Miracle-dz/UniFAR-FTUs huggingface dataset:
Balancing Methodological Rigor and the Needs of Research Participants

Despite growing consideration of the needs of research participants in studies related to sensitive issues, discussions of alternative ways to design sensitive research are scarce. Structured as an exchange between two researchers who used different approaches in their studies with childhood sexual abuse survivors, in this article, we seek to advance understanding of methodological and ethical issues in designing sensitive research. The first perspective, which is termed protective, promotes the gradual progression of participants from a treatment phase into a research phase, with the ongoing presence of a researcher and a social worker in both phases. In the second perspective, which is termed minimalist, we argue for clear boundaries between research and treatment processes, limiting the responsibility of researchers to ensuring that professional support is available to participants who experience emotional difficulties. Following rebuttals, lessons are drawn for ethical balancing between methodological rigor and the needs of participants.
Title Scalable Correlation-aware Virtual Machine ConsolidationUsing Two-phase Clustering

Server consolidation is the most common and effective method to save energy and increase resource utilization in data centers, and virtual machine (VM) placement is the usual way of achieving server consolidation. VM placement is however challenging given the scale of IT infrastructures nowadays and the risk of resource contention among co-located VMs after consolidation. Therefore, the correlation among VMs to be colocated need to be considered. However, existing solutions do not address the scalability issue that arise once the number of VMs increases to an order of magnitude that makes it unrealistic to calculate the correlation between each pair of VMs. In this paper, we propose a correlation-aware VM consolidation solution ScalCCon, which uses a novel two-phase clustering scheme to address the aforementioned scalability problem. We propose and demonstrate the benefits of using the two-phase clustering scheme in comparison to solutions using one-phase clustering (up to 84% reduction of execution time when 17, 446 VMs are considered). Moreover, our solution manages to reduce the number of PMs required, as well as the number of performance violations, compared to existing correlation-based approaches. Keywords—Scalability; Correlation; Consolidation; Clustering; Performance degradation; VM placement;
Expanding Paraphrase Lexicons by Exploiting Generalities

Techniques for generating and recognizing paraphrases, i.e., semantically equivalent expressions, play an important role in a wide range of natural language processing tasks. In the last decade, the task of automatic acquisition of subsentential paraphrases, i.e., words and phrases with (approximately) the same meaning, has been drawing much attention in the research community. The core problem is to obtain paraphrases of high quality in large quantity. This article presents a method for tackling this issue by systematically expanding an initial seed lexicon made up of high-quality paraphrases. This involves automatically capturing morpho-semantic and syntactic generalizations within the lexicon and using them to leverage the power of large-scale monolingual data. Given an input set of paraphrases, our method starts by inducing paraphrase patterns that constitute generalizations over corresponding pairs of lexical variants, such as “amending” and “amendment,” in a fully empirical way. It then searches large-scale monolingual data for new paraphrases matching those patterns. The results of our experiments on English, French, and Japanese demonstrate that our method manages to expand seed lexicons by a large multiple. Human evaluation based on paraphrase substitution tests reveals that the automatically acquired paraphrases are also of high quality.
Parallel Execution of Test Runs for Database Application Systems

In a recent paper [8], it was shown how tests for database application systems can be executed efficiently. The challenge was to control the state of the database during testing and to order the test runs in such a way that expensive reset operations that bring the database into the right state need to be executed as seldom as possible. This work extends that work so that test runs can be executed in parallel. The goal is to achieve linear speed-up and/or exploit the available resources as well as possible. This problem is challenging because parallel testing can involve interference between the execution of concurrent test runs.
Partial Truthfulness in Minimal Peer Prediction Mechanisms with Limited Knowledge

We study minimal single-task peer prediction mechanisms that have limited knowledge about agents' beliefs. Without knowing what agents' beliefs are or eliciting additional information, it is not possible to design a truthful mechanism in a Bayesian-Nash sense. We go beyond truthfulness and explore equilibrium strategy profiles that are only partially truthful. Using the results from the multi-armed bandit literature, we give a characterization of how inefficient these equilibria are comparing to truthful reporting. We measure the inefficiency of such strategies by counting the number of dishonest reports that any minimal knowledge-bounded mechanism must have. We show that the order of this number is θ(log n), where n is the number of agents, and we provide a peer prediction mechanism that achieves this bound in expectation.
Fault tolerant synchronization for a class of uncertain chaotic systems versus external disturbances using fuzzy sliding mode control

In this paper, fault tolerant synchronization (FTS) for a class of uncertain chaotic systems is investigated. The FTS system is to increase the safety and reliability of synchronization when the slave system is subjected to actuator and sensor faults. In this paper, the faulty slave system is considered with model uncertainty and external disturbances. A sliding surface is adopted to ensure the stability of the synchronization dynamics and employed to design a fuzzy control system. Based on Lyapunov stability theory and fuzzy rules, the nonlinear controller and some generic sufficient conditions for global asymptotic synchronization are attained. The fuzzy rules are directly constructed subject to a defined Lyapunov function such that the synchronization error dynamics of two identical chaotic motions satisfy stability in the Lyapunov sense. The fuzzy sliding control system can compensate the actuator faults, model uncertainties, and disturbances occurred in the slave system. The proposed method is applied to two chaotic systems; Genesio and Duffing systems. Numerical simulation results demonstrate the validity and feasibility of the proposed FTS method.
Incorporation of Crack Propagation in the M-E Fatigue Cracking Prediction

ABSTRACT Fatigue cracking process includes both crack initiation and crack propagation. However, crack propagation is often not directly considered in various existing mechanistic-empirical (M-E) fatigue analysis models. This paper presents a practical approach for incorporating crack propagation caused by variable traffic loading spectrum into the M-E fatigue cracking prediction model. This Paris' law based practical approach consists of stress intensity factor (SIF) and fracture properties (A and n) as the fundamental input parameters for modelling crack propagation. For practical application, 8 SIF regression equations have been developed based on extensive finite element simulations and computations. In this approach, the HMA fracture properties (A and n) are easily determined using the Overlay Tester that has unique features with respect to specimen size, specimen preparation, and short testing time. Thus far, this proposed methodological approach is exhibiting great potential as a practical means to incorporate crack propagation in M-E fatigue crack prediction analyses.
RSSI-based self-localization with perturbed anchor positions

We consider the problem of self-localization by a resource-constrained mobile node given perturbed anchor position information and distance estimates from the anchor nodes. We consider normally-distributed noise in anchor position information. The distance estimates are based on the log-normal shadowing path-loss model for the RSSI measurements. The available solutions to this problem are based on complex and iterative optimization techniques such as semidefinite programming or second-order cone programming, which are not suitable for resource-constrained environments. In this paper, we propose a closed-form weighted least-squares solution. We calculate the weights by taking into account the statistical properties of the perturbations in both RSSI and anchor position information. We also estimate the bias of the proposed solution and subtract it from the proposed solution. We evaluate the performance of the proposed algorithm considering a set of arbitrary network topologies in comparison to an existing algorithm that is based on a similar approach but only accounts for perturbations in the RSSI measurements. We also compare the results with the corresponding Cramer-Rao lower bound. Our experimental evaluation shows that the proposed algorithm can substantially improve the localization performance in terms of both root mean square error and bias.
Imidacloprid affects rat liver mitochondrial bioenergetics by inhibiting FoF1-ATP synthase activity

ABSTRACT Imidacloprid (IMD) is a neonicotinoid insecticide widely used in crops, pets, and on farm animals for pest control. Several studies were conducted examining the adverse effects of IMD on animals often exhibiting hepatic damage. The aim of this study was to determine the effects of IMD on bioenergetics of mitochondria isolated from rat liver. Imidacloprid (50–200 µM) produced a concentration-dependent decrease in oxygen consumption and ATP production without markedly affecting mitochondrial membrane potential (MMP). Oxygen consumption experiments showed that IMD did not significantly affect the respiratory chain, and this was similar to findings with oligomycin and carboxyatractyloside, suggesting a direct action on FoF1-ATP synthase and/or the adenine nucleotide translocator (ANT). Imidacloprid inhibited FoF1-ATP synthase activity only in disrupted mitochondria and induced a partial inhibition of ADP-stimulated depolarization of the MMP. Our results indicate that IMD interacts specifically with FoF1-ATP synthase resulting in functional inhibition of the enzyme with consequent impairment of mitochondrial bioenergetics. These effects of IMD on mitochondrial bioenergetics may be related to adverse effects of this insecticide on the liver.
Energy consumption scheduling in smart grid: A non-cooperative game approach

This paper is concerned with an energy consumption scheduling problem for consumers in smart grid, based on a real time pricing strategy. Firstly, the energy consumption scheduling problem is cast into a non-cooperative energy consumption game, where consumers compete with each other in order to minimize their electricity usage cost. Secondly, we prove that the non-cooperative energy consumption game has a unique Nash equilibrium point, i.e., optimal energy consumption solution. Thirdly, the energy consumption solution is obtained by a discrete iterative algorithm. Simulation results show that the energy consumption scheduling scheme is effective in matching the varying generation capacity in a day.
A Unified Framework for Tracking Based Text Detection and Recognition from Web Videos

Video text extraction plays an important role for multimedia understanding and retrieval. Most previous research efforts are conducted within individual frames. A few of recent methods, which pay attention to text tracking using multiple frames, however, do not effectively mine the relations among text detection, tracking and recognition. In this paper, we propose a generic Bayesian-based framework of Tracking based Text Detection And Recognition (T <inline-formula><tex-math notation="LaTeX">$^2$</tex-math><alternatives> <inline-graphic xlink:href="yin-ieq1-2692763.gif"/></alternatives></inline-formula>DAR) from web videos for embedded captions, which is composed of three major components, i.e., text tracking, tracking based text detection, and tracking based text recognition. In this unified framework, text tracking is first conducted by tracking-by-detection. Tracking trajectories are then revised and refined with detection or recognition results. Text detection or recognition is finally improved with multi-frame integration. Moreover, a challenging video text (embedded caption text) database (USTB-VidTEXT) is constructed and publicly available. A variety of experiments on this dataset verify that our proposed approach largely improves the performance of text detection and recognition from web videos.
Genetic polymorphisms in TERT are associated with increased risk of esophageal cancer

Single nucleotide polymorphisms (SNPs) in TERT may be associated with susceptibility to esophageal cancer. In this study, we analyzed the association between TERT SNPs and risk of esophageal cancer in 386 esophageal cancer patients and 495 healthy subjects from the Xi’an area of China. Of the four SNPs examined, rs10069690 and rs2242652 were correlated with esophageal cancer risk. Additionally, after adjusting for age and gender, the “Trs10069690Ars2242652”, “Trs10069690Grs2242652” haplotypes were associated with an increased risk of esophageal cancer, while the and “Crs10069690Grs2242652” haplotype was associated with a decreased risk of esophageal cancer. These findings suggest that TERT polymorphisms may contribute to the development of esophageal cancer.
Computational verification of C protocol implementations by symbolic execution

We verify cryptographic protocols coded in C for correspondence properties with respect to the computational model of cryptography. The first step uses symbolic execution to extract a process calculus model from a C implementation of the protocol. The new contribution is the second step in which we translate the extracted model to a CryptoVerif protocol description, such that successful verification with CryptoVerif implies the security of the original C implementation. We implement our method and apply it to verify several protocols out of reach of previous work in the symbolic model (using ProVerif), either due to the use of XOR and Diffie-Hellman commitments, or due to the lack of an appropriate computational soundness result. We analyse only a single execution path, so our tool is limited to code following a fixed protocol narration. This is the first security analysis of C code to target a verifier for the computational model. We successfully verify over 3000 LOC. One example (about 1000 LOC) is independently written and currently in testing phase for industrial deployment; during its analysis we uncovered a vulnerability now fixed by its author.
Automatic recognition of pain, anxiety, engagement and tiredness for virtual rehabilitation from stroke: A marginalization approach

Virtual rehabilitation taps affective computing to personalize therapy. States of anxiety, pain and engagement (affective) and tiredness (physical or psychological) were studied to be inferable from metrics of 3D hand location-proxy of hand movement- and fingers' pressure relevant for upper limb motor recovery. Features from the data streams characterized the motor dynamics of 2 stroke patients attending 10 sessions of motor virtual rehabilitation. Experts tagged states manifestations from videos. We aid classification contributing with a marginalization mechanism whereby absent input is reconstructed. With the hand movement information absent, marginalization statistically outperformed a base model where such input is ignored. Marginalized classification performance was (Area below ROC curve: μ ± σ) 0.880 ± 0.173 and 0.738 ± 0.177 for each patient. Marginalization aid classification sustaining performance under input failure or permitting different sensing settings.
Primary user detection in cognitive radio using spectral-correlation features and stacked denoising autoencoder

This paper proposes a primary user detection method in cognitive radio system based on the features of cyclic spectral correlation and the stacked denoising autoencoder. The cyclic spectral correlation, which is sensitive to OFDM signal, is utilized as the feature-extraction tool. Then, we use the SDAE as a classifier due to its high classification accuracy. Concretely, we suppose that OFDM is applied by PU because OFDM is more commonly used than other single carrier modulations. Therefore, our mission turns to the classification of OFDM signal. In addition, a long symbols sequence is not required for this method simplifying the detection procedure and rendering rapid detection more achievable. The results of the probability of successful detection, the change in the probability of successful detection corresponding to different corruption levels are presented, demonstrating significant performance advantages over energy detection.
IRLbot: scaling to 6 billion pages and beyond

This paper shares our experience in designing a web crawler that can download billions of pages using a single-server implementation and models its performance. We show that with the quadratically increasing complexity of verifying URL uniqueness, BFS crawl order, and fixed per-host rate-limiting, current crawling algorithms cannot effectively cope with the sheer volume of URLs generated in large crawls, highly-branching spam, legitimate multi-million-page blog sites, and infinite loops created by server-side scripts. We offer a set of techniques for dealing with these issues and test their performance in an implementation we call IRLbot. In our recent experiment that lasted 41 days, IRLbot running on a single server successfully crawled 6.3 billion valid HTML pages ($7.6$ billion connection requests) and sustained an average download rate of 319 mb/s (1,789 pages/s). Unlike our prior experiments with algorithms proposed in related work, this version of IRLbot did not experience any bottlenecks and successfully handled content from over 117 million hosts, parsed out 394 billion links, and discovered a subset of the web graph with 41 billion unique nodes.
Effectiveness of Heuristic Based Approach on the Performance of Indexing and Clustering of High Dimensional Data

Data in practical applications (e.g., images, molecular biology, etc) is mostly characterised by high dimensionality and huge size or number of data instances. Though, feature reduction techniques have been successful in reducing the dimensionality for certain applications, dealing with high dimensional data is still an area which has received considerable attention in the research community. Indexing and clustering of high dimensional data are two of the most challenging techniques that have a wide range of applications. However, these techniques suffer from performance issues as the dimensionality and size of the processed data increases. In our effort to tackle this problem, this paper demonstrates a general optimisation technique applicable to indexing and clustering algorithms which need to calculate distances and check them against some minimum distance condition. The optimisation technique is a simple calculation that finds the minimum possible distance between two points, and checks this distance against the minimum distance condition; thus reusing already computed values and reducing the need to compute a more complicated distance function periodically. Effectiveness and usefulness of the proposed optimisation technique has been demonstrated by applying it with successful results to clustering and indexing techniques. We utilised a number of clustering techniques, including the agglomerative hierarchical clustering, k-means clustering, and DBSCAN algorithms. Runtime for all three algorithms with this optimisation scenario was reduced, and the clusters they returned were verified to remain the same as the original algorithms. The optimisation technique also shows potential for reducing runtime by a substantial amount for indexing large databases using NAQ-tree; in addition, the optimisation technique shows potential for reducing runtime as databases grow larger both in dimensionality and size.
Predicting the Change on Stock Market Index Using Emotions of Market Participants with Regularization Methods

Stock market index as the composite of a series of representative stocks plays a very crucial role in the financial market. Predicting the change of stock market index is vital for investors and stock holders to capture the trend of stocks which they are interested. Recently research from behavioral finance suggests that emotions of market participates can influence stock market index. However, variable selection becomes a major challenge. Normally, lots of key words related to emotions can be extracted from the social media, meaning that the number of predictor variables p for the data mining methods is very large. Traditional variable selection methods require that the number of observations n is sufficient lager and regularization methods could select variables for high dimensional conditions. However, it is common that n is close to p when analyzing the emotions data within a specific time period. Under this condition, both variable selection methods are applicable, but few research has been done on it. In this paper, we compare the traditional variable selection method with the regularization method under the condition that n is close to p. Then we apply typical data mining methods to predict the SSE Composite Index in China with the selected variables. The results show that the regularization methods give much better performance compared with traditional variable infliction factor (VIF) analysis.
A comprehensive evaluation of normalization methods for Illumina high-throughput RNA sequencing data analysis

During the last 3 years, a number of approaches for the normalization of RNA sequencing data have emerged in the literature, differing both in the type of bias adjustment and in the statistical strategy adopted. However, as data continue to accumulate, there has been no clear consensus on the appropriate normalization method to be used or the impact of a chosen method on the downstream analysis. In this work, we focus on a comprehensive comparison of seven recently proposed normalization methods for the differential analysis of RNA-seq data, with an emphasis on the use of varied real and simulated datasets involving different species and experimental designs to represent data characteristics commonly observed in practice. Based on this comparison study, we propose practical recommendations on the appropriate normalization method to be used and its impact on the differential analysis of RNA-seq data.
Precoded OFDM for asynchronous uplink with transparency to OFDM receiver

A novel waveform design approach is taken to develop a waveform based on precoding of data symbols, referred to as Precoded OFDM. The waveform is tailored for robustness against asynchronous transmission and a simple OFDM receiver to keep its intricacies hidden from the system. These features are most desired in massive Machine Type Communications (mMTC) where better intrinsic robustness is critical in avoiding the overheads used in cellular systems for synchronization.
A New Harmony Search Approach for Optimal Wavelets Applied to Fault Classification

This paper presents a novel approach based on the harmony search algorithm to optimally determine suitable wavelet functions and wavelet decomposition levels for accurate fault classification in transmission lines, unlike previous works in which only one arbitrary wavelet function is used. Discrete wavelet transform is used to extract the features in the voltage and/or current signals using the identified wavelet functions. Machine learning classifiers are then used to build a proper classification model to automate the fault classification process. The results of applying the proposed approach are presented and discussed, and conclusions are drawn.