Miracle-dz/UniFAR-FTUs huggingface dataset:
The CLaC System at the TREC 2019 News Track

This paper describes our approach to the TREC 2019 News Track. The goal of the News Track is to provide background links and entity linking to target news articles within a collection of articles. Our approach ﬁrst represents all articles in the collection using Doc2Vec embeddings then computes the cosine similarity between the target article with all other articles in the collection and outputs the 100 closest ones. Although simple, this approach allows minimum pre-processing and is ag-nostic to the content of the documents. The overall results of the shared tasks are below the median with a nDCG@5 of 0.4298, however, for spe-ciﬁc topics, the approach achieved the best scores among all participating teams.
Reading Contemporary Art: Comparative Art History Education through Themes

Article History: Purpose: Conducting contemporary art lessons with a traditional art history view and a chronological approach leads to misinterpretations and even to prejudices of contemporary art as it is not progressing chronologically, cannot be assessed within specific stylistic forms and defined with strict lines, unlike previous traditional art disciplines. Based upon this problem, in this study, a new course process was planned in which contemporary art pieces are compared to traditional pieces focusing on the five major themes contemporary art differs from traditional art. Received: 27 Jan. 2020 Received in revised form: 14 Nov. 2020 Accepted: 17 Feb. 2021 DOI: 10.14689/ejer.2021. 92.12
Psychometric evaluation of recall and recognition tasks for the measurement of young spectators’ theatrical memory

The purpose of this research is the construction and psychometric evaluation of seven categories of recall and recognition tasks for the measurement of young spectators’ theatrical memory, based on the respective theatre codes of a specific performance for young spectators. The aim of those tasks is the evaluation of the young spectators’ mnemonic recordings on a level a) the actors’ representation of the characters on stage (acting), b) the visual frame of the performance, c) the audio code d) the lights, e) the dramatic text, f) the plot/action and g) the Shadow Theatre technique. The recall and recognition tasks were constructed according to the Classical Test Theory of Question Analysis on a sample of 5th Grade Primary School pupils (i.e. ten-year-old pupils), who had seen this specific performance addressed to young spectators and were evaluated with the Factor Analysis method. For the evaluation of the quality of the questions, we took into consideration the difficulty coefficient of each question, the discriminant coefficient and the evaluation of the correlation level of expert judges. For the psychometric control of the tests we examined the validity of their conceptual construct via inquiry factor analysis and the internal validity coefficient. The result of the above tasks was the creation of seven reliable and valid measurement tools, in which no gender effect is inferred.
The Relationship between Identity and Acculturation in International Students in Turkey

Article History: Purpose: This study aimed to discover identity statuses and acculturation strategies of international students in Turkey, as well as revealing the relationship between these concepts. Research Methods: To investigate the relationship, a correlational research design was used. Turkish version of Acculturation Questionnaire and Turkish version of Ego-Identity Process Questionnaire were administered to 156 international students in Turkey. Findings: The findings showed that the most common acculturation strategy was assimilation and the most seen identity statuses were achievement and diffusion. Received: July 31, 2020 Received in revised form: August 14, 2020 Accepted: February 26, 2021 DOI: 10.14689/ejer.2021. 92.15
Adversarial Attacks Against Network Intrusion Detection in IoT Systems

Deep learning (DL) has gained popularity in network intrusion detection, due to its strong capability of recognizing subtle differences between normal and malicious network activities. Although a variety of methods have been designed to leverage DL models for security protection, whether these systems are vulnerable to adversarial examples (AEs) is unknown. In this article, we design a novel adversarial attack against DL-based network intrusion detection systems (NIDSs) in the Internet-of-Things environment, with only black-box accesses to the DL model in such NIDS. We introduce two techniques: 1) model extraction is adopted to replicate the black-box model with a small amount of training data and 2) a saliency map is then used to disclose the impact of each packet attribute on the detection results, and the most critical features. This enables us to efficiently generate AEs using conventional methods. With these tehniques, we successfully compromise one state-of-the-art NIDS, Kitsune: the adversary only needs to modify less than 0.005% of bytes in the malicious packets to achieve an average 94.31% attack success rate.
A Two-Dimensional Spectral Model of Blood Plasma Flow with Oxygen Transport and Blood Cell Membrane Deformation

Sickle cell disease (SCD) is a hereditary blood disorder in which the red blood cells (RBC) have abnormal cell structure and function. These abnormal RBC cannot efficiently transport and release oxygen as in a normal cell. In this investigation spectral methods in a 2-D computational model of flow dynamics are implemented in order to simulate plasma velocities, oxygen diffusion, and RBC deformation in the microcirculation. A mixed Chebyshev and Fourier spectral scheme with finite difference scheme is used to compute the flow field, oxygen diffusion, and RBC deformation. A level set computational method is used to advect the RBC membrane on a staggered grid. Simulations are made to compare the computed profiles with the results obtained by Tekleab and Harris [1]. Differences in profiles are discussed and both sets of profiles are compared with the three-dimensional model of Le Floch-Yin [2].
Lessons Learned From the Development of Mobile Applications for Fall Detection

Falls are the leading cause of older adults injuries and solutions are needed to address this issue. One way to meet this is by developing applications that use sensors embedded in devices like smartphones and smartwatches. This paper presents our experience in developing such applications and the lessons learned during their development and evolution. First, we developed an application called fAlert to identify a fall using data from a smartphone’s accelerometer. However, the usage of this kind of mobile device for detecting falls is not natural, because it needs to be positioned at the level of the user’s chest. Then, we developed a new app called WatchAlert, which runs in smartwatches. In that case, we also created an algorithm that uses two sensors, accelerometer and gyroscope, and later evolved it to use only the accelerometer with better results. Moreover, we use first a fall detection threshold algorithm in this solution. Next, we expanded this strategy to use threshold and machine learning algorithms, which were evaluated considering the accuracy, false negative, and time criteria as well as their features. We believe that this study can support the development of new systems and devices for detecting falls. As future work, it would be interesting to assess the related energy cost of the fall detection approaches studied. Keywords–Falls; Lessons; IoT Health; IS for healthcare.
Artificial Intelligence's Grand Challenges: Past, Present, and Future

Copyright © 2021, Association for the Advancement of Artificial Intelligence. All rights reserved. ISSN 0738-4602 Spring 2021 61 grand challenges are important as they act as compasses for researchers and practitioners alike — especially young professionals — who are pondering worthwhile problems to work on, testing the boundaries of what is possible! Challenge tasks also unleash the competitive spirit in participants as evidenced by the plethora of active participants in Kaggle competitions (and forum discussions therein). prize money and research bragging rights also accrue to the winners. The Defense Advanced research projects Agency grand Challenges1 and X prizes2 are some of the best-known successful programs that have helped make significant progress across many domains applying artificial intelligence (Ai). As grand challenges are accomplished, other than the long-term benefits the solutions engender, the positive press they garner helps rally society behind the field. Trickle-down benefits include renewed respect for and trust in science and technology by citizens, as well as a desirable focus on science, technology, engineering, and mathematics education.  Innovative, bold initiatives that capture the imagination of researchers and system builders are often required to spur a field of science or technology forward. A vision for the future of artificial intelligence was laid out by Turing Award winner Raj Reddy in his 1988 Presidential address to the Association for the Advancement of Artificial Intelligence. It is time to provide an accounting of the progress that has been made in the field, over the last three decades, toward the challenge goals. While some tasks such as the world-champion chess machine were accomplished in short order, many others, such as self-replicating systems, require more focus and breakthroughs for completion. A new set of challenges for the current decade is also proposed, spanning the health, wealth, and wisdom spheres. Artificial Intelligence’s Grand Challenges: Past, Present, and Future
Fast and slow lifetime degradation in boron‐doped Czochralski silicon described by a single defect

A demonstration that boron–oxygen related degradation in boron‐doped Czochralski silicon could be caused by a single defect with two trap energy levels is presented. In this work, the same two‐level defect can describe the fast and slow lifetime decay with a capture cross‐section ratio of electrons and holes for the donor level of σn/σp = 19 ± 4. A model is proposed for the multi‐stage degradation involving a single defect, in which the product of the slow reaction is a reactant in the fast reaction. After thermal processing, a population of interstitial oxygen (Oi) exists in a certain state (the precursor state) that can rapidly form defects (fast degradation) and another population of Oi exists in a state that is required to undergo a slow transformation into the precursor state before defect formation can proceed (slow degradation). Kinetic modelling is able to adequately reproduce the multi‐stage degradation for experimental data. Dark annealing is also shown to impact the extent of ‘fast’ degradation. By decreasing the dark annealing time on pre‐degraded wafers, a more severe ‘fast’ degradation of the samples can be enabled during subsequent illumination, consistent with this theory. The paper then discusses possible candidates for the chemical species involved. (© 2016 WILEY‐VCH Verlag GmbH &Co. KGaA, Weinheim)
Adaptation of Secondary Traumatic Stress Scale to Turkish for Social Media Users: Reliability and Validity Study

Authors Information Seher Balcı Çelik Professor, Ondokuz Mayıs University, Samsun, Turkey sbalci@omu.edu. tr Mustafa Selim Altınışık Lecturer, Ondokuz Mayıs University, Samsun, Turkey mustafaselim.altinisik@omu.edu. tr ABSTRACT When the literature is analysed, the gap in the researches related to STS for social media users is big. This study aims to adapt the Secondary Traumatic Stress Scale (STSS-SM) for Social Media Users to Turkish, which will contribute to filling the gap in social media users. In the study, data of 708 participants aged 18-26 were analysed. Validity and reliability study of STSS-SM was carried out and its three-factor structure was examined. According to the findings in adaptation to Turkish, STSS-SM consists of a single-factor structure rather than a three-factor structure as in the original. EFA results indicate that scale items can be collected under this single-factor structure. In conclusion, based on validity and reliability studies, it can be said that the Turkish version of STSS-SM is applicable in scientific studies.
Aesthetic Intelligence and Aesthetic Experience in Children

How do children experience music, art, emotions, and beauty? Is aesthetic potential in children innate or acquired? Do children understand the world around aesthetically? Is aesthetic experience a developmental process? How can teachers provide students with opportunities to experience aesthetics for cognitive, social, cultural, and psychological development? These are some of the fascinating questions in the field of contemporary aesthetic education. This paper presents what is aesthetic experience and how it enriches the lives of children. Children should be exposed to fine arts and support from teachers will help them to find meaning in fine arts. Visual arts curriculum will help children in cognitive development by continuously constructing new knowledge and integrating it into existing knowledge. Curriculum designed to teach aesthetic experience should incorporate both art viewing and art making. Aesthetic experience integrates mind, body, and emotion. It can induce personal growth in children, and it is intrinsically satisfying.
Maintaining Persistence and Contour Trees for Time Varying Functions on 2 or 3-Manifolds

Topological methods have been gaining in prominence in the studies of scalar functions over a given domain. With the proliferation of time-varying functions in scientific data, the question of maintaining the data structures associated with topological methods over time becomes more and more important. In particular, suppose we are given a time-varying scalar function f defined over a manifold M, and a topological data structure associated with f . As f varies over time, the question is how to maintain the associated topological data structure such that it is always consistent with the scalar function f at any given point in time, without recomputing the data structure for f at each time instant. In this paper, we consider two topological structures: topological persistence [16] and contour trees [26]. We present two algorithms: The first one maintains the persistence pairings for a time-varying function defined over a triangulation of a 2-manifold in O(log n) time per crossing event, where at a crossing event, two vertices switch the order of their function values. The second one maintains the contour tree of a time-varying function defined over a triangulation of a topological 3-sphere in O(| lnk(p) ∩ lnk(q) | log n) time per crossing event, where lnk(p) is the link of a vertex p in the 3-manifold. The second algorithm can also be extended to time-varying functions on d-manifolds with a slightly worse running time. It turns out that both these topological structures make use of a tree-like structure, and as such their time-varying versions can be solved efficiently with the help of the wellknown dynamic tree data structure [23].
Study of calculation method of pure mode II stress intensity factor of fine-grained concrete using different numerical models

Measuring and calculating methods of critical stress intensity factors (SIFs) have become hot topics which attracted large attention recently. In this work, anti-symmetrical four-point bending tests of cracked fine-grained concrete specimens were conducted experimentally and numerically by using a computer-controlled universal testing machine and ABAQUS code. A comparative study of the calculation method of pure mode II stress intensity factor of a fine-grained concrete was performed by utilizing the conventional finite element method (FEM) in two and three dimensions as well as the extended finite element method (XFEM) in three dimensions. The results show that in three-dimensional models, the crack mode is closest to the pure mode II at the center of specimen thickness. Pure mode II stress intensity factors obtained by SEAM and XFEM are 1.013 and 1.0617 times that by SEAM, respectively. Pure mode II stress intensity factors of the fine-grained concrete obtained by the conventional FEM are more stable than that by XFEM. The number of mesh circles has slight influence on the calculation results of pure mode II stress intensity factor.
Afghanistan’s Food Security: Evidence from Pakistan and Afghanistan Wheat Price Transmission using Threshold Vector Error Correction Model (TVECM))

Afghanistan's food security mainly depends on Pakistan's wheat prospect, circumstances, agriculture policies, and market price dynamics. This study explores the price transmission mechanism of the wheat flour and wheat grain between Pakistan and Afghanistan using monthly price pairs from January 2003 through October 2017. The paper investigates the existing knowledge of how Pakistan’s agricultural policy and wheat market affects the wheat market and food security of Afghanistan. The results confirm that the wheat flour price of Pakistan is found to be driving the price of wheat flour of Afghanistan. This implies that wheat flour price of Pakistan evolves independently, and that wheat flour price of Afghanistan balances any divergence in the long-run relationship between the two markets prices. The policy implication is to eradicate transaction costs as well as procuring timely wheat grain and flour, in order to maintain price stability between Pakistan and Afghanistan wheat markets.
Kinetic modeling of nitric oxide removal by ultraviolet/H2O2 advanced oxidation process

Aims: In present study, the mass transfer-reaction kinetic parameters of nitric oxide (NO) removal by ultraviolet (UV)/H 2 O 2 process in a bubble column reactor in the presence of SO 2 are calculated. Materials and Methods: The mass balance equation for NO through a layer thickness of δ, under the steady state condition is solved, and NO absorption rate is calculated. The value of rate constants and Ha numbers are obtained based on experimental data under different conditions. Results: The calculations indicate that the values of Ha number are >3. The values of rate constants (k obs) are fitted to some empirical equations for different operating conditions. It is observed that the value of k obs increases with an increase in H 2 O 2 concentration and UV radiation intensity while it decreases with an increase in NO and SO 2 inlet concentrations. The values of rate constants are in order of 10−5 , expect for SO 2 , which are in order of 10−7 . The results reveal that there is a good agreement between calculated and experimental values where the maximum absolute error is 16.18% related to UV light intensities between 0 and 0.012 W/m 3 . Conclusion: The obtained values of Ha numbers under different condition confirm that the absorption process of gas in the liquid phase is a fast reaction. The maximum error values resulted from a comparison between the calculated NO absorption rates and the experimental ones are acceptable.
Does Portfolio Disclosure Make Money Smarter?

We provide causal evidence that mandatory portfolio disclosure helps investors evaluate and select hedge fund managers. Using a staggered difference-in-differences analysis, we demonstrate that investor capital flows better predict fund performance among funds that publicly disclose their portfolio holdings. Additional cross-sectional analyses suggest that this gain in selection ability varies with the informational value of disclosure. Furthermore, examining investor-level allocations, we find that institutional investors earn higher returns on their allocations to disclosing funds. Overall, these results help contribute to the cost-benefit analysis of mandatory portfolio disclosure.
General discussion : Adoption , Risk , and Protection

This thesis examined the development of adopted children to shed more light on the effects of deprivation on child development and the potential for catch-up and recovery after placement in a more advantageous environment. In the first part of the thesis a meta-analysis is presented in which we compared adopted children’s attachment relationships with the normative attachment distribution of nonadopted children raised by their biological parents, and as a comparison we also compared the attachment distribution of foster children with the normative distribution. The second part of the thesis focused on the development of former foster and post-institutionalized children, 11 to 16 months old at arrival, two and six months after their adoption from China. Several salient developmental domains were studied: attachment, cognitive and motor development, physical growth, stress regulation, and social-emotional behavior. In this chapter the results are summarized and discussed, and recommendations for practice and future research are presented.
Fully-Connected Tensor Network Decomposition and Its Application to Higher-Order Tensor Completion

The popular tensor train (TT) and tensor ring (TR) decompositions have achieved promising results in science and engineering. However, TT and TR decompositions only establish an operation between adjacent two factors and are highly sensitive to the permutation of tensor modes, leading to an inadequate and inflexible representation. In this paper, we propose a generalized tensor decomposition, which decomposes an Nth-order tensor into a set of Nth-order factors and establishes an operation between any two factors. Since it can be graphically interpreted as a fully-connected network, we named it fully-connected tensor network (FCTN) decomposition. The superiorities of the FCTN decomposition lie in the outstanding capability for characterizing adequately the intrinsic correlations between any two modes of tensors and the essential invariance for transposition. Furthermore, we employ the FCTN decomposition to one representative task, i.e., tensor completion, and develop an efficient solving algorithm based on proximal alternating minimization. Theoretically, we prove the convergence of the developed algorithm, i.e., the sequence obtained by it globally converges to a critical point. Experimental results substantiate that the proposed method compares favorably to the state-of-the-art methods based on other tensor decompositions.
Stock Prediction using Sentiment analysis and Long Short Term Memory

: The Stock market is a shambolic place for prediction as there are plenty of factors that affect the stock market simultaneously. Numerous studies have been conducted regarding this field, in hopes that one day accurate stock values can be predicted. This paper introduces a hybrid algorithm that incorporates Twitter sentiment analysis and Long Short Term Memory to predict next day closing values of a stock. Our proposed algorithm exploits the temporal correlation between public sentiment and its effect on stock values. We use Part-of-speech tagging to perform sentiment analysis and Long Short Term Memory for foretelling the next day closing price of the stock, both of these combined gives us a decent picture regarding the future of the stock.