Miracle-dz/UniFAR-FTUs huggingface dataset:
Relationship between depression and anxiety symptoms in studies conducted in Turkey: a meta-analysis study

Relationship between depression and anxiety symptoms in studies conducted in Turkey: a meta-analysis study Objective: This meta-analysis study aimed to test the relationship between depression and anxiety symptoms by analyzing the studies on depression and anxiety symptoms in Turkey. The study was carried out in two stages to determine the effect of depression sypmtoms on the anxiety sypmtoms, and the moderators that could affect the mean effect size obtained in this study. Method: In order to determine the studies that will be included in the meta-analysis, a search was made in the National Higher Education Council’s National Thesis Center, ULAKBIM Social and Humanities Database, ULAKBIM Medical Data Base and Google Academic Database. In this phase, all studies including depression and anxiety were recorded by using the terms related with anxiety and depression. The screening process was reduced to title, keyword and summary fields to create study pool (143 studies). Subsequently, with the detailed examination of studies, 74 studies were excluded and 69 studies were included in the sample in accordance with the study criteria. The Pearson Correlation Coefficient (r) was calculated as the effect size in the analysis and the random effect model was used. Results: In the study, the mean effect size depression on anxiety was calculated as 0.53, with a lower limit value of 0.48 and an upper limit value of 0.58. In addition, there was a difference between the size of effect observed in the study and the size of the virtual effect generated according to the random effects model aimed to correct the effect caused by the publication bias. Conclusion: Confidence intervals of 69 studies included in meta-analysis study were narrow, meaning that the decisions which will be made taking into consideration the data obtained from these studies are reliable. It has been found that depression has a positive effect on a wide range of anxiety. According to these findings, as depression levels increase, anxiety levels also increase, or as depression levels decrease, anxiety levels decrease. Overall, moderator variables of anxiety type, anxiety scale, and depression scale affect the relationship between depression and anxiety of individuals. Anxiety and depression levels can vary according to these moderator variables.
OCR correction of documents generated during Argentina's national reorganization process

In this paper we present work done to automatically correct OCRed text from a digital archive setup to preserve documents created during Argentina's 1976-1983 dictatorship, also known as the National Reorganization Process (Proceso de Reorganización Nacional). These documents are quite unique in their structure, content and state of preservation, making it a challenging corpus. We adopted a post-processing approach, in which we create a specific dictionary and correct the OCRed text based on edit distances and typographical characteristics of the text. On a representative test set we were able to correct about 30% of the OCR errors.
Gait recognition using smartphone

Gait recognition on smartphones could be considered as one of the most user-friendly biometric modalities. The main benefit of gait recognition is that it is an unobtrusive biometric modality, since it requires little interaction with the user. Users would only have to carry the sensor device and walk as normally. Its unobtrusiveness make it suitable for a user-friendly access system. Up to date, most studies on gait recognition have been done using dedicated hardware acquisition sensors. Nevertheless, one possible solution for gait recognition is using sensors embedded on smartphones. This paper compares the performance of four state-of-art algorithms on a smartphone. These algorithms have already been tested on dedicated hardware but not in a commercial phone. For such purpose, a database using a smartphone as acquisition device has been obtained. State-of-art gait recognition algorithms have been tested on this data base, as well as a new cycle detection algorithm which has been designed to have the same starting point. As a result, the algorithms have shown EER ranging from 16.38% to 29.07%, These EERs are significantly higher than the ones obtained in dedicated hardware which ranged from 5.7% to 13%.
Identification of a Severe Acute Respiratory Syndrome Coronavirus-Like Virus in a Leaf-Nosed Bat in Nigeria

ABSTRACT Bats are reservoirs for emerging zoonotic viruses that can have a profound impact on human and animal health, including lyssaviruses, filoviruses, paramyxoviruses, and severe acute respiratory syndrome coronaviruses (SARS-CoVs). In the course of a project focused on pathogen discovery in contexts where human-bat contact might facilitate more efficient interspecies transmission of viruses, we surveyed gastrointestinal tissue obtained from bats collected in caves in Nigeria that are frequented by humans. Coronavirus consensus PCR and unbiased high-throughput pyrosequencing revealed the presence of coronavirus sequences related to those of SARS-CoV in a Commerson's leaf-nosed bat (Hipposideros commersoni). Additional genomic sequencing indicated that this virus, unlike subgroup 2b CoVs, which includes SARS-CoV, is unique, comprising three overlapping open reading frames between the M and N genes and two conserved stem-loop II motifs. Phylogenetic analyses in conjunction with these features suggest that this virus represents a new subgroup within group 2 CoVs. IMPORTANCE Bats (order Chiroptera, suborders Megachiroptera and Microchiroptera) are reservoirs for a wide range of viruses that cause diseases in humans and livestock, including the severe acute respiratory syndrome coronavirus (SARS-CoV), responsible for the global SARS outbreak in 2003. The diversity of viruses harbored by bats is only just beginning to be understood because of expanded wildlife surveillance and the development and application of new tools for pathogen discovery. This paper describes a new coronavirus, one with a distinctive genomic organization that may provide insights into coronavirus evolution and biology. Bats (order Chiroptera, suborders Megachiroptera and Microchiroptera) are reservoirs for a wide range of viruses that cause diseases in humans and livestock, including the severe acute respiratory syndrome coronavirus (SARS-CoV), responsible for the global SARS outbreak in 2003. The diversity of viruses harbored by bats is only just beginning to be understood because of expanded wildlife surveillance and the development and application of new tools for pathogen discovery. This paper describes a new coronavirus, one with a distinctive genomic organization that may provide insights into coronavirus evolution and biology.
Impact of Cognitive Distractions on Drivers’ Anticipation Behavior in Vehicle-bicycle Conflict Situations

Overall, the rate of vehicle-bicycle collisions is continually increasing. In the United States alone, bicyclist fatalities contributed to 2.3 percent of all crash related fatalities in 2015. In most of these cases, crashes occur due to distracted drivers who are unable to correctly anticipate the bicyclists at the hazardous locations on the roadways such as, intersections and curves. The objective of the current study is to contribute to the divisive literature surrounding cell phone use while driving by specifically measuring, the effects of a secondary mock cell phone task on hazard anticipation performance across common vehicle-bicycle conflict situations. Two groups of 20 drivers each, navigated seven unique scenarios on a driving simulator while being monitored by an eye tracker. One group of participants performed a hands free mock cellphone task while driving, while the second group drove without any additional tasks outside of the primary task of driving. Analysis of the proportion of anticipatory glances using a logistic regression model revealed a significant main effect of the mock cellphone task at reducing the proportion of such glances made by the drivers towards potential bicyclist threats on the roadway.
CNN-Based Methods for Object Recognition With High-Resolution Tactile Sensors

Novel high-resolution pressure-sensor arrays allow treating pressure readings as standard images. Computer vision algorithms and methods such as convolutional neural networks (CNN) can be used to identify contact objects. In this paper, a high-resolution tactile sensor has been attached to a robotic end-effector to identify contacted objects. Two CNN-based approaches have been employed to classify pressure images. These methods include a transfer learning approach using a pre-trained CNN on an RGB-images dataset and a custom-made CNN (TactNet) trained from scratch with tactile information. The transfer learning approach can be carried out by retraining the classification layers of the network or replacing these layers with an SVM. Overall, 11 configurations based on these methods have been tested: eight transfer learning-based, and three TactNet-based. Moreover, a study of the performance of the methods and a comparative discussion with the current state-of-the-art on tactile object recognition is presented.
How Do I Look? Publicity Mining From Distributed Keyword Representation of Socially Infused News Articles

Previous work on opinion mining and sentiment analysis mainly concerns product, movie, or literature reviews; few applied this technique to analyze the publicity of person. We present a novel document modeling method that utilizes embeddings of emotion keywords to perform reader’s emotion classiﬁcation, and calculates a publicity score that serves as a quantiﬁable measure for the publicity of a person of interest. Experiments are conducted on two Chinese corpora that in total consists of over forty thousand users’ emotional response after reading news articles. Results demonstrate that the proposed method can outperform state-of-the-art reader-emotion classiﬁcation methods, and provide a substantial ground for publicity score estimation for candidates of political elections. We believe it is a promising direction for mining the publicity of a person from online social and news media that can be useful for propaganda and other purposes.
Predicting Next Changes at the Fine-Grained Level

Changing source code is not an easy task. Developers occasionally change source code incorrectly. Such mistakes entail additional cost in having to reedit the source code correctly, and repeated changes themselves can be a vulnerability to software quality. We are conducting research into realizing automated code changing as a countermeasure for human errors. As the first step of this research, we propose a technique to predict the types of program elements deleted and added in a next change to Java methods. This technique is designed to support developers in deciding how to change source code after they have identified a method to be changed. We evaluated predictions using the proposed technique with two thresholds, which are sizes of source code changes. For predictions with the smaller threshold where only a single type of program element was added or deleted, the accuracy of the proposed technique was 74% -- 85%. However, for the larger threshold, where 5 or fewer types of program elements were added or deleted, the accuracy was 44% -- 48%.
New Challenges in Data Integration: Large Scale Automatic Schema Matching

Today schema matching is a basic task in almost every data intensive distributed application, namely enterprise information integration, collaborating web services, ontology based agents communication, web catalogue integration and schema based P2P database systems. There has been a plethora of algorithms and techniques researched in schema matching and integration for data interoperability. Numerous surveys have been presented in the past to summarize this research. The requirement for extending the previous surveys has been created because of the mushrooming of the dynamic nature of these data intensive applications. Indeed, evolving large scale distributed information systems are further pushing the schema matching research to utilize the processing power not available in the past and directly increasing the industry investment proportion in the matching domain. This article reviews the latest application domains in which schema matching is being utilized. The paper gives a detailed insight about the desiderata for schema matching and integration in the large scale scenarios. Another panorama which is covered by this survey is the shift from manual to automatic schema matching. Finally the paper presents the state of the art in large scale schema matching, classifying the tools and prototypes according to their input, output and execution strategies and algorithms.
Local: a model geared towards ubiquitous learning

The increasing use of mobile devices and the dissemination of wireless networks have stimulated mobile and ubiquitous computing research. In this context, education is being considered one of the main application areas. New pedagogical opportunities are created through the use of location systems to track learners, and through context awareness support. This paper proposes a model to explore these opportunities using location information and context management as learning support tools. This model, called LOCAL, was conceived for small scale learning environments, but can be applied in large-scale as well. The model was implemented and the initial results show its utility to assist the teaching and learning processes.
Achievable rates for multiple descriptions

Consider a sequence of independent identically distributed (i.i.d.) random variables X_{l},X_{2}, \cdots, X_{n} and a distortion measure d(X_{i},X_{i}) on the estimates X_{i} of X_{i} . Two descriptions i(X)\in \{1,2, \cdots ,2^{nR_{1}\} and j(X)\in \{1,2, \cdots,2^{nR_{2}\} are given of the sequence X=(X_{1}, X_{2}, \cdots ,X_{n}) . From these two descriptions, three estimates (i(X)), X2(j(X)) , and \hat{X}_{O}(i(X),j(X)) are formed, with resulting expected distortions E \frac{1/n} \sum^{n}_{k=1} d(X_{k}, \hat{X}_{mk})=D_{m}, m=0,1,2. We find that the distortion constraints D_{0}, D_{1}, D_{2} are achievable if there exists a probability mass distribution p(x)p(\hat{x}_{1},\hat{x}_{2},\hat{x}_{0}|x) with Ed(X,\hat{x}_{m})\leq D_{m} such that R_{1}>I(X;\hat{X}_{1}), R_{2}>I(X;\hat{X}_{2}), where I(\cdot) denotes Shannon mutual information. These rates are shown to be optimal for deterministic distortion measures.
Gender-dependent and speaker-dependent speech enhancement

Our work introduces a speech enhancement technique that can explicitly incorporate prior information about the gender or speaker time-frequency characteristics in its formalism. We approximate the multimodal, clean speech linear spectrum magnitude with a mixture of Gaussians pdfs using the Expectation-Maximization algorithm (EM). Subsequently. we apply the Bayesian inference framework to the degraded spectral coefficients and by employing Minimum Mean Square Error Estimation (MMSE) we derive a closed fonn solution for the spectral magnitude estimation task adapted to the spectral characteristics and noise variance of each band. We suggest that 2–3 minutes of phonetically balanced non-degraded gender or speaker dependent speech is adequate to tune our algorithm. We demonstrate the benefit of using an enhancement technique tailored to a specific gender or speaker and propose its use in cases where message ambiguity is of critical importance. We evaluate of our algorithm using Lynx helicopter and White Gaussian noise on the task of improving the quality of speech and in combination with a speech coder and demonstrate its robustness at very low SNRs. Implementation code is available at: http://slt.wcl. ee.upatras. gr/potamitis/index.html
Effect of Cerium Precursor in the Synthesis of Ce-MCM-41 and in the Efficiency for Liquid-Phase Oxidation of Benzyl Alcohol

Understanding the effects of synthetic parameters in the catalytic activity of heterogeneous catalysts is of utmost importance when aiming for optimal reaction conditions. Hence, we disclose in this work the synthesis and characterization of cerium-modified MCM-41 materials. In addition, it was observed for the first time, differences in catalytic activity when using different cerium synthetic precursors: CeCl3·7H2O and Ce(NO3)3·6H2O (Ce-MCM-Cl and Ce-MCM-NO3, respectively). A mechanism for cerium incorporation in MCM-41 was proposed, where [Ce(OH)3] species were hydrogen bonded to silicate anions, forming framework Ce-O-Si bonds during condensation and, consequently, causing distortion of the typical hexagonal mesophase. It was also observed that Ce(OH)3 formed aggregated layers with template assemblies during synthesis, resulting in non-framework CeO2 species on the MCM-41 surface after calcination. These CeO2 species were preferentially formed for Ce-MCM-NO3 and were attributed to the nitrate ions’ strong binding to template molecules. In the solvent free liquid-phase oxidation of benzyl alcohol (BzOH), Ce-MCM-Cl achieved better BzOH conversions and benzaldehyde (BzD) yields, while Ce-MCM-NO3 offered increased BzD selectivity. The catalysts’ reusability was also studied over three catalytic runs, where Ce-MCM-NO3 was more resistant than Ce-MCM-Cl towards deactivation. The observed catalytic behavior shows the importance of metal precursors in the obtainment of materials with desirable final properties.
Fast and Robust Mid-Sagittal Plane Location in 3D MR Images of the Brain

Extraction of the mid-sagittal plane (MSP) is an important s tep for brain image registration and asymmetry analysis. We present a fast MSP extraction method for 3D MR images, which is based on automatic segmentation of the brain and on heuristic maximi zation of cerebro-spinal fluid within the MSP. The method is shown to be robust to severe anatomical asy mmetries between the hemispheres, caused by surgical procedures and lesions. The experiments used 64 MR images (36 pathological, 20 healthy, 8 synthetic) and the method found an acceptable app roximation of the MSP in all images with a mean time of 60.0 seconds per image.
Support vector machines, Decision Trees and Neural Networks for auditor selection

The selection of a proper auditor is driven by several factors. Here, we use three data mining classification techniques to predict the auditor choice. The methods used are Decision Trees, Neural Networks and Support Vector Machines. The developed models are compared in term of their performances. The wrapper feature selection technique is used for the Decision Tree model. Two models reveal that the level of debt is a factor that influences the auditor choice decision. This study has implications for auditors, investors, company decision makers and researchers.
Variable Rate LDPC Codes for Wireless Applications

This paper deals with the problem of designing good rate-variable LDPC codes for wireless applications, where fast link adaptation is required in order to maximize the system efficiency. In particular, we show that the so-called "pseudo-puncturing" strategy, recently introduced, can be used as a valid alternative to more conventional solutions, by ensuring good performance while maintaining limited complexity
Classifying Articles as Fake or Real Language and Statistics Spring 2007

Is it real or fake? That is the question. A discrimination task that may seem trivial to humans can be extremely complicated for a machine. Humans make use of a “makes sense” feature, which relies on world knowledge including Linguistics, to distinguish between real and fake articles. Unfortunately such a feature does not exist for machines yet. As such, to solve a relatively mundane problem for humans, it is necessary to rely on varied and sometimes complicated statistical and linguistic techniques to attempt to allow a machine to make such a distinction. Our goal in this project was to determine what combination of various different statistical and linguistic features would best discover and exploit the deficiencies in a convential trigram model that was used to generate fake articles from a broadcast news vocabulary. We used features that were statistically (backward bigrams for example), syntactically (parsing), and semantically (topic modeling) motivated to try to determine deficiencies in the trigram model. By exploiting these deficiences (namely the inability to track long-distance relations), we hoped to design a program that could discriminate between real Broadcast News articles and articles generated by the trigram model. The rest of this project paper is organized as follows: §2 describes some preprocessing we did to the data. §3 describes the features that we used to construct a discriminating model. §4 describes the tools we used to actually construct the model. §5 describes the results we had with the best model.
Evaluating Dynamic Resource Allocation Strategies in Virtualized Data Centers

Virtualization technology allows a dynamic allocation of VMs to servers. It reduces server demand and increases energy efficiency of data centers. Dynamic control strategies migrate VMs between servers in dependence to their actual workload. A concept that promises further improvements in VM allocation efficiency. In this paper we evaluate the applicability of DSAP in a deterministic environment. DSAP is a linear program, calculating VM allocations and live-migrations on workload patterns known a priori. Efficiency is evaluated by simulations as well as an experimental test bed infrastructure. Results are compared against alternative control approaches that we studied in preliminary works. Our findings are, dynamic allocation can reduce server demand at a reasonable service quality. Countermeasures are required to keep the number of live-migrations under control.
Weight based Precision oriented metrics for Multilingual Information Retrieval System

Multilingual Information Retrieval System (MLIR) provides users to apply query in one language and retrieve the resultant documents in more than one language. Assessing the performance of these systems is a challenging task. Amongst the available metrics Precision is considered as the basic measure for assessing the performance of IR/MLIR systems. In the literatures a few metrics are available to analyse the performance of MLIR System. Therefore in this paper Precision oriented metrics are proposed based on the weight of languages and the results are very promising and demonstrated their existence in MLIR research domain.
Karush-Kuhn-Tuker type conditions for optimality of non-smooth multiobjective semi-infinite programming

Abstract In this paper, for a nonsmooth semi-infinite multiobjective programming with locally Lipschitz data, some weak and strong Karush-KuhnTucker type optimality conditions are derived. The necessary conditions are proposed under a constraint qualification, and the sufficient conditions are explored under assumption of generalized invexity. All results are expressed in terms of Clarke subdifferential.
Of Massive Static Analysis Data

The Software Assurance Metrics and Tool Evaluation (SAMATE) project at the National Institute of Standards and Technology (NIST) has organized four Static Analysis Tool Expositions (SATE). SATE is designed to advance research in static analysis tools that find security-relevant defects in source code. Briefly, participating tool makers run their tools on a set of programs. Researchers led by NIST analyze the tool outputs. The results and experiences are reported at a workshop. These expositions have accumulated large amounts of data. This collection allowed for the development and validation of practical metrics in regard to static analysis tool effectiveness and independence. In this paper, we discuss the role of the data in determining which metrics can be derived. Specifically, we detail the three characteristics test data should exhibit and explain why the data we use express each combination of two out of these three properties.