Miracle-dz/UniFAR-FTUs huggingface dataset:
Estimating wage equations for Hungarian higher-education gradu- ates by Peter Galasi Introduction

The paper investigates the labour market output of Hungarian higher education in terms of wages based on samples from two cohorts of Hungarian career-beginners. It focuses on five elements of the problem: how 1. university education as compared to college degree, 2. in-school labour market experience, 3. types of education, 4. learning foreign language and information technology (IT) affect school-leavers’ wages, and 5. whether cost-priced (statefunded) education plays a role in wage determination. First, changes on the Hungarian labour market during the ’90s, and their consequences for the labour market situation of employees with higher-education diploma are considered. Second, data and variables, and estimation strategy are discussed. Then estimation results are presented, and the final section concludes
Cluster-based Particle Swarm Algorithm for Solving the Mastermind Problem

—In this paper we present a metaheuristic algorithm that is inspired by Particle Swarm Optimization. The algorithm maintains a set of intercommunicating particle clusters and equips each particle with a specialized local search function. To demonstrate the effectiveness of the algorithm, we analyze its ability to solve Mastermind codes and compare its performance with other algorithms found in the literature. For the Master-mind problem, we have found that our algorithm is comparable to other algorithms for small problem sizes, but has much more efﬁcient scaling behavior.
A Low Voltage Grid Monitoring System using Wireless Mesh Networks

—This work develops a LV grid monitoring solution using a Wireless Mesh Network. A Wireless Mesh Node is developed, which runs the Contiki OS on a Atmega AVR. The network uses 6LoWPAN header compression on an IPv6 network. The sensor implements a standards compliant DLMS/COSEM server with over 50 OBIS objects. The reliability of the solution has been demonstrated in a real-life test-bed in Batalha, Portugal and a success ratio of over 99% was obtained.
NONPARAMETRIC ESTIMATOR OF FALSE DISCOVERY RATE BASED ON BERNˇ IN POLYNOMIALS STE ̆

Under a local dependence assumption about the p-values, an estimator of the proportion ˇ0 of true null hypotheses, having a closed-form expression, is derived based on Bernštĕın polynomial density estimation. A nonparametric estimator of false discovery rate (FDR) is then obtained. These estimators are proved to be consistent, asymptotically unbiased, and normal. Confdence intervals for ˇ0 and the FDR are also given. The usefulness of the proposed method is demonstrated through simulations and its application to a microarray dataset.
Edge formation in Social Networks to Nurture Content Creators

Social networks act as major content marketplaces where creators and consumers come together to share and consume various kinds of content. Content ranking applications (e.g., newsfeed, moments, notifications) and edge recommendation products (e.g., connect to members, follow celebrities or groups or hashtags) on such platforms aim at improving the consumer experience. In this work, we focus on the creator experience and specifically on improving edge recommendations to better serve creators in such ecosystems. The audience and reach of creators – individuals, celebrities, publishers and companies – are critically shaped by these edge recommendation products. Hence, incorporating creator utility in such recommendations can have a material impact on their success, and in turn, on the marketplace. In this paper, we (i) propose a general framework to incorporate creator utility in edge recommendations, (ii) devise a specific method to estimate edge-level creator utilities for currently unformed edges, (iii) outline the challenges of measurement and propose a practical experiment design, and finally (iv) discuss the implementation of our proposal at scale on LinkedIn, a professional network with 645M+ members, and report our findings.
ISSA: An Integrated System for Sequence Analysis

ISSA is a system integrating different sequential mining tasks on the basis of a common unifying framework: a Galois lattice adapted to ordered contexts. This paper surveys some results obtained within this framework; we will see that, after characterizing the Galois lattice for a set of input sequences, it is possible to calculate either partial orders, or frequent sequential patterns, or also a new notion of association rules with order by just traversing its nodes. We describe a decision support tool that brings into practice all the theoretical results. Our aim is to provide a system based on a sound theory that can be used for the analysis of sequences from different contexts.
IRIT at TREC 2019: Incident Streams and Complex Answer Retrieval Tracks

This paper presents the approaches proposed by the IRIS team of the IRIT laboratory for the TREC Incident Streams and Complex Answer Retrieval tracks. The Incident Streams (IS) track aims to categorize and prioritize tweets in disaster situation to assist emergency service operators. In our participation, we applied supervised learning techniques based on features extracted from tweets. Then, the 2019 edition of the Complex Answer Retrieval (CAR) track aims to answer complex questions expressed as outlines using paragraphs from Wikipedia. In our participation, we used the Terrier retrieval system to rank paragraphs for each section of the outline and keep the most relevant according to three diﬀerent strategies.
Secondhand Smoking and Poor Mental Health in Adolescents

In Korea, the prevalence of depression is increasing in adolescents and the most common cause of death of adolescents has been reported as suicide. At a time of increasing predicament of mental health of adolescents, there are few studies on whether secondhand smoking is associated with mental health in adolescents. The objective of this study was to determine whether exposure to secondhand smoke is associated with mental health-related variables, such as depression, stress, and suicide, in Korean adolescents. Data from the eleventh Korea youth risk behavior web-based survey, a nationally representative survey of 62,708 participants (30,964 males and 31,744 females), were analyzed. For students of aged 12 to 18 years, extensive data including secondhand smoking, mental health, sociodemographic variables, and physical health were collected. Chi-square analysis, multiple logistic regression analysis and ordered logistic regression analysis were performed to estimate the association and dose-response relation between secondhand smoking and mental health. Compared with the non-exposed group, the odds ratios (OR) of depression, stress, suicidal ideation, suicidal planning and suicidal attempt in the secondhand smoking exposed group were 1.339, 1.192, 1.303, 1.437 and 1.505, respectively (all P < 0.001). When subjects were classified into two secondhand smoke exposure groups, with increasing secondhand smoking experience, higher was the OR for each mental health related variable, in a dose-response relation. Our findings suggest that secondhand smoking is associated with poor mental health such as depression, stress, and suicide, showing a dose-response relation in Korean adolescents.
Learning Conversational Web Interfaces

Automating user tasks with natural language instructions, such as booking a movie ticket, while keeping them engaged is a nontrivial and open problem. Previous work has focused on a particular scenario where users need to give entire instructions before a task can be handled. Aside from the difﬁculty of uttering a long instruction, this setup is also less realistic as the instructions could depend on future observations and needs to be delayed. In this work, we introduce the dialogue-based web navigation problem where the objective is to fulﬁll a hidden user goal by having multi-turn conversations with users and navigating a given web page simultaneously. We study joint learning of dialogue and navigation policies using reinforcement learning with actor-critic method. An architecture where user dialogue and web page observations are attentively encoded into policy actions is developed. We build a novel dialogue-based web environment by wrapping a user simulator and the Fandango movie ticket booking website into a single environment. We evaluate the performance of our models and discuss their biases and shortcomings.
Running Head: EXECUTIVE FUNCTION MOOD AND AROUSAL 1 The effects of musical mood and arousal on executive function performance By

We perform a variety of our daily tasks while listening to background music. There has been a growing interest in psychology and cognitive neuroscience to determine how certain properties of music can influence cognition, specifically the mood and arousal a piece of music induces on its listener. There has been research conducted on musical mood and arousal’s effects on visuospatial, attentional and memory tasks. However the effect of musical mood and arousal on executive functions remains a novel area of investigation. In the current study, we recruited 100 undergraduate participants and assigned them to five musical listening groups: High Arousal Positive Mood (HAP) music (n=20); High Arousal Negative Mood (HAN) music (n=20); Low Arousal Positive Mood (LAP) music; Low Arousal Negative Mood (LAN) and a silent (control) condition (n=20). All participants completed three executive function tasks: the Double Stroop Task which measured conflict monitoring, the Stimulus Detection Task which measured selective attention, and the Simon Task which measured working memory and conflict monitoring. We found that participants were performed as accurately regardless of the musical listening group they were assigned to, on all three of the tasks. However participants that listened to HAP music had significantly faster reaction times on the Double Stroop Task, and the Simon Task; both of which measure conflict monitoring. The results of our study suggest that listening to HAP music may have an effect on attenuating conflict costs during conflict monitoring tasks. Future studies should explore if these results can be repeated in other conflict monitoring paradigms. EXECUTIVE FUNCTION MOOD AND AROUSAL 3
Applying the Tag Technique for Cooperation in Peer-to-Peer Networks

— We propose a simple algorithm that allows selfish adaptive peers in a network to maintain high levels of cooperation whilst performing the collective task of file sharing. The algorithm is adapted from novel “tag” models of cooperation that do not rely on explicit reciprocity, reputation or trust mechanisms. A sequence of three simulation models is presented - starting with an abstract model of tag-based cooperation (TagWorld) and finishing with a peer-to-peer file-sharing model (FileWorld) that puts the technique to work. From analysis of extensive computer simulations, we demonstrate the technique to be scalable, robust and decentralized – requiring no central servers or authorities. The simplicity of the algorithm means peers do not need to store additional trust information about other nodes or to perform significant additional processing.
Building Customer Relationship for Gaining Customer Loyalty in the Pharmaceutical Industry

As the competition in the pharmaceutical industry becoming more intense, building collaborative relationships with patients, physicians, and other stakeholders are the key to sustain demand for pharmacy services on a long-term basis. Thus, the purpose of this study is to identify and understand the importance of key dimension used to assess the success of customer relationship and how it affects customer loyalty in the pharmaceutical industry. Data was collected from 325 customers who purchase their needs from pharmacies throughout Kuching. A reliability and validity analysis suggests that Trust have a significant relationship to customer loyalty and a subsequent multiple regression analysis revealed that Conflict Handling is the most important dimensions of relationship marketing to customer loyalty. This study finding is to gain better understanding of the dimension of relationship marketing as perceived by the customers in pharmaceutical industry.
Temporal Concept Localization within Video using a Mixture of Context-Aware and Context-Agnostic Segment Classiﬁers

This paper describes my solution to the 3rd YouTube-8M Video Understanding Challenge. To deal with the limited number of annotated segments, video-level models were pre-trained on the YouTube-8M frame-level features dataset to create meaningful video representations from frames. The weights of the two models were used to create two types of segment classiﬁers: context-aware and context-agnostic. The created classiﬁers were ﬁne-tuned on the YouTube-8M segment-rated frame-level features dataset, with negative label mining, segment expansion, and a custom weighted cross-entropy loss. The ﬁnal ensemble was created by taking arithmetic means of the class probabilities from six context-aware and six context-agnostic classiﬁers. It achieved seventh place in the private leaderboard. From the experiment results, a mixture of the two types of the classiﬁer has been shown to have stronger performances on both the public and private test dataset. The code is publicly available at https: //github.com/ceshine/yt8m-2019 .
Detecting Intruders in the Web Using CNN RNN Algorithms in Deep Learning

-Cyber security is more important in many fields such as government, military etc., because the government, military, corporate, financial and medical organizations collect process and store unprecedented amounts of data on computers and other devices. A significant portion of that data can be sensitive information whether that be intellectual, financial data, personal information or other types of data for which unauthorized access or exposure could have negative consequences. In our approach is we can find the attack types using in deep learning methods. In this paper we develop a desktop application which identifies the attack occurred in the web application and sends a notification to the web server system
THE PERCEPTION AND INTENTION OF THE MALAYSIAN ENFORCEMENT AGENCIES’ EMPLOYEES ON THE WHISTLEBLOWER PROTECTION ACT

This study aims to measure the level of perception and intention of the Malaysia Enforcement Agencies’ employees on the Whistleblower Protection Act 2010 (WPA). The WPA was enforced in 10 June 2010 and intended to combat the wrongdoings in the public and private sectors, as well as facilitate the management of the whistleblowing protection matters. The enforcement agencies that involved in this study were Royal Malaysian Police, Royal Malaysian Customs, Immigration Department of Malaysia, Road Transport Department, and Malaysian Anti-Corruption Commission. A total of 300 questionnaires were distributed to the five agencies with equal number of questionnaire (60). However, only 185 are returned, with a percentage of 61.67%. Based on the statistical test results, the key findings of the study reveal that there are positive correlations between ethics and locus of control towards perception and intention of the Malaysian Enforcement Agencies’ employees on WPA 2010. Although there are a lot of research that have been conducted in whistleblowing, but none from the perspective of the Enforcement Agencies’ employees; who are supposedly enforced this Act in Malaysia. Thus, this paper aimed to contribute to the extant literature by choosing the employees of the Enforcement Agencies as our respondents. Relevant authorities and agencies may use the findings from this study to formulate the strategies to promote and enhance the enforcement of the WPA to be more effective.
WHO EMRO | Validation of Persian version of WHOQOL-HIV BREF questionnaire in Islamic Republic of Iran

The aim of this cross-sectional study was to validate the first Persian version of the WHOQOL-HIV BREF questionnaire. The study sample comprised 61 patients regularly attending the outpatient infectious disease clinic consultation centre for patients with behavioural disorders in 2013–2014. The internal consistency, content related validity and reliability of WHOQOL-HIV BREF were evaluated. Content validity was quantified using the
Changes in levels of hydrogen peroxide and phenolic compounds in grapevine latent buds during the annual cycle

The quantitative analysis of phenolic compounds and of hydrogen peroxide (H2O2) amounts in the latent buds of the vine (Vitis vinifiera. L) showed seasonal variations. These compounds change also as function of the vine development cycle. The total amounts of phenolic compounds are strongly accumulated in the buds in beginning of dormancy phase in midAugust. Nevertheless, the ending of dormancy phase in midNovember in the buds is associated with increasing of the hydrogen peroxide yield and a considerable drop in the content of the total amount of phenolic compounds. These variations appear to be strictly related to the climatic conditions. This is particularly visible in ending of dormancy phase. This is coinciding with the cold period where the temperature is below 10 ° C for at least 7 consecutive days.
Comparison of Two Methods in Detecting Late-night Talk Shows using Pattern Recognition

This paper presents two alternative approaches to detecting images taken from videos of Leno talk-shows: a Support Vector Machine (SVM) and an Eigen-classifier based on principal components analysis. On a testing set of 2952 images collected from 88 videos, the SVM approach produced an experimentally calculated 90.41% accuracy using color features. On the same set, the Eigen-classifier produced 97.37% accuracy employing thresholds derived from Eigen images. The paper describes strengths and weaknesses of both methods, as well as their potential use on the difficult problem of video copyright violation detection. As internet based video websites, such as YouTube and Metacafe, continue to increase in popularity, the need for systems to detect and remove copyrighted material increases. Copyright owners often create their own websites to display advertisements along with their videos to earn money. Allowing the same material to remain on social video websites can reduce traffic to, and thus revenue earned from, the owner’s website. This paper focuses on one small example of copyrighted material: Jay Leno of the Tonight Show performing interviews. Due to the relative consistency, both in background and layout, of the images captured from Tonight Show videos, frames of these videos can be identified by both color analysis and principal components analysis. Using the ideas presented in this paper, it may be possible to develop a system that could detect almost any relatively static video clips, such as other talk shows, news programs, or other various shows with a consistent background.
POS Tagging and Named Entity Recognition on Handwritten Documents

Parts of Speech (POS) tagging and Named Entity Recognition (NER) on handwritten document images can help in keyword detection during document image processing. In this paper, we propose an approach to detect POS and Named Entity tags directly from ofﬂine handwritten document images without explicit character/word recognition. We observed that POS tagging on handwritten text sequences increases the predictability of named entities and also brings a linguistic aspect to handwritten document analysis. As a pre-processing step, the document image is bi-narized and segmented into word images. The proposed approach comprising of a CNN-LSTM model, trained on word image sequences produces encouraging re-sults on challenging IAM dataset.
First protocol for DNA isolation in Indian charophyta

Charophyta is a highly diversified assemblage of six macrophytic algal genera, namely Chara, Nitella, Tolypella, Lychnothamnus, Lamprothamnium and Nitellopsis. Several attempts have been made to resolve the biodiversity of this group on the basis of morphology, sexual dimorphism, cytotaxonomy, palaeobiogeography and fossil history (Sundaralingam, 2002). Bhatnagar et al. (1996) tried to resolve charophycean biodiversity and trace the phylogeny of this group on the basis of the Giemsa C-banding technique. In view of the revision of Characeae by Wood & Imahori (1965), in which the nomenclature of many taxa was altered without adequate experimental verification, molecular characterization of charophyta taxa became inevitable. Several researchers have isolated DNA from various algal species (Bancroft & Smith, 1988; Dutcher & Kapraun, 1994; Alberto et al., 1997; Satoh et al., 1997; Winkler et al., 2002). Among charophyta, although the isolation of cDNA from Chara corallina (Nakanishi et al., 1999) and mitochondrial DNA from Chara vulgaris (Turmel et al., 2003) are on record, protocols for genomic DNA isolation from Indian Charophyta are not yet available. Hence, this study is aimed at developing an efficient protocol for DNA isolation from Indian charophyta. MATERIALS AND METHODS
NUMERICAL CONSIDERATIONS FOR THREE-DIMENSIONAL SOUND PROPAGATION MODELING: COORDINATE SYSTEMS AND GRID SIZES

Three-dimensional sound propagation models with a parabolic approximate wave equation (PE) solved in either Cartesian (x,y,z) or cylindrical (r,θ,z) coordinate systems are compared. The Split-step Fourier marching algorithm is employed. The solutions can be made arbitrarily more accurate by increasing grid resolution. Both models have limited valid area in horizontal azimuth with respect to the marching direction, which is radial for the cylindrical model and is along the x axis for the Cartesian model. Selection between these two models for a given problem depends on the type of sound wave field. For a cylindrical wave-like field the cylindrical model is more suitable; on the other hand, for a plane wave-like field the Cartesian model is advantageous. In terms of numerical implementation, the cylindrical model requires entry or calculation of the free-space propagator at each step, while this numerical item is spatially uniform for the Cartesian method thus reducing computation time. Conventional implementation of three-dimensional PE models in a cylindrical coordinate system with fixed azimuth grids suffers from loss of resolution in far field, and a method of re-sampling azimuth grids is proposed in this paper to overcome this defect. Further analysis of errors for specific frequencies and grid sizes applicable to ocean acoustics problems will be given in the presentation.
University of Amsterdam at the TREC 2019 Complex Answer Retrieval Track

This paper documents the University of Amsterdam’s participation in the TREC 2019 Complex Answer Retrieval Track. This is the ﬁrst year we actively participate in TREC CAR, attracted by the introduction to the limited “budget” of 20 passages per heading in the outline. We conducted initial exploratory experiments on making each heading contain a unique set of passages within the outline, and even do this hierarchical for each subtree and main title/article level, hence remove any redundancy between passages for diﬀerent “queries” within the same title. We experimented with top-down and bottom-up ﬁltering approaches. At the time of writing we are still in the process of analyzing the results. Some initial observations are the following. First, the restriction makes the task very challenging, as assigning any passage to the right heading in the outline is highly non-trivial. Qualitative analysis shows that our simple heuristics often make a diﬀerent decision than the editorial judges on the heading under which a passage relevant to the title’s topic is assigned. Second, the fraction of judged and relevant passages per individual query or leave node is very small, making it hard to draw any deﬁnite conclusions on our experiments, and also resulting in a too small recall base to evaluate our non-pooled runs in a meaningful way. Third, when aggregating all qrels and runs to the title level, there is reasonable eﬀectiveness of the underlying BM25 rankings, showing that the underlying passage ranking is not unreasonable, and that the hard and interesting problem is in the exact assignment of passages to the “right” headings.