structured roads without remarkable boundaries and marking is a very difficult task for computer. For a given image of any road, that may difficult to identify clear edges or texture orientation or priori known color, is it possible for computer to find road? This paper gives answer to that question. First step is to find vanishing point, which uses Gabor filters to find texture orientation at each pixel and voting scheme is used to find vanishing point. Second step is to identify boundaries from this estimated vanishing point. This paper describes the considerable work that has been done towards vanishing point identification methods so that it can be used in real time for further applications.
Discriminative Optimization: Theory and Applications to Point Cloud Registration

Many computer vision problems are formulated as the optimization of a cost function. This approach faces two main challenges: (1) designing a cost function with a local optimum at an acceptable solution, and (2) developing an efficient numerical method to search for one (or multiple) of these local optima. While designing such functions is feasible in the noiseless case, the stability and location of local optima are mostly unknown under noise, occlusion, or missing data. In practice, this can result in undesirable local optima or not having a local optimum in the expected place. On the other hand, numerical optimization algorithms in high-dimensional spaces are typically local and often rely on expensive first or second order information to guide the search. To overcome these limitations, this paper proposes Discriminative Optimization (DO), a method that learns search directions from data without the need of a cost function. Specifically, DO explicitly learns a sequence of updates in the search space that leads to stationary points that correspond to desired solutions. We provide a formal analysis of DO and illustrate its benefits in the problem of 2D and 3D point cloud registration both in synthetic and range-scan data. We show that DO outperforms state-of-the-art algorithms by a large margin in terms of accuracy, robustness to perturbations, and computational efficiency.
TV ratings vs. social media engagement: Big social data analytics of the Scandinavian TV talk show Skavlan

This paper explores the relationship between TV viewership ratings for Scandinavian's most popular talk show, Skavlan and public opinions expressed on its Facebook page. The research aim is to examine whether the activity on social media affects the number of viewers per episode of Skavlan, how the viewers are affected by discussions on the Talk Show, and whether this creates debate on social media afterwards. By analyzing TV viewer ratings of Skavlan talk show, Facebook activity and text classification of Facebook posts and comments with respect to type of emotions and brand sentiment, this paper identifes patterns in the users' real-world and digital world behaviour.
The Influence of Smart Phones on Human Health and Behavior : Jordanians ’ Perceptions

The advent of new touch technologies and the wide spread of smart mobile phones made humans embrace technology more and depend on it extensively in their lives. With new communication technologies and smart phones the world really became a small village. Although these technologies provided many positive features, we cannot neglect the negative influences inherited in these technologies. One of the major negative sides of smart phones is their side effects on human health. This paper will address this issue by exploring the exiting literature related to the negative side of smart phones on human health and behavior by investigating the literature related to three major dimensions: health, addiction and behavior. The third section will describe the research method used. The fourth section will discuss the analysis side followed by a section on the conclusions and future work. Index Terms Mobile phones, smart phone, touch screen, health effects, ergonomics, addiction, behavior.
Statistical Assumptions as Empirical Commitments

Researchers who study punishment and social control, like those who study other social phenomena, typically seek to generalize their findings from the data they have to some larger context: in statistical jargon, they generalize from a sample to a population. Generalizations are one important product of empirical inquiry. Of course, the process by which the data are selected introduces uncertainty. Indeed, any given dataset is but one of many that could have been studied. If the dataset had been different, the statistical summaries would have been different, and so would the conclusions, at least by a little. How do we calibrate the uncertainty introduced by data collection? Nowadays, this question has become quite salient, and it is routinely answered using wellknown methods of statistical inference, with standard errors, t-tests, and P-values, culminating in the “tabular asterisks” of Meehl (1978). These conventional answers, however, turn out to depend critically on certain rather restrictive assumptions, for instance, random sampling.1 When the data are generated by random sampling from a clearly defined population, and when the goal is to estimate population parameters from sample statistics, statistical inference can be relatively straightforward. The usual textbook formulas apply; tests of statistical significance and confidence intervals follow. If the random-sampling assumptions do not apply, or the parameters are not clearly defined, or the inferences are to a population that is only vaguely defined, the calibration of uncertainty offered by contemporary statistical technique is in turn rather questionable.2 Thus, investigators who use conventional statistical technique
Speech outcomes at age 5 and 10 years in unilateral cleft lip and palate after one-stage palatal repair with minimal incision technique - a longitudinal perspective.

To investigate speech outcomes in 5- and 10-year-old children with unilateral cleft lip and palate (UCLP) treated according to minimal incision technique (MIT) - a one-stage palatal method.



A retrospective, longitudinal cohort study of a consecutive series of 69 patients born with UCLP, treated with MIT (mean age 13 months) was included. Forty-two children (43%) received a velopharyngeal flap; 12 before 5 years and another 18 before 10 years of age. Cleft speech variables were rated from standardized audio recordings at 5 and 10 years of age, independently by three experienced, external speech-language pathologists, blinded to the material. The prevalences of cleft speech characteristics were determined, and inter- and intra-rater agreement calculated.



More than mild hypernasality, weak pressure consonants and perceived incompetent velopharyngeal function were present in 19-22% of the children at 5 years, but improved to less than 5% at 10 years. However, audible nasal air leakage, prevalent in 23% at 5 years, did not improve by age 10. Thirty percent had frequent or almost always persistent compensatory articulation at 5 years, and 6% at age 10. The general impression of speech improved markedly, from 57% giving a normal impression at 5 years to 89% at 10 years. A high prevalence of distorted/s/was found at both 5 and 10 years of age.



A high occurrence of speech deviances at 5 years of age after MIT was markedly reduced at 10 years in this study of children with unilateral cleft lip and palate. The high pharyngeal flap rate presumably accounted for the positive speech development.
Power-Aware Optimized RRH to BBU Allocation in C-RAN

Wireless networks have faced increasing demand to cope with the exponential growth of data. Conventional architectures have hindered the evolution of network scalability. However, the introduction of cloud technology has brought tremendous flexible and scalable on demand resources. Thus, cloud radio access networks (C-RANs) have been introduced as a new trend in wireless technologies. Despite the novel advancements that C-RAN offers, remote radio head (RRH)-to-base band unit (BBU) resource allocation can cause significant downgrade in efficiency, particularly the allocation of computational resources in the BBU pool to densely deployed small cells. This causes an increase in power consumption and wasted resources. Consequently, an efficient resource allocation method is vital for achieving efficient resource consumption. In this paper, the optimal allocation of computational resources between RRHs and BBUs is modeled. This is dependent on having an optimal physical resource allocation for users to determine the required computational resources. For this purpose, an optimization problem that models the assignment of resources at these two levels is formulated. A decomposition model is adopted to solve the problem by formulating two binary integer programming subproblems; one for each level. Furthermore, two low complexity heuristic algorithms are developed to solve each subproblem. Results show that the computational resource requirements and the power consumption of BBUs and the physical machines decrease as the channel quality worsens. Moreover, the developed heuristic solution achieves a close to optimal performance while having a lower complexity. Finally, both models achieve high resource utilization, cementing the efficiency of the proposed solutions.
A Needle in a Haystack: What Do Twitter Users Say about Software?

Users of the Twitter microblogging platform share a vast amount of information about various topics through short messages on a daily basis. Some of these so called tweets include information that is relevant for software companies and could, for example, help requirements engineers to identify user needs. Therefore, tweets have the potential to aid in the continuous evolution of software applications. Despite the existence of such relevant tweets, little is known about their number and content. In this paper we report on the results of an exploratory study in which we analyzed the usage characteristics, content and automatic classification potential of tweets about software applications by using descriptive statistics, content analysis and machine learning techniques. Although the manual search of relevant information within the vast stream of tweets can be compared to looking for a needle in a haystack, our analysis shows that tweets provide a valuable input for software companies. Furthermore, our results demonstrate that machine learning techniques have the capacity to identify and harvest relevant information automatically.
Facial expression transfer method based on frequency analysis

We propose a novel expression transfer method based on an analysis of the frequency of multi-expression facial images. We locate the facial features automatically and describe the shape deformations between a neutral expression and non-neutral expressions. The subtle expression changes are important visual clues to distinguish different expressions. These changes are more salient in the frequency domain than in the image domain. We extract the subtle local expression deformations for the source subject, coded in the wavelet decomposition. This information about expressions is transferred to a target subject. The resulting synthesized image preserves both the facial appearance of the target subject and the expression details of the source subject. This method is extended to dynamic expression transfer to allow a more precise interpretation of facial expressions. Experiments on Japanese Female Facial Expression (JAFFE), the extended Cohn-Kanade (CK+) and PIE facial expression databases show the superiority of our method over the state-of-the-art method.
Learning Compiler Design as a Research Activity

This paper describes the application of a pedagogical model called \learning as a research activity" [D. Gil-P erez and J. Carrascosa-Alis, Science Education 78 (1994) 301{315] to the design and implementation of a two-semester course on compiler design for Computer Engineering students. In the new model, the classical pattern of classroom activity based mainly on one-way knowledge transmission/reception of pre-elaborated concepts is replaced by an active working environment that resembles that of a group of novice researchers under the supervision of an expert. The new model, rooted in the now commonly-accepted constructivist postulates, strives for meaningful acquisition of fundamental concepts through problem solving |in close parallelism to the construction of scienti c knowledge through history.
Resveratrol Mediated Modulation of Sirt-1/Runx2 Promotes Osteogenic Differentiation of Mesenchymal Stem Cells: Potential Role of Runx2 Deacetylation

Osteogenic repair in response to bone injury is characterized by activation and differentiation of mesenchymal stem cells (MSCs) to osteoblasts. This study determined whether activation of Sirt-1 (a NAD(+)-dependent histone deacetylase) by the phytoestrogen resveratrol affects osteogenic differentiation.



Monolayer and high-density cultures of MSCs and pre-osteoblastic cells were treated with an osteogenic induction medium with/without the Sirt-1 inhibitor nicotinamide or/and resveratrol in a concentration dependent manner.



MSCs and pre-osteoblastic cells differentiated to osteoblasts when exposed to osteogenic-induction medium. The osteogenic response was blocked by nicotinamide, resulting in adipogenic differentiation and expression of the adipose transcription regulator PPAR-γ (peroxisome proliferator-activated receptor). However, in nicotinamide-treated cultures, pre-treatment with resveratrol significantly enhanced osteogenesis by increasing expression of Runx2 (bone specific transcription factor) and decreasing expression of PPAR-γ. Activation of Sirt-1 by resveratrol in MSCs increased its binding to PPAR-γ and repressed PPAR-γ activity by involving its cofactor NCoR (nuclear receptor co-repressor). The modulatory effects of resveratrol on nicotinamide-induced expression of PPAR-γ and its cofactor NCoR were found to be mediated, at least in part, by Sirt-1/Runx2 association and deacetylation of Runx2. Finally, knockdown of Sirt-1 by using antisense oligonucleotides downregulated the expression of Sirt-1 protein and abolished the inhibitory effects of resveratrol, namely nicotinamide-induced Sirt-1 suppression and Runx2 acetylation, suggesting that the acetylated content of Runx2 is related to downregulated Sirt-1 expression.



These data support a critical role for Runx2 acetylation/deacetylation during osteogenic differentiation in MSCs in vitro. (242 words in abstract).
Evolving intraday foreign exchange trading strategies utilizing multiple instruments price series

We propose a Genetic Programming architecture for the generation of foreign exchange trading strategies. The system’s principal features are the evolution of free-form strategies which do not rely on any prior models and the utilization of price series from multiple instruments as input data. This latter feature constitutes an innovation with respect to previous works documented in literature. In this article we utilize Open, High, Low, Close bar data at a 5 minutes frequency for the AUD.USD, EUR.USD, GBP.USD and USD.JPY currency pairs. We will test the implementation analyzing the in-sample and out-of-sample performance of strategies for trading the USD.JPY obtained across multiple algorithm runs. We will also evaluate the differences between strategies selected according to two different criteria: one relies on the fitness obtained on the training set only, the second one makes use of an additional validation dataset. Strategy activity and trade accuracy are remarkably stable between in and out of sample results. From a profitability aspect, the two criteria both result in strategies successful on out-of-sample data but exhibiting different characteristics. The overall best performing out-of-sample strategy achieves a yearly return of 19%.
DIGS: A Framework for Discovering Goals for Security Requirements Engineering

The security goals of a software system provide a foundation for security requirements engineering. Identifying security goals is a process of iteration and refinement, leveraging the knowledge and expertise of the analyst to secure not only the core functionality but the security mechanisms as well. Moreover, a comprehensive security plan should include goals for not only preventing a breach, but also for detecting and appropriately responding in case a breach does occur.  The objective of this research is to support analysts in security requirements engineering by providing a framework that supports a systematic and comprehensive discovery of security goals for a software system.  We develop a framework, Discovering Goals for Security (DIGS), that models the key entities in information security, including assets and security goals. We systematically develop a set of security goal patterns that capture multiple dimensions of security for assets. DIGS explicitly captures the relations and assumptions that underlie security goals to elicit implied goals. We map the goal patterns to NIST controls to help in operationalizing the goals. We evaluate DIGS via a controlled experiment where 28 participants analyzed systems from mobile banking and human resource management domains.  Participants considered security goals commensurate to the knowledge available to them. Although the overall recall was low given the empirical constraints, participants using DIGS identified more implied goals and felt more confident in completing the task.  Explicitly providing the additional knowledge for the identification of implied security goals significantly increased the chances of discovering such goals, thereby improving coverage of stakeholder security requirements, even if they are unstated.
Analog Circuit Design Optimization based on Symbolic Simulation and Simulated Annealing

A methodology for the automatic design optimization of analog circuits is presented. A non-fixed topology approach is followed. A symbolic simulator, called ISAAC, generates an analytic AC model for any analog circuit, time-continuous or time-discrete, CMOS or bipolar. ISAAC's expressions can be fully symbolic or mixed numeric-symbolic, exact or simplified. The model is passed to the design optimization program OPTIMAN. For a user selected circuit topology, the independent design variables are automatically extracted and OPTIMAN sizes all elements to satisfy the performance constraints, thereby optimizing a user defined design objective. The optimization algorithm is simulated annealing. Practical examples show that OPTIMAN quickly designs analog circuits, closely meeting the specifications, and that it is a flexible and reliable design and exploration tool.
Examining the Role of Social Media in Disaster Management from an Attribution Theory Perspective

This paper is related to the use of social media for disaster management by humanitarian organizations. The past decade has seen a significant increase in the use of social media to manage humanitarian disasters. It seems, however, that it has still not been used to its full potential. In this paper, we examine the use of social media in disaster management through the lens of Attribution Theory.  Theory posits that people look for the causes of events, especially unexpected and negative events. The two major characteristics of disasters are that they are unexpected and have negative outcomes/impacts. Thus, Attribution Theory may be a good fit for explaining social media adoption patterns by emergency managers. We propose a model, based on Attribution Theory, which is designed to understand the use of social media during the mitigation and preparedness phases of disaster management. We also discuss the theoretical contributions and some practical implications. This study is still in its nascent stage and is research in progress.
A new trend for knowledge-based decision support systems design

HAL is a multi-disciplinary open access archive for the deposit and dissemination of scientific research documents, whether they are published or not. The documents may come from teaching and research institutions in France or abroad, or from public or private research centers. L’archive ouverte pluridisciplinaire HAL, est destinée au dépôt et à la diffusion de documents scientifiques de niveau recherche, publiés ou non, émanant des établissements d’enseignement et de recherche français ou étrangers, des laboratoires publics ou privés. A new trend for knowledge-based decision support systems design Pascale Zaraté,