Miracle-dz/UniFAR-FTUs huggingface dataset:
Model View Controller in iOS mobile applications development

—Due to the increased number of mobile applications and their popularity, many software developers have begun to focus on mobile platforms. While this focus has positive effects (e.g. a larger developer community, new open source projects, new tools), it also has a down side. With the migration of developers from different software development areas, where they have used other programming paradigms or architectural approaches, the topic of software architecture on mobile platforms become more trending and hype in the mobile development communities. Even though several new architectural solution were proposed for solving some of the issues which arise from using the classical architectural patterns popularised by the creators of the mobile platforms, we want to emphasise the principles of software architecture in mobile computing, why they have to be respected and how their adoption impacts the development process. Therefore, this paper focuses on showing that the Model View Controller (MVC) — one of the most common classical architectural patterns — can be used successfully for building mobile applications and the problems which might arise are by products of the wrong usage of the pattern rather than pattern issues. We show that by analysing the most common architectural misuses of the MVC pattern in both open-source and private projects and offers solutions to those problems.
Multi-Location Program Repair Strategies Learned from Successful Experience (S)

Automated program repair (APR) has great potential to reduce the effort and time-consumption in software maintenance and becomes a hot topic in software engineering recently with many approaches being proposed. Multi-location program repair has always been a challenge in this field since its complexity in logic and structure. While some approaches do not claim to have the features for solving multi-location bugs, they generate correct patches for these defects in practice. In this paper, we first make an observation on multi-location bugs in Defects4J and divide them into two categories (i.e., similar and relevant multi-location bugs) based on the repair actions in their patches. We then summarize the situation of multi-location bugs in Defects4J fixed by current tools. We analyze the twenty-two patches generated by current tools and propose two feasible strategies for fixing multi-location bugs, illustrating them through two detailed case studies. At last, the experimental results prove the feasibility of our methods with the repair of two bugs that have never been fixed before. By learning from successful experience in the past, this paper points out possible ways ahead for multi-location program repair.
C-QWERTY: a text entry method for circular smartwatches (S)

We present a study aimed at comparing different circular layouts for entering text on smartwatches. In particular, we measured the extent through which the use of a QWERTY layout increases user performance in the earliest sessions of use. To this aim, we designed C-QWERTY, a soft keyboard designed for circular smartwatches in which the keys are arranged along the edge of the screen in a circular layout. In order to make the keyboard more familiar to users, the order of the keys is similar to the one of traditional rectangular QWERTY keyboards. The method supports two interaction modes: tapping and gesture, in which a whole word can be written with a single gesture. As an evaluation, we compared the C-QWERTY layout with Cirrin, another circular layout in witch the order of the characters has been optimized to minimize the distance between successive keys during writing. The experimental results showed that the C-QWERTY, with a text entry speeds of 9.1 wpm and 7.7 wpm (tapping and gesture mode, respectively), outperformed the Cirrin layout with entry speeds of 6.6 wpm and 5.5 wpm. The increase in text entry speed due to the use of the QWERTY layout was 38 percent.
Generating SQL Statements from Natural Language Queries: A Multitask Learning Approach (S)

—NL2SQL advocates an idea of helping engineers and/or end users generate SQL statements from natural language queries. However, it still remains a strong challenge in improving its precision and scalability. This paper introduces MultiSQL, a multitask deep learning approach to performing NL2SQL. MultiSQL uniﬁes the task representations and trains a model in parallel on multiple tasks, including NL2SQL, machine translation, etc. It employs a multitask question-answering network for jointly learning all tasks and transferring knowledge among tasks. We have evaluated MultiSQL on two query datasets: WikiSQL (an open sourced dataset) and CnSQL (a Chinese dataset we created). The evaluation results clearly show the effectiveness of MultiSQL. In particular, the accuracies achieved by MultiSQL approximate those achieved by the state-of-the-art NL2SQL methods on WikiSQL, and its accuracy is 78%, which is 17% higher than the “Chinese2English + NL2SQL” method on CnSQL.
Three-Dimensional Localization Algorithm of WSN Nodes Based on RSSI-TOA and Single Mobile Anchor Node

Aimed at the shortcomings of low localization accuracy of the fixed multianchor method, a three-dimensional localization algorithm for wireless sensor network nodes is proposed in this paper, which combines received signal strength indicator (RSSI) and time of arrival (TOA) ranging information and single mobile anchor node. A mobile anchor node was introduced in the proposed three-dimensional localization algorithm for wireless sensor networks firstly, and the mobile anchor node moves according to the Gauss–Markov three-dimensional mobility model. Then, based on the idea of using RSSI ranging in the near end and TOA ranging in the far end, a ranging method combining RSSI and TOA ranging information is proposed to obtain the precise distance between the anchor node and the unknown node. Finally, the maximum-likelihood estimation method is used to estimate the position of unknown nodes based on the obtained ranging values. The MATLAB simulation results show that the proposed algorithm had a higher localization accuracy and lower localization energy consumption compared with the traditional RSSI localization method or TOA localization method.
Empirical Studies Concerning the Maintenance of BPMN Diagrams: A Systematic Mapping Study

— Business process models help understand the organizational process and the software that supports it. BPMN (Business Process Modeling and Notation) is the standard notation for business process modeling, and it is widely accepted in industry. BPMN models can elucidate the activities carried out by a software during its construction and maintenance. However, during the maintenance of the software that supports an organizational process, usually only the source code of the software undergoes modifications, even when inserting new features. The software design models, including the BPMN models, often become outdated over time and, in future maintenance, they will not help understand the business process in which the software is inserted and which the software aims to support. Such scenario highlights the importance of supporting the maintenance of BPMN models. However, what has been experimentally investigated regarding the maintenance of BPMN models? To answer this question, we performed a systematic mapping, which showed experimental studies, factors and technologies that influence the maintenance of BPMN models. These results present conclusions about the state of the art and gaps that can be explored in this field of research.
TL-GAN: Generative Adversarial Networks with Transfer Learning for Mode Collapse (S)

—Image generation based on the generative adversarial network (GAN) has been widely used in the ﬁeld of computer vision. It helps generate images similar to the given data by learning their distribution. However, in many tasks, training on small datasets of scenes may lead to mode collapse, such that the generated images are often blurred and almost the same. To solve this problem, we propose a generative adversarial network with transfer learning for mode collapse called TL-GAN. Owing to the size of the training dataset, we introduce transfer learning (VGG pre-training network) to extract more useful features from the underlying pixels and add them to the discriminator, which can be used to calculate the distance between samples, and to provide the discriminator with a new training target. The discriminator thus learns the best features that can distinguish between real data and generated data using the proposed model. This also enhances the learning capability of the generator, which learn further about the distribution of real data. Meanwhile, generator can produce new images more realistic. The results of experiments show that the TL-GAN can guarantee the diversity of samples. A qualitative comparison with several prevalent methods conﬁrmed its effectiveness.
Multilevel Visualisation of Topic Dependency Modelsfor Assessment Design and Delivery: A Hypergraph Based Approach

The effective design and delivery of assessments in a wide variety of evolving educational environments remains a challenging problem. Proposals have included the use of learning dashboards, peer learning environments, and grading support systems; these embrace visualisations to summarise and communicate results. In an on-going project, the investigation of graph based visualisation models for assessment design and delivery has yielded promising results. Here, an alternative graph foundation, a two-weighted hypergraph, is considered to represent the assessment material (e.g., questions) and their explicit mapping to one or more learning objective topics. The visualisation approach considers the hypergraph as a collection of levels; the content of these levels can be customized and presented according to user preferences. A case study on generating hypergraph models using commonly available assessment data and a flexible visualisation approach using historical data from an introductory programming course is presented.
Grouping Semantically Related Change-Sets to Enhance Identification of Logical Coupling

Identifying dependency between various artifacts in a large scale software system is a non-trivial task. As the software evolves, multiple artifacts like files, docs, classes, database scripts, etc., are likely to undergo change concurrently. Such artifacts tend to have a dependency between them, otherwise referred to as logical coupling. Researchers have used Support and Confidence as an association rule based measurement to predict the levels of logical coupling among the software artifacts. However, employing a single change on a software artifact can span across various closely related changes when many code contributors are working on the same change. Thus it is important to preprocess and group these semantically related change-sets before identifying logical coupling. In this paper, we propose a method to identify logical coupling and group semantically related change sets. We evaluate our method on real-world git repositories and document our observations.
Multistep Flow Prediction on Car-Sharing Systems: A Multi-Graph Convolutional Neural Network with Attention Mechanism

Multistep flow prediction is an essential task for the car-sharing systems. An accurate flow prediction model can help system operators to pre-allocate the cars to meet the demand of users. However, this task is challenging due to the complex spatial and temporal relations among stations. Existing works only considered temporal relations (e.g. using LSTM) or spatial relations (e.g. using CNN) independently. In this paper, we propose an attention to multi-graph convolutional sequence-to-sequence model (AMGC-Seq2Seq), which is a novel deep learning model for multistep flow prediction. The proposed model uses the encoder–decoder architecture, wherein the encoder part, spatial and temporal relations are encoded simultaneously. Then the encoded information is passed to the decoder to generate multistep outputs. In this work, specific multiple graphs are constructed to reflect spatial relations from different aspects, and we model them by using the proposed multi-graph convolution. Attention mechanism is also used to capture the important relations from previous information. Experiments on a large-scale real-world car-sharing dataset demonstrate the effectiveness of our approach over state-of-the-art methods.
Improvement of the Bias Stress Stability in 2D MoS2 and WS2 Transistors with a TiO2 Interfacial Layer

The fermi-level pinning phenomenon, which occurs at the metal–semiconductor interface, not only obstructs the achievement of high-performance field effect transistors (FETs) but also results in poor long-term stability. This paper reports on the improvement in gate-bias stress stability in two-dimensional (2D) transition metal dichalcogenide (TMD) FETs with a titanium dioxide (TiO2) interfacial layer inserted between the 2D TMDs (MoS2 or WS2) and metal electrodes. Compared to the control MoS2, the device without the TiO2 layer, the TiO2 interfacial layer deposited on 2D TMDs could lead to more effective carrier modulation by simply changing the contact metal, thereby improving the performance of the Schottky-barrier-modulated FET device. The TiO2 layer could also suppress the Fermi-level pinning phenomenon usually fixed to the metal–semiconductor interface, resulting in an improvement in transistor performance. Especially, the introduction of the TiO2 layer contributed to achieving stable device performance. Threshold voltage variation of MoS2 and WS2 FETs with the TiO2 interfacial layer was ~2 V and ~3.6 V, respectively. The theoretical result of the density function theory validated that mid-gap energy states created within the bandgap of 2D MoS2 can cause a doping effect. The simple approach of introducing a thin interfacial oxide layer offers a promising way toward the implementation of high-performance 2D TMD-based logic circuits.
Topologically inferring active miRNA‐mediated subpathways toward precise cancer classification by directed random walk

Accurate predictions of classification biomarkers and disease status are indispensable for clinical cancer diagnosis and research. However, the robustness of conventional gene biomarkers is limited by issues with reproducibility across different measurement platforms and cohorts of patients. In this study, we collected 4775 samples from 12 different cancer datasets, which contained 4636 TCGA samples and 139 GEO samples. A new method was developed to detect miRNA‐mediated subpathway activities by using directed random walk (miDRW). To calculate the activity of each miRNA‐mediated subpathway, we constructed a global directed pathway network (GDPN) with genes as nodes. We then identified miRNAs with expression levels which were strongly inversely correlated with differentially expressed target genes in the GDPN. Finally, each miRNA‐mediated subpathway activity was integrated with the topological information, differential levels of miRNAs and genes, expression levels of genes, and target relationships between miRNAs and genes. The results showed that the proposed method yielded a more robust and accurate overall performance compared with other existing pathway‐based, miRNA‐based, and gene‐based classification methods. The high‐frequency miRNA‐mediated subpathways are more reliable in classifying samples and for selecting therapeutic strategies.
From Work Ability Research to Implementation

Work ability research started in Finland in the 1990s due to the challenges of work force aging. The employment rates of older workers (55+) were below 40% and early retirement and work disability rates were rather common in many European countries. The work ability concept and methods were developed and broad international research activities started in the 1990s. A comprehensive promotion model for work ability was created aiming to prevent work ability from declining during aging. However, to be able to impact the work ability is a complicated and difficult task, and requires effects on human resources, work arrangements, and management. Therefore, only a limited number of intervention studies have shown an improvement of work ability during aging. This article introduces some possibilities regarding how to make work ability interventions more successful.
Automatic Neural Question Generation using Community-based Question Answering Systems

In this thesis, we address the problem of opinion question generation. The motivation behind this task is to provide users with more question samples related to their query when using search engines. In our view, one of the datasets that is closest to peoples’ thoughts, informal and casual speech are Community Question Answering (CQA) forums, where one can post questions, and other users can answer them. Specifically, we perform experiments on the Amazon question/answer dataset. Unlike the conventional approaches that have tackled the question generation problem with hand-crafted rules, our approach is entirely data-driven. We model our problem with the sequence to sequence approach using an encoder-decoder structure, which has shown significant improvement in different natural language processing research areas in recent years. Our model benefits from the attention mechanism, which assists the model in focusing on a specific part of the input sentence. Furthermore, we provide solutions to the following problems: repetition of words and generating outside of vocabulary tokens. We provide a detailed explanation of the performance of the system. Experimental results show an improvement in automatic evaluation metrics such as the BLEU score over the state-ofthe-art question generation system.
RETRACTED ARTICLE: MiR-140-5p inhibits cell proliferation and metastasis by regulating MUC1 via BCL2A1/MAPK pathway in triple negative breast cancer

Statement of Retraction We, the Editors and Publisher of the journal Cell Cycle, have retracted the following article: Bofan Yu, Wei You, Guang Chen, Yang Yu, and Qinheng Yang. MiR-140-5p inhibits cell proliferation and metastasis by regulating MUC1 via BCL2A1/ MAPK pathway in triple negative breast cancer. Cell Cycle. 2019;18(20):2641–2650. doi: 10.1080/15384101.2019. 1653107 Since publication, concerns have been raised about the integrity of the data in the article. When approached for an explanation, the authors have been unable to address all the concerns raised and have not been able to provide all their original data. As verifying the validity of published work is core to the integrity of the scholarly record, we are therefore retracting the article. The corresponding author listed in this publication has been informed. The authors have agreed to retract the article. We have been informed in our decision-making by our policy on publishing ethics and integrity and the COPE guidelines on retractions. The retracted article will remain online to maintain the scholarly record, but it will be digitally watermarked on each page as ‘Retracted’.
Trait self-control, exercise and exercise ambition: Evidence from a healthy, adult population

ABSTRACT This study explores if self-control predicts exercise ambition and actual exercise, and if exercise ambition predicts actual exercise. Members and guests at a not-for-profit sports club were invited to participate. 264 individuals completed a self-reported (Self-Control Scale) measure of self-control, and responded to questions about actual exercise as well as exercise ambition prior to their workout. Main Outcome Measures are exercise ambition, actual exercise, and the difference between them. We find that trait self-control predicts both actual exercise and exercise ambition. Exercise ambition also predicts actual exercise. The results suggest a path from self-control, via exercise ambition, to actual exercise. Individuals with relatively low self-control might benefit from some aid in setting goals for their workouts and committing to exercise.
Structure and dynamics of inactive and active MARK4: conformational switching through the activation process

Abstract Microtubule affinity-regulating kinase 4 (MARK4) is a serine/threonine protein kinase belonging to a highly-conserved group of PAR proteins that phosphorylate microtubule-associated proteins (tau, MAP2 and MAP4) and regulate cell polarity. MARK4 isoform is mainly found in brain tissue, causing microtubule destabilization in neuronal cells and also tau-protein phosphorylation seen in Alzheimer’s disease. In this study, the dynamic behavior of inactive and active structures of human MARK4 was studied by modeling and molecular dynamics simulations and motions of the protein through activation process were interpolated. Structure and dynamics of the protein active state in presence of Mg-ATP were also studied. The results suggest for occlusion of ATP binding site by activation loop, as the main inactivation mechanism. Data also justify the necessity of UBA (ubiquitin-associated) domain auto-inhibitory role. Within the inactive and active state, G-loop is highly fluctuating and assumes an open conformation and away from Mg-ATP complex. This behavior raises the assumption that this loop may experience other stabilizing interactions with residues out of the kinase core which help its stabilization during the phospho-transfer reaction. Mg-ATP complex localization is well preserved within the catalytic cleft through the electrostatic interactions. However, minor rearrangements of water molecules around this complex are allowed, which may further refine the delicate position of this complex for phospho-transfer reaction. Communicated by Ramaswamy H. Sarma
Quantifying energy expenditure in childhood: utility in managing pediatric metabolic disorders

ABSTRACT Background Energy expenditure prediction equations are used to estimate energy intake based on general population measures. However, when using equations to compare with a disease cohort with known metabolic abnormalities, it is important to derive one's own equations based on measurement conditions matching the disease cohort. Objective We aimed to use newly developed prediction equations based on a healthy pediatric population to describe and predict resting energy expenditure (REE) in a cohort of pediatric patients with thyroid disorders. Methods Body composition was measured by DXA and REE was assessed by indirect calorimetry in 201 healthy participants. A prediction equation for REE was derived in 100 healthy participants using multiple linear regression and z scores were calculated. The equation was validated in 101 healthy participants. This method was applied to participants with resistance to thyroid hormone (RTH) disorders, due to mutations in either thyroid hormone receptor β or α (β: female n = 17, male n = 9; α: female n = 1, male n = 1), with deviation of REE in patients compared with the healthy population presented by the difference in z scores. Results The prediction equation for REE = 0.061 * Lean soft tissue (kg) − 0.138 * Sex (0 male, 1 female) + 2.41 (R2 = 0.816). The mean ± SD of the residuals is −0.02 ± 0.44 kJ/min. Mean ± SD REE z scores for RTHβ patients are −0.02 ± 1.26. z Scores of −1.69 and −2.05 were recorded in male (n = 1) and female ( n = 1) RTHα patients. Conclusions We have described methodology whereby differences in REE between patients with a metabolic disorder and healthy participants can be expressed as a z score. This approach also enables change in REE after a clinical intervention (e.g., thyroxine treatment of RTHα) to be monitored.