Miracle-dz/UniFAR-FTUs huggingface dataset:
Survey of Energy-Efficient and Power-Proportional Storage Systems

Increasingly, large-scale computing systems are consuming more power each passing year. As power consumption is on the rise, concern has been raised over the growing implications on power bills, carbon emissions, and power supply limitations for data centers. Computing systems use hardware components that are not power proportional and machines comprising large systems tend to be underutilized, resulting in great energy wastage. Motivated by this fact, researchers aim to improve the energy efficiency of these systems by increasing resource utilization and exploiting system characteristics in order to achieve power proportionality. However, many challenges burden the task of producing a poweraware, energy-efficient large scale system that provides the same performance as today’s systems. Particularly, storage systems are of great concern since storage consumes a large amount of power in the data center. This paper outlines the main problems and challenges of delivering power efficient storage solutions, and proposes a taxonomy of power-aware techniques. This work aims to provide a detailed exposition of current trends in power-aware storage systems in a comprehensive and organized manner, outlining and comparing current solutions and challenges.
Inferring Latent Attributes of an Indian Twitter User using Celebrities and Class Influencers

In this paper we classify a user into three categories: "Gender", "Age" and "Political Affiliation" with an application to Indian Twitter users. Our approach automatically predicts these attributes by leveraging observable information such as the tweet behavior, linguistic content of the user's Twitter feed and the celebrities followed by the user. This paper would also use a novel feature that we would define in this paper as "class influencers". Class influencers are the Twitter users which influence a particular class so much that, they themselves can be used as a discriminating feature. Our approach first extracts the linguistic content based features using LIWC dictionary. Then, we derive features like smiley types, smiley count, tweet frequency, night-time tweet frequency, etc. We have also derived celebrity based feature: age, genre, gender (using Wikipedia and Freebase) of the celebrities a user is following. Finally, we refine the results using class influencers. Results show that rich linguistic features combined with popular neighborhood and influencers prove valuables and promising for additional user classification needs.
Dynamic Spectrum Assignment and Access Scenarios, System Architecture, Functional Architecture and Procedures for IEEE P1900.4 Management System

Implementing dynamic spectrum assignment and access technologies in heterogeneous wireless networks may lead to considerable improvement in spectrum usage. For this purpose, management system shall be added on top of existing heterogeneous wireless system. IEEE P1900.4 working group has been developing such management system. In this paper, we propose different scenarios for IEEE P1900.4 dynamic spectrum assignment and access use cases. We propose enhancements to IEEE P1900.4 system architecture. Also, we propose functional architecture for IEEE P1900.4 management system. Finally, we propose generic procedures to implement proposed scenarios using proposed system architecture.
Predicting the Execution Time of Workflow Activities Based on Their Input Features

The ability to accurately estimate the execution time of computationally expensive e-science algorithms enables better scheduling of workflows that incorporate those algorithms as their building blocks, and may give users an insight into the expected cost of workflow execution on cloud resources. When a large history of past runs can be observed, crude estimates such as the average execution time can easily be provided. We make the hypothesis that, for some algorithms, better estimates can be obtained by using the histories to learn regression models that predict execution time based on selected features of their inputs. We refer to this property as input predictability of algorithms. We are motivated by e-science workflows that involve repetitive training of multiple learning models. Thus, we verify our hypothesis on the specific case of the C4.5 decision tree builder, a well-known learning method whose training execution time is indeed sensitive to the specific input dataset, but in non-obvious ways. We use the case study to demonstrate a method for assessing input predictability. While this yields promising results, we also find that its more general applicability involves a trade off between the black-box nature of the algorithms under analysis, and the need for expert insight into relevant features of their inputs.
Emotional states discrimination in voice in secure environments

In this paper we present the use of emotions in security. The access control security systems by speech recognition can be complemented by means of an emotions discrimination system from the speech. An emotions recognition system has the advantage of increasing the security allowing identifying if an authorized user in the system is being coerced while he executes speech identification, reflecting his speech some nervousness. In this study we use four emotional states. We use three emotional states that would produce alarm in a security system: anxiety, hot anger and panic. and a fourth emotional state that corresponds to emotions set without interest for the security, like happiness, shame, sadness, boredom or the absence of emotion (neutral), and that we have called rest. In our simulations, the proposed emotions discrimination system has obtained an average accuracy about 79%.
Prognostic Value of DNA and mRNA E6/E7 of Human Papillomavirus in the Evolution of Cervical Intraepithelial Neoplasia Grade 2

Objective This study aimed at evaluating whether human papillomavirus (HPV) groups and E6/E7 mRNA of HPV 16, 18, 31, 33, and 45 are prognostic of cervical intraepithelial neoplasia (CIN) 2 outcome in women with a cervical smear showing a low-grade squamous intraepithelial lesion (LSIL). Methods This cohort study included women with biopsy-confirmed CIN 2 who were followed up for 12 months, with cervical smear and colposcopy performed every three months. Results Women with a negative or low-risk HPV status showed 100% CIN 2 regression. The CIN 2 regression rates at the 12-month follow-up were 69.4% for women with alpha-9 HPV versus 91.7% for other HPV species or HPV-negative status (P < 0.05). For women with HPV 16, the CIN 2 regression rate at the 12-month follow-up was 61.4% versus 89.5% for other HPV types or HPV-negative status (P < 0.05). The CIN 2 regression rate was 68.3% for women who tested positive for HPV E6/E7 mRNA versus 82.0% for the negative results, but this difference was not statistically significant. Conclusions The expectant management for women with biopsy-confirmed CIN 2 and previous cytological tests showing LSIL exhibited a very high rate of spontaneous regression. HPV 16 is associated with a higher CIN 2 progression rate than other HPV infections. HPV E6/E7 mRNA is not a prognostic marker of the CIN 2 clinical outcome, although this analysis cannot be considered conclusive. Given the small sample size, this study could be considered a pilot for future larger studies on the role of predictive markers of CIN 2 evolution.
Improving the Prediction of Survival in Cancer Patients by Using Machine Learning Techniques: Experience of Gene Expression Data: A Narrative Review

Background: Today, despite the many advances in early detection of diseases, cancer patients have a poor prognosis and the survival rates in them are low. Recently, microarray technologies have been used for gathering thousands data about the gene expression level of cancer cells. These types of data are the main indicators in survival prediction of cancer. This study highlights the improvement of survival prediction based on gene expression data by using machine learning techniques in cancer patients. Methods: This review article was conducted by searching articles between 2000 to 2016 in scientific databases and e-Journals. We used keywords such as machine learning, gene expression data, survival and cancer. Results: Studies have shown the high accuracy and effectiveness of gene expression data in comparison with clinical data in survival prediction. Because of bewildering and high volume of such data, studies have highlighted the importance of machine learning algorithms such as Artificial Neural Networks (ANN) to find out the distinctive signatures of gene expression in cancer patients. These algorithms improve the efficiency of probing and analyzing gene expression in cancer profiles for survival prediction of cancer. Conclusion: By attention to the capabilities of machine learning techniques in proteomics and genomics applications, developing clinical decision support systems based on these methods for analyzing gene expression data can prevent potential errors in survival estimation, provide appropriate and individualized treatments to patients and improve the prognosis of cancers.
Batch self-organizing maps for mixed feature-type symbolic data

The Kohonen Self-Organizing Map (SOM) is an unsupervised neural network method with a competitive learning strategy which has both clustering and visualization properties. In this paper, we present batch SOM algorithms based on adaptive and non-adaptive distances for mixed feature-type symbolic data that, for a fixed epoch, optimize a cost function. The performance, and usefulness of these SOM algorithms are illustrated with real mixed feature-type symbolic data sets.
Determinants of Price Differentials in Oklahoma Value-Added Feeder Cattle Auctions

Many value-added practices cannot be observed by feeder cattle buyers. Third-party verification can decrease market inefficiency associated with this asymmetric information. We evaluate the effectiveness of a verification program, the Oklahoma Quality Beef Network, in increasing received prices. We estimate the value of verification, weaning, vaccinating, certification and phenotypic traits of feeder cattle at Oklahoma auctions. Results indicate that the OQBN program adds $2.39 to $5.74/cwt. Vaccinating calves adds $1.44/cwt, and weaning calves adds $2.05/cwt. Differential values for lot size, average weight, hide color, frame size, conditioning, Brahman influence, gender and other characteristics are also reported.
ARV-based Array Grouping and Data Distribution in OpenMP/JIAJIA

In order to improve the performance of applications on OpenMP/JIAJIA, we present a new abstraction, Array Relation Vector (ARV), to describe the relation between the data elements of two consistent shared arrays accessed in one computation phase. Based on ARV, we use array grouping to eliminate the pseudo data distributing of small shared data and improve the page locality. Experimental results show that ARV-based array grouping can greatly improve the performance of applications with non-continuous data access and strict access affinity on OpenMP/JIAJIA cluster. For applications with small shared arrays, array grouping can improve the performance obviously when the processor number is small.
Corneal Topographic and Aberrometric Measurements Obtained with a Multidiagnostic Device in Healthy Eyes: Intrasession Repeatability

Purpose. To evaluate the intrasession repeatability of corneal curvature, eccentricity, and aberrometric measurements obtained with a multidiagnostic device in healthy eyes. Methods. This study enrolled 107 eyes of 107 patients ranging in age from 23 to 65 years. All of them underwent a complete anterior segment examination with the VX120 system (Visionix-Luneau Technologies, Chartres, France). Three consecutive measurements were obtained. The within-subject standard deviation (Sw), intrasubject precision (1.96 × Sw), and intraclass correlation coefficient (ICC) were calculated. Results. All Sw for corneal power measurements were below 0.26 D, with ICC above 0.982. The Sw for corneal astigmatism at different areas (3, 5, and 7 mm) was below 0.21 D, with ICC above 0.913. Concerning the axis of astigmatism, its Sw was below 11.27°, with ICC above 0.975. The Sw and ICC for corneal eccentricity were 0.067 and 0.957, respectively. The Sw and ICC for high-order aberration root mean square (RMS) were 0.048 µm and 0.901, respectively. For 3rd- and 4th-order aberrometric parameters, all Sw were below 0.037 µm and all ICC were higher than 0.84, except for quadrafoil RMS (ICC: 0.689). Conclusions. The multidiagnostic device evaluated is able to provide consistent measurements of corneal power, eccentricity, and third- and fourth-order aberrations in healthy eyes.
Cross Quality Distillation

We propose a technique for training recognition models when high-quality data is available at training time but not at testing time. Our approach, called Cross Quality Distillation (CQD), first trains a model on the high-quality data and encourages a second model trained on the low-quality data to generalize in the same way as the first. The technique is fairly general and only requires the ability to generate low-quality data from the high-quality data. We apply this to learn models for recognizing low-resolution images using labeled high-resolution images, non-localized objects using labeled localized objects, edge images using labeled color images, etc. Experiments on various fine-grained recognition datasets demonstrate that the technique leads to large improvements in recognition accuracy on the low-quality data. We also establish connections of CQD to other areas of machine learning such as domain adaptation, model compression, and learning using privileged information, and show that the technique is general and can be applied to other settings. Finally, we present further insights into why the technique works through visualizations and establishing its relationship to curriculum learning.
Marker based simple non-overlapping camera calibration

This paper describes a method for calibrating non-overlapping cameras in a simple way: using markers on the cameras. By adding an AR (Augmented Reality) marker to a camera, we can find the transformation between the fixed AR marker and the camera's center. With such information, relative pose of cameras can be easily found as long as the marker located on them is visible. Our method consists of the following two steps: (1) use an extra camera and a chessboard to find the transformation between the AR marker and the camera center. (2) Use the information from (1) and transformation between different markers to calibrate non-overlapping cameras. Compare to other non-overlapping calibration methods, our method can work with as less as one image and does not suffer from the degenerate cases.
Mycobacterium Strain and Type of Resistance in Pulmonary Tuberculosis Patients: A Missed Link in Iran’s National Tuberculosis Plan

Background: The Incidence of multi-drug resistant tuberculosis (MDR-TB) is constantly increasing. Objectives: This study aimed to clarify an important missed link in Iran's national TB plan. Patients and Methods: Through a 9-month period, all pulmonary TB patients diagnosed based on the national TB protocol, in Shiraz TB center, were selected and culture of TB colonies, drug susceptibility testing (DST), polymerase chain reaction (PCR) for detection of IS6110 gene, Isoniazid (INH) and Rifampin (RIF) tuberculosis resistance were done to collect data. Data were analyzed using SPSS. Results: In 92 included patients, (mean age 45.4 ± 15 years), DST results showed that 16 cases (17.4%) were resistant to INH, 19 (20.7 %) to RIF and 24 (26.1%) to both INH and RIF. Polymerase chain reaction identified IS6110 gene in 71 cases (77.2%) and gene mutations in 3 (3.2%) KatG, 3 (3. 2%) InhA, 9 (9.7%) both KatG and InhA, 17 (18.4%) rpoB and 20 (21.7%) in KatG, InhA and rpoB genes. Patients with INH-resistant tuberculosis were more than those with RIF-resistant (OR = 7.1). Conclusions: Findings of the present study show that 4 out of five new cases of pulmonary TB patients who were diagnosed based on the national TB protocol (clinical symptoms and acid fast bacilli staining) had IS6110 gene (MTB, Mycobactrium TB) and at least one-fifth of this group had A kind of Drug Resistant TB. Therefore, by using PCR ,as a complementary test, it could be possible to start 1st line anti-TB drugs for only MTB cases (up to 77% of the patients) and 2nd line drugs for MDR cases (15% of cases). This policy aims to achieve safety and better outcome for patients while saving human and financial resources in health care system.
Ziv-Zakai lower bound for UWB based TOA estimation with multiuser interference

The aim of this paper is to derive a Ziv-Zakai lower bound (ZZLB) for the time of arrival (TOA) estimation in single-path (SP) additive white Gaussian noise (AWGN) channels and in the presence of multiuser interference (MUI). Time-hopping pulse position modulated (TH-PPM) ultra-wideband (UWB) signals are considered. Following a classical approach and extending it for the scenario under consideration, we compute the ZZLB by converting the related estimation problem into a binary detection one. To obtain the bit error rate (BER) of the corresponding detection problem, we both consider the exact handling of the MUI and the modeling of the MUI by means of a Gaussian approximation (GA). We compare the performance of the single-user maximum likelihood (ML) TOA estimator with the bounds.
Qualitative fusion-based traffic signal preemption

Using Intelligent Transportation Systems to provide emergency vehicles a green light at intersections can reduce driver confusion, reduce conflicts, and improve emergency response times. Nowadays, the technology of wireless sensor networks can solve many problems and can offer a good management of the crossroad. In this paper, we develop a new approach based on the technique of clustering and the graphical possibilistic fusion modeling. So, the proposed model is elaborated in three phases. The first one consists to decompose the environment into clusters, following by the local fusion performed inside the cluster then a global fusion is applied at level of head clusters. Finally, we will show some experimental results that proves the efficiency of our proposed approach.
Event Detection from Millions of Tweets Related to the Great East Japan Earthquake Using Feature Selection Technique

Social media offers a wealth of insight into howsignificant events -- such as the Great East Japan Earthquake, the Arab Spring, and the Boston Bombing -- affect individuals. The scale of available data, however, can be intimidating: duringthe Great East Japan Earthquake, over 8 million tweets weresent each day from Japan alone. Conventional word vector-based event-detection techniques for social media that use Latent SemanticAnalysis, Latent Dirichlet Allocation, or graph communitydetection often cannot scale to such a large volume of data due to their space and time complexity. To alleviate this problem, we propose an efficient method for event detection by leveraging a fast feature selection algorithm called CWC. While we begin withword count vectors of authors and words for each time slot (inour case, every hour), we extract discriminative words from eachslot using CWC, which vastly reduces the number of features to track. We then convert these word vectors into a time series of vector distances from the initial point. The distance betweeneach time slot and the initial point remains high while an eventis happening, yet declines sharply when the event ends, offeringan accurate portrait of the span of an event. This method makes it possible to detect events from vast datasets. To demonstrateour method's effectiveness, we extract events from a dataset ofover two hundred million tweets sent in the 21 days followingthe Great East Japan Earthquake. With CWC, we can identifyevents from this dataset with great speed and accuracy.
2 Two Strands of Development Leading to Computers

The common view that the notion of a Turing machine is directl y re evant to AI is criticised. It is argued that computers are the result of a convergence of two str ands of development with a long history: development of machines for automating various physical pr ocesses and machines for performing abstract operations on abstract entities, e.g. doing numer ical calculations. Various aspects of theseoperations on abstract entities, e.g. doing numer ical calculations. Various aspects of these developments are analysed, along with their relevance to AI , and the similarities between computers viewed in this way and animal brains. This comparison depend s o a number of distinctions: between energy requirements and information requirements of machi nes, between ballistic and online control, between internal and external operations, and between vari ous kinds of autonomy and self-awareness. The ideas are all intuitively familiar to software engineer s, though rarely made fully explicit. Most of this has nothing to do with Turing machines or most of the ma thematical theory of computation. But it has everything to do with both the scientific task of und erstanding, modelling or replicating human or animal intelligence and the engineering applicati ons of AI, as well as other applications of
Extracting Keyphrases to Represent Relations in Social Networks from Web

Social networks have recently garnered considerable interest. With the intention of utilizing social networks for the Semantic Web, several studies have examined automatic extraction of social networks. However, most methods have addressed extraction of the strength of relations. Our goal is extracting the underlying relations between entities that are embedded in social networks. To this end, we propose a method that automatically extracts labels that describe relations among entities. Fundamentally, the method clusters similar entity pairs according to their collective contexts in Web documents. The descriptive labels for relations are obtained from results of clustering. The proposed method is entirely unsupervised and is easily incorporated with existing social network extraction methods. Our experiments conducted on entities in researcher social networks and political social networks achieved clustering with high precision and recall. The results showed that our method is able to extract appropriate relation labels to represent relations among entities in the social networks.
On the Need to Divide the Signature Creation Environment

Electronic signatures have been legally recognized as the key element for boosting e-commerce under secure conditions. Several legislations throughout the world establish electronic signatures as legally equivalent to hand-written signatures, assigning them the property of evidence in legal proceedings. In addition, international standards define electronic signatures as non-repudiation evidence respecting the signed information. Bearing this in mind, it is obvious that the reliability of electronic signatures is paramount. However, the results show that several attacks on signature creation environments are feasible and easy to perform. As a result, the reliability of evidence is drastically undermined. We claim that the division of the environment becomes the most effective solution to counteract current threats. The formal proofs that support this statement are given along with an overview of the legal background and a summary of main potential threats on signature creation environments.