Miracle-dz/UniFAR-FTUs huggingface dataset:
Cache Storage Channels: Alias-Driven Attacks and Verified Countermeasures

Caches pose a significant challenge to formal proofs of security for code executing on application processors, as the cache access pattern of security-critical services may leak secret information. This paper reveals a novel attack vector, exposing a low-noise cache storage channel that can be exploited by adapting well-known timing channel analysis techniques. The vector can also be used to attack various types of security-critical software such as hypervisors and application security monitors. The attack vector uses virtual aliases with mismatched memory attributes and self-modifying code to misconfigure the memory system, allowing an attacker to place incoherent copies of the same physical address into the caches and observe which addresses are stored in different levels of cache. We design and implement three different attacks using the new vector on trusted services and report on the discovery of an 128-bit key from an AES encryption service running in TrustZone on Raspberry Pi 2. Moreover, we subvert the integrity properties of an ARMv7 hypervisor that was formally verified against a cache-less model. We evaluate well-known countermeasures against the new attack vector and propose a verification methodology that allows to formally prove the effectiveness of defence mechanisms on the binary code of the trusted software.
Solutions For Optimizing The Data Parallel Prefix Sum Algorithm Using The Compute Unified Device Architecture

In this paper, we analyze solutions for optimizing the data parallel prefix sum function using the Compute Unified Device Architecture (CUDA) that provides a viable solution for accelerating a broad class of applications. The parallel prefix sum function is an essential building block for many data mining algorithms, and therefore its optimization facilitates the whole data mining process. Finally, we benchmark and evaluate the performance of the optimized parallel prefix sum building block in CUDA.
Multilayer Matter-Element Extension Synthesis Evaluation of Software Quality

According to the needs and characteristics of software quality synthesis evaluation, a new multilayer matter-element extension evaluation algorithm is introduced in the reference [5]. This paper will continue to study and discuss the application of the new algorithm. Practice application has shown that the metric results of the multilayer matter-element extension synthesis evaluation algorithm is more accurate, practical and reliable than original algorithm for software quality metrics.
A SAF Binding Site in the Promoter Region of Human γ-Fibrinogen Gene Functions as an IL-6 Response Element 1

Expression of fibrinogen is highly induced during inflammation, and such abnormal expression of this protein is considered as a major cardiovascular risk factor. IL-6 is one of the main mediators of abnormal expression of fibrinogen leading to the pathogenic conditions. Transient transfection and EMSA were performed to investigate the molecular mechanism of IL-6-induced γ-fibrinogen gene expression in hepatic cells. Using progressively deleted 5′ fragments of the γ-fibrinogen promoter coupled to chloramphenicol acetyltransferase gene, an IL-6 responsive element located between positions −273 and −259 was identified. Mutation of this element abrogates IL-6 responsiveness of the γ-fibrinogen promoter. Interaction of this promoter with a zinc finger transcription factor, serum amyloid A activating factor (SAF)-1, was demonstrated by EMSA. Furthermore, overexpression of wild-type SAF-1 in transfected liver cells can increase transcription of the γ-fibrinogen promoter. These data show that transcription factor SAF-1 is involved in the regulation of IL-6-mediated induction of the human γ-fibrinogen gene in liver cells.
Sentence Extraction by Spreading Activation with Refined Similarity Measure

Although there has been a great deal of research on automatic summarization, most methods are based on a statistical approach, disregarding relationships between extracted textual segments. To ensure sentence connectivity, we propose a novel method to extract a set of comprehensible sentences that centers on several key points. This method generates a similarity network from documents with a lexical dictionary and applies spreading activation to rank sentences. Also, we show evaluation results of a multi-document summarization system based on the method, participating in a competition of summarization, TSC (Text Summarization Challenge) task organized by the third NTCIR (NII-NACSIS Test Collection for IR Systems) project.
Multiple-Locus Variable-Number Tandem Repeat Analysis Reveals Genetic Relationships within Bacillus anthracis

ABSTRACT Bacillus anthracis is one of the most genetically homogeneous pathogens described, making strain discrimination particularly difficult. In this paper, we present a novel molecular typing system based on rapidly evolving variable-number tandem repeat (VNTR) loci. Multiple-locus VNTR analysis (MLVA) uses the combined power of multiple alleles at several marker loci. In our system, fluorescently labeled PCR primers are used to produce PCR amplification products from eight VNTR regions in the B. anthracisgenome. These are detected and their sizes are determined using an ABI377 automated DNA sequencer. Five of these eight loci were discovered by sequence characterization of molecular markers (vrrC1, vrrC2,vrrB1, vrrB2, and CG3), two were discovered by searching complete plasmid nucleotide sequences (pXO1-aat and pXO2-at), and one was known previously (vrrA). MLVA characterization of 426 B. anthracis isolates identified 89 distinct genotypes. VNTR markers frequently identified multiple alleles (from two to nine), with Nei's diversity values between 0.3 and 0.8. Unweighted pair-group method arithmetic average cluster analysis identified six genetically distinct groups that appear to be derived from clones. Some of these clones show worldwide distribution, while others are restricted to particular geographic regions. Human commerce doubtlessly has contributed to the dispersal of particular clones in ancient and modern times.
Characteristic Male Urine Microbiomes Associate with Asymptomatic Sexually Transmitted Infection

Background The microbiome of the male urogenital tract is poorly described but it has been suggested that bacterial colonization of the male urethra might impact risk of sexually transmitted infection (STI). Previous cultivation-dependent studies showed that a variety of non-pathogenic bacteria colonize the urethra but did not thoroughly characterize these microbiomes or establish links between the compositions of urethral microbiomes and STI. Methodology/Findings Here, we used 16S rRNA PCR and sequencing to identify bacteria in urine specimens collected from men who lacked symptoms of urethral inflammation but who differed in status for STI. All of the urine samples contained multiple bacterial genera and many contained taxa that colonize the human vagina. Uncultivated bacteria associated with female genital tract pathology were abundant in specimens from men who had STI. Conclusions Urine microbiomes from men with STI were dominated by fastidious, anaerobic and uncultivated bacteria. The same taxa were rare in STI negative individuals. Our findings suggest that the composition of male urine microbiomes is related to STI.
On The Impact of Target Technology in SHA-3 Hardware Benchmark Rankings

Both FPGAs and ASICs are widely used as the technology for comparing SHA-3 hardware benchmarking process. However, the im- pact of target technology in SHA-3 hardware benchmark rankings has hardly been considered. A cross-platform comparison between the FPGA and ASIC results of the 14 second round SHA-3 designs demonstrates the gap between two sets of benchmarking results. In this paper we de- scribe a systematic approach to analyze a SHA-3 hardware benchmark process for both FPGAs and ASICs, and we present our latest results for FPGA and ASIC evaluation of the 14 second round SHA-3 candidates.
Skeleton-based Temporal Segmentation of Human Activities from Video Sequences

This paper presents a new multi-step, skeleton-based approach for the temporal segmentation of human activities from video sequences. Several signals are first extracted from a skeleton sequence. These signals are then segmented individually to localize their cyclic segments. Finally, all individual segmentations are merged with respect to the global set of signals. Our approach requires no prior knowledge on human activities and can use any generic stick-model. Two different techniques for signal segmentation and for the fusion of the individual segmentations are proposed and tested on a database of fifteen video sequences of variable level of complexity.
An Incremental Ensemble Evolved by using Genetic Programming to Efficiently Detect Drifts in Cyber Security Datasets

Unbalanced classes, the ability to detect changes in real-time, the speed of the streams and other peculiar characteristics make most of the data mining algorithms not apt to operate with datasets in the cyber security domain. To overcome these issues, we propose an ensemble-based algorithm, using a distributed Genetic Programming framework to generate the function to combine the classifiers and efficient strategies to react to changes in data. After that the base classifiers are trained, the combining function of the ensemble, based on non-trainable functions, can be generated without any extra phase of training, while the drift detection function adopted, together with a strategy for replacing classifiers, permits to respond in an efficient way to changes. Preliminary experiments conducted on an artificial dataset and on a real intrusion detection dataset show the effectiveness of the approach.
PERCEIVED ADVANTAGES IN THE USE OF E-EXAMS - COMPARATIVE TAM-MODEL STUDY AMONG STUDENTS AND TEACHERS

Exploratory survey about the transformation from traditional examinations to e-exams. This study was conducted among the higher education students and teachers in technical university and business school in Finland. We wanted to find out the perceived usefulness and ease-of-use of e-exam system among students and teachers. Furthermore, we wanted to compare the attitude and acceptance rate of students and teachers towards the new system. The research was conducted in the phase where there was still possibility to choose between traditional examination in lecture hall with pen and paper and novel electronic examination.
Large-Scale Tattoo Image Retrieval

In current biometric-based identification systems, tattoos and other body modifications have shown to provide a useful source of information. Besides manual category label assignment, approaches utilizing state-of-the-art content-based image retrieval (CBIR) techniques have become increasingly popular. While local feature-based similarities of tattoo images achieve excellent retrieval accuracy, scalability to large image databases can be addressed with the popular bag-of-word model. In this paper, we show how recent advances in CBIR can be utilized to build up a large-scale tattoo image retrieval system. Compared to other systems, we chose a different approach to circumvent the loss of accuracy caused by the bag-of-word quantization. Its efficiency and effectiveness are shown in experiments with several tattoo databases of up to 330,000 images.
Robust Support Vector Machine Training via Convex Outlier Ablation

One of the well known risks of large margin training methods, such as boosting and support vector machines (SVMs), is their sensitivity to outliers. These risks are normally mitigated by using a soft margin criterion, such as hinge loss, to reduce outlier sensitivity. In this paper, we present a more direct approach that explicitly incorporates outlier suppression in the training process. In particular, we show how outlier detection can be encoded in the large margin training principle of support vector machines. By expressing a convex relaxation of the joint training problem as a semide finite program, one can use this approach to robustly train a support vector machine while suppressing outliers. We demonstrate that our approach can yield superior results to the standard soft margin approach in the presence of outliers.
Using transgenic reporters to visualize bone and cartilage signaling during development in vivo

Green fluorescent protein was first used as a marker of protein expression in vivo 18 years ago, heralding the beginning of what became known as the Green Revolution. Since then, there has been an explosion in the number of transgenic lines in existence, and these transgenic tools are now being applied to skeletal research. Advances in transgenesis are also leading to increasing use of new model organisms for studying skeletogenesis. Such new models include the small teleosts zebrafish and medaka, which due to their optical translucency offer imaging possibilities in the live animals. In this review, we will introduce a number of recent advances in genetic engineering and transgenesis and the new genetic tools that are currently being developed. We will provide examples of how zebrafish and medaka transgenic lines are helping us to understand the behavior of skeletal cells in vivo. Finally, we will discuss future prospects for the application of transgenic technology to skeletal research.
A Likelihood-Based Approach for Missing Genotype Data

Missing genotype data in a candidate gene association study can make it difficult to model the effects of multiple genetic variants simultaneously. In particular, when regression models are used to model phenotype as a function of SNP genotypes in several different genes, the most common approach is a complete case analysis, in which only individuals with no missing genotypes are included. But this can lead to substantial reduction in sample size and thus potential bias and loss in efficiency. A number of other methods for handling missing data are applicable, but have rarely been used in this context. The purpose of this paper is to describe how several standard methods for handling missing data can be applied or adapted to this problem, and to compare their performance using a simulation study. We demonstrate these techniques using an Alzheimer’s disease association study. We show that the expectation-maximization algorithm and multiple imputation with a bootstrapped expectation-maximization sampling algorithm have the best properties of all the estimators studied.
Mastiff: A MapReduce-based System for Time-Based Big Data Analytics

Existing MapReduce-based warehousing systems are not specially optimized for time-based big data analysis applications. Such applications have two characteristics: 1) data are continuously generated and are required to be stored persistently for a long period of time, 2) applications usually process data in some time period so that typical queries use time-related predicates. Time-based big data analytics requires both high data loading speed and high query execution performance. However, existing systems including current MapReduce-based solutions do not solve this problem well because the two requirements are contradictory. We have implemented a MapReduce-based system, called Mastiff, which provides a solution to achieve both high data loading speed and high query performance. Mastiff exploits a systematic combination of a column group store structure and a lightweight helper structure. Furthermore, Mastiff uses an optimized table scan method and a column-based query execution engine to boost query performance. Based on extensive experiments results with diverse workloads, we will show that Mastiff can significantly outperform existing systems including Hive, HadoopDB, and GridSQL.
From pedagogy to quality: the Europeanised experience of higher education in post-Soviet Armenia

The article reports on a study of everyday university life and the academic life-world of professionals in education management in Armenia, focusing on the impact of Europeanisation on their professional identity construction. It also examines the impact of broader ideologies associated with quality assurance on the higher education provision in the country. The need for European funding, together with pressures for quality enhancement, create a shared sense of obligation to meet the perceived goals of Europeanisation and, in the specific context of Armenia, construct the identity of ‘imitator’ among education managers. These managers also turn away from pedagogy, in the mistaken belief that it is contaminated by the Soviet legacy. Consequently, students experience national higher education as a newly emergent space preoccupied with articulating ‘quality assurance’, rather than an environment supportive of teaching and learning.
The Effect of a Structured Method on Mental Model Accuracy and Performance in a Complex Task

In comparison to their performance with normative standards or even simple heuristics, humans do not perform well in complex decision-making. The application of systems thinking to help people to understand and handle interdependent and complex systems is proposed as a means of improving this poor performance. The aim of this study is to investigate the effect of a generic systems thinking method, i.e., a structured method, on performance. A laboratory experiment was conducted using a dynamic and complex simulation task. The results demonstrated that subjects provided with a structured method achieved a higher performance. In addition, mental model accuracy had a significant effect on performance, as already shown by several previous studies. The results of our study provide a way of teaching subjects how to improve their performance when coping with complex systems in general. This has implications for education in the fields of complex systems and system dynamics.
Integrating Planning and Reacting in a Heterogeneous Asynchronous Architecture for Controlling Real-World Mobile Robots

This paper presents a heterogeneous, asynchronous architecture for controlling autonomous mobile robots which is capable of controlling a robot performing multiple tasks in real time in noisy, unpredictable environments. The architecture produces behavior which is reliable, task-directed (and taskable), and reactive to contingencies. Experiments on real and simulated realworld robots are described. The architecture smoothly integrates planning and reacting by performing these two functions asynchronously using heterogeneous architectural elements, and using the results of planning to guide the robot's actions but not to control them directly. The architecture can thus be viewed as a concrete implementation of Agre and Chapman's plans-ascommunications theory. The central result of this work is to show that completely unmodified classical AI programming methodologies using centralized world models can be usefully incorporated into real-world embedded reactive systems.
A dynamic voltage scaling with single power supply and varying speed factor for multiprocessor system using genetic algorithm

With growing of applications of the embedded system technology to mobile systems, energy efficiency is becoming an important issue for designing real time embedded systems. One of the possible techniques to reduce the energy consumption is the Dynamic Voltage Scaling (DVS). DVS utilizes the slack time and adjusts the supply voltage so as to reduce the energy expense. However, how to optimally adjust the supply voltage is a NP hard problem. This paper focuses the combinational optimization problem, namely, the problem of minimizing schedule length with energy consumption constraint and the problem of minimizing energy consumption with schedule length constraint. These problems emphasize the tradeoff between power and performance and are defined such that the power-performance product is optimized by fixing one factor and minimizing the other. We propose the analytical result which gives the variation factor of each power supply which depends on the workload and provides the same power supply while meeting the constraints. We address to the use of genetic algorithm to schedule the tasks and then find the optimal power supplies and determine the schedule length on the multiprocessor system.
Exploration strategies in developmental robotics: A unified probabilistic framework

We present a probabilistic framework unifying two important families of exploration mechanisms recently shown to be efficient to learn complex non-linear redundant sensorimotor mappings. These two explorations mechanisms are: 1) goal babbling, 2) active learning driven by the maximization of empirically measured learning progress. We show how this generic framework allows to model several recent algorithmic architectures for exploration. Then, we propose a particular implementation using Gaussian Mixture Models, which at the same time provides an original empirical measure of the competence progress. Finally, we perform computer simulations on two simulated setups: the control of the end effector of a 7-DoF arm and the control of the formants produced by an articulatory synthesizer.
The effects of docosahexaenoic acid-rich fish oil on behavior, school attendance rate and malaria infection in school children--a double-blind, randomized, placebo-controlled trial in Lampung, Indonesia.

BACKGROUND There are only a very limited number of reports of intervention studies on the effects of fish oil on behavior in normal school children. OBJECTIVE To observe the effects of fish oil on behavior and school attendance rates in school children. DESIGN Fourth to sixth graders (mostly 9-12 years of age) of an elementary school in Lampung Province, Indonesia, were randomly divided into either a docosahexaenoic acid (DHA) group (n=116) or a control group (n=117) in a double-blind manner. The subjects in the DHA group took 6 fish oil capsules per day (0.65 g DHA and 0.10 g eicosapentaenoic acid (EPA)/day) for 3 months. Controls took soybean oil capsules. Two questionnaires were administered and blood was taken at the start and end of the study. Two questionnaires were administered at the start and end of the study: Hostility-Aggression Questionnaire for Children (HAQ-C) and Barratt Impulsiveness Scale, version 11 (BIS-11), for measurement of aggression and impulsivity, respectively. Attendance was recorded during the study period. OUTCOMES The concentrations of DHA and EPA in the phospholipid fraction in red blood cells were significantly increased in the DHA group. Behavior checked with HAQ-C or BIS-11 did not show any differences between groups. However, the odds ratio of inability to attend school regularly during the study period was 0.40 (95%CI: 0.23-0.71) in the DHA group compared with controls (p=0.002). CONCLUSIONS DHA-rich fish oil may improve the school attendance rate of children in Lampung, Indonesia.