Miracle-dz/UniFAR-FTUs huggingface dataset:
Logic and lattices for distributed programming

In recent years there has been interest in achieving application-level consistency criteria without the latency and availability costs of strongly consistent storage infrastructure. A standard technique is to adopt a vocabulary of commutative operations; this avoids the risk of inconsistency due to message reordering. Another approach was recently captured by the CALM theorem, which proves that logically monotonic programs are guaranteed to be eventually consistent. In logic languages such as Bloom, CALM analysis can automatically verify that programs achieve consistency without coordination. In this paper we present BloomL, an extension to Bloom that takes inspiration from both of these traditions. BloomL generalizes Bloom to support lattices and extends the power of CALM analysis to whole programs containing arbitrary lattices. We show how the Bloom interpreter can be generalized to support efficient evaluation of lattice-based code using well-known strategies from logic programming. Finally, we use BloomL to develop several practical distributed programs, including a key-value store similar to Amazon Dynamo, and show how BloomL encourages the safe composition of small, easy-to-analyze lattices into larger programs.
From Human Gesture to Synthetic Action

Embodied Agents are still looking for their own body language. Our e orts aim at analyzing human speakers to extract individual repertoires of gesture. We argue that TV personalities or actors are better suited for this purpose than ordinary subjects. For analysis, we propose an annotation scheme and present the tool Anvil for transcoding gesture and posture. For transfer to synthetic agents, we suggest to think of gestures as categorizable in equivalence classes, a subset of which can make up an agent's nonverbal repertoire. Results of investigating di erent levels of gesture organisation and posture shifts are presented. The concept of GGroups is introduced which is hypothesized to correspond to rhetorical devices. Also, we give a brief sketch on how these results are planned to be integrated in a multimodal generation system for presentation teams.
Modelling Agent Communication Languages

This paper proposes the use of the Unified Modelling Language (UML) as a formalism for defining an abstract syntax for Agent Communication Languages (ACLs) and their associated content languages. It describes an approach supporting a formal automatic mapping from high-level abstract specifications of language structures to specific computer language bindings and in-memory structures that can be directly used by an agent platform. Some advantages of this approach are that it provides a formal framework for specifying and experimenting with alternative agent communication languages, reduces the error-prone manual process of generating compatible bindings and grammars for different syntaxes. A proof-of-concept prototype implementation supporting an automatic conversion from abstract communication language expressed in UML to a native Java API and a Resource Description Framework (RDF) serialisation format is described.
Audio Environment Recognition using Zero Crossing Features and MPEG-7 Descriptors

Problem statement: This study investigated zero crossing features and selected MPEG-7 audio descriptors for environment sound recognition applications such as audio forensics. Approach: The study implemented several experiments focusing on the problems of environment recognition from audio particularly for forensic applications. Results: It was investigated the effect of the temporal zero crossing feature as well as selected MPEG-7 audio low level descriptors on environment sound recognition. The performance was evaluated against a varying number of training sounds and samples per training file. Conclusion/Recommendations: Experimental results showed that higher recognition accuracy is achieved by increasing the number of training files and by decreasing the number of samples per training file. This study presented an audio environment recognition using zero crossing features and MPEG-7 Descriptors.
A Random Walk Model for Optimization of Search Impact in Web Frontier Ranking

Large-scale web search engines need to crawl the Web continuously to discover and download newly created web content. The speed at which the new content is discovered and the quality of the discovered content can have a big impact on the coverage and quality of the results provided by the search engine. In this paper, we propose a search-centric solution to the problem of prioritizing the pages in the frontier of a crawler for download. Our approach essentially orders the web pages in the frontier through a random walk model that takes into account the pages' potential impact on user-perceived search quality. In addition, we propose a link graph enrichment technique that extends this solution. Finally, we explore a machine learning approach that combines different frontier prioritization approaches. We conduct experiments using two very large, real-life web datasets to observe various search quality metrics. Comparisons with several baseline techniques indicate that the proposed approaches have the potential to improve the user-perceived quality of web search results considerably.
Design a system based on PSO algorithm to analyze commercial data

The economic and fiscal forecasts and identify something that has long been of interest to scholars and practitioners in the field of economy. In the past, experts predict models of a manual analyzer used for this work. As long as the number of companies and their parameters were low Using manual methods seem appropriate, but with the increasing complexity of the issues, traditional analysis methods have limited performance and accuracy are not enough. Follow companies and the increasing complexity of economic relations users of financial information, which is named as beneficiaries, And Analysts attempting to use the newer methods, to predictive power of the financial condition companies and institutions to increase. Data mining techniques and tools are very common nowadays in different areas of application have been used. Diagnosis and prediction of bankruptcy of companies, one of the most useful data mining is economically which in recent years, many researchers have been done about it. The main objective of this paper, design and present an intelligent computer model to predict bankruptcy situation on the stock exchange is based on financial information so does this work with the greatest possible precision because It is one of the most important features of intelligent algorithms such as PSO is to reach the optimal point on these issues.
The Recurrent Nova U Scorpii in the 1999 Outburst : the First Detection of a Significant Orbital-Period Change

In this paper we present and discuss our time-resolved photometry of an eclipsing recurrent nova, U Sco, during an outburst in 1999, which was conducted from immediately after the optical maximum to the final fading toward the quiescence. In the first-ever complete light-curve, a few primary and secondary eclipses of the binary system were detected, and the timings of the minima were determined. We found that the eclipses showed no totality during the outburst. The depth of the primary eclipses was 0.4–0.8 mag, much shallower than that in quiescence. In the plateau phase, very little irradiation (≤ 0.1mag) was observed in the orbital light curve, which implies the existence and a large flaring rim of the accretion disk during the outburst. The minima of the eclipses were detected at earlier orbital phases for the predicted ephemerides. Thus, we obtained an orbital period change of the binary system as u P/P= −1.7(±0.7) ×10 −6 yr −1 from the O −C. Assuming that this period change is a result of the conservative mass transfer between the component stars, its mass-transfer rate reaches u
Discounting by Intervals: A Generalized Model of Intertemporal Choice

According to most models of intertemporal choice, an agent's discount rate is a function of how far the outcomes are removed from the present, and nothing else. This view has been challenged by recent studies, which show that discount rates tend to be higher the closer the outcomes are to one another (subadditive discounting) and that this can give rise to intransitive intertemporal choice. We develop and test a generalized model of intertemporal choice, the Discounting By Intervals (DBI) model, according to which the discount rate is a function of both how far outcomes are removed from the present and how far the outcomes are removed from one another. The model addresses past challenges to other models, most of which it includes as special cases, as well as the new challenges presented in this paper: Our studies show that when the interval between outcomes is very short, discount rate tends to increase with interval length (superadditive discounting). In the discussion we place our model and evidence in a broader theoretical context.
Gender Differences in Severity and Symptoms of Post War Trauma and the Effects of Persisting Psychological Trauma on Quality of Life Among Bosnian Refugees Living in the United States

Bosnian refugees, living in Cleveland Ohio, completed questionnaires during the months of March and April of 2009. This study sample consisted of 41 participants, 21 males and 20 females, who have lived in Bosnia for any duration of time during the civil war that took place between May of 1992 to November of 1995. This study employed several assessment measures: Posttraumatic Stress Disorder Checklist (PCL), Impact of Event Scale-Revised (IES-R), Symptom Checklist-90-R (SCL-90-R) and Multidimensional Index of Life Quality (MILQ). The Following hypothesis were proposed: 1) Manifestation of PTSD symptoms of post war trauma will be significantly higher amongst the female Bosnian refugees. 2) The presence of PTSD symptoms and their severity will be negatively associated with the quality of life currently experienced by Bosnia refugees. Present sample of Bosnian war refugees did not present a higher prevalence of PTSD symptoms in the female population of this sample. However, results from the second hypothesis analysis revealed a strong negative relationship between health and quality of life experienced and PTSD manifestation in the current sample. This shows that the presence of PTSD and its symptoms indicate diminished health and quality of life among Bosnian war refugees.
A Two-stage Hybrid Model for Intrusion Detection

As the number of networked computers grows, intrusion detection is an essential component in keeping networks secure. Various approaches to intrusion detection are currently being in use with each one has its own merits and demerits. This paper presents a hybrid approach for modeling intrusion detection system (IDS). Rule based classifier and simple K-means clustering are combined as a hybrid intelligent system. The initial prototype developed by the rule base classifier improves the performance of K-means clustering. The results show that the developed hybrid model provides better IDS.
Pre-Launch Validation of ADM-Aeolus with an airborne direct-detection wind lidar.

The Atmospheric Dynamics Mission ADM-Aeolus of ESA will be the first lidar mission to sense the global wind field from space. It is based on a direct-detection Doppler lidar operating at 355 nm with two interferometers for aerosol/cloud and molecular backscatter. In order to assess the radiometric and wind measurement performance of the Doppler lidar ALADIN on ADM-Aeolus, an airborne version – the ALADIN Airborne Demonstrator A2D – was developed. The A2D is the first airborne directdetection Doppler lidar worldwide. The A2D receiver and laser transmitter performance characterization will be presented. Findings from two ground campaigns at the Observatory Lindenberg of German Weather Service in 2006 and 2007 using a wide variety of collocated observations will be discussed. The first results of an airborne campaign with the A2D and a coherent 2-μm wind lidar aboard the same aircraft will be shown.
Multidimensional Management of Information Security – A Metrics Based Approach Merging Business and Information Security Topics

Currently as even more in the future, enterprises of whatever size and structure highly dependent on information and information processing technologies. A lot effort has been made for securing these assets, focusing on technical and selected organizational solutions mainly. As the rising dependability on information security comes along with an even stronger increase in the necessity to manage information security also by using a business pair of glasses, activities like communication to security stakeholders or to justify resources needed substantiate the development of models and methods to support information security management entities. A multidimensional approach able to cope with these challenges by integrating business and security topics is presented in the current paper.
Computing the Expected Value and Variance of Geometric Measures

Let P be a point set in ℝd, and let M be a function that maps any subset of P to a positive real. We examine the problem of computing the mean and variance of M when a subset in P is selected according to a random distribution. We consider two distributions; in the first distribution (the Bernoulli distribution), each point p in P is included in the random subset independently, with probability π(p). In the second distribution (the fixed-size distribution), exactly s points are selected uniformly at random among all possible subsets of s points in P. We present efficient algorithms for computing the mean and variance of several geometric measures when point sets are selected under one of the described random distributions. We also implemented four of those algorithms: an algorithm that computes the mean 2D bounding box volume in the Bernoulli distribution, an algorithm for the mean 2D convex hull area in the fixed-size distribution, an algorithm that computes the exact mean and variance of the mean pairwise distance (MPD) for d-dimensional point sets in the fixed-size distribution, and an (1 − ε)-approximation algorithm for the same measure. We conducted experiments where we compared the performance of our implementations with a standard heuristic approach, and we show that our implementations are very efficient. We also compared the implementation of our exact MPD algorithm and the (1 − ε)-approximation algorithm; the approximation method performs faster on real-world datasets for point sets of up to 13 dimensions, and provides high-precision approximations.
Of PCOS Symptoms, Hirsutism Has the Most Significant Impact on the Quality of Life of Iranian Women

Introduction Polycystic ovary syndrome is a common endocrine disorder affecting women both physically and psychologically and can lead to a poor quality of life compared to their normal counterparts. The aim of the present study was to assess the impact of various clinical features of polycystic ovary syndrome on the health-related quality of life of Iranian women diagnosed with this syndrome. Materials and Methods A total of 796 women diagnosed with polycystic ovary syndrome, aged 15–49 years, completed the questionnaires, interviews, and medical assessments required for this study. A reliable and validated Persian version of the health-related quality of life questionnaire for polycystic ovary syndrome patients was used. Linear regression models were used to assess the association between the symptoms of polycystic ovary syndrome and health-related quality of life. Results The mean age of participants was 28.02 years. 35.4% of the subjects were classified as overweight or obese. Hirsutism, was reported to have the strongest impact on the patients’ health-related quality of life, followed in descending order by body mass index, irregular menses and infertility. The relative level of hirsutism was directly proportional to decrease in health-related quality of life score (p<0.001). Conclusions The results of the study found that hirsutism had the strongest impact on the health-related quality of life measures in Iranian women diagnosed with polycystic ovary syndrome. Health care officials need to evaluate in depth the effect of each clinical feature of polycystic ovary syndrome separately and design management strategies, keeping in mind the psychological and physical manifestations.
An ID/locator split architecture for future networks

The ID/locator split concept has recently been introduced in the standardization activities of ITU-T study group 13 for use in future networks. To contribute to ITU-T's initiative, we first propose a naming system to configure host-names and identifiers and map them to locators. We then propose a network architecture that is based on the ID/locator split concept and the naming system. The proposed architecture allows the network layer to change protocols and locators without disturbing the upper-layer communication sessions. This capability is helpful for designing efficient solutions for mobility, multi-homing, routing, and security as well as for integrating heterogeneous network layer protocols.
Mining top-k granular association rules for recommendation

Recommender systems are important for e-commerce companies as well as researchers. Recently, granular association rules have been proposed for cold-start recommendation. However, existing approaches reserve only globally strong rules; therefore some users may receive no recommendation at all. In this paper, we propose to mine the top-k granular association rules for each user. First we define three measures of granular association rules. These are the source coverage which measures the user granule size, the target coverage which measures the item granule size, and the confidence which measures the strength of the association. With the confidence measure, rules can be ranked according to their strength. Then we propose algorithms for training the recommender and suggesting items to each user. Experimental are undertaken on a publicly available data set MovieLens. Results indicate that the appropriate setting of granule can avoid over-fitting and at the same time, help obtaining high recommending accuracy.
Extension of ISOMAP for Imperfect Manifolds

As one of the most promising nonlinear dimensionality reduction techniques, Isometric Mapping (ISOMAP) performs well only when the data belong to a single well-sampled manifold, where geodesic distances can be well approximated by the corresponding shortest path distances in a suitable neighborhood graph. Unfortunately, the approximation gets less and less precise generally as the number of edges of the corresponding shortest path increases, which makes ISOMAP tend to overlap or overcluster the data, especially for disjoint or imperfect manifolds. To alleviate this problem, this paper presented a variant of ISOMAP, i.e. Edge Number-based ISOMAP (ENISOMAP), which uses a new variant of Multidimensional Scaling (MDS), i.e. Edge Number-based Multidimensional Scaling (EN-MDS), instead of the classical Multidimensional Scaling (CMDS) to map the data into the low-dimensional embedding space. As a nonlinear variant of MDS, ENMDS gives larger weight to the distances with fewer edges, which are generally better approximated and then more trustworthy than those with more edges, and thus can preserve the more trustworthy distances more precisely. Finally, experimental results verify that not only imperfect manifolds but also intrinsically curved manifold can be visualized by EN-ISOMAP well.
Methods for Long-Term Environmental Policy Challenges

This article provides a concise overview of methods for analyzing policy choices that have been used in the study of long-term environmental challenges. We open with an overview of the broad classes of methods used for long-term policy analysis, and subsequent sections will describe in depth three particular methods. They are: statistical models, such as employed in the debate on the environmental Kuznets curve, which infer past patterns from data and project them into the future; robust decision-making, a decision analytic framework that supports choices under deep uncertainty, and relates near-term policy interventions to different clusters of long-term environmental futures; and adaptive control and agent-based modeling, which provide an approach to simulation modeling that focuses on cooperation and conflict among multiple actors and their choice of strategies. While all three approaches can be used for various applications, this article focuses on the challenge of a potential transition to a low-carbon future to illustrate the strengths, weaknesses, and synergies among the respective methods. In the final section, we offer guidance for choosing among methods.
Supporting context-aware database querying in an Ambient Intelligent environment

Advances in wireless communication and powerful computing technologies will eventually lead to a digital environment in which computing functionality will be embedded in all kinds of objects, which are capable of recognizing and responding to individual needs in a seamless, unobtrusive, and often invisible way. Such a vision was coined Ambient Intelligence (AmI) [1], [5], whose aim is to bring information and communication technologies to everyone, every home, every school, and every business. Different from the traditional desktop-based database access paradigm, users in an AmI environment may request information anytime anywhere via any handheld device. The digital environment continuously monitors users' context (e.g., location, activity, etc.) to ensure the right information for the right user at the right context. The aim of this paper is to investigate the influence of users' context upon database querying, as reflected from an SQL-based context-aware database query language.
Effect of Stimulus Parameters, Age and Gender on Cubic Difference Distortion Product Otoacoustic Emission Measurement for Hearing Screening and Diagnosis

Distortion product otoacoustic emissions (DPOAEs) are a series of combination tones generated in the ear when stimulated with two pure tones of frequency f1 and f2 (in audio range). The most prominent distortion product is the cubic difference tone at 2f1-f2. Maximum level of the DPOAE signals are observed when the frequency ratio f2/f1 is chosen to be 1.22. The objective of this study is to determine the DPOAE response characteristics from normal and hearing impaired subjects and to find the influence of stimulus parameters, age and gender on cubic difference DPOAE responses. The DSP platform controls the microphone/speaker system which generates the acoustical stimuli for presentation to the ear canal and collects the acoustic responses recorded in the ear canal. The signal processing problem involves estimation of the weak DPOAE response under two strong stimuli and background noise. A total of 43 DPOAE responses from neonatal to adult subjects (both male and female) are analyzed. The DPOAE data of both adults and children are compared with individual pure tone audiometric data to investigate how DPOAE responses differentiated ears with normal hearing from impaired ears. DPOAE responses are analyzed under different stimulus parameters. Both age and gender difference affects DPOAE response. DPOAE assessment provides detailed frequency specific information on cochlear function. This property of DPOAEs is used for clinical applications to find hearing impairments and hearing screening.
Code-mixing : linguistic form and socio-cultural meaning

Code-mixing is the change of one language to another within the same utterance or in the same oral/ written text. It is a common phenomenon in societies in which two or more languages are used. Studies of code-mixing enhance our understanding of the nature, processes and constraints of language (Myers-Scotton, 1993a; Boeschoten, 1998; Azuma, 1998), and of the relationship between language use and individual values, communicative strategies, language attitudes and functions within particular socio-cultural contexts. (Auer, 1998; Jacobson, 1998; Myers-Scotton, 1993b; Ludi, 2003)