snu-aidas/HVMap-dataset huggingface dataset:
Android anti-forensics through a local paradigm

Mobile devices are among the most disruptive technologies of the last years, gaining even more diffusion and success in the daily life of a wide range of people categories. Unfortunately, while the number of mobile devices implicated in crime activities is relevant and growing, the capability to perform the forensic analysis of such devices is limited both by technological and methodological problems. In this paper, we focus on Anti-Forensic techniques applied to mobile devices, presenting some fully automated instances of such techniques to Android devices. Furthermore, we tested the effectiveness of such techniques versus both the cursory examination of the device and some acquisition tools. a 2010 Digital Forensic Research Workshop. Published by Elsevier Ltd. All rights reserved.
Hole-filling Based on Disparity Map and Inpainting for Depth- Image-Based Rendering

Due to the changes in viewpoint, holes may appear in the novel view synthesized by depth-image-based rendering (DIBR). A hole-filling method combining disparity-mapbased hole-filling and inpainting is proposed. The method first eliminates matching errors in destination image according to the disparity map. Then, holes are classified into different types according to their sizes. Finally, a disparity-map-based approach and an improved exemplar-based inpainting algorithm are used to fill different types of holes according to the type of hole. Experimental results show that the artifacts at the edge of foreground objects can be reduced in synthesized view since the matching errors are eliminated before hole-filling. In addition, the proposed method can achieve more natural and satisfactory results in filled areas in comparison with the disparity-mapbased approach and Gautier’s inpainting algorithm, though it may result in higher time complexity. Keyword: Depth-image-based Rendering, Hole-filling, Holes, Disparity Map, Inpainting
Performance on Raven ’ s Advanced Progressive Matrices by African , East Indian , and White engineering students in South Africa

The hypothesis is tested that the Raven’s Advanced Progressive Matrices (APM) has the same construct validity in African university students as it does in non-African university students. Analyses were made of scores from 294 highly select 17–23-year-olds in the Faculties of Engineering and the Built Environment at the University of the Witwatersrand (187 Africans, 40 East Indians, 67 Whites; 70 women, 224 men). Out of a total of 36 problems, the African students solved an average of 22, the East Indian students, 25, and the White students, 29 (P < .001), placing them at the 57th, 64th, and 86th percentiles, respectively, and yielding IQ equivalents of 103, 106, and 117 on the 1993 US norms. Four months earlier, they had completed the Standard Progressive Matrices. The two tests correlated .60 or higher for both the Africans and the non-Africans, and both tests predicted final end-of-year grades with mean r’s = .30 (P’s < .05). Items found difficult by one group were difficult for the others; items found easy by one group were easy for the others (mean r’s = .90, P < .001). The African–East Indian–White differences were ‘‘Jensen Effects,’’ being most pronounced on the general factor of intelligence (measured in this instance by items with the highest item-total correlations). Indeed, the g loadings showed cross-cultural generality: For example, item-total correlations calculated on the East Indian students predicted the magnitude of the African–White differences. When each of the 0160-2896/03/$ – see front matter D 2003 Elsevier Science Inc. All rights reserved. PII: S0160 -2896 (02 )00140 -X * Corresponding author. Tel.: +1-519-661-3685; fax: +1-519-850-2302. E-mail addresses: Rushton@uwo.ca (J.P. Rushton), 135skuy@mentor.edcm.wits.ac.za (M. Skuy). 1 Tel.: +27-11-716-5286; fax: +27-11-339-3844. Intelligence 31 (2003) 123–137 36 g loadings and race effects were aggregated into nine four-item ‘‘subtests,’’ the magnitude of the Jensen Effect was Spearman’s r = .52. There were no sex differences. D 2003 Elsevier Science Inc. All rights reserved.
Key performance indicators for traffic intensive web-enabled business processes

Purpose – Intensive traffic often occurs in web-enabled business processes hosted by travel industry and government portals. An extreme case for intensive traffic is flash crowd situations when the number of web users spike within a short time due to unexpected events caused by political unrest or extreme weather conditions. As a result, the servers hosting these business processes can no longer handle overwhelming service requests. To alleviate this problem, process engineers usually analyze audit trail data collected from the application server and reengineer their business processes to withstand unexpected surge in the visitors. However, such analysis can only reveal the performance of the application server from the internal perspective. This paper aims to investigate this issue. Design/methodology/approach – This paper proposes an approach for analyzing key performance indicators of traffic intensive web-enabled business processes from audit trail data, web server logs, and stress testing logs. Findings – The key performance indicators identified in the study’s approach can be used to understand the behavior of traffic intensive web-enabled business processes and the underlying factors that affect the stability of the web server. Originality/value – The proposed analysis also provides an internal as well as an external view of the performance. Moreover, the calculated key performance indicators can be used by the process engineers for locating potential bottlenecks, reengineering business processes, and implementing contingency measures for traffic intensive situations.
Prevalence of kidney stones in the United States.

BACKGROUND
The last nationally representative assessment of kidney stone prevalence in the United States occurred in 1994. After a 13-yr hiatus, the National Health and Nutrition Examination Survey (NHANES) reinitiated data collection regarding kidney stone history.


OBJECTIVE
Describe the current prevalence of stone disease in the United States, and identify factors associated with a history of kidney stones.


DESIGN, SETTING, AND PARTICIPANTS
A cross-sectional analysis of responses to the 2007-2010 NHANES (n=12 110).


OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS
Self-reported history of kidney stones. Percent prevalence was calculated and multivariable models were used to identify factors associated with a history of kidney stones.


RESULTS AND LIMITATIONS
The prevalence of kidney stones was 8.8% (95% confidence interval [CI], 8.1-9.5). Among men, the prevalence of stones was 10.6% (95% CI, 9.4-11.9), compared with 7.1% (95% CI, 6.4-7.8) among women. Kidney stones were more common among obese than normal-weight individuals (11.2% [95% CI, 10.0-12.3] compared with 6.1% [95% CI, 4.8-7.4], respectively; p<0.001). Black, non-Hispanic and Hispanic individuals were less likely to report a history of stone disease than were white, non-Hispanic individuals (black, non-Hispanic: odds ratio [OR]: 0.37 [95% CI, 0.28-0.49], p<0.001; Hispanic: OR: 0.60 [95% CI, 0.49-0.73], p<0.001). Obesity and diabetes were strongly associated with a history of kidney stones in multivariable models. The cross-sectional survey design limits causal inference regarding potential risk factors for kidney stones.


CONCLUSIONS
Kidney stones affect approximately 1 in 11 people in the United States. These data represent a marked increase in stone disease compared with the NHANES III cohort, particularly in black, non-Hispanic and Hispanic individuals. Diet and lifestyle factors likely play an important role in the changing epidemiology of kidney stones.
Improving Neural Knowledge Base Completion with Cross-Lingual Projections

In this paper we present a cross-lingual extension of a neural tensor network model for knowledge base completion. We exploit multilingual synsets from BabelNet to translate English triples to other languages and then augment the reference knowledge base with cross-lingual triples. We project monolingual embeddings of different languages to a shared multilingual space and use them for network initialization (i.e., as initial concept embeddings). We then train the network with triples from the cross-lingually augmented knowledge base. Results on WordNet link prediction show that leveraging cross-lingual information yields significant gains over exploiting only monolingual triples.
How does EMDR work ?

Eye movement desensitisation and reprocessing (EMDR) is an effective treatment for alleviating trauma symptoms, and the positive effects of this treatment have been scientifically confirmed under wellcontrolled conditions. This has provided an opportunity to explore how EMDR works. The present paper reports on the findings of a long series of experiments that disproved the hypothesis that eye movements or other ‘dual tasks’ are unnecessary. These experiments also disproved the idea that ‘bilateral stimulation’ is needed; moving the eyes up and down produces the same effect as horizontal eye movement, and so do tasks that require no eye movement at all. However, it is important that the dual task taxes working memory. Several predictions can be derived from the working memory explanation for eye movements in EMDR. These seem to hold up extremely well in critical experimental tests, and create a solid explanation on how eye movements work. This paper discusses the implications that this theory and the empirical findings may have for the EMDR technique. © Copyright 2012 Textrum Ltd. All rights reserved.
Data-Efficient Hierarchical Reinforcement Learning

Hierarchical reinforcement learning (HRL) is a promising approach to extend traditional reinforcement learning (RL) methods to solve more complex tasks. Yet, the majority of current HRL methods require careful task-specific design and on-policy training, making them difficult to apply in real-world scenarios. In this paper, we study how we can develop HRL algorithms that are general, in that they do not make onerous additional assumptions beyond standard RL algorithms, and efficient, in the sense that they can be used with modest numbers of interaction samples, making them suitable for real-world problems such as robotic control. For generality, we develop a scheme where lower-level controllers are supervised with goals that are learned and proposed automatically by the higher-level controllers. To address efficiency, we propose to use off-policy experience for both higherand lower-level training. This poses a considerable challenge, since changes to the lower-level behaviors change the action space for the higher-level policy, and we introduce an off-policy correction to remedy this challenge. This allows us to take advantage of recent advances in off-policy model-free RL to learn both higherand lower-level policies using substantially fewer environment interactions than on-policy algorithms. We term the resulting HRL agent HIRO and find that it is generally applicable and highly sample-efficient. Our experiments show that HIRO can be used to learn highly complex behaviors for simulated robots, such as pushing objects and utilizing them to reach target locations,1 learning from only a few million samples, equivalent to a few days of real-time interaction. In comparisons with a number of prior HRL methods, we find that our approach substantially outperforms previous state-of-the-art techniques.2
The ArchC Architecture Description Language and Tools

This paper presents an architecture description language (ADL) called ArchC, which is an open-source SystemC-based language that is specialized for processor architecture description. Its main goal is to provide enough information, at the right level of abstraction, in order to allow users to explore and verify new architectures, by automatically generating software tools like simulators and co-verification interfaces. ArchC’s key features are a storage-based co-verification mechanism that automatically checks the consistency of a refined ArchC model against a reference (functional) description, memory hierarchy modeling capability, the possibility of integration with other SystemC IPs and the automatic generation of high-level SystemC simulators and assemblers. We have used ArchC to synthesize both functional and cycle-based simulators for the MIPS and Intel 8051 processors, as well as functional models of architectures like SPARC V8, TMS320C62x, XScale and PowerPC.
Hardware-In-the-Loop Simulation of Electric Vehicle Powertrain System

This paper presents a design procedure of HardwareIn-the-Loop (HIL) for Electric Vehicle powertrain system modeling and simulation. First, simulation is performed in MATLAB/Simulink platform. Then, an Electric Vehicle Powertrain Hardware-In-the-Loop system including BMS (Battery Manage System), MCU (Motor Control Unit) and VMS (vehicle management system) is built based on the dSPACE with MATLAB/Simulink, along with the RTW toolbox (real time workshop). Through this system, the Electric Vehicle Control strategy is simulated and validated, and the results of offline simulation & real time simulation with laboratory prototype are presented. Keywords-Hardware In the Loop; Electric Vehicle; Simulation; dSPACE; DSP
Introduction: Hybrid intelligent adaptive systems

This issue of International Journal of Intelligent Systems includes extended versions of selected papers from the 4th International Conference on Soft Computing, held in Iizuka, Japan, September 30]October 5, 1996. The topic of the special issue is ‘‘Hybrid Intelligent Adaptive Systems.’’ Research on hybrid systems is one of the key issues of developing intelligent systems and it can apply a wide range of tools, including artificial neural networks, fuzzy logic, knowledge-based systems, genetic algorithms, evolutionary computation, and chaos models. The papers in this issue have been carefully reviewed and modified to give the readers a comprehensive overview of theoretical aspects, design, and implementation issues of hybrid intelligent adaptive systems. In the first paper by Kasabov and Kozma, a general framework of developing hybrid, intelligent, and adaptive systems is given. This work develops multimodular, fuzzy neural network systems and applies it to phoneme-based speech recognition. The second paper by Miyata, Furuhashi, and Uchikawa proposes fuzzy abductive inference with degrees of manifestations. This method infers irredundant combinations of candidates with degrees of belief for the manifestations. It is also demonstrated that the results of the inference method are applicable to medical diagnosis and system fault detection. Cho adopts ideas of artificial life in his work to develop evolutionary neural networks. The introduced modular neural network can evole its structure autonomously using a structural genetic code. The effectiveness of the method is demonstrated on the example of handwritten digit recognition. Feuring and Lippe study theoretical aspects of fuzzy neural networks. They propose a training algorithm for fuzzy neural networks that satisfies a certain goodness criterion. The second part of the special issue contains articles related to time series analysis and systems control. Yamazaki, Kang, and Ochiai introduce a hierarchical neural network system for adaptive, intelligent control, based on the analogy with the human thinking process. The optimum parameter space of the neural network system is found by a self-controllable algorithm, which can lead to either equilibrium or to nonequilibrium, chaotic behavior. The results of this study are applied, e.g., to laser beam analysis, semiconductor design, and design of magnetic devices. The work by Kozma, Kasabov, Kim, and Cohen presents a chaotic neuro-fuzzy method for time series analysis and process control. The
Long-term stability of anterior open-bite treatment by intrusion of maxillary posterior teeth.

INTRODUCTION
Anterior open bite results from the combined influences of skeletal, dental, functional, and habitual factors. The long-term stability of anterior open bite corrected with absolute anchorage has not been thoroughly investigated. The purpose of this study was to examine the long-term stability of anterior open-bite correction with intrusion of the maxillary posterior teeth.


METHODS
Nine adults with anterior open bite were treated by intrusion of the maxillary posterior teeth. Lateral cephalographs were taken immediately before and after treatment, 1 year posttreatment, and 3 years posttreatment to evaluate the postintrusion stability of the maxillary posterior teeth.


RESULTS
On average, the maxillary first molars were intruded by 2.39 mm (P<0.01) during treatment and erupted by 0.45 mm (P<0.05) at the 3-year follow-up, for a relapse rate of 22.88%. Eighty percent of the total relapse of the intruded maxillary first molars occurred during the first year of retention. Incisal overbite increased by a mean of 5.56 mm (P<0.001) during treatment and decreased by a mean of 1.20 mm (P<0.05) by the end of the 3-year follow-up period, for a relapse rate of 17.00%. Incisal overbite significantly relapsed during the first year of retention (P<0.05) but did not exhibit significant recurrence between the 1-year and 3-year follow-ups.


CONCLUSIONS
Most relapse occurred during the first year of retention. Thus, it is reasonable to conclude that the application of an appropriate retention method during this period clearly enhances the long-term stability of the treatment.
Adverse Effects Associated with Protein Intake above the Recommended Dietary Allowance for Adults

Background. While high-protein consumption-above the current recommended dietary allowance for adults (RDA: 0.8 g protein/kg body weight/day)-is increasing in popularity, there is a lack of data on its potential adverse effects. Objective. To determine the potential disease risks due to high protein/high meat intake obtained from diet and/or nutritional supplements in humans. Design. Review. Subjects. Healthy adult male and female subjects. Method. In order to identify relevant studies, the electronic databases, Medline and Google Scholar, were searched using the terms:"high protein diet," "protein overconsumption," "protein overuse," and "high meat diet." Papers not in English were excluded. Further studies were identified by citations in retrieved papers. Results. 32 studies (21 experimental human studies and 11 reviews) were identified. The adverse effects associated with long-term high protein/high meat intake in humans were (a) disorders of bone and calcium homeostasis, (b) disorders of renal function, (c) increased cancer risk, (d) disorders of liver function, and (e) precipitated progression of coronary artery disease. Conclusions. The findings of the present study suggest that there is currently no reasonable scientific basis in the literature to recommend protein consumption above the current RDA (high protein diet) for healthy adults due to its potential disease risks. Further research needs to be carried out in this area, including large randomized controlled trials.
The Art of Multiple Sequence Alignment in R

Figure 1: The art of multiple sequence alignment. This document is intended to illustrate the art of multiple sequence alignment in R using DECIPHER. Even though its beauty is often concealed, multiple sequence alignment is a form of art in more ways than one. Take a look at Figure 1 for an illustration of what is happening behind the scenes during multiple sequence alignment. The practice of sequence alignment is one that requires a degree of skill, and it is that art which this vignette intends to convey. It is simply not enough to “plug” sequences into a multiple sequence aligner and blindly trust the result. An appreciation for the art as well a careful consideration of the results are required. What really is multiple sequence alignment, and is there a single correct alignment? Generally speaking, alignment seeks to perform the act of taking multiple divergent biological sequences of the same “type” and fitting them to a form that reflects some shared quality. That quality
Fast and Accurate Calibration of a Kinect Sensor

The article describes a new algorithm for calibrating a Kinect sensor that achieves high accuracy using only 6 to 10 image-disparity pairs of a planar checkerboard pattern. The method estimates the projection parameters for both color and depth cameras, the relative pose between them, and the function that converts kinect disparity units (kdu) into metric depth. We build on the recent work of Herrera et. al [8] that uses a large number of input frames and multiple iterative minimization steps for obtaining very accurate calibration results. We propose several modifications to this estimation pipeline that dramatically improve stability, usability, and runtime. The modifications consist in: (i) initializing the relative pose using a new minimal, optimal solution for registering 3D planes across different reference frames, (ii) including a metric constraint during the iterative refinement to avoid a drift in the disparity to depth conversion, and (iii) estimating the parameters of the depth distortion model in an open-loop post-processing step. Comparative experiments show that our pipeline can achieve a calibration accuracy similar to [8] while using less than 1/6 of the input frames and running in 1/30 of the time.
The Power of Word Clusters for Text Classification

The recently introducedInformation Bottleneck method [21] provides an information theoretic framework, for extr acting features of one variable, that are relevant for the values of an ther variable. Several previous works already suggeste d applying this method for document clustering, gene expression data a nalysis, spectral analysis and more. In this work we present a novel implementation of this method for supervised text cla ssification. Specifically, we apply the information bottlen eck method to find word-clusters that preserve the information a bout document categories and use these clusters as features for classification. Previous work [1] used a similar clustering procedure to show that word-clusters can significantly redu c the feature space dimensionality, with only a minor change in cl assification accuracy. In this work we present similar resul ts and go further to show that when the training sample is small word clusters can yield significant improvement in classificatio n accuracy (up to18%) over the performance using the words directly.