Miracle-dz/UniFAR-FTUs huggingface dataset:
Energy-Aware Disk Storage Management: Online Approach with Application in DBMS

Energy consumption has become a first-class optimization goal in design and implementation of data-intensive computing systems. This is particularly true in the design of database management systems (DBMS), which was found to be the major consumer of energy in the software stack of modern data centers. Among all database components, the storage system is one of the most power-hungry elements. In previous work, dynamic power management (DPM) techniques that make real-time decisions to transition the disks to low-power modes are normally used to save energy in storage systems. In this paper, we tackle the limitations of DPM proposals in previous contributions. We introduced a DPM optimization model integrated with model predictive control (MPC) strategy to minimize power consumption of the disk-based storage system while satisfying given performance requirements. It dynamically determines the state of disks and plans for inter-disk data fragment migration to achieve desirable balance between power consumption and query response time. Via analyzing our optimization model to identify structural properties of optimal solutions, we propose a fast-solution heuristic DPM algorithm that can be integrated in large-scale disk storage systems for efficient state configuration and data migration. We evaluate our proposed ideas by running simulations using extensive set of synthetic workloads based on popular TPC benchmarks. Our results show that our solution significantly outperforms the best existing algorithm in both energy savings and response time.
Design of nanoscale enzyme complexes based on various scaffolding materials for biomass conversion and immobilization

The utilization of scaffolds for enzyme immobilization involves advanced bionanotechnology applications in biorefinery fields, which can be achieved by optimizing the function of various enzymes. This review presents various current scaffolding techniques based on proteins, microbes and nanomaterials for enzyme immobilization, as well as the impact of these techniques on the biorefinery of lignocellulosic materials. Among them, architectural scaffolds have applied to useful strategies for protein engineering to improve the performance of immobilized enzymes in several industrial and research fields. In complexed enzyme systems that have critical roles in carbon metabolism, scaffolding proteins assemble different proteins in relatively durable configurations and facilitate collaborative protein interactions and functions. Additionally, a microbial strain, combined with designer enzyme complexes, can be applied to the immobilizing scaffold because the in vivo immobilizing technique has several benefits in enzymatic reaction systems related to both synthetic biology and metabolic engineering. Furthermore, with the advent of nanotechnology, nanomaterials possessing ideal physicochemical characteristics, such as mass transfer resistance, specific surface area and efficient enzyme loading, can be applied as novel and interesting scaffolds for enzyme immobilization. Intelligent application of various scaffolds to couple with nanoscale engineering tools and metabolic engineering technology may offer particular benefits in research.
Modeling a bipedal humanoid robot using inverted pendulum towards push recovery

Push recovery is an important technology needs to be developed for the effective use of bipedal humanoid robot in the household applications. In this paper, a three mass inverted pendulum model equivalent to Humanoid Open Architecture Platform (HOAP -2) has been developed and the effectiveness of the model has been demonstrated using a software framework known as WEBOTS. It has been developed as a test bed for the development of sophisticated Push recovery techniques for HOAP-2 humanoid robot. Functionality of this model is general and adaptable to any kind of three mass models. Ankle strategy technique has been implemented to verify the correctness of the model. The correctness of the model has been verified successfully by the simulation results.
USER PROFILING : A MOBILE NETWORKS PERSPECTIVE

In present scenario, when increasing number of users are using mobile devices to search for information from the Internet, it has become quite necessary to personalize and filter the vast amount of information available. User Profiles are efficiently used to cache only the relevant information on the user’s mobile device. Furthermore, User Profiles can also be used to improve QoS, by including the Mobility Profiles. In this paper, we discuss User Profiling successfully implemented for efficient caching and improving QoS.
Persistent self-supervised learning: From stereo to monocular vision for obstacle avoidance

Self-supervised learning is a reliable learning mechanism in which a robot uses an original, trusted sensor cue for training to recognize an additional, complementary sensor cue. We study for the first time in self-supervised learning how a robot’s learning behavior should be organized, so that the robot can keep performing its task in the case that the original cue becomes unavailable. We study this persistent form of self-supervised learning in the context of a flying robot that has to avoid obstacles based on distance estimates from the visual cue of stereo vision. Over time it will learn to also estimate distances based on monocular appearance cues. A strategy is introduced that has the robot switch from flight based on stereo to flight based on monocular vision, with stereo vision purely used as “training wheels” to avoid imminent collisions. This strategy is shown to be an effective approach to the “feedback-induced data bias” problem as also experienced in learning from demonstration. Both simulations and real-world experiments with a stereo vision equipped ARDrone2 show the feasibility of this approach, with the robot successfully using monocular vision to avoid obstacles in a 5 × 5 m room. The experiments show the potential of persistent self-supervised learning as a robust learning approach to enhance the capabilities of robots. Moreover, the abundant training data coming from the own sensors allow to gather large data sets necessary for deep learning approaches.
Motion Segmentation Using Convergence Properties

Motion segmentation is traditionally coupled with motion detection, where each image region corresponds to a particular motion model which accounts for the temporal changes in the region. Using the motion model to estimate the second frame from the (cid:12)rst frame, for example, should give a very low prediction error in the corresponding region. To relax the need for accurate motion models, it is proposed to examine the convergence of the prediction error, rather than the prediction error itself. In an iterative process of motion computation followed by computing the prediction error, those points for which the prediction error is being reduced are considered as a coherent region. This segmentation approach works well even with approximate motion models that don’t eliminate the prediction error.
The General and Oral Health Status in Older Adults from Rural Environment of Iasi County, Romania

The older adults present different combinations of chronic multi-morbidity and functional limitations which determines health and social problems. The aim of the study was to assess the health status (general and oral) of the older adults because it can offer valuable information for estimation of the necessary health services. The targeted objectives were: the evaluation of oral morbidity, oral health related behaviors and attitudes towards oral health; evaluation of general morbidity; study of the addressability to health services and evaluation supplying medicines for rural area. From January–December 2016, we conducted a prevalence study focused on the main indicators of morbidity (general and oral). In our study were included 196 subjects, aged 65-74 years, 45.41% males, 54.59% females, from the rural community of Iasi County, in North Eastern region of Romania. In the studied group, the general morbidity model was the same for both male and female gender: in male gender, the morbidity was dominated by the cardiovascular diseases (63.24%), chronic digestive diseases (33.82%), diabetes mellitus (29.41%), osteoarticular diseases (27.94%), accidents and traumatisms (26.47%) and chronic respiratory diseases (23.53%); in female gender, it was also dominated by the cardiovascular diseases (67.82%), followed by diabetes mellitus (32.18%), osteoarticular diseases (31.03%) and chronic digestive diseases (24.14%). The oral morbidity model was relatively different between genders. At male gender, the predominant diseases were: dental caries (57.30%), lip and mucosal diseases (28.09%, OR=1.51, p<0.05), trauma (24.24%, OR=2.68, p<0.05) and total edentulism (25.84%). At female gender, the predominant diseases were: caries disease (59.81%), non-carious dental disorders (42.67%), periodontal disease (41.33%, OR=1.48, p<0.05) and total edentulism (29.91%.
International Asset Allocation: The Benchmark Effect

We study the impact of well-known benchmark indexes as a coordinating mechanism on asset allocation and capital flows across countries. Using unique monthly micro-level data between 1996 and 2012 on equity and bond mutual funds from around the world, we find that benchmarks have significant and large effects on mutual fund international investments, generating significant pro-cyclicality. Benchmarks are closely related to market capitalization, so shocks to returns get fully transmitted. Moreover, because mutual funds tend to follow their respective benchmarks, their weights significantly affect how mutual funds allocate their injections across countries. Benchmarks explain between 60 and 90 percent of the allocations. On average, a 1 percent change in benchmark weights implies a 0.7 percent change in country weights. Moreover, we also find that when deviating from the benchmarks, the less active funds are more pro-cyclical (investing in countries with higher relative returns), while more active funds are more counter-cyclical. JEL Classification Codes: F32, F36, G11, G15, G23
DBT-5: A Fair Usage Open-Source TPC-E Implementation for Performance Evaluation of Computer Systems

The TPC-E is the new benchmark recently approved by the TPC council. It is designed to exercise an On-Line Transaction Processing workload of a brokerage firm and be representative of current database server work. In this paper we present DBT-5, a fair usage open-source implementation of the TPC-E benchmark. In addition to reporting on the design and implementation details of the tool, experimental results on a system running the PostgreSQL database engine are also described. The significance of this work is that it provides an environment where recent innovations in the OLTP workload field can be evaluated. Resumo. O TPC-E e o novo benchmark aprovado recentemente pelo conselho TPC. Foi projetado para exercitar uma carga de trabalho de Processamento de Transacao On-Line de uma corretora de acoes, sendo centrada no trabalho executado pelo servidor de banco de dados. Neste artigo nos apresentamos a ferramenta DBT-5, uma implementacao de codigo aberto, para uso nao comercial, do benchmark TPC-E. Alem de descrevermos a arquitetura e a implementacao do sistema, mostramos resultados experimentais em um sistema rodando o banco de dados PostgreSQL. A importância deste trabalho se da pela provisao de um ambiente onde as inovacoes no campo das cargas de trabalho transacionais podem ser avaliadas.
The clinical utility of the Personal Questionnaire (PQ): a mixed methods study

Abstract The demand to implement routine outcome assessment in mental health care services calls for measures with clinical utility, i.e. feasible to therapists, acceptable to clients and generalizable to settings. An exploratory sequential research design was used to study the clinical utility of an individualized outcome measure, the Personal Questionnaire (PQ). In a qualitative first phase, a focus group of therapists was conducted to guide the development of a survey. In a quantitative second phase, the survey was administered to an international sample of 25 therapists that integrate PQ in routine clinical work. Results suggested that the PQ is perceived as a clinically significant and fairly practical measure, useful not only in assessing outcome but also for various clinical tasks. Furthermore, it is relatively well accepted by clients and generalizable to different clients, clinical approaches and settings. However, therapists gave specific suggestions for increasing the PQ’s clinical utility. Exploring therapists’ perspectives and practices will improve the appropriateness of measures for real-world clinical settings.
Traffic flow characterization of cloud applications used in campus network

The paper reports the results from flow-based measurements carried in campus network of AGH-UST. We have conducted detailed analysis of traffic streams in order to find most active online applications. Measurements were carried with the focus to find most active applications, anomalies and also malicious users. It was shown that Facebook, YouTube and Dropbox, they constitute highest and rather stable contributions to whole traffic exchanged with Internet. Presented results show sizes, lengths, inter-arrival times supplemented by average packet sizes of flows belonging to analysed applications.
Towards a One-Way American Sign Language Translator

Inspired by the Defense Advanced Research Projects Agency’s (DARPA) recent successes in speech, we introduce a new task for sign language recognition research: a mobile one-way American Sign Language translator. We argue that such a device should be feasible in the next few years, may provide immediate practical benefits for the Deaf community, and leads to a sustainable program of research comparable to early speech recognition efforts. We ground our efforts in a particular scenario, that of a Deaf individual seeking an apartment, and discuss the system requirements and our interface for this scenario. Finally we describe initial recognition results of 94% accuracy on a 141 sign vocabulary signed in phrases of fours signs using a one-handed glove-based system and hidden Markov models (HMMs).
A Comparison of Culture- and PCR-Based Methods to Detect Six Major Non-O157 Serogroups of Shiga Toxin-Producing Escherichia coli in Cattle Feces

Culture-based methods to detect the six major non-O157 (O26, O45, O103, O111, O121 and O145) Shiga toxin-producing E. coli (STEC) are not well established. Our objectives of this study were to develop a culture-based method to detect the six non-O157 serogroups in cattle feces and compare the detection with a PCR method. Fecal samples (n = 576) were collected in a feedlot from 24 pens during a 12-week period and enriched in E. coli broth at 40° C for 6 h. Enriched samples were subjected to immunomagnetic separation, spread-plated onto a selective chromogenic medium, and initially pooled colonies, and subsequently, single colonies were tested by a multiplex PCR targeting six serogroups and four virulence genes, stx1, stx2, eae, and ehxA (culture method). Fecal suspensions, before and after enrichment, were also tested by a multiplex PCR targeting six serogroups and four virulence genes (PCR method). There was no difference in the proportions of fecal samples that tested positive (74.3 vs. 77.4%) for one or more of the six serogroups by either culture or the PCR method. However, each method detected one or more of the six serogroups in samples that were negative by the other method. Both culture method and PCR indicated that O26, O45, and O103 were the dominant serogroups. Higher proportions (P < 0.05) of fecal samples were positive for O26 (44.4 vs. 22.7%) and O121 (22.9 vs. 2.3%) serogroups by PCR than by the culture method. None of the fecal samples contained more than four serogroups. Only a small proportion of the six serogroups (23/640; 3.6%) isolated carried Shiga toxin genes. The culture method and the PCR method detected all six serogroups in samples negative by the other method, highlighting the importance of subjecting fecal samples to both methods for accurate detection of the six non-O157 STEC in cattle feces.
Implicit and Explicit Stigma of Mental Illness: Links to Clinical Care

This study examined implicit and explicit measures of bias toward mental illness among people with different levels of mental health training, and investigated the influence of stigma on clinically-relevant decision-making. Participants (N = 1539) comprised of (1) mental health professionals and clinical graduate students, (2) other health care/social services specialists, (3) undergraduate students, and (4) the general public self-reported their attitudes toward people with mental illness, and completed implicit measures to assess mental illness evaluations that exist outside of awareness or control. In addition, participants predicted patient prognoses and assigned diagnoses after clinical vignettes. Compared with people without mental health training, individuals with mental health training demonstrated more positive implicit and explicit evaluations of people with mental illness. Further, explicit (but not implicit) biases predicted more negative patient prognoses, but implicit (and not explicit) biases predicted over-diagnosis, underscoring the value of using both implicit and explicit measures.
Preposition Disambiguation: Still a Problem

Considerable recent progress has been made in preposition disambiguation using the SemEval 2007 corpus, with results reaching accuracy of over 88 percent. However, with a new corpus of tagged instances, use of the models shows a decline in performance to around 43 percent. This suggests that recent efforts suffer from an out-of-domain problem. Detailed examination of the dimensions of this problem suggests that the sampling methodology used in creating the SemEval corpus was biased to the underlying reliance on FrameNet. In view of the demonstration of increasing importance attached to prepositions in semantic role labeling, further efforts to understand preposition behavior seem warranted. Initial examination of the new data may provide a basis for future directions in preposition disambiguation.
Robust and Reversible Image Watermarking Scheme Using Combined DCT-DWT-SVD Transforms

We present a secure and robust image watermarking scheme that uses combined reversible DWT-DCT-SVD transformations to increase integrity, authentication, and confidentiality. The proposed scheme uses two different kinds of watermarking images: a reversible watermark, , which is used for verification (ensuring integrity and authentication aspects); and a second one, , which is defined by a logo image that provides confidentiality. Our proposed scheme is shown to be robust, while its performances are evaluated with respect to the peak signal-to-noise ratio (PSNR), signal-to-noise ratio (SNR), normalized cross-correlation (NCC), and running time. The robustness of the scheme is also evaluated against different attacks, including a compression attack and Salt & Pepper attack.
The role of selective attention in short-term memory and goal-directed behavior

Selective attention enables the prioritization of goal-relevant aspects of our sensory environment in order to guide our actions, or to store goal-relevant information in short-term memory. Yet, it remains largely unclear how attention prioritizes goal-relevant information. For example, selective attention may enhance processing of goal-relevant information, suppress processing of distracting information, or both. The research presented in this dissertation examined the neurophysiological mechanisms through which selective attention supports visual short-term memory and action selection using electroencephalography, focusing on the role of brain oscillations and modulations of sensory activity. A first question concerned if selective attention predominantly modulates sensory processing during perception, or may also support maintenance of stored representations to optimize behavior. A second question concerned whether attentional modulation of anticipated and unanticipated irrelevant information rely on similar neurophysiological mechanisms. Thirdly, we addressed whether these mechanisms generalize across different sensory dimensions during action selection. Results provided supporting evidence for an important role of selective attention in action selection, and during both short-term memory encoding and maintenance. However, they also revealed that attentional selection is often imperfect, especially when irrelevant information cannot be anticipated. Whereas interregional modulation of alpha oscillations was associated with filtering of anticipated irrelevant information, filtering of unanticipated distraction relied, at least partly, on representation-specific sensory modulations. Furthermore, individuals differed markedly in the degree to which they exhibited attentional modulation of irrelevant information for different sources of interference, demonstrating the importance of an individual differences approach to increase our understanding of the neural mechanisms that enable selective processing of goal-relevant information.
Evaluation of background subtraction algorithms for object extraction

There is an increase in need of video surveillance applications. Intelligent video surveillance (IVS) includes public safety and security applications, including authenticity control, crowd flow direction and crowd analysis, human behaviour detection and analysis etc. The critical part of IVS system is proper foreground estimation using background subtraction algorithms. This is a challenging task due to variations in illumination, background motion due cluttering noise like swaying trees, flowing water, etc. and the slow moving objects introduce noise in the background estimated. We have precisely concentrated on such challenges. The purpose of this evaluation is to give an overview and categorization of the approaches based on the performance measures like Precision, Recall, F measures (F1), Similarity, Matching Index and Average Classification Error. And also the available techniques are compared based on the computational complexity parameters in terms of Big-O along with their limitations for the improvement in the efficiency of the background subtraction algorithms.