Reviewerly/exHarmony huggingface dataset:
Impact in Software Engineering Activities After One Year of COVID-19 Restrictions for Startups and Established Companies

The restrictions imposed by the COVID-19 pandemic required software development teams to adapt, being forced to work remotely and adjust the software engineering activities accordingly. In the studies evaluating these effects, a few have assessed the impact on software engineering activities from a broader perspective and after a period of time when teams had time to adjust to the changes. No studies have been found comparing software startups and established companies either. This paper aims to investigate the impacts of COVID-19 on software development activities after one year of the pandemic restrictions, comparing the results between startups and established companies. Our approach was to design a cross-sectional survey and distribute it online among software development companies worldwide. The participants were asked about their perception of COVID-19’s pandemic impact on different software engineering activities: requirements engineering, software architecture, user experience design, software implementation, and software quality assurance. The survey received 170 valid answers from 29 countries, and for all the software engineering activities, we found that most respondents did not observe a significant impact. The results also showed that software startups and established companies were affected differently since, in some activities, we found a negative impact in the former and a positive impact in the latter. Regarding the time spent on each software engineering activity, most of the answers reported no change, but on those that did, the result points to an increase in time. Thus, we cannot find any relation between the change in time of effort and the reported positive or negative impact.
Knowledge Transfer to Solve Split and Rephrase

The usage of large pre-trained language models like BERT and GPT has brought a transformative impact on various natural language processing (NLP) tasks in recent times. However, a significant challenge faced in many NLP tasks is the scarcity of high-quality datasets required for fine-tuning these models for specific tasks. In this study, we introduce an innovative framework designed to address the issue of dataset insufficiency in the Split-and- Rephrase (SR) task. We achieve this by leveraging the knowledge embedded in a rule-based model and employing it to supervise the fine-tuning process of large pre-trained language models, enabling them to perform the SR task effectively without relying on labeled data. Our framework for knowledge transfer holds promise as a potential solution for other NLP tasks involving rule-based models.
Using Accessible Motion Capture in Educational Games for Sign language Learning

Various studies show that multimodal interaction technologies, especially motion capture in educational environments, can significantly improve and support educational purposes such as language learning. In this paper, we introduce a prototype that implements finger tracking and teaches the user different letters of the German fingerspelling alphabet. Since most options for tracking a user’s movements rely on hardware that is not commonly available, a particular focus is laid on the opportunities of new technologies based on computer vision. These achieve accurate tracking with consumer webcams. In this study, the motion capture is based on Google MediaPipe. An evaluation based on user feedback shows that the prototype’s capabilities meet the functional requirements. This study corroborates the thesis that new technologies, such as motion-tracking-based software, can make language learning more accessible.
Models and mechanisms for managing competencies of IT services users

Abstract The subject of the article is the models and mechanisms for managing the competencies of IT services users applied as intellectual support of decision-makers on issues of professional retraining and professional development of personnel. Models and mechanisms correspond to two problem situations, requiring, respectively, the implementation of a unified or personalized approach to the competencies formation, and are an integration of expert information, models of scalar and vector optimization, game models, as well as discrete optimization methods, simulation and game modeling. The use of the proposed models and mechanisms is demonstrated by examples of the formation of professional retraining and development programs for users of ERP system. However, they can be used to manage the competencies of specialists in other fields.
Research on the Influence of BBL and EBL on Students’ Learning Factors in Digital Sculpture Course

In recent years, the development of 3D animation and game industry has promoted the demand for digital sculpture, making digital sculpture technology an indispensable application for 3D learning. Because digital sculpting software is complex and difficult to learn, the course combines blend-based learning and example-based learning to enhance students’ motivation and learning outcomes. At the end of the course, 96 students were questionnaired and a total of 71 valid questionnaires were collected. 66.2% of students believe that digital sculpture is easier to create character models than 3D animation software, 60.6% of students rely on course videos to improve learning, and 58.9% of students think course videos are very useful. 69% of students think the sample design is interesting and shows the impact of the EBL design. The questionnaire was analyzed by factor analysis and summarized into three constructs. The results show that blend-based learning and example-based learning have a clear contribution to curriculum design, learning response and motivation.
A Novel Design for Voice-Enabled Home Automation and Personalized Recommendation System

Technology has completely changed the way we interact with the machines around us. Today, with everyone having access to devices like smartphones, voice has now become the primary and the most preferred mode of communication. We can also use this mode of communication to control various devices and applications at our home—a voice-controlled standalone device. We can develop a device with the help of Raspberry Pi and Relays to automate some of the manual day-to-day activities like switching on/off electrical appliances at home. Further, with the help of content-based recommendation, we can enable a device to suggest recommendations based on that individual’s past activities. Hence, this device can serve as a voice-controlled personalized home assistant.
Use of Agile Practices in Start-ups

Context. Software start-ups have shown their ability to develop and launch in- novative software products and services. Small, motivated teams and uncertain project scope makes start-ups good candi ...
Assessing Holistic Impacts of Major Events on the Bitcoin Blockchain Network.

As the pioneer of blockchain technology, Bitcoin is the most popular cryptocurrency to date. Given its dramatic price spikes (and crashes) along with the never-ending news from SEC regulations to security breaches, there seems to be a lack of understanding about the dynamics of cryptocurrencies. These dynamics are believed to be affected by various political, security, financial, and regulatory events. In this paper, we present an efficient framework for holistic analysis of cryptocurrency fluctuations by introducing the Impact-Score metric to distinguish event-induced changes from normal variations. We have applied our framework to 16 major worldwide events and the Bitcoin blockchain network (defined as Bitcoin transaction and users, blockchain data, and memory pool data) from 2016-2018. The results show that a majority of the events are correlated with substantial network changes. We observed roughly generalizable correlations between event types (e.g. financial events) and sub-structures of the Bitcoin blockchain network. Subgroups of these events have strongly consistent temporal impacts on specific facets (e.g. activity or fees) of the Bitcoin ecosystem. Furthermore, we demonstrate the robustness of our process by correlating a majority of spikes in network/subnetwork change with major events.
Inferring finer-grained human information with multi-modal cross-granularity learning

Existing machine learning algorithms for human information inference are typically data-driven models trained on carefully labeled datasets. Given the significant labeling effort, traditional pure data-driven approaches are challenging to implement for emerging smart applications requiring long-term finer-grained information. Taking activities of daily life (ADL) tracking for elders as an example, prior work mostly focused on context-level information learning such as cooking and cleaning. [8]. However, new applications such as evaluating elders' cognitive impairments progress by tracking their ADL engagement requires finer-grained, i.e., action-level information [7]. In practice, labeling the day-length data at such granularity can be very expensive and requires a lot of human efforts [9]. My research focuses on the inference problems in the scope of human physical condition monitoring and activity recognition with limited labeled data. To alleviate the effort of labeling large amounts of data, prior works on semi-supervised learning combine a small amount of labeled data with a large amount of unlabeled data to train the model. However, as the label granularity (number of classes) increasing, the difficulty to distinguish nuance distinctions between finer-grained classes escalates as well. This makes training a robust semi-supervised model for finer-grained classification with less labels difficult if not impossible. Fortunately, coarse-grained (context-level) labels is usually available or cheaper to obtain in practice. In this case, the multi-granularity hierarchy between finer and coarse labels follows the aggregation relation defined in [5]. This hierarchical relation can be leveraged in the tasks of inferring finer-grained information. In addition, it is illustrated by the previous study that co-located multi modality sensing systems capture complementary aspects of the same event [6]. The research question I focus on is how to infer finer-grained human information with coarse-grained labeled data leveraging complementary multi-modal sensing? I target three directions: 1) a cross-granularity semi-supervised setting: how to utilize coarse-grained labeled data with a small amount of finer-grained labeled data to infer finer-grained human information, 2) cross-granularity relationship learning: how to learn the multi-granularity class hierarchy from data and further help the finer-grained human information acquisition, 3) enhancing inference granularity by leveraging multi-model sensing: how to leverage the complimentary co-located multiple sensing modalities to accurately infer finer-grained human information?
Domain-Dependent Speaker Diarization for the Third DIHARD Challenge

This report presents the system developed by the ABSP Laboratory team for the third DIHARD speech diarization challenge. Our main contribution in this work is to develop a simple and efficient solution for acoustic domain dependent speech diarization. We explore speaker embeddings for \emph{acoustic domain identification} (ADI) task. Our study reveals that i-vector based method achieves considerably better performance than x-vector based approach in the third DIHARD challenge dataset. Next, we integrate the ADI module with the diarization framework. The performance substantially improved over that of the baseline when we optimized the thresholds for agglomerative hierarchical clustering and the parameters for dimensionality reduction during scoring for individual acoustic domains. We achieved a relative improvement of $9.63\%$ and $10.64\%$ in DER for core and full conditions, respectively, for Track 1 of the DIHARD III evaluation set.
Research on recommendation algorithm combining item social regularization and variational encoder

Variational autoencoder is a very concise and effective unsupervised learning method, which can achieve excellent performance when applied in the field of recommendation systems. At present, most of the research on recommendation algorithms consider the user's social relationship, and rarely consider the social relationship between items. Focused on the problem of how to improve the recommendation accuracy of the recommendation system under the condition of sparse scoring data, a recommendation algorithm (SR-CVAE) for fusion item social relationship regularization and variational encoder are proposed. In this paper, the user-item feedback information, item content information, social relationship between items, and tag information are combined, and integrated into a variational encoder with a hidden variable generation model, and then combing the variational encoder with the discriminant model and applying it to the collaborative filtering recommendation algorithm to improve the accuracy of the algorithm. Experimental results on two CiteULike datasets show that the proposed algorithm has better interpretation and recommendation performance than other models.
The Morumotto / Sicaqo project

Morumotto is a software part of the Siqaco seismic quality control project. The software is designed to automatically retrieve data from a set of different sources (seismic station, local archives...) in order to build a seismic data archive. It also corrects overlaps and flags missing data. Morumotto is modular, as it is able to integrate new source plugins to adapt to any type of seismic network and to any new data format and structure. It comes with a web interface for visualization, quality control and configuration. This poster was presented during the Resif Scientific and Technical Meetings that took place in Biarritz in November 2019. Resif is a national research infrastructure dedicated to the observation and understanding of the Earth's internal structure and dynamics. Resif is based on high technology observation networks, composed of seismological, geodetic and gravimetric instruments deployed in a dense manner throughout the French territory. The data collected allow the study of ground deformation, surface and deep structures, local and global seismicity and natural hazards, particularly seismic, on the French territory with a high spatio-temporal resolution. Resif is integrated into the European (EPOS - European Plate Observing System) and worldwide instruments that allow to image the Earth's interior in its entirety and to study many natural phenomena.
A Linkage-based Doubly Imbalanced Graph Learning Framework for Face  Clustering

In recent years, benefiting from the expressive power of Graph Convolutional Networks (GCNs), significant breakthroughs have been made in face clustering area. However, rare attention has been paid to GCN-based clustering on imbalanced data. Although imbalance problem has been extensively studied, the impact of imbalanced data on GCN- based linkage prediction task is quite different, which would cause problems in two aspects: imbalanced linkage labels and biased graph representations. The former is similar to that in classic image classification task, but the latter is a particular problem in GCN-based clustering via linkage prediction. Significantly biased graph representations in training can cause catastrophic over-fitting of a GCN model. To tackle these challenges, we propose a linkage-based doubly imbalanced graph learning framework for face clustering. In this framework, we evaluate the feasibility of those existing methods for imbalanced image classification problem on GCNs, and present a new method to alleviate the imbalanced labels and also augment graph representations using a Reverse-Imbalance Weighted Sampling (RIWS) strategy. With the RIWS strategy, probability-based class balancing weights could ensure the overall distribution of positive and negative samples; in addition, weighted random sampling provides diverse subgraph structures, which effectively alleviates the over-fitting problem and improves the representation ability of GCNs. Extensive experiments on series of imbalanced benchmark datasets synthesized from MS-Celeb-1M and DeepFashion demonstrate the effectiveness and generality of our proposed method. Our implementation and the synthesized datasets will be openly available on https://github.com/espectre/GCNs_on_imbalanced_datasets.
PERFORMANCE EVALUATION OF CLUSTER BASED HETEROGENEOUS SYSTEMS USING ADAPTIVE TASK TUNING

MapReduce deployment in datacenters and clouds presents a number of challenges in terms of attaining optimal task performance. As a result of server replacement and multi-tenant performance, datacenters and clouds are generally very heterogeneous compared to in-house clusters. As most Mapreduce deployments assume homogeneous clusters, heterogeneity can lead to substantial load imbalances in workflow performance and low utilization of clusters. Notwithstanding the optimisations of task scheduling and load balance, adaptive self-tuning on heterogeneous clusters still is inadequately carried out. The interest in delivering a simple scalable and cost-efficient parallel computing solution for large-scale applications has recently been enormsous with cluster computing platforms. Several adaptive methods have been proposed for the scheduling of adapted and homogenous clusters to increase the performance. However, commodity clusters are naturally adaptive self-tuning task-controlled framework, which is generally upgraded to improve clustering hardware and also introduced new quick machines to boost cluster performance.
Hamiltonian Monte Carlo for Regression with High-Dimensional Categorical  Data

Latent variable models are increasingly used in economics for high-dimensional categorical data like text and surveys. We demonstrate the effectiveness of Hamiltonian Monte Carlo (HMC) with parallelized automatic differentiation for analyzing such data in a computationally efficient and methodologically sound manner. Our new model, Supervised Topic Model with Covariates, shows that carefully modeling this type of data can have significant implications on conclusions compared to a simpler, frequently used, yet methodologically problematic, two-step approach. A simulation study and revisiting Bandiera et al. (2020)'s study of executive time use demonstrate these results. The approach accommodates thousands of parameters and doesn't require custom algorithms specific to each model, making it accessible for applied researchers
A Transformer-based Math Language Model for Handwritten Math Expression  Recognition

Handwritten mathematical expressions (HMEs) contain ambiguities in their interpretations, even for humans sometimes. Several math symbols are very similar in the writing style, such as dot and comma or 0, O, and o, which is a challenge for HME recognition systems to handle without using contextual information. To address this problem, this paper presents a Transformer-based Math Language Model (TMLM). Based on the self-attention mechanism, the high-level representation of an input token in a sequence of tokens is computed by how it is related to the previous tokens. Thus, TMLM can capture long dependencies and correlations among symbols and relations in a mathematical expression (ME). We trained the proposed language model using a corpus of approximately 70,000 LaTeX sequences provided in CROHME 2016. TMLM achieved the perplexity of 4.42, which outperformed the previous math language models, i.e., the N-gram and recurrent neural network-based language models. In addition, we combine TMLM into a stochastic context-free grammar-based HME recognition system using a weighting parameter to re-rank the top-10 best candidates. The expression rates on the testing sets of CROHME 2016 and CROHME 2019 were improved by 2.97 and 0.83 percentage points, respectively.
Reviewing Enron Scandal

According to Enron cases, it was the biggest scandal ever made in the U.S. history. A company that huge finally collapses within one night. All those problems occur during the responsibilities of two biggest scams, Jeff Skilling and Andy Fastows. No one knows that these two persons are a very bad person. No one thought so until the day Enron falls to the ground.
Application of deep learning algorithms and architectures in the new generation of mobile networks

Operators of modern mobile networks are faced with significant challenges in providing the requested level of service to an ever increasing number of user entities. Advanced machine learning techniques based on deep architectures and appropriate learning methods are recognized as promising ways of tackling the said challenges in many aspects of mobile networks, such as mobile data and mobility analysis, network control, network security and signal processing. Having firstly presented the background of deep learning and related technologies, the paper goes on to present the architectures used for deployment of deep learning in mobile networks. The paper continues with an overview of applications and services related to the new generation of mobile networks that employ deep learning methods. Finally, the paper presents practical use case of modulation classification as implementation of deep learning in an application essential for modern spectrum management. We complete this work by pinpointing future directions for research.
Improved Search Pattern with Discrete Wavelet Transform for Video Steganography

The scope of this research is to overcome the computational complexity of the methods for hiding messages inside video frames using various advanced search procedures. For this purpose, the optimal binary coding scheme with low-complexity frame adjustment has been implemented for wireless video transmission applications. To minimize the computational complexity of concealing the existence of messages and the ability to accurately extract them, the Improved Search Pattern with Discrete Wavelet Transform Based Phase Correlation (ISP-DWT-PC) was computed. The approach has also been used to improve the speed of the hidden data retrieval reliably using a simpler search pattern. This paper emphasizes computer forensics procedures, which acts as a tool for data security, reliability, and authentication in video steganography applications works. The results that have been achieved is surpassed the traditional methods in terms of PSNR, MSE, SSIM, Accuracy, surpassed, sensitivity and specificity.
B-Handy: An Augmented Reality System for Biomechanical Measurement

The study of bio-mechanics allows us to infer measurements for every day objects without needing measurement tools. A limitation of this comes from the complex mental transformations of space involved. The efficiency of this task degrades the larger these measurements become. We present B-Handy, a system that offloads this mental workload by providing visual transformations of space in the form of tracking and duplicating the user&#x0027;s hand in AR. It is our hope that this system will simplify the complexity of these mental transformations and increase the efficiency of bio-mechanical measurements.
An Approach to Reduce Error in Regression Problems with an Implementation

Regression is one of the most used techniques in the machine learning domain, so it is very important to have high accuracies in the model. There is a standard machine learning approach to the build model. The basic machine learning pipeline does not perform efficiently in terms of accuracies. In this paper, we will propose a slightly enhanced version of the basic machine learning pipeline which helps attain better model performance. The proposed algorithm works on the principle of retraining the model. The predictions of the first training process are used to train the model a second time. The proposed approach was seen to have improved the accuracies by one to seven percent. We have tested the proposed approach over six algorithms on a traffic prediction dataset.