Miracle-dz/UniFAR-FTUs huggingface dataset:
Speculatively Granting Conflicting Accesses on Hardware Transactional Memory

Transactional memory (TM) [1] is a promising mechanism for improving both of productivity and performance of parallel processing programs on shared-memory systems such as multi-core processors. It can be complementary and/or alternative to traditional lock-based mechanisms that are awkward and intractable for programmers. Hardware transactional memories (HTMs), hardware implementations of TM, are now widely installed to the several latest processors, such as IBM zEC12, Power8, and Intel Haswell. Programmers define critical sections as transactions, and the transactions can be speculatively executed in parallel by HTMs, while traditional lock-based systems completely serialize them. HTMs dynamically detect access conflicts on shared variables between transactions and cancel speculative execution of transactions. Because the canceled execution comes to nothing and causes some performance overhead for rolling back the transaction execution, how to avoid and reduce access conflicts is very important for the performance of HTMs. Many studies [2, 5, 8–11] have been conducted on improving HTM performance, but some type of programs still suffer many access conflicts and large performance overhead, and they should be relieved. Transactions must guarantee several properties including Atomicity and Isolation, and access requests that may violate them are generally denied by HTMs. However, we found that some of the requests can be granted without violating Isolation, and this can improve the HTM performance. On HTM, execution of a transaction is naturally speculative, and our proposal boosts its speculativeness by speculatively permitting some accesses that are judged as conflicting accesses. In this presentation, we aim to make the following contributions:
Studies to See the Effect of Nutrition on the Health Status of Selected Retired and Active Working Elderly People

In the present study an effort was made to assess the nutritional status of 120 elderly residents of Baijnath and Panchrukhi blocks of District Kangra. From the collected information it was observed that 44.17 per cent of subjects had Grade I and Grade II obesity. Energy, carbohydrate and fat intake was higher as compare to recommended dietary allowances and found to be positively correlated with anthropometric parameters. Age had significant and negative association with anthropometric parameters namely Body Mass Index (BMI) (-0.343**), Arm Muscle Circumference (AMC) (-0.284**), Arm Muscle Area (AMA) (-0.351**), Arm Fat Area (AFA) (0.215*). Health conditions like diabetes, hypertension, heart disease, arthritis and gout were positively associated with nutrient intake such as energy, carbohydrate and fat. These health conditions were also positively and significantly associated with anthropometric parameters viz. BMI (0.853*), WHR (0.219*), WHtR (0.192*), Percent Body Fat (PBF) and Fat Mass (FM) (0.199*). [*Significant at p<0.05, **Significant at p<0.01]
A Bigram Supported Generic Knowledge-Assisted Malware Analysis System: BiG2-KAMAS

Malicious software, short “malware”, refers to software programs that are designed to cause damage or to perform unwanted actions on the infected computer system. Behaviorbased analysis of malware typically utilizes tools that produce lengthy traces of observed events, which have to be analyzed manually or by means of individual scripts. Due to the growing amount of data extracted from malware samples, analysts are in need of an interactive tool that supports them in their exploration efforts. In this respect, the use of visual analytics methods and stored expert knowledge helps the user to speed up the exploration process and, furthermore, to improve the quality of the outcome. In this paper, the previously developed KAMAS prototype is extended with additional features such as the integration of a bi-gram based valuation approach to cover further malware analysts’ needs. The result is a new prototype which was evaluated by two domain experts in a detailed user study.
On exponential domination of graphs

Exponential domination in graphs evaluates the influence that a particular vertex exerts on the remaining vertices within a graph. The amount of influence a vertex exerts is measured through an exponential decay formula with a growth factor of one-half. An exponential dominating set consists of vertices who have significant influence on other vertices. In non-porous exponential domination, vertices in an exponential domination set block the influence of each other. Whereas in porous exponential domination, the influence of exponential dominating vertices are not blocked. For a graph G, the non-porous and porous exponential domination numbers, denoted γe(G) and γ∗ e (G), are defined to be the cardinality of the minimum non-porous exponential dominating set and cardinality of the minimum porous exponential dominating set, respectively. This dissertation focuses on determining lower and upper bounds of the non-porous and porous exponential domination number of the King grid Kn, Slant grid Sn, n-dimensional hypercube Qn, and the general consecutive circulant graph Cn,[`]. A method to determine the lower bound of the non-porous exponential domination number for any graph is derived. In particular, a lower bound for γ∗ e (Qn) is found. An upper bound for γ∗ e (Qn) is established through exploiting distance properties of Qn. For any grid graph G, linear programming can be incorporated with the lower bound method to determine a general lower bound for γ∗ e (G). Applying this technique to the grid graphs Kn and Sn yields lower bounds for γ∗ e (Kn) and γ∗ e (Sn). Upper bound constructions for γ∗ e (Kn) and γ∗ e (Sn) are also derived. Finally, it is shown that γe(Cn,[`]) = γ ∗ e (Cn,[`]).
Path Length Resolved Dynamic Light Scattering Measurements with Suppressed Influence of Optical Properties Using Phase Modulated Low Coherence Interferometry

In optical Doppler measurements, the path length of the light is unknown. This complicates the noninvasive diagnosis of tissue with light. For example, in laser Doppler blood flowmetry, the coherent light delivered into the tissue interacts with static as well as moving scatterers, e.g. red blood cells and it records values averaged over different and basically unknown path lengths. One of the important limitations of this technique is the dependence of the perfusion signal on the optical properties of the tissue, i.e. absorption coefficient, scattering coefficient and anisotropy factor. These dependences result from the varying optical path length of detected photons; the longer the optical path length the greater is the probability for Doppler scattering events to occur, thus yielding an overestimation of the blood perfusion, compared to the short path length situation [1].
Spherical k-Means Clustering

Clustering text documents is a fundamental task in modern data analysis, requiring approaches which perform well both in terms of solution quality and computational efficiency. Spherical k-means clustering is one approach to address both issues, employing cosine dissimilarities to perform prototype-based partitioning of term weight representations of the documents. This paper presents the theory underlying the standard spherical k-means problem and suitable extensions, and introduces the R extension package skmeans which provides a computational environment for spherical k-means clustering featuring several solvers: a fixed-point and genetic algorithm, and interfaces to two external solvers (CLUTO and Gmeans). Performance of these solvers is investigated by means of a large scale benchmark experiment.
Compiling SU(4) quantum circuits to IBM QX architectures

The Noisy Intermediate-Scale Quantum (NISQ) technology is currently investigated by major players in the field to build the first practically useful quantum computer. IBM QX architectures are the first ones which are already publicly available today. However, in order to use them, the respective quantum circuits have to be compiled for the respectively used target architecture. While first approaches have been proposed for this purpose, they are infeasible for a certain set of SU(4) quantum circuits which have recently been introduced to benchmark corresponding compilers. In this work, we analyze the bottlenecks of existing compilers and provide a dedicated method for compiling this kind of circuits to IBM QX architectures. Our experimental evaluation (using tools provided by IBM) shows that the proposed approach significantly outperforms IBM's own solution regarding fidelity of the compiled circuit as well as runtime. Moreover, the solution proposed in this work has been declared winner of the IBM QISKit Developer Challenge. An implementation of the proposed methodology is publicly available at http://iic.jku. at/eda/research/ibm_qx_mapping.
Dive Patterns of Belugas ( Delphinapterus leucas ) in Waters Near Eastern Devon Island

Data were obtained for six belugas or white whales ( Delphinapterus leucas ) instrumented with satellite-linked dive recorders in September-November 1995 in waters near eastern Devon Island, Canada. The mean of the daily maximum depths of dives was 483-665 m for the 31-51 days when maximum depth measurements were taken. The deepest dive recorded was 872 m. Both the dive rate (number of dives per hour to depths > 8 m) and the time at surface (time spent within the uppermost 5 m of the water column) declined from mid-September through mid-October. The four females had significantly elevated dive rates during the nights (2300-0500), whereas the males showed no effects of time of day on the dive rates. Few dives lasted more than 18 min, and most lasted either less than 1 min or for 9-18 min. A trend from short dives to longer dives was noted from mid-September through October, along with a decline in the number of dives to 8-20 m and a corresponding increase in the number of dives to 200-452 m during the same period. The small whales made more dives and had longer times at the surface than the large whales, but they did not dive as deeply or for as long periods as did the large whales. Vertical speeds ranged from . 05 ms to . 19 ms for depths of 52-800 m. These speeds are significantly faster than vertical speeds recorded from narwhals ( Monodon monoceros ). Normal.dotm 0 0 1 12 70 University of Calgary 1 1 85 12.0 0 false 18 pt 18 pt 0 0 false false false Key words: beluga, Delphinapterus leucas , diving, Devon Island, satellite telemetry
A Preliminary Literature Review of Digital Transformation Case Studies

 Abstract — While struggling to succeed in today’s complex market environment and provide better customer experience and services, enterprises encompass digital transformation as a means for reaching competitiveness and foster value creation. A digital transformation process consists of information technology implementation projects, as well as organizational factors such as top management support, digital transformation strategy, and organizational changes. However, to the best of our knowledge, there is little evidence about digital transformation endeavors in organizations and how they perceive it – is it only about digital technologies adoption or a true organizational shift is needed? In order to address this issue and as the first step in our research project, a literature review is conducted. The analysis included case study papers from Scopus and Web of Science databases. The following attributes are considered for classification and analysis of papers: time component; country of case origin; case industry and; digital transformation concept comprehension, i.e. focus. Research showed that organizations – public, as well as private ones, are aware of change necessity and employ digital transformation projects. Also, the changes concerning digital transformation affect both manufacturing and service-based industries. Furthermore, we discovered that organizations understand that besides technologies implementation, organizational changes must also be adopted. However, with only 29 relevant papers identified, research positioned digital transformation as an unexplored and emerging phenomenon in information systems research. The scarcity of evidence-based papers calls for further examination of this topic on cases from practice.
Use of detection of coherent flow structures for better understanding of 3D flow fields in urban environment

The rapid development of flow measurement (particularly PIV) and numerical simulation results in a huge amount of information: 2D or 3D velocity, pressure and turbulent kinetic energy distribution of high resolution. Particularly in case of 3D distribution it is not at all easy to “understand” the flow field. Without clear understanding of the flow field characteristics it is difficult to develop proposals addressing e.g. improvement of a given air pollution situation. This paper discusses the use of advanced flow field structure extraction methods enabling a comprehensive understanding of the flow field structure presented on cases of parts of urban environment like streets, square, intersection of streets, as well as on simplified models of built environment. The turbulent flow is measured or modeled via RANS approach solved by means of Finite Volume Method using the commercial software FLUENT 6 or MISKAM 5. Post-processing of the results is based on simultaneous observation and analyses of streamlines, wall streak lines, vortex cores, iso-surfaces of the second invariant of the velocity gradient tensor (usually denoted by Q) and iso-surfaces of the total pressure (in case of CFD). Applying these methods, one can not only show the structure of separated flow regions but can determine which of these structures are predominantly influential in terms of development of aerodynamic characteristics and transport processes of the flow in urban areas. The gathered additional information may extend the support of city and traffic planning in making conceptual or optimization decisions.
Association between Nutritional Status and Life Style Practices of Primary School Children in the Colombo District: A Pilot Study

The objective of this study was to determine the association between nutritional status and life-style practices of primary school children in the Colombo district. A cross-sectional study involving 1347 children was conducted on randomly selected 8 schools in Colombo in 2008. Life style practices were assessed using a questionnaire. Height and weight were measured and height-for-age, weight-for-age and body-mass-index for age were used to define stunting, underweight and obesity respectively. The prevalence of stunting and underweight was 3.8% and 16.6%, respectively. According to the age specific body-mass-index, 5.1% were obese, 8.9% were overweight, and 30.9% were thin. Overweight and obesity were significantly high among boys while underweight was high among girls. Daily intake of breakfast was seen only in 67.1%. Daily intake of fruits, vegetables and green leaves were 52.1%, 71.8% and 52.9%, respectively. Children who consumed breakfast daily showed significantly high daily intake of vegetables and green leaves. The frequency of consuming fast foods was significantly high among boys studied. According to the leisure activities, 73.3% were involved predominantly in sedentary type of activities. Television viewing or computing was significantly high among boys. Children who engaged in watching television while consuming food reported poor diet consuming comparatively small quantities of vegetables, green leaves, and fruits daily with a high habitual intake of soft drinks, and fast foods. It can be concluded that the nutrition challenge among primary school children in Colombo is shifting from undernutrition to overweight and obesity, with boys subjected to a higher risk. Key words: Life-style practices; Nutritional status; Obesity; School children; Undernutrition DOI: http://dx.doi. org/10.4038/tar.v22i4. 3790 TAR 2011; 22(4): 392-401
Investigating the Moderating Role of Self-Knowledge Processes and self-control in the Relationship between Childhood Traumas and the Severity of Borderline Personality Traits

Background: Previous research shows that neglecting the basic needs of the child or physical and sexual abuse during the first years of life has a significant relationship with developing of BPD symptoms. So, the present study aimed to investigate the moderating role of self-knowledge processes and self-control in the relationship between childhood traumas and the severity of borderline personality traits. Materials & Method: Based on convenience sampling method 109 individuals were assessed by Child Abuse and Trauma Scale (Sanders & Becker-Lausen), Integrative Self-Knowledge Scale (Ghorbani, Watson & Hargis), Mindfulness Scale (Brown & Ryan), Self-Control Scale (Tangney, Baumeister & Boone), and the Borderline Personality Scale (Claridge & Broks). Result: The hierarchical regressions showed that mindfulness, self-knowledge and self-control can predict the severity of borderline personality traits directly (p 0.01). Conclusion: Self-regulation processes do not moderate the severity of traumas and borderline personality traits and they merely act as predictor variables.
Isolation and cellular fatty acid composition of psychrotrophic Halomonas strains from Antarctic sea water

Microorganisms from extreme environments such as Arctic, Antarctic and Polar regions modulate their membrane fatty acids to survive in such habitats. Characterization of such microorganisms helps in understanding their physiological behavior. In view of this, the present article describes isolation, characterization and cellular fatty acid composition of three bacterial isolates from Antarctic sea water samples. All the three isolates (BRI 6, 29 and 31) were psychrotrophic Gram negative rods. Their 16S rRNA sequencing (around 1200 bp) revealed that all three of them belong to genus Halomonas. Each of them showed 99% sequence similarity to Halomonas neptunia Eplume1 (NR 027218), H. boliviensis LC1 (NR 029080) and H. variabilis DSM 3051 (NR 042068). The fatty acid analysis of our isolates indicated i) predominance of C 18:1, C 16:0 and C16:1 fatty acids and absence of trans fatty acids in all of them and ii) higher percentage of anteiso fatty acids than of iso fatty acids in BRI 6. These characteristic features may contribute to their adaptation to the Antarctic habitat.
MODELLING THE IMPACT OF WIND FARMS ON HOUSE PRICES IN THE UK

This paper discusses the findings from a UK study to determine the likely impact of a wind farm on house prices using a hedonic pricing model. The Government's commitment to wind power has resulted in a massive increase in the number of wind farms sited in the UK. This has led to concerns that their visual and aural presence could have a negative impact on proximate house prices. This paper presents an analysis of 201sales transactions from houses situated within half a mile of a 16 turbine wind farm in Cornwall, UK. Whilst no causal link was established between the presence of the wind farm and house price, there was some evidence to suggest that both noise and flicker from the turbine blades could blight certain property and that the view of countryside enjoyed by the occupier had some value which may be affected by a wind farm.
TIL as the Logic of Communication in a Multi-Agent System

Textual Entailment Recognition (RTE) was proposed as a generic task, aimed at building modules capable of capturing the semantic variability of texts and performing natural language inferences. These modules can be then included in any NLP system, improving its performance in fine-grained semantic differentiation. The first part of the article describes our approach aimed at building a generic, language-independent TE system that would eventually be used as a module within a QA system. We evaluated the accuracy of this system by building two instances of it for English and Romanian and testing them on the data from the RTE3 competition. In the second part we show how we applied the steps described in [1] and adapted this system in order to include it as module in a QA system architecture. Lastly, we show the results obtained, which point out significant growth in precision.
Conversion of Biomass to Bio-Oil in Sub- and Supercritical Water

Biomass-derived energy is widely considered to be the most potential and feasible among many forms of available energy on earth. Unlike any other renewable energy sources, such as geothermal, solar and wind which only generate heat and power, biomass can be converted to solid, liquid and gaseous fuels [1]. However, the choice of conversion methods suitable for a particular type of biomass and the target fuels becomes a challenge because of the complex three-dimensional (3D) structure connecting the main biomass components (i.e. lignin, cellulose and hemicellulose). This structure gives strength to the materials, and offers resist‐ ance that protects them against chemicals and microbial attack making it very difficult to hydrolyze. An effective dissociation of these components and their subsequent separation are needed for the production of high value products from lignocellulosic biomass [2]. One promising approach introduced recently is “hydrothermal liquefaction method” or commonly referred to as “hydrothermal upgrading” in many parts of Europe, wherein biomass is liquefied using water as a solvent at elevated temperatures and pressures.
A Fuzzy-Rough Feature Selection Based on Binary Shuffled Frog Leaping Algorithm

Feature selection and attribute reduction are crucial problems, and widely used techniques in the field of machine learning, data mining and pattern recognition to overcome the well-known phenomenon of the Curse of Dimensionality. This paper presents a feature selection method that efficiently carries out attribute reduction, thereby selecting the most informative features of a dataset. It consists of two components: 1) a measure for feature subset evaluation, and 2) a search strategy. For the evaluation measure, we have employed the fuzzy-rough dependency degree (FRFDD) of the lower approximation-based fuzzy-rough feature selection (L-FRFS) due to its effectiveness in feature selection. As for the search strategy, a modified version of a binary shuffled frog leaping algorithm is proposed (B-SFLA). The proposed feature selection method is obtained by hybridizing the B-SFLA with the FRDD. Nine classifiers have been employed to compare the proposed approach with several existing methods over twenty two datasets, including nine high dimensional and large ones, from the UCI repository. The experimental results demonstrate that the B-SFLA approach significantly outperforms other metaheuristic methods in terms of the number of selected features and the classification accuracy. Keywords—Binary shuffled frog leaping algorithm, feature selection, fuzzy-rough set, minimal reduct.