Reviewerly/exHarmony huggingface dataset:
Improving Traceability Link Recovery Using Fine-grained Requirements-to-Code Relations

Traceability information is a fundamental prerequisite for many essential software maintenance and evolution tasks, such as change impact and software reusability analyses. However, manually generating traceability information is costly and error-prone. Therefore, researchers have developed automated approaches that utilize textual similarities between artifacts to establish trace links. These approaches tend to achieve low precision at reasonable recall levels, as they are not able to bridge the semantic gap between high-level natural language requirements and code. We propose to overcome this limitation by leveraging fine-grained, method and sentence level, similarities between the artifacts for traceability link recovery. Our approach uses word embeddings and a Word Mover's Distance-based similarity to bridge the semantic gap. The fine-grained similarities are aggregated according to the artifacts structure and participate in a majority vote to retrieve coarse-grained, requirement-to-class, trace links. In a comprehensive empirical evaluation, we show that our approach is able to outperform state-of-the-art unsupervised traceability link recovery approaches. Additionally, we illustrate the benefits of fine-grained structural analyses to word embedding-based trace link generation.
Smart objects recommendation based on pre-training with attention and the thing–thing​ relationship in social Internet of things

In Internet of things (IoT) and Social Internet of things (SIoT), how to select or recommend suitable smart objects from an ocean of smart objects has become an increasingly critical issue. In this paper, we propose a novel neural network model called BLA (BERT and Bi-LSTM with attention) for smart objects scoring tasks to make recommendations in social Internet of things. The model uses a BERT network to obtain the sentence vectors for a smart object related text, and then uses Bi-LSTM with two types of attention mechanisms to extract representations of the smart object vectors. The devised attention mechanism contains a self-attention (SA) layer and a global-attention (GA) layer. The SA layer is able to estimate the importance of sentences or fields, which in a certain sense can substitute for manually defined features at the sentence and field level. The GA layer can measure the relationships between sentences (or fields) and user requirements, which further helps the model obtain more effective smart object vectors. The thing–thing relationship of Internet of things is introduced into the model to inprove the recommendation effect. Experimental results on the datasets demonstrate that our model outperforms other baseline methods.
Assessment and Prediction of Maize Production Considering Climate Change by Extreme Learning Machine in Czechia

Machine learning algorithms have been applied in the agriculture field to forecast crop productivity. Previous studies mainly focused on the whole crop growth period while different time windows on yield prediction were still unknown. The entire growth period was separated into each month to assess their corresponding predictive ability by taking maize production (silage and grain) in Czechia. We present a thorough assessment of county-level maize yield prediction in Czechia using a machine learning algorithm (extreme learning machine (ELM)) and an extensive set of weather data and maize yields from 2002 to 2018. Results show that sunshine in June and water deficit in July were vastly influential factors for silage maize yield. The two primary climate parameters for grain maize yield are minimum temperature in September and water deficit in May. The average absolute relative deviation (AARD), root mean square error (RMSE), and coefficient (R2) of the proposed models are 6.565–32.148%, 1.006–1.071%, 0.641–0.716, respectively. Based on the results, silage yield will decrease by 1.367 t/ha (3.826% loss), and grain yield will increase by 0.337 t/ha (5.394% increase) when the max temperature in May increases by 2 °C. In conclusion, ELM models show a great potential application for predicting maize yield.
On the impact of using different templates on creating and understanding user stories

Context: User stories are often used for elicitation and prioritisation of requirements. However, the lack of a widely adopted user story template, covering benefit and the usage (or not) of a persona, can affect user stories’ quality, leading to ambiguity, lack of completeness, or accidental complexity. Objectives: Our goal was to analyse the differences between 4 alternative user story templates when creating and understanding user stories. Methods: We conducted a quasi-experiment. We asked 41 participants to perform creation and understanding tasks with the user story templates. We measured their accuracy, using metrics of task success; their speed, with task duration; visual effort, collected with an eye-tracker; and participants’ perceived effort, evaluated with NASA-TLX. Results: Regarding the impact of the different templates in creating user stories, we observed statistically significant differences in some of the metrics for accuracy, speed and visual effort. For understanding user stories, we observed small differences in terms of visual effort. Conclusions: Although some templates outperformed others in a few metrics, no template obtained the best overall result. As such, we found no compelling evidence that one template is "better" than the others.
An Optimized K-means with Density and Distance-Based Clustering Algorithm for Multidimensional Spatial Databases

From massive and complex spatial database, the useful information and knowledge are extracted using spatial data mining. To analyze the complexity, efficient clustering algorithm for spatial database has been used in this area of research. The geographic areas containing spatial points are discovered using clustering methods in many applications. With spatial attributes, the spatial clustering problem have been designed using many approaches, but non-overlapping constraints are not considered. Most existing data mining algorithms suffer in high dimensions. With non-overlapping named as Non Overlapping Constraint based Optimized K-Means with Density and Distance-based Clustering (NOC-OKMDDC),a multidimensional optimization clustering is designed to solve this problem by the proposed system and the clusters with diverse shapes and densities in spatial databases are fast found. Proposed method consists of three main phases. Using weighted convolutional Neural Networks(Weighted CNN), attributes are reduced from the multidimensional dataset in this first phase. A partition-based algorithm (K-means) used by Optimized K-Means with Density and Distance-based Clustering (OKMDD) and several relatively small spherical or ball-shaped sub clusters are made by Clustering the dataset in this second phase. The optimal sub cluster count is performed with the help of Adaptive Adjustment Factor based Glowworm Swarm Optimization algorithm (AAFGSO). Then the proposed system designed an Enhanced Penalized Spatial Distance (EPSD) Measure to satisfy the non-overlapping condition. According to the spatial attribute values, the spatial distance between two points are well adjusted to achieving the EPSD. In third phase, to merge sub clusters the proposed system utilizes the Density based clustering with relative distance scheme. In terms of adjusted rand index, rand index, mirkins index and huberts index, better performance is achieved by proposed system when compared to the existing system which is shown by experimental result.
COVID-19 and online education: Digital inequality and other dilemmas of rural students in accessing online education during the pandemic

It is widely assumed among academicians that the COVID-19 pandemic has negative implications for the education of school students. However, institutions tried to balance that limitation by using online education, and there exist some inequalities among students. Most of the studies conducted during COVID-19 on online education focused on urban school students and their access to online education. In particular, rural school students and their online education remain an open question. Twenty in-depth interviews with rural student respondents determine the fundamental problems and challenge the rural school students’ face in online education during the COVID-19 pandemic lockdown. The study identifies six major problems of rural students: inadequate technology, unacquainted academic atmosphere, digital disconnect, physical well-being, the distractions inherent with the medium, and digital illiteracy. The identified constraints draw inferences to a critical concept in online learning that is digital inequality. Digital inequality refers to the disparity in the access, distribution of technology, information because of various socio-economic and cultural factors. The study also discusses the suggestions of rural students regarding the betterment of online education. The recommendations from the rural students include providing appropriate technological infrastructure, facilitating technical assistance and providing a convenient academic atmosphere. The suggestions are pointing towards the idea of digital inclusion that is vital in online education. Digital inclusion is defined as the ability of individuals or groups of people to access and use information and communication technologies. It is not only about access in a broader sense the opportunities of using innovative hardware and software technology, content and services, getting proper digital literacy pieces of training and the effective use of these services. The findings of the study will help to bridge the disparities in online education. These findings will help the academic community to identify the needs of rural children. It will help build infrastructure for online learning and give extensive support to the school children of rural communities. These findings are also vital for the communication scholars as the disparity in the distribution of information and knowledge is a prime concern for them.
DevOps-RAF: An assessment framework to measure DevOps readiness in software organizations

DevOps is a software process that embraces agility practices on software development and delivery, requires that organization meet strict culture and tooling requirements, and has become widely used by leading software development companies, as well as Latin-American Small and Medium Enterprises (SME). Several DevOps maturity models have been proposed around CAMS (Culture, Automation, Measurement, Sharing) or variations, but they do not allow to identify capability gaps in specific organizations; and altough some have been evaluated at European and USA companies, none are LatAm SME-focused. This article describes DevOps-RAF, an assessment framework to (1) evaluate SME’s readiness to adapt and adopt DevOps practices, culture and tooling, (2) identify capability gaps, and (3) propose improvement roadmaps. DevOps-RAF exploits the notion of DevOps as Dev + CI + Ops, and recognizes four domains for them (Agile Development, Agile Operations, Continuous Integration) and the cross-cutting Culture; 17 key performance indicators (KPI); and four maturity levels (Initial, Intermediate, Advanced, and Optimized). This domain structure allows to assess both development organizations (doing Dev and Integration) and their customers (doing Integration and Ops). An initial validation of DevOps-RAF formulation and structure was carried with a survey to several DevOps Chilean industry experts; respondents suggested to retain 71% of proposed KPI’s, change 29%, and drop none. Once pilot-tested and stabilized, DevOps-RAF will allow to systematically assess and mitigate the capacity gaps that bedevil LatAm software SME’s in adopting DevOps.
Research on News Text Classification Based on Deep Learning Convolutional Neural Network

Aiming at the problems of low classification accuracy and low efficiency of existing news text classification methods, a new method of news text classification based on deep learning convolutional neural network is proposed. Determine the weight of the news text data through the VSM (Viable System Model) vector space model, calculate the information gain of mutual information, and determine the characteristics of the news text data; on this basis, use the hash algorithm to encode the news text data to calculate any news. The spacing between the text data realizes the feature preprocessing of the news text data; this article analyzes the basic structure of the deep learning convolutional neural network, uses the convolutional layer in the convolutional neural network to determine the change value of the convolution kernel, trains the news text data, builds a news text classifier of deep learning convolutional neural network, and completes news text classification. The experimental results show that the deep learning convolutional neural network can improve the accuracy and speed of news text classification, which is feasible.
Secure online payment through facial recognition and proxy detection with the help of TripleDES encryption

Nowadays, there is a tremendous increase in the use of online secure payment systems. Even roadside shops all over India and other countries have access to online payment which made each and every person to go cash-free. But there is one important issue we need to consider which is the security during online transactions. There are several new techniques that are being developed nowadays but each has its own pros and cons. In order to enhance the security during online transaction, we have introduced a two-way authentication system which increases the security during online transaction. This system includes facial and proxy detection before entering the UPI pin. The algorithm used in this paper, will further improve the security and efficiency of facial recognition since it embeds 128 feature points of the user’s face thereby increasing the accuracy of the system. While the data is being transmitted for verification, we have included tripleDES encryption to further enhance the security of the work. After the verification is done by the system, the bank balance of the user is verified and the transaction is done. The proposed system has better efficiency when compared to the existing works and also its simple UI makes it easy to use.
Abnormal Detection in Big Data Video with an Improved Autoencoder

With the rapid growth of video surveillance data, there is an increasing demand for big data automatic anomaly detection of large-scale video data. The detection methods using reconstruction errors based on deep autoencoders have been widely discussed. However, sometimes the autoencoder could reconstruct the anomaly well and lead to missing detections. In order to solve this problem, this paper uses a memory module to enhance the autoencoder, which is called the memory-augmented autoencoder (Memory AE) method. Given the input, Memory AE first obtains the code from the encoder and then uses it as a query to retrieve the most relevant memory items for reconstruction. In the training phase, the memory content is updated and encouraged to represent prototype elements of normal data. In the test phase, the learned memory elements are fixed, and reconstruction is obtained from several selected memory records of normal data. So, the reconstruction will tend to be close to normal samples. Therefore, the reconstruction of abnormal errors will be strengthened for abnormal detection. The experimental results on two public video anomaly detection datasets, i.e., Avenue dataset and ShanghaiTech dataset, prove the effectiveness of the proposed method.
Deep network based stereoscopic image quality assessment via binocular summing and differencing

With the development of deep networks in dealing with various visual tasks, the deep network based on binocular vision is expected to tackle the issue of stereoscopic image quality assessment. Here, we present a stereoscopic image quality assessment method using the deep network with four channels together, which takes the left view, right view, binocular summing view, and binocular differencing view as the inputs of the network. The visual features are enhanced through the concatenation in a weighted way, so that the binocular vision can be adequately included in the binocular addition and subtraction information. Compared with the state-of-the-art metrics, the proposed method exhibits relatively high performances on four benchmark databases.
RoGPT2: Romanian GPT2 for Text Generation

Text generation is one of the most important and challenging tasks in NLP, where models have shown a significant performance increase in recent years. However, most generative models are available only for English, whereas low-resource languages like Romanian have no available alternatives. As such, we introduce RoGPT2, a Romanian version of the GPT2 model, trained on the largest corpus available for the Romanian language. Three versions of the model were trained, namely base (124M parameters), medium (354M parameters), and large (774M parameters). Six tasks from the LiRo benchmark were selected to test the performance and limitations of our encoder versus BERT-Base models for Romanian (RoBERT, BERT-ro-base, and RoDiBERT). RoGPT2 manages to achieve similar or even better performance, except for the task of zero-shot learning cross-lingual question answering. RoGPT2 also obtains state-of-the-art results for grammar error correction (RoGEC) using the RONACC corpus, thus arguing for the model’s capability to generate grammatically correct text (F <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0.5</inf> = 69.01). In addition, we introduce two use cases in which we showcase the different versions and explore the extent to which RoGPT2 is able to continue Romanian news articles. After fine-tuning, the model generated rather long text which accounts for the context of the news.
Explanatory Variables of Dropout in Colombian Public Education: Evolution Limited to Coronavirus Disease

&lt;p style="text-align: justify;"&gt;Student dropout, defined as the temporary or definitive suspension of the exercise of the right to education, is attributable to multiple variables classified into individual, academic, institutional, and socioeconomic determinants which may be exacerbated in the context of the Coronavirus disease (COVID-19) pandemic. Consequently, this work aims to synthesize, from the available evidence, the behaviour and influence of the explanatory variables of school dropout in infant school, primary school and, high school in Colombia for the period 2014-2019 compared to the period 2020-2021 under the COVID-19 pandemic conditions. The research methodology consisted of a systematic review of 125 indexed articles for 2014-2019 and 32 reports related to dropout in Colombian Basic education for the 2020-2021 period. The systematic review of the 157 articles revealed that dropout was studied and explained in both time periods, mainly from the academic determinant whose most cited explanatory variables were: ‘teachers’, ‘curriculum’ and ‘methodologies used’. Moreover, it could be perceived that in the period 2014-2019, the socioeconomic variable was the second dropout determinant, considering ‘family income” as the most important indicator, while in 2020-2021 the “infrastructure” and the ‘political environment’ remained as the most dominant. Lastly, in 2020-2021, the variable ‘teachers’ was highly cited showing that their practice made students maintain their interest despite the physical distance.&lt;/p&gt;
Gun Detection in Real-Time, using YOLOv5 on Jetson AGX Xavier

Automating the detection of weapons from video surveillance images is a difficult task due to: lighting, focus, resolution, among others. Solving this problem would be very useful for citizen security purposes. In this sense, this research work trains a weapon detection system based on YOLOv5 (You Only Look Once) for different data sources, reaching an accuracy of 98.56 % in video surveillance images, performing Real-Time inferences reaching 33 fps on Nvidia's Jetson AGX Xavier which is a good result compared to other existing research in the state of the art.
Attention Based Deep Multiple Instance Learning Approach for Lung Cancer Prediction using Histopathological Images

Deep Neural Networks using histopathological images as an input currently embody one of the gold standards in automated lung cancer diagnostic solutions, with Deep Convolutional Neural Networks achieving the state of the art values for tissue type classification. One of the main reasons for such results is the increasing availability of voluminous amounts of data, acquired through the efforts employed by extensive projects like The Cancer Genome Atlas. Nonetheless, whole slide images remain weakly annotated, as most common pathologist annotations refer to the entirety of the image and not to individual regions of interest in the patient's tissue sample. Recent works have demonstrated Multiple Instance Learning as a successful approach in classification tasks entangled with this lack of annotation, by representing images as a bag of instances where a single label is available for the whole bag. Thus, we propose a bag/embedding-level lung tissue type classifier using Multiple Instance Learning, where the automated inspection of lung biopsy whole slide images determines the presence of cancer in a given patient. Furthermore, we use a post-model interpretability algorithm to validate our model's predictions and highlight the regions of interest for such predictions.
Using Different Error Handling Strategies to Facilitate Older Users’ Interaction With Chatbots in Learning Information and Communication Technologies

To support older users' accessibility and learning of the prevalent information and communication technologies (ICTs), libraries, as informal learning institutes, are committed to information literacy education activities with friendly interfaces. Chatbots using Voice User Interfaces (VUIs) with natural and intuitive interactions have received growing research and practical attention; however, older users report regular frustrations and problems in using them. To serve as a basis for the subsequent design and development of an automated dialog mechanism in senior-friendly chatbots, a between-subject user experiment was conducted with 30 older adults divided into three groups. The preliminary findings on their interactions with the voice chatbots designed with different error handling strategies were reported. Participants' behavioral patterns, performances, and the tactics they employed in interacting with the three types of chatbots were analyzed. The results of the study showed that the use of multiple error handling strategies is beneficial for older users to achieve effectiveness and satisfaction in human-robot interactions, and facilitate their attitude toward information technology. This study contributes empirical evidence in the genuine and pragmatic field of gerontechnology and expands upon voice chatbots research by exploring conversation errors in human-robot interactions that could be of further application in designing educational and living gerontechnology.