Miracle-dz/UniFAR-FTUs huggingface dataset:
Empirical Evaluation of the Usefulness of Graph-based Visualization Techniques to Support Software Understanding

Many researchers have highlighted the scarcity of empirical studies that systematically examine the advantages and disadvantages of the use of visualization techniques for software understanding activities. Such studies are crucial for gathering and analyzing objective and quantifiable evidence about the usefulness of proposed visualization techniques and tools, and ultimately, for guiding the research in software visualization. This paper presents a controlled experiment aimed at assessing the impact of a graph-based visualization technique on comprehension tasks. Six common comprehension tasks were performed by 20 undergraduate software engineering students. The completion time and the accuracy of the participants’ responses were measured. The results indicate that on one hand the use of the graph-based visualization increases the correctness (by 21.45% in average) but on the other hand it does not reduce the completion time in program comprehension tasks. Keywords— software visualization; controlled experiment; software comprehension
EFFECT OF UREATED SUGAR BEET PULP ON LACTATING GOAT PERFORMANCE

Forty female Barki goat does were used to investigate the effect of inclusion of sugar beet pulp (SBP) treated with urea at levels of 2, 4 and 6% urea to replace about 30% of common concentrate feed mixture in the diet of goats on lactating goat performance, milk yield and composition, birth and weaning weight. Five experimental diets were formulated: T1 (control): concentrate feed mixture (CFM)+ berseem hay (BH), T2: CFM + untreated SBP+BH, T3: CFM+SBP treated with 2% urea on DM basis+ BH, T4: CFM + SBP treated with 4% urea on DM basis+ BH and T5: CFM + SBP treated with 6% urea on DM basis+ BH. The experiment feeding period lasted for 140 days. At the end of lactation stage a digestibility trial was carried out to study the effect of the experimental diets on digestibility coefficients, rumen parameters, microbial protein, protozoal count, and blood constituents. The obtained results revealed that T5, T4 and T3 increased (P≤0.05) DM, OM, EE, NFE and CP contents, and decreased ash, CF, NDF, ADF, DAL, cellulose and hemicellulose contents as compared to T2 and T1. Urea treatments of SBP increased (P≤0.05) body weight and feed intake. T4 and T5 had the highest (P≤0.05) DM, OM, EE, CP, CF, NFE, NDF, ADF, ADL, cellulose and hemicellulose digestibility coefficients as well as the highest (P≤0.05) TDN and DCP intakes. T4, T5 and T3 improved (P<0.05) nitrogen balance, water balance, rumen fermentations, rumen protozoal count, milk yield and composition, birth and weaning weight. The present study suggests the replacement of dried sugar beet pulp treated with 4% urea by a part of common concentrate feed mixture of lactating goats.
Diabetes, Metabolic Syndrome and Obesity: Targets and Therapy Dovepress

php and incorporate the Creative Commons Attribution – Non Commercial (unported, v3.0) License (http://creativecommons.org/licenses/by-nc/3.0/). By accessing the work you hereby accept the Terms. Non-commercial uses of the work are permitted without any further permission from Dove Medical Press Limited, provided the work is properly attributed. For permission for commercial use of this work, please see paragraphs 4.2 and 5 of our Terms (https://www.dovepress.com/terms.php). Diabetes, Metabolic Syndrome and Obesity: Targets and Therapy 2018:11 53–64 Diabetes, Metabolic Syndrome and Obesity: Targets and Therapy Dovepress
Invariant Bag of Words for Image Retrieval

The avalanche of images from personal devices and steaming servers in the recent years has made the visual search problem timely and important. In this paper, we propose an algorithm which encodes spatial information into the BoV representation. Unlike previous approaches in spatial bag of words, our algorithm forms spatial visual phrases which are invariant to translation and rotation. Furthermore, we demonstrate that our algorithm outperforms other recently proposed spatial image retrieval algorithms in terms of accuracy.
Evaluating the Power Grid Investment Behavior in China: From the Perspective of Government Supervision

Power grid investment behavior (PGIB) is a necessary prerequisite to ensure safe and reliable power supply. The supervision of PGIB is an important measure to reduce electricity price. It helps to release the dividends of the new power system reform and promote the sustainable development of the power industry. However, for a long time, China’s regulation of PGIB was relatively weak, lacking clear supervision awareness and effective supervision methods. On the basis of clarifying the contents of PGIB supervision, this paper establishes an evaluation index system for the compliance of PGIB from the government supervision perspective, involving four dimensions and 14 secondary indicators. Furthermore, a hybrid evaluation model based on the combined decision-making trial and evaluation laboratory method (DEMATEL-ANP), analytical network process method, and vague set is proposed. The results reveal that the control over cost and efficiency of the PGIB in China is not enough. According to the results, this paper puts forward the indicators that close attention should be paid to and the work priorities in China’s supervision of PGIB, which has certain practical value for the implementation of new power system reform and the sustainable development of Chinese power industry.
Temporal sequence mining for improved situation understanding in robotics applications

For both humans and robots, a key to understanding the world is to understand causal relationships. This position paper describes how causal relationships, implicitly given in the form of frequently occurring action sequences, can be retrieved from data sets containing series of actions performed as part of everyday household tasks. 1 Background and motivation Unlike humans, robots are depending on detailed instructions even when performing tasks of comparatively low complexity. The robotics community is working to improve the robots’ cognitive skills and abilities to perform intelligent information processing, with the aim of creating a new generation of robots that will be able to interact with the surrounding world with a higher degree of independence. To interact smoothly with the world, both humans and robots need a certain degree of situation understanding. Situation understanding implies, among other things, an awareness and understanding of causal relationships. Understanding of causal relations helps to predict the actions of others and to understand what is an apropriate way to act in a given situation. Adult humans normally have a very good understanding of causal relationships, aquired through logical reasoning and long experience. For robots, on the other hand, causal relations poses a problem. It seems reasonable, however, that a robot with access to sufficient amounts of relevant background data, such as observations of people acting in situations similar to the one considered, should be able to speed up the ”learningfrom-experience” process. Based on training data it should be possible to draw conclusions of the type ”action B follows action A with a probability of 90%”. Systematically going through all possible action combinations in order to detect strong dependencies between actions may not be feasible, due to the computational complexity. Especially if action sequences that involves more than two actions needs to be considered, one needs to turn to more sophisticated approaches. The work presented in this paper shows how frequently occurring action sequences can be retrieved, using data mining techniques, from data sets containing series of actions performed as part of everyday household tasks. Once the frequent sequences i.e. sequences that show up ”sufficiently often” in the reference data set have been found, the amount of data that is still interesting to examine has hopefully been significantly reduced. The frequent sequences can then be further processed and used to identify strong causal dependencies that involve series of consecutive actions.
Fingerprinting Bluetooth-Low-Energy Devices Based on the Generic Attribute Profile

Bluetooth Low Energy (BLE) is a short range wireless technology included in many consumer devices such as smartphones, earphones and wristbands. As part of the Attribute (ATT) protocol, discoverable BLE devices expose a data structure called Generic Attribute (GATT) profile that describes supported features using concepts of services and characteristics. This profile can be accessed by any device in range and can expose users to privacy issues. In this paper, we discuss how the GATT profile can be used to create a fingerprint that can be exploited to circumvent anti-tracking features of the BLE standard (i.e. MAC address randomization). Leveraging a dataset of more than 13000 profiles, we analyze the potential of this fingerprint and show that it can be used to uniquely identify a number of devices. We also shed light on several issues where GATT profiles can be mined to infer sensitive information that can impact privacy of users. Finally, we suggest solutions to mitigate those issues.
State Ownership, Corporate Performance, and Parameter Heterogeneity: A Quantile Analysis on China’s Listed Companies*

Assessing the effect of government shareholding on corporate performance in the context of China has been a hot topic. Documented results are mainly empirical, and the findings are diverse. In this paper we present a formal model that establishes a theoretical link between government shareholding and corporate performance, where firms receive private signals about their potential profitability and make private effort. Predicting a negative impact of government shareholding on corporate performance, our model further shows that this impact is more significant when the firms’ perceived profitability is high. Using a panel dataset of China’s listed companies during 19942000, we find that the estimations of conditional quantile regression models are indeed consistent with these predictions. Journal of Economic Literature Classification Numbers: D82, G32, G34, P31. Discussion Paper 60 Centre for Financial & Management Studies
Revisiting the Hybrid Attack on Sparse Secret LWE and Application to HE Parameters

In the practical use of the Learning With Error (LWE) based cryptosystems, it is quite common to choose the secret to be extremely small: one popular choice is ternary ((±1,0),coefficient vector, and some further use ternary vector having only small numbers of nonzero coefficient, what is called \em sparse and ternary vector. This use of small secret also benefits to attack algorithms against LWE, and currently LWE-based cryptosystems including homomorphic encryptions (HE) set parameters based on the attack complexity of those improved attacks. In this work, we revisit the well-known Howgrave-Graham's hybrid attack, which was originally designed to solve the NTRU problem, with respect to sparse and ternary secret LWE case, and also refine the previous analysis for the hybrid attack in line with LWE setting. Moreover, upon our analysis we estimate attack complexity of the hybrid attack for several LWE parameters. As a result, we argue the currently used HE parameters should be raised to maintain the same security level by considering the hybrid attack; for example, the parameter set (n,log q,σ) = (65536, 1240, 3.2) with Hamming weight of secret key h = 64, which was estimated to satisfy ≥ 128$ bit-security by the previously considered attacks, is newly estimated to provide only 113 bit-security by the hybrid attack.
Quantity Premia and Within-Store Search Costs

We take note of the phenomenon that the unit price of a multi-pack of products is observed to be higher than the unit price of the same product sold by the piece. First, with the assumption of costly price information, we devise a simple game between a retailer and consumers. Under certain conditions, our model generates equilibria where a quantity premium is observed with a positive probability. Next, using the retailer’s equilibrium strategies, we show that the greater the horizontal or vertical distance between multi-packs and single units is, the more likely quantity premia is observed and the greater the unit price dispersion is.
BAMB

The discovery of Markov blanket (MB) for feature selection has attracted much attention in recent years, since the MB of the class attribute is the optimal feature subset for feature selection. However, almost all existing MB discovery algorithms focus on either improving computational efficiency or boosting learning accuracy, instead of both. In this article, we propose a novel MB discovery algorithm for balancing efficiency and accuracy, called BAlanced Markov Blanket (BAMB) discovery. To achieve this goal, given a class attribute of interest, BAMB finds candidate PC (parents and children) and spouses and removes false positives from the candidate MB set in one go. Specifically, once a feature is successfully added to the current PC set, BAMB finds the spouses with regard to this feature, then uses the updated PC and the spouse set to remove false positives from the current MB set. This makes the PC and spouses of the target as small as possible and thus achieves a trade-off between computational efficiency and learning accuracy. In the experiments, we first compare BAMB with 8 state-of-the-art MB discovery algorithms on 7 benchmark Bayesian networks, then we use 10 real-world datasets and compare BAMB with 12 feature selection algorithms, including 8 state-of-the-art MB discovery algorithms and 4 other well-established feature selection methods. On prediction accuracy, BAMB outperforms 12 feature selection algorithms compared. On computational efficiency, BAMB is close to the IAMB algorithm while it is much faster than the remaining seven MB discovery algorithms.
The Impact of Bridges on the Process of Water Turbidity on the Example of Large Lowland Rivers

The transformations of river channels as a result of human activity may reflect the level of anthropogenic pressure. Recorded in macroscale, they are indicators of transformations for the larger part of the river basin, while in the microscale they usually refer to its fragment. The human activity that has the strongest influence on the fluvial processes, including suspended sediment transport, is the interference in the riverbed, such as hydrotechnical construction (create of water stages) or transformation of the river banks (concreting of the river banks). The aim of the research was to determine the impact of bridges on the process of water turbidity in the longitudinal profile of large lowland rivers. The research area was the lower reach of Vistula – bridge profiles: Torun, Bydgoszcz, Kiezmark (Poland) and bridge profile on Sukhona – a tributary of the Northern Dvina River near the city of Veliky Ustyug (Russia – Arkhangelsk Oblast). The research conducted in the years 2013–2017 included the measurements of water turbidity in the cross-sections of the above-mentioned rivers. The determinations of water turbidity were carried out using traditional (granulometry analysis) and nephelometric methods (devices: LISST-25X and turbidimeter). The impact of bridges on the variability of water turbidity was determined. The obtained results were related to the variability of depth conditions (bathymetric measurements) and hydrodynamics in the cross-section profile of the channel.
Size Dependent Variation in Cholesterol and Fatty Acids Profile of Different Tissues of Carnivore Freshwater Catfish, Wallago attu

The purpose of the study was to find out variation in cholesterol and fatty acid content in various tissues viz., muscle, skin, liver and skull of two different sized groups of carnivore catfish (Wallago attu) i.e. small sized group (411.66±10.40 g) and a large sized group (1006.66 ± 8.82 g). Cholesterol contents in different tissues of small and large sized groups of Wallago attu showed significant variations (P<0.05) and ranged from 40.1 to 183.9 mg/100 g. Higher cholesterol levels were observed in muscle (135.23±12.93 g/100g), skin (102.1±10.76 g/100g) and liver tissues (183.9±13.56 g/100g) of large sized group of Wallago attu. Fatty acid analyses were performed on GC-MASS. Fatty acid compositions of different tissues of small and large sized groups of Wallago attu consisted of 29.82 to 49.89% saturated fatty acids (SFA), 27.37 to 38% monounsaturated fatty acids (MUFAs) and 17.25 to 39.74% polyunsaturated fatty acids (PUFAs). The SFA and MUFA levels were higher in muscle, skin and skull tissues of small sized group of Wallago attu, whereas PUFAs levels were significantly higher (P<0.001) in muscle, skin and skull tissues of larger sized group. The Ω-3/Ω-6 ratio in all tissues of Wallago attu ranged from 0.60 1.79. The percentage of DHA, Docosahexanoic acid (22:6n-3) exceeded that of EPA, Eicosapentanoic acid (20:5n-3) in all tissues of Wallago attu. Our present study suggests that Wallago attu have high reserves of essential fatty acids like Ω-3 and Ω-6, which provides a strong shield against coronary heart diseases.
A scalable galerkin multigrid method for real-time simulation of deformable objects

We propose a simple yet efficient multigrid scheme to simulate high-resolution deformable objects in their full spaces at interactive frame rates. The point of departure of our method is the Galerkin projection which is simple to construct. However, a naïve Galerkin multigrid does not scale well for large and irregular grids because it trades-off matrix sparsity for smaller sized linear systems which eventually stops improving the performance. Given that observation, we design our special projection criterion which is based on skinning space coordinates with piecewise constant weights, to make our Galerkin multigrid method scale for high-resolution meshes without suffering from dense linear solves. The usage of skinning space coordinates enables us to reduce the resolution of grids more aggressively, and our piecewise constant weights further ensure us to always deal with reasonably-sparse linear solves. Our projection matrices also help us to manage multi-level linear systems efficiently. Therefore, our method can be applied to different optimization schemes such as Newton's method and Projective Dynamics, pushing the resolution of a real-time simulation to orders of magnitudes higher. Our final GPU implementation outperforms the other state-of-the-art GPU deformable body simulators, enabling us to simulate large deformable objects with hundred thousands of degrees of freedom in real-time.
Vertebrate ecology in caatinga: A. Distribution of wildlife in relation to water. B. Diet of pumas (Puma concolor) and relative abundance of felids

Caatinga is a seasonally dry forest in northeastern Brazil. Despite the strong seasonality of rainfall, the fauna of caatinga is characteristic of mesic biomes and shows no special physiological adaptations for coping with water limitation. Overall, the fauna of caatinga is poor in endemics and species richness is lower than in other systems. This study has two major objectives. First, I describe the seasonal distribution of common mediumand large-sized vertebrates in relation to water sources. Second, I analyze the feeding ecology of a large, widespread predator in caatinga. This study was conducted from October 1999 until August 2000 in Serra da Capivara National Park. The first part of this study describes the seasonal distribution of tegu lizards (Tupinambis teguixin), rusty-margined guans (Penelope superciliaris), seriemas (Cariama cristata), crab-eating foxes (Cerdocyon thous), pumas (Puma concolor), gray brocket deer (Mazama gouazoubira), white-collared peccaries (Tayassu tajacu) and agoutis (Dasyprocta prymnolopha) based on observational data. During the dry season, foxes and seriemas moved from the dry plateau of the park to valleys where water holes are present. Other species showed seasonal site fidelity and were observed mostly at lower (deer) or higher (agouti) elevations. With the exception of pumas and agoutis, all species showed close association with available water sources, at least during the dry season. The year-round availability of water at lower elevations and overall differences in vegetation along the elevational gradient influenced the distribution of species are discussed. The second part of this study analyzes the diet of pumas in relation to the relative abundance and biomass of prey. The relative abundance of felids in caatinga is compared with other neotropical systems. Relative abundance of prey and felids were estimated using remote cameras.
Learning predict-and-simulate policies from unorganized human motion data

The goal of this research is to create physically simulated biped characters equipped with a rich repertoire of motor skills. The user can control the characters interactively by modulating their control objectives. The characters can interact physically with each other and with the environment. We present a novel network-based algorithm that learns control policies from unorganized, minimally-labeled human motion data. The network architecture for interactive character animation incorporates an RNN-based motion generator into a DRL-based controller for physics simulation and control. The motion generator guides forward dynamics simulation by feeding a sequence of future motion frames to track. The rich future prediction facilitates policy learning from large training data sets. We will demonstrate the effectiveness of our approach with biped characters that learn a variety of dynamic motor skills from large, unorganized data and react to unexpected perturbation beyond the scope of the training data.
Discrete geodesic parallel coordinates

Geodesic parallel coordinates are orthogonal nets on surfaces where one of the two families of parameter lines are geodesic curves. We describe a discrete version of these special surface parameterizations and show that they are very useful for specific applications, most of which are related to the design and fabrication of surfaces in architecture. With the new discrete surface model, it is easy to control strip widths between neighboring geodesics. This facilitates tasks such as cladding a surface with strips of originally straight flat material or designing geodesic gridshells and timber rib shells. It is also possible to model nearly developable surfaces. These are characterized by geodesic strips with almost constant strip widths and are used for generating shapes that can be manufactured from materials which allow for some stretching or shrinking like felt, leather, or thin wooden boards. Most importantly, we show how to constrain the strip width parameters to model a class of intrinsically symmetric surfaces. These surfaces are isometric to surfaces of revolution and can be covered with doubly-curved panels that are produced with only a few molds when working with flexible materials like metal sheets.
An effective identification method of video ships and vehicles

In usual videos, there are problems such as small amplitude motion interference and sudden change of illumination in the background. It is difficult for ship and vehicle recognition. This paper takes full advantage of classical mixture Gauss background model in the context of small-scale motion. The inter frame difference method is not sensitive to illumination changes. So this paper proposes an identification method combining mixture of Gauss background model and three-frame difference method, and achieves potential region acquisition. The connected components are analyzed and merged to obtain the minimum outer rectangles, and the geometric characteristics of the targets are extracted from the rectangles. This paper uses the LS_SVM classifier to classify each potential region and outputs whether there are targets and the location of the targets. The experiment results show the average accuracy of the target recognition method in this paper is significantly improved.