Miracle-dz/UniFAR-FTUs huggingface dataset:
Prevalence of major depressive disorder in the general population of Iran: A systematic review and meta-analysis

Background: Major depressive disorder (MDD) is the most common psychological disorder and affects many people worldwide. Surveying the prevalence of mental disorders, especially general disorders, such as major depressive disorder, is highly important for health professionals and policymakers. Due to a wide variation of studies concerning the prevalence of major depressive disorder in Iran, the present study was conducted to integrate such results. Methods: To conduct this study, English (PubMed/Medline, Scopus and Web of Science) and Persian (MHRN, Noormgs, Magiran, Iran-Medex, Irandoc, and SID) databases, from 1990 to 2018, were explored. All articles related to the point prevalence of depressive disorders in the general population of Iran were reviewed. The inclusion criteria of the present study were as follow: cross sectional studies based on general populations; the expression of the point prevalence rate and incidence of major depressive disorder for people over 15 years; and the use of a structured or semi-structured clinical interview for diagnosis of the disorder. Studies that were not original (such as review studies) and those that did not represent general populations were excluded from the study. To analyze the data obtained from this study, the second version of Comprehensive Meta-Analysis (CMA) software was used. The total point prevalence was calculated using the meta-analysis method, and the I2 index was used to measure the heterogeneity of the studies. Results: Of 6734 studies, 30 studies with a total sample size of 37 867 were finally included based on proposed inclusion and exclusion criteria. The sample included 20641 women (55%), and 17 226 (45%) men. In the present meta-analysis study, due to significant heterogeneity (I=97.1%, p=0.001) among studies, the randomized method was used, and the total point prevalence was 4.1% (CI: 3.1% 5.5%). The overall point prevalence of major depressive disorder was 4.8% (CI: 3.6% -5.6%) in women and 2.3% (CI: 1.6% -3.6%)in men, which indicated that the incidence of this disorder was 1.95 times more in women than in men. Conclusion: This meta-analysis study found a considerable prevalence of major depressive disorder among Iranian participants and revealed that the point prevalence of the disease was more than two-fold in women compared to men. Considering these findings and the high burden of this disorder for communities, the need to formulate and implement prevention and treatment programs is highly required.
Second Language Learning Using eBooks

—Current Computer Assisted Language Learning (CALL) tools, which have largely remained unchanged over the past few years, are often inadequate for attaining ﬂuency in a second language. The goal of this work was to build a CALL tool based on bilingual eBooks, following the EPUB3 format. The tool should have an intuitive user interface, include word deﬁnitions, and also incorporate a set of serious games. The project comprised the development of a Python script which took the parallel texts in both languages and pre-recorded audio in the target language and merged them into a single structured eBook document, complete with dynamic features. These features offer the user an improved control over the audio playback, and are not included by default in the target platform, Apple’s iPad in this work. The script also retrieves dictionary deﬁnitions for every word in the eBook and includes them in the ﬁnal document. Additionally, the project also comprised the development of a Javascript application that implements the serious games without relying on online external resources. A preliminary user study was conducted with a demonstration eBook generated by this system in order to improve its features and understand the expectations users would have for the eBook. Although positive feedback reassures the usefulness of this application, the demand for features that require deeper analysis of the dual language book raises concerns over the viability of the chosen reading platform, Apple’s ibooks application, and the EPUB3 format versus implementing the solution as a standalone mobile application.
A prediction based model for forex markets combining Genetic Algorithms and Neural Networks

Investing in ﬁnancial markets is always a complex and diﬃcult task. To raise the small chances of beating the market, investors usually rely on several techniques that attempt to determine the underlying trading signal, and hopefully predict future market entry and exit points. This thesis proposes a trading system optimized for the Foreign Exchange Market, widely known as FOREX. To perform such task, we use a Feedforward Neural Network (FNN), that take as input features a set of technical indicators (TI), calculated using FOREX hourly data. To optimize the TI and FNN parameters, we deployed an Evolutionary Strategy (ES) based on a Genetic Algorithm (GA). The GA also deploys an automatic Feature Selection (FS) mechanism that enables the FNN to use only relevant features for the given problem. The proposed system is tested with real hourly data from 5 diﬀerent markets, each one exhibiting diﬀerent behavior during the sampled time. The produced investment strategies are compared with classical trading strategies for the sake of comparision. The achieved results show that this approach is capable to outperform the Buy and Hold strategy (B&H) in the GBP/USD market, achieving an average result of 14.19% of Return of Investment (ROI), against 10.69% of ROI for B&H. The system also outperformed the Sell&Hold (S&H) strategy for the USD/CHF, achieving a result of 4.45% of ROI against 4.09% for S&H. Furthermore, it is also discussed the usage of Batch Normalization as preprocessement technique during the development of each market strategy.
TECHNOLOGICAL OPTIONS FOR THE PRODUCTION OF LACTOFERRIN

Lactoferrin (LTF) is a multifunctional protein of the transferrin family present in human and other mammals’ milk. Global demand for originated protein is currently much higher than the supply, and lactoferrin remains the one of the most expensive proteins. Therefore, the development of cost efficient methods to produce lactofferin is extremely important. In this study, human lactoferrin is obtained in bacterial system. ltf gene, coding sequence of protein human lactoferrin (hLTF), was cloned into the expression vector pET28a+, modified signal sequence IMKKTAIAIAVALAGFATVAQA AS. E.coli BL21DE3 strain was selected for expression. Productivity of the producing strain in regard to recombinant human lactoferrin was 30–35% of the cellular protein. The strain produced the recombinant lactoferrin in the form of inclusion bodies. The maximum yield of the target protein (including in soluble form) was achieved by culturing of the recombinant strain at 25°C, induction by 1.1 mM concentration of IPTG and 8-h period after induction. Optimization of solubilization and renaturation conditions allowed once to recover lactoferrin in a concentration of 2.9 mg/ml by the pulsed system. The protein of high purity at least 90% was obtained by means of affinity chromatography on Ni+-agarose.
Small Integrated Navigator for PLanetary EXploration

Executive Summary: The main goal of SINPLEX was to develop an innovative solution to reduce signi cantly the mass of the navigation subsystem for exploration missions which include a landing and/or a rendezvous and capture phase. It is a contribution to the strengthening of the European position for space exploration. It targets increasing the scienti c return of exploration missions, enabling new types of missions and targets, and reducing launch cost and travel time. Future space exploration missions target asteroids, comets, planets and planetary moons. They will bring robotic vehicles to these targets and will provide the capability to return samples to Earth. In general for all space mission but in particular for this kind of missions, mass is one of the most critical factors. Thus reducing the mass of components or complete subsystems of an exploration vehicle is a key enabling factor for the future exploration of our solar system and beyond. The mass reduction while still creating a good navigation performance is achieved by applying functional integration of the different sensors, utilizing micro and nanotechnologies for compacting electronics, and using sensor hybridization approaches to improve the performance of the complete navigation subsystem. The project objectives results: the development of an integrated novel navigation subsystem architecture, the production of a breadboard and the demonstration of its applicability for object relative robotic navigation for space applications.
Combining Physical Simulators and Object-Based Networks for Prediction and Control

Simulators serve an important role in robot controller designs; however, practical real-world control problems involve difﬁcult to model complex contact mechanics. Most simulators employ approximations that lead to a loss in precision. In this abstract, we introduce a hybrid dynamics model, simulator-augmented interaction networks (SAIN), combining a physics engine with an object-based neural network for dynamics modeling. SAIN captures the frictional interaction between objects more accurately and efﬁciently than either purely data-driven or analytical approaches. We demonstrate SAIN’s capabilities on experiments in simulation and on a real robot. The results suggest that it also leads to better performance when used in complex control tasks with better generalization capabilities.
The Architecture of the Computation

Most syntacticians, of whatever theoretical persuasion, will agree that a fundamental property of all human languages is that they pair meanings with sounds or signs (let’s call these forms) in an unbounded fashion. The syntactic structures of a human language are not appropriately thought of as a finite list. Further, most syntacticians will agree that the form-meaning relationship in human languages is, at heart, hierarchical: forms do not syntactically combine linearly, rather larger form-meaning pairings are built out of smaller ones creating a self-similar hierarchy of phrases. It is the job of syntactic theory to give an account of this pairing and its unbounded nature. One of Chomsky’s earliest contributions is the recognition that a theory of the unbounded construction of hierarchical structures must incorporate a computational system that generates the structures (e.g. Chomsky 1959). The history of generative grammar is to a great extent the history of attempts to specify what that computational system is and how it interacts ∗Thanks to Nick Allott, Miriam Butt, Stefan Müller, Martin Haspelmath, Peter Svenonius and an anonymous referee for comments on an earlier draft.
APPROXIMATE PATTERN MATCHING WITH RULE BASED AHO-CORASICK INDEX

Language processing application areas such as Information Retrieval, Information Extraction, and Machine translation etc are using pattern matching as the basic requirement for extraction of information from the documents. Nowadays, Searching through the multimedia database for the particular pattern is the biggest issues due to the tremendous amount of records to be dealt to search. On the internet, the pattern matching mechanism is used to retrieve the documents that are similar to the given query. The exact pattern matching mechanisms are not suitable for this situation since it searches only for perfect matches. So that the approximate pattern matching mechanism is introduced. The reduction in database search space is attainable by using appropriate data structure used for searching. Moreover, the entire pattern matching algorithms support the error detection and correction of the pattern to be searched, not in the searching text. In this paper, a new mechanism is proposed to search a single and multiple patterns in the given database and performs correction in the text if it contains misspelled word.
Deep Learning for Denoising of Fluorescence Microscopy Images

Fluorescence microscopy images are often taken at low light and short exposure times to preserve the integrity of cell samples. However, imaging under these conditions leads to severely degraded images with low signal to noise ratios. To computationally restore these images, we introduce novel loss functions to denoise microscopy images. These loss functions will be folded into the CARE algorithm. The results produced by this modiﬁcation will be evaluated against traditional TV ﬁl-tering and NL means techniques. The modiﬁed model will also be compared against its CARE predecessor us-ing standard image quality metrics
An Introductory approach to Audio Signal Processing for Music Transcription

This thesis is focused on signal processing techniques applied to the transcription of an excerpt of a three voice J.S. Bach’s fugue. The ideia is to study and apply some basic signal processing tools to an audio input signal and retrieve relevant music information that will allow to convert the signal into a symbolic representation. Music transcription is a complex task. So in order to narrow down the problem this challenge naturally imposes, it was chosen a piano solo recording of a fugue, which is a western composition technique based on a strong melodic independance. This introduces several simpliﬁcations like no need for instrument source detection or both harmonic and melodic complex analysis. The challenges faced in this task involve the computation of the spectrum of the input signal, rhythm events detection, background spectrum removal, fundamental frequencies detection and pitch detection.
OP-NARE120754 801..807

The Human Metabolome Database (HMDB) (www. hmdb.ca) is a resource dedicated to providing scientists with the most current and comprehensive coverage of the human metabolome. Since its first release in 2007, the HMDB has been used to facilitate research for nearly 1000 published studies in metabolomics, clinical biochemistry and systems biology. The most recent release of HMDB (version 3.0) has been significantly expanded and enhanced over the 2009 release (version 2.0). In particular, the number of annotated metabolite entries has grown from 6500 to more than 40 000 (a 600% increase). This enormous expansion is a result of the inclusion of both ‘detected’ metabolites (those with measured concentrations or experimental confirmation of their existence) and ‘expected’ metabolites (those for which biochemical pathways are known or human intake/exposure is frequent but the compound has yet to be detected in the body). The latest release also has greatly increased the number of metabolites with biofluid or tissue concentration data, the number of compounds with reference spectra and the number of data fields per entry. In addition to this expansion in data quantity, new database visualization tools and new data content have been added or enhanced. These include better spectral viewing tools, more powerful chemical substructure searches, an improved chemical taxonomy and better, more interactive pathway maps. This article describes these enhancements to the HMDB, which was previously featured in the 2009 NAR Database Issue. (Note to referees, HMDB 3.0 will go live on 18
LVPDA: A Lightweight and Verifiable Privacy-Preserving Data Aggregation Scheme for Edge-Enabled IoT

Edge computing is envisioned to be a powerful platform that provides efficient data storage and computation services in the smart Internet-of-Things (IoT) systems. In this data-intensive architecture, protecting user-side data privacy is one of the most critical concerns to prevent privacy leakage from any other untrusted entities. Aiming to resist this concern, many privacy-preserving data aggregation (PPDA) schemes have been proposed for various cloud-enabled IoT applications. However, due to the resource-constrained nature of the smart IoT devices, the conventional PPDA solutions, in terms of both privacy and performance requirements, are unsuitable in edge computing. To address this challenge, we propose a lightweight and verifiable PPDA scheme, named LVPDA, for the edge-computing-enabled IoT system, where the Paillier homomorphic encryption method and an online/offline signature technique are combined to ensure the privacy preserving and integrity verification during the data aggregation process. A detailed security analysis indicates that LVPDA is existentially unforgeable under the chosen message attack (EU-CMA) and the data integrity can be guaranteed with formal proof under $q$ -strong Diffie–Hellman ( $q$ -SDH) assumptions. Compared with other PPDA methods, our scheme can achieve lightweight PPDA in terms of less computational complexity and communication overhead.
Modelling and Verifying Dependability of Hybrid Systems in HCSP

Hybrid systems are dynamic systems with interacting discrete computation and continuous physical processes. They have become ubiquitous in our daily life, e.g., automotive, aerospace, medical systems and so on, particular, many of them are safety-critical . For these safety critical systems, it is demanded to guarantee not only the correctness (safety normally), i.e., its functions satisfying the given requirements, but also the dependability, i.e., the resistance to the unexpected behaviour from its environment, as many of them are deployed in highly uncertain environment, and the unexpected behaviour from the environment may result in a correct system malfunctioning. For example, the interactions between a controller and a physical processes are possibly realised via (wireless) communications. In case that the communications fail, the expected control from the controller may get lost and as a consequence the physical processes cannot behave as expected. In the literature, how to guarantee the correctness of hybrid systems has been extensively investigated, but there is little work on dependability of hybrid systems. To address this issue, this paper proposes a formal framework by extending HCSP, a formal modeling language for hybrid systems, for modelling and veriﬁcation of hybrid systems in the presence of communication failure. Thus, safety and dependability of hybrid systems can be considered in the unform framework. Furthermore, by leveraging the expressivity and efﬁciency, we present two inference systems for the extension, and correspondingly implement two theorem provers in Isabelle/HOL. To illustrate our approach, we consider a case study on train control system originating from Chinese Train Control System, for which the two theorem provers are applied separately and the proof results are compared.
Effectiveness of PEERS social intervention program in Taiwanese young adults with autism spectrum disorder

Objective: Social deficits among individuals with autism spectrum disorder (ASD) are lifelong, which may impact occupational function and mental health particularly for the young adults. Despite the urgent needs of this population, evidence-based programs which builds social skills for young adults were not yet available until recently. Program for the Education and Enrichment of Relational Skills (PEERS®) has been proved to effectively improve social skills for adolescents with ASD across different social cultures. Whether such effectiveness can be replicated in young adults beyond North America waits to be examined. This study aimed to investigate the effectiveness of PEERS intervention in Taiwanese young adults with ASD, as well as its durability and clinical correlates. Method: We recruited 82 young adults with ASD and normal IQ, 41 randomized to the PEERS group, 41 in the control group. A total of 36 young adults completed the PEERS intervention. For those participants who completed the program, we compared their social deficits, autistic symptoms, social interaction anxiety, empathy and social skill knowledge by questionnaires before and after the PEERS intervention. We also rated on the communicative behaviors observed through sessions. The durability of the intervention was followed up at 3-month and 6-month after. Results: We found significant effectiveness on social deficits, autistic severity, social interaction anxiety, empathy, and social skill knowledge either by self-report or coachreport. The communicative behaviors objectively by observers improved throughout the sessions, showing a trend of more appropriate eye contact, gestures, facial expression during conversation, and appropriately maintain conversation and reciprocity. Most effect maintained at 3-month and 6-month follow-ups. The improvement of social deficits were correlated with baseline severity, while the learning on social skills knowledge was correlated with IQ. Additionally, the improvement of social deficits, autistic severity, and empathy were correlated with each other. Conclusion: The PEERS intervention effectively improves social deficits, social interaction anxiety, social skill knowledge, and empathy in Taiwanese young adults with ASD.
Learning Credal Sum-Product Networks ∗

Probabilistic representations, such as Bayesian and Markov networks, are fundamental to much of statistical machine learning. Thus, learning probabilistic representations directly from data is a deep challenge, the main computational bottleneck being inference that is intractable. Tractable learning is a powerful new paradigm that attempts to learn distributions that support e ﬃ cient probabilistic querying. By leveraging lo-cal structure, representations such as sum-product networks (SPNs) can capture high tree-width models with many hidden layers, essentially a deep architecture, while still admitting a range of probabilistic queries to be computable in time polynomial in the network size. While the progress is impressive, numerous data sources are incomplete, and in the presence of missing data, structure learning methods nonetheless revert to single distributions without characterizing the loss in conﬁdence. In recent work, credal sum-product networks, an imprecise extension of sum-product networks, were proposed to capture this robustness angle. In this work, we are interested in how such representations can be learnt and thus study how the computational machinery underlying tractable learning and inference can be generalized for imprecise probabilities.
UNLOCKING OUR BEHAVIORAL KNOWLEDGE INHERITANCE THROUGH ONTOLOGY LEARNING: A DESIGN FRAMEWORK, AN INSTANTIATION, AND A RANDOMIZED EXPERIMENT

The accumulated literature base in the behavioral sciences represents the most significant source of knowledge about human behavior, yet the same literature has grown beyond human comprehension, resulting in a knowledge inaccessibility problem. Existing IT artifacts such as search engines have not been able to address this issue and in fact, may have intensified it by both rendering low-precision search results and escalating confirmation biases. Following the design science research paradigm, we propose a novel design framework and an instantiation to unlock behavioral knowledge embedded in large-scale behavioral articles. Based on an ontology learning layer cake framework and the state-of-the-art text analytics, we implemented a three-step process of extraction and assembly of behavioral theories through hypothesis , construct , and construct-relationship extraction . Linguistics-vs. statistics-based techniques were evaluated and compared in order to determine the best extraction methods. We also developed an ontology-based search engine— TheoryOn —that allows researchers to directly search for constructs and synonymous constructs , construct relationships , antecedents , and consequents , and to easily integrate related behavioral theories . We conducted a randomized experiment comparing four information-retrieval tasks for behavioral literature review between TheoryOn and EBSCOhost (a full-text search engine) among 38 IS and Management researchers. We found that TheoryOn users are significantly better at retrieving relevant constructs, construct relationships, and theories, suggesting significant benefit of our proposed design artifact for addressing the knowledge accessibility problem.