Miracle-dz/UniFAR-FTUs huggingface dataset:
Nearest-neighbour Searching in Files of Text Signatures Using Transputer Networks

SUMMARY This paper discusses the implementation of nearest-neighbour document retrieval in serial files using transputer networks. The system uses a two-stage retrieval algorithm in which an initial text-signature search is used to exclude large numbers of documents from the detailed and time-consuming pattern-matching search. The latter is implemented using a processor farm, so that documents which match at the signature level can be examined in parallel to determine whether they are, in fact, a good match for the query. The results demonstrate that communication is the critical factor in all of the transputer networks that were investigated. A high degree of speed-up can be obtained when only the pattern-matching search is carried out. When text signatures are used, however, the speed-up is less, decreasing in line with an increase in the size of the text signatures that are used.
Multiscale Analysis and Computation for a Stationary Schrödinger-Poisson System in Heterogeneous Nanostructures

This paper discusses the multiscale analysis and computation of a stationary Schrodinger--Poisson system with rapidly oscillating discontinuous coefficients arising from the modeling of a heterogeneous nanostructure with a periodic microstructure. The multiscale asymptotic expansions of the solutions for the above nonlinear equations are presented, and the convergence result with an explicit rate is derived. The efficient numerical method based on the multiscale asymptotic expansions is advanced. Numerical simulations are then carried out to validate the above theoretical results. The physical model used to describe the low-dimensional nanometer-sized systems will be introduced in an appendix.
Detection of De-Authentication DoS Attacks in Wi-Fi Networks: A Machine Learning Approach

Media Access Layer (MAC) vulnerabilities are the primary reason for the existence of the significant number of Denial of Service (DoS) attacks in 802.11 Wi-Fi networks. In this paper we focus on the de-authentication DoS (Deauth-DoS) attack in Wi-Fi networks. In Deauth-DoS attack an attacker sends a large number of spoofed de-authentication frames to the client (s) resulting in their disconnection. Existing solutions to mitigate Deauth-DoS attack rely on encryption, protocol modifications, 802.11 standard up gradation, software and hardware upgrades which are costly. In this paper we propose a Machine Learning (ML) based Intrusion Detection System (IDS) to detect the Deauth-DoS attack in Wi-Fi network which does not suffer from these drawbacks. To the best of our knowledge ML based techniques have never been used for detection of Deauth-DoS attack. We have used a variety of ML based classifiers for detection of Deauth-DoS attack enabling an administrator to choose among a host of classification algorithms. Experiments performed on in-house test bed shows that the proposed ML based IDS detects Deauth-DoS attack with precision (accuracy) and recall (detection rate) exceeding 96% mark.
Virtual disk based centralized management for enterprise networks

The rapid advances in hardware, software, and networks have made the management of enterprise network systems an increasingly challenging task. Due to the tight coupling between hardware, software, and data, every one of the hundreds or thousands of PCs that are connected in an enterprise environment has to be administered individually, leading to high Total Cost of Ownership (TCO). We argue that centralized management with distributed, diskless clients, yet centralized repositories of all software and data can reduce the management complexity with reduced software maintenance time, improved system availability, and enhanced security. We instantiate such paradigm with a diskless, thick client based system that supports heterogeneous OSes including Windows---the dominant commodity OS in the current market. The prototype requires no or minimum OS modification, nor application modification. Our initial deployment and experiment results demonstrate that our approach is a feasible and efficient solution for managing enterprise network systems.
Modeling speech parameter sequences with latent trajectory Hidden Markov model

This paper proposes a probabilistic generative model of a sequence of vectors called the latent trajectory hidden Markov model (HMM). While a conventional HMM isonly capable of describing piecewise stationary sequences of data vectors, the proposed model is capable of describing continuously time-varying sequences of data vectors, governed by discrete hidden states. This feature is noteworthy in that it can be used to model many kinds of time series data that are continuous in nature such as speech spectra. Given a sequence of observed data, the optimal state sequence can be decoded using the expectation-maximization (EM) algorithm. Given a set of training examples, the underlying model parameters can be trained by either the expectation-maximization algorithm or the variational inference algorithm.
Voltage balancing strategy for a five-level flying capacitor converter using phase disposition PWM with sawtooth-shaped carriers

The flying capacitor (FC) multilevel converter has attracted a great deal of interest in the recent years because of its easier extension to a higher number of levels (n>;3), as compared to its counterpart, the diode-clamped converter (DCC). The main focus of this paper is to develop a voltage balancing scheme of FCs for a five-level FC converter based on phase disposition pulse-width modulation (PD-PWM). Since there are multiple states that produce the same output voltage at the leg of the converter, such a redundancy is used to regulate the FC voltages at their desired levels. The selection of the optimal states is performed by minimizing a cost function. A drawback observed when using standard symmetrical triangular carriers for the PD-PWM, is the additional switching events that are produced due to transitions within the same voltage level. Nevertheless, this fact can be avoided by using sawtooth carrier waveforms instead. Simulation results verify the robustness of the proposed voltage balancing scheme against static and dynamic load conditions. Moreover, using sawtooth carriers a significant reduction of the switching frequency is achieved as compared to the use of standard triangle carriers while maintaining the FC voltage balanced.
An efficient routing protocol for green communications in vehicular ad-hoc networks

Vehicular ad-hoc networks (VANETs) provide the communications required to deploy Intelligent Transportation Systems (ITS). In the current state of the art there is a lack of studies on Green Communications (energy-efficiency) in VANETs. However, due to the possible interaction with devices that are fed with different electrical sources and the proliferation of electrical vehicles, the power consumption by the wireless communications might become a major concern in VANET design. In this paper, we study the energy-efficiency of a quality-of-service optimized version of OLSR by means of Differential Evolution (DE-OLSR). We have conducted a series of VANET simulations aiming at analyzing the power consumption and the QoS in order to compare DE-OLSR with the standard version of OLSR. An extensive performance evaluation shows that DE-OLSR clearly outperforms the standard version in terms of energy consumption, while offering a competitive QoS.
Adaptive Machinery to Support Natural Conversations

Real-time adaptation and ongoing learning promise to provide computational machinery to support fluid conversational interaction. We will focus on representations and inferential strategies that can provide real-time adaptation to multiple characteristics of a conversation. The work approaches conversation from a perspective of inference and decision making under uncertainty. Automated methods for engaging users in conversations must address critical uncertainties about users’ attentional status, utterances, and goals.
Proceedings: Pathogenesis and Treatment of Renal Osteodystrophy

Renal osteodystrophy is the term used to describe the many different patterns of the skeletal abnormalities that occur in patients with chronic kidney disease. The main two conditions are osteitis fibrosa, characterized by high bone turnover, increased osteoclastic and osteoblastic activity, and high levels of circulating parathyroid hormone (PTH) and adynamic bone disease characterized by low bone turnover and low levels of circulating PTH. Retention of phosphorus, decreased levels of calcitriol in blood, decreased levels of serum ionized calcium, reduced numbers of vitamin D receptors and calcium sensors in the parathyroid gland, and skeletal resistance to the calcemic action of PTH play a major role in the development of renal osteodystrophy. This review will describe the current approach for the treatment of renal osteodystrophy.
Enhancing versatility and safety of industrial grippers with adaptive robotic fingers

This paper introduces a novel concept for a (self-)adaptive, a.k. a. underactuated, finger which presents the particularity of exploiting the traditional motion generated by standard robotic grippers for actuation. The kinematic structure of this finger allows for seizing objects of various shapes in both enveloping and precision grasps without having any actuator in its joints. Following a kinetostatic analysis, the forces generated by the finger will be obtained and analyzed. The performance of a practical implementation of the design will be subsequently evaluated and compared with another architecture inspired by a commercially available product. Finally, collision modes for safe human interaction will be presented and followed by the demonstration of a prototype.
Hunting Killer Tasks for Cloud System through Machine Learning: A Google Cluster Case Study

Motivated by frequent failures in cloud computing systems, we analyze failure frequency and failure continuity of tasks from the Google cloud cluster, and find what we call killer tasks that suffer from frequent failures and repeated rescheduling. Killer tasks cause unnecessary resource wasting and significant increase of scheduling workloads, which can be a big concern in cloud systems. We aim to recognize killer tasks at the very early stage of their occurrence so that they can be addressed proactively instead of being rescheduled repeatedly, so as to promote reliability and save resources. To recognize killer tasks from a large amount of tasks in real time is really challenging. In this paper, we first investigate characteristics and behavior patterns of killer tasks and then develop two machine learning based methods, K-HUNTER and C-HUNTER, for online recognition of killer tasks. The empirical results show that our approach performs at 97% of precision in recognizing killer tasks with an 89% timing advance and 88% of resource saving for the cloud system on average.
High-throughput screening to estimate single or multiple enzymes involved in drug metabolism: microtitre plate assay using a combination of recombinant CYP2D6 and human liver microsomes

1. The purpose of this study was to estimate readily involvement of single or multiple enzymes in the metabolism of a drug through inhibitory assessment. Inhibitory effects of various compounds on CYP2D6 activity assayed by formation of fluorescent metabolite from 3-[2- (N, N -diethyl-N-methyl-ammonium)ethyl]-7-methoxy-4-methyl-coumarin (AMMC) were assessed using microtitre plate (MTP) assays with a combination of recombinant CYP2D6 and human liver microsomes (HLM). 2. Among various compounds studied, antipsychotic drugs extensively inhibited recombinant CYP2D6 activity and the IC 50 values were generally lower than those of antidepressants and antiarrhythmic drugs. 3. After pre-incubation, the IC 50 values of mianserin, chlorpromadine, risperidone, thioridazine, alprenolol, propafenone and dextromethorphan increased but the values of timolol, S- metoprolol and propranolol substantially decreased compared with those in case of co-incubation 4. The IC 50 values of typical substrates of CYP2D6 (bufuralol and dextromethorphan at lower substrate concentration) in inhibition studies using HLM, were similar to those in the case of recombinant CYP2D6, but the values of the compounds that are metabolized by multiple CYP forms (perphenazine and chlorpromazine) in HLM were much larger. 5. If the ratio (HLM/rCYP ratio) of IC 50 values between HLM and recombinant CYP2D6 exceeds approximately 2, it suggests that other CYP forms in addition to CYP2D6 might be involved in the metabolism of the test compounds. From the advantage such as speed, high throughput and ease of the technique, the MTP assay using a combination of the recombinant CYP2D6 and HLM is useful to estimate the involvement of single or multiple enzymes in the metabolism of drugs at the stage of drug discovery.
Gender Identification and Age Estimation of Users Based on Music Metadata

Music recommendation is a crucial task in the field of music information retrieval. However, users frequently withhold their real-world identity, which creates a negative impact on music recommendation. Thus, the proposed method recognizes users’ real-world identities based on music metadata. The approach is based on using the tracks most frequently listened to by a user to predict their gender and age. Experimental results showed that the approach achieved an accuracy of 78.87% for gender identification and a mean absolute error of 3.69 years for the age estimation of 48403 users, demonstrating its effectiveness and feasibility, and paving the way for improving music recommendation based on such personal information.
Effect of receiver Signal to Noise Ratio on the classification performance of RF fingerprinting

Traditionally, Radio Frequency (RF) fingerprinting classification is performed by selecting an “unknown” signal from the pool, generating its RF fingerprint, and correlating the RF fingerprint with each profile RF fingerprint stored in the database. Most existing RF fingerprinting techniques have used high received SNR signals for generating the profile RF fingerprint of the transmitters and promising classification results has been reported in the literature. However, receiver SNR changes due to mobility of the transmitter/receiver and environment. Therefore, the RF fingerprints changes with the received SNR of the signals, which affect the classification results of the RF fingerprinting. This paper analyzes the effect of the receiver SNR on the overall RF fingerprinting classification. Three scenarios are considered, where profile RF fingerprint is generated by training the classifier with low, high and low/high SNR signals. These three scenarios correspond to the situation, when either transmitter or receiver is mobile and SNR changes from low to high or vice versa. The testing results show that accurate classification largely depends on the received SNR signal. Whereas high receiver SNR yields accurate results but high SNR is not typical in a wireless environment.
Inter-individual variation of DNA methylation and its implications for large-scale epigenome mapping

Genomic DNA methylation profiles exhibit substantial variation within the human population, with important functional implications for gene regulation. So far little is known about the characteristics and determinants of DNA methylation variation among healthy individuals. We performed bioinformatic analysis of high-resolution methylation profiles from multiple individuals, uncovering complex patterns of inter-individual variation that are strongly correlated with the local DNA sequence. CpG-rich regions exhibit low and relatively similar levels of DNA methylation in all individuals, but the sequential order of the (few) methylated among the (many) unmethylated CpGs differs randomly across individuals. In contrast, CpG-poor regions exhibit substantially elevated levels of inter-individual variation, but also significant conservation of specific DNA methylation patterns between unrelated individuals. This observation has important implications for experimental analysis of DNA methylation, e.g. in the context of epigenome projects. First, DNA methylation mapping at single-CpG resolution is expected to uncover informative DNA methylation patterns for the CpG-poor bulk of the human genome. Second, for CpG-rich regions it will be sufficient to measure average methylation levels rather than assaying every single CpG. We substantiate these conclusions by an in silico benchmarking study of six widely used methods for DNA methylation mapping. Based on our findings, we propose a cost-optimized two-track strategy for mammalian methylome projects.
Association of cognitive dysfunction with cardiovascular disease events in elderly hypertensive patients

Objectives: This study assesses whether presence of cognitive dysfunction can be a marker associated with the development of cardiovascular disease (CVD) events independent of ambulatory blood pressure (BP) or other indices of target organ damage (TOD) in elderly hypertensive patients. Methods: We recruited 585 hypertensive patients (mean age, 73 years; 41% men) who were ambulatory, lived independently, and were without clinically overt dementia. Cognitive function was assessed by Mini-Mental State Examination (MMSE) at baseline, and CVD events (coronary artery disease, stroke, congestive heart failure, and sudden death) were prospectively ascertained. Cognitive dysfunction was defined as the lowest quartile of MMSE scores (n = 183, median 24 points). Results: CVD events occurred in 42 people over an average of 2.8 years (1644 person-years). The prevalence of cognitive dysfunction was higher in patients with CVD events than those without (57 vs. 29%; both P <0.001) at baseline. Cognitive dysfunction was associated with CVD events, after adjustment for nocturnal SBP and evidence of TOD [i.e. albuminuria, cardiac hypertrophy, and carotid-artery intima–media thickness (IMT)], hazard ratio 2.5–2.9 (all P <0.01). Incorporation of MMSE in the risk model (including age, estimated glomerular filtration rate, and preexisting CVD) improved the C-statistics (from 0.691 to 0.741) and resulted in a net reclassification improvement of 17.6% (P = 0.02). In contrast, incorporation of albuminuria, cardiac hypertrophy, and high carotid-artery IMT added little further improvement in the risk prediction. Conclusion: Cognitive dysfunction is an independent marker associated with increased risk of CVD events in elderly hypertensive patients.
Smart technologies for long-term stress monitoring at work

Due to the growing pace of life, stress became one of the major factors causing health problems. We have developed a framework for measuring stress in real-life conditions continuously and unobtrusively. In order to provide meaningful, useful and actionable information, we present stress information, derived from sensor measurements, in the context of person's activities. In this paper, we describe our framework, discuss how we address arising challenges and evaluate our approach on basis of the field studies we have conducted. The main results of the evaluation are that the results of long-term measurements of stress reveal people information about their behavioral patterns that they perceive as meaningful and useful, and trigger their ideas about behavioral changes necessary to achieve a better stress balance.
Bestrophin-2 is involved in the generation of intraocular pressure.

PURPOSE The bestrophin family of proteins has been demonstrated to generate or regulate Ca2+-activated Cl(-) conductances. Mutations in bestrophin-1 (Best1) cause several blinding eye diseases, but little is known about other bestrophin family members. This study involved disruption of the Best2 gene in mice. METHODS The mouse Best2 gene was disrupted by replacing exons 1, 2, and part of exon 3 with a Lac Z. The expression profile of Bestrophin-2 (Best2) was examined using RT-PCR, X-gal staining, and immunohistochemistry. Intraocular pressure (IOP) was measured by anterior chamber cannulation. RESULTS RT-PCR of mouse tissues revealed Best2 mRNA in eye, colon, nasal epithelia, trachea, brain, lung, and kidney. X-gal staining, confirmed expression in colon epithelia and in the eye, in the nonpigmented epithelia (NPE). Best2 was not expressed in RPE cells. Best2 protein was observed only in NPE and colon epithelia. The absence of Best2 had no obvious deleterious effect on the mice. However, the Best2-/- mice were found to have significantly (P < 0.02) diminished IOP with respect to the Best2+/+ and Best2+/- littermates. The Best2-/- and Best2+/- mice responded better to the carbonic anhydrase inhibitor brinzolamide than did their Best2+/+ littermates, although the beta-blocker timolol brought IOP to the same level, regardless of genotype. CONCLUSIONS Best2 plays a role in the generation of IOP by regulating formation of aqueous humor, and inhibition of Best2 function represents an attractive new avenue for regulating IOP in individuals with glaucoma.
Archiving scientific data

We present an archiving technique for hierarchical data with key structure. Our approach is based on the notion of timestamps whereby an element appearing in multiple versions of the database is stored only once along with a compact description of versions in which it appears. The basic idea of timestamping was discovered by Driscoll et. al. in the context of persistent data structures where one wishes to track the sequences of changes made to a data structure. We extend this idea to develop an archiving tool for XML data that is capable of providing meaningful change descriptions and can also efficiently support a variety of basic functions concerning the evolution of data such as retrieval of any specific version from the archive and querying the temporal history of any element. This is in contrast to diff-based approaches where such operations may require undoing a large number of changes or significant reasoning with the deltas. Surprisingly, our archiving technique does not incur any significant space overhead when contrasted with other approaches. Our experimental results support this and also show that the compacted archive file interacts well with other compression techniques. Finally, another useful property of our approach is that the resulting archive is also in XML and hence can directly leverage existing XML tools.
Query mapping techniques for XML documents: A comparative study

Extensible Markup Language (XML) has grown to be a promising medium for data interchange and representation over the Internet. In the presence of the database technology there is a growing need to develop efficient storage and querying large repositories of XML documents. Numerous researchers proposed various mapping approaches using Relational Database (RDB) to store and query XML documents. These approaches are categorized into two: structure-mapping and model-mapping approaches. Various query mapping techniques (Edge-based, Node-based, and Path-based) are proposed and applied on the existing model mapping approaches. These techniques translate XPath query into Structured Query Language (SQL) statement with less number of join operations and reduce the search space, and improved the XML query processing. Therefore, the aim of this paper is to compare these query mapping techniques in terms of their advantages and limitations. The implication of these query mapping techniques on the existing model mapping approaches are also discussed. The discussion is focus on the complexity of translated SQL and query response time, which affect the efficiency of the XML query processing. Finally, the effectiveness for each technique on XML query processing and the solutions are pointed out.