Reviewerly/exHarmony huggingface dataset:
TACT: An insTrument to Assess the organizational ClimaTe of agile teams - A Preliminary Study

Background: Measuring the organizational climate of agile teams is a challenge for organizations, mainly because of the shortages of specific instruments to agile methodologies. On the other hand, finding companies willing to participate in the preliminary validation of an instrument is a challenge for researchers of the organizational climate. The preliminary validation allows identifying problems and improvements in the instrument. Objective: We present the preliminary evaluation of TACT. TACT is an instrument to assess the organizational climate of agile teams. Its initial version comprises the Communication, Collaboration, Leadership, Autonomy, Decision-Making, and Client Involvement dimensions. Method: We planned and executed a case study considering three development teams. We evaluated TACT using open-ended questions, quantitative methods, and TAM dimensions of Intention to Use, Perceived Usefulness, and Output Quality. Results: TACT allowed to classify the organizational climate of the teams for the Communication, Collaboration, Leadership, Autonomy, Decision-Making, and Client Involvement dimensions. Some items were assessed negatively or neutrally, which represent points of attention. TACT captured the lack of agile ceremonies, the difficulty of the product owner in planning iterations, and the distance in leadership. In addition, TACT dimensions presented high levels of reliability. Conclusions: TACT captured the organizational climate of the teams adequately. The team leaders reported intention of future use. The items that compose TACT can be used by researchers investigating the influence of human factors in agile teams and practitioners who need to design organizational climate assessments of agile teams. By using an instrument adapted to assess the organizational climate of agile teams, an organization can better identify issues and improvement actions aligned with agile values, principles, and practices.
Efficient Nested Key Equation Solver for Short Generalized Integrated Interleaved BCH Codes

Generalized integrated interleaved (GII) codes can nest BCH sub-codewords to form stronger BCH codewords. They are among the best candidates for error correction in the new storage class memories (SCMs). However, SCMs require short codeword length and low redundancy. In this case, the nested key equation solver (KES), which is a key step in GII decoding, has a small number of iterations. The initialization and/or scalar pre-computation in previous nested KES designs have large area and may take even longer time than the iterations themselves. This paper proposes an efficient nested KES design for short GII-BCH codes. The polynomial updating is decomposed into two steps to reduce the critical path without requiring scalar pre-computation. Besides, the KES is reformulated to reduce the number of clock cycles without incurring any area overhead. For an example code over $GF(2^{10})$ that protects 2560 bits with 10% redundancy, the proposed design achieves at least 25% area reduction and 37% reduction on the area-time product averaged over the nested decoding rounds compared to prior efforts.
ARSearch

Stack Overflow and GitHub are two popular platforms containing API-related resources for developers to learn how to use APIs. The platforms are good sources for information about API such as code examples, usages, sentiment, bug reports, etc. However, it is difficult to collect the correct resources regarding a particular API due to the ambiguity of an API method name. An API method name mentioned in the text would only refer to one API, but the method name could match with different APIs. To help people in finding the correct resources for a particular API, we introduce ARSearch. ARSearch finds Stack Overflow threads that mention the particular API and their relevant code examples from GitHub. We demonstrate our tool by a video available at https://youtu.be/Rr-zTfUD_z0.
Design and Build a Web-Based CCTV Sales Information System

In this era of globalization, the use of computers and their systems has become a major requirement in order to improve performance in a company. The author made this Final Project based on research results from CV. Electronic Center. CV is a company engaged in the sale of electronic goods. The increasing sales at CV.Centra Elektronik then, problems arise that occur because the system is still manual. So it is necessary to create a web- based application system in order to be able to handle problems that arise in the sales process and at the same time increase the work and productivity of existing resources. Therefore the author designed a computerized data processing system of goods and sales starting from the process of inputting goods data, sales transactions and report generation.
An Efficient Reliable Federated Learning Technology

Many organizations in this competitive world use data analysis to provide useful insights for decision making and problem solving tasks that exist in real - time scenarios. To preserve the information provided, technological improvements are needed to handle data privacy in the system. In real-world circumstances, large amounts of data will be collected from a variety of sources and need to be analyzed and protected from unauthorized access. As a result, machine learning models must be deployed across all sites without the need to upload data, as this could compromise privacy. Federated learning helps to provide this privacy for the data. In this paper, we have analyzed two methods of building machine learning models - the centralized or conventional method of building machine learning models and the federated approach of building models. The performance metrics in terms of accuracy has been examined. The results are used to highlight the improved accuracy of federated approach compared to the centralized approach without compromising the privacy of the data.
Scholarly big data quality assessment

Recently, the Allen Institute for Artificial Intelligence released the Semantic Scholar Open Research Corpus (S2ORC), one of the largest open-access scholarly big datasets with more than 130 million scholarly paper records. S2ORC contains a significant portion of automatically generated metadata. The metadata quality could impact downstream tasks such as citation analysis, citation prediction, and link analysis. In this project, we assess the document linking quality and estimate the document conflation rate for the S2ORC dataset. Using semi-automatically curated ground truth corpora, we estimated that the overall document linking quality is high, with 92.6% of documents correctly linking to six major databases, but the linking quality varies depending on subject domains. The document conflation rate is around 2.6%, meaning that about 97.4% of documents are unique. We further quantitatively compared three near-duplicate detection methods using the ground truth created from S2ORC. The experiments indicated that locality-sensitive hashing was the best method in terms of effectiveness and scalability, achieving high performance (F1=0.960) and a much reduced runtime. Our code and data are available at https://github.com/lamps-lab/docconflation.
A Flexible Encoding/Decoding Procedure for 6G SCMA Wireless Networks via Adversarial Machine Learning Techniques

5G networks may not be competent in fully supporting the high data rates, system capacity, and spectral efficiency expected in the upcoming 6G networks. An enabling factor to achieve these requirements is the adoption of the Sparse Code Multiple Access (SCMA), which is one of the most promising code-domain Non-Orthogonal Multiple Access (NOMA) techniques. Nowadays, there are two major challenges for the implementation of SCMA in practical networks. First, the design of a near-optimal codebook set at the transmitter, which heavily impacts the network performance. Second, an efficient decoding algorithm at the receiver, which dominates the computational complexity of the system. The two aspects are not independent from each other; therefore the optimization of an SCMA network should address jointly both the Encoder and the Decoder side. However, most of the works available in the literature address only one aspect and also are not tailored for a given radio channel. In this paper, we design an end-to-end SCMA en/deconding structure based on the integration between a state-of-the-art AutoEncoder (AE) architecture and a novel Wasserstein Generative Adversarial Network (WGAN). The aim is to jointly create a near-optimal codebook design and a non-computationally heavy Decoder that are robust to the given channel statistics. Finally, we trained our model to be flexible to support different channel conditions and compared its performance against the state-of-the-art AE and the best conventional Decoders. The simulation results show that our innovative design exhibits decoding performance very similar to conventional Decoders with a reduced decoding latency.
Fisher Information Neural Estimation

Fisher information is a fundamental quantity in information theory and signal processing. A direct analytical computation of the Fisher information is often infeasible or intractable due to the lack or sophistication of statistical models. In this paper, we propose a Fisher Information Neural Estimator (FINE) which is computationally efficient, highly accurate, and applicable for both cases of deterministic and random parame-ters. The proposed method solely depends on measured data and does not require knowledge or an estimate of the probability density function and is therefore universally applicable. We validate our approach using some experiments and compare with existing works. Numerical results show the high efficacy and low-computational complexity of the proposed estimation approach.
Image Enlargement Scheme based on Singular Value Decomposition and Cubic Spline Interpolation Through Random Numbers

A image enlargement technique based on decomposition and interpolation through random numbers is proposed in the present article. Singular Value Decomposition (SVD) along with Cubic Spline (CS) interpolation is combined to enlarge the color images. This approach transfers the feature of the Low-Resolution (LR) original image into the High-Resolution (HR) interpolated image with less complexity. First, the SVD is performed on the color images having three Red, Green, and Blue (RGB) components. This is done to extract the features of the color images. After extracting features of these three components the CS interpolation is applied to it to interpolate the image. Random numbers have been used to perform this interpolation process. To demonstrate the presented technique's computational effectiveness, its images are compared to those acquired through three existing methodologies using six performance parameters.
Bayesian Active Learning for Sim-to-Real Robotic Perception

While learning from synthetic training data has recently gained an increased attention, in real-world robotic applications, there are still performance deficiencies due to the so-called Sim-to-Real gap. In practice, this gap is hard to resolve with only synthetic data. Therefore, we focus on an efficient acquisition of real data within a Sim-to-Real learning pipeline. Concretely, we employ deep Bayesian active learning to minimize manual annotation efforts and devise an autonomous learning paradigm to select the data that is considered useful for the human expert to annotate. To achieve this, a Bayesian Neural Network (BNN) object detector providing reliable un-certainty estimates is adapted to infer the informativeness of the unlabeled data. Furthermore, to cope with misalignments of the label distribution in uncertainty-based sampling, we develop an effective randomized sampling strategy that performs favorably compared to other complex alternatives. In our experiments on object classification and detection, we show benefits of our approach and provide evidence that labeling efforts can be reduced significantly. Finally, we demonstrate the practical effectiveness of this idea in a grasping task on an assistive robot.
Deep Residual Learning with Attention Mechanism for OFDM Channel Estimation

In this paper, we apply deep learning to the channel estimation problem of OFDM. Precisely speaking, to reduce the influence of noise on LS channel estimation, we design a channel estimation model based on attention mechanism and residual network. AttRNet-Conv and AttRNet-FC use attentional mechanism to change the weight of the feature to suppress noise. Compared with other channel estimation algorithms based on deep learning, AttRNet-Conv and AttRNet-FC avoid damaging channel information. The simulation results show that AttRNet-Conv and AttRNet-FC are superior to existing channel estimation algorithms based on deep learning in both high and low pilot frequency conditions. Even at low SNR, the estimation accuracy is higher than that of LMMSE. Experimental results show thatAttRNet-Conv and AttRNet-FC has strong robustness. In addition, we find that AttRNet-Conv is more suitable for channel estimation at low SNR in EPA, and the AttRNet-FC model is used in other cases.
Remote Sensing Fine-Grained Ship Data Augmentation Pipeline With Local-Aware Progressive Image-to-Image Translation

Remote sensing image ship fine-grained classification is a challenging vision problem due to factors such as inter-class similarity, real-world image scarcity, and class imbalance. Data augmentation aims to solve these problems from the data perspective. Most of these methods cannot work effectively in complex scenes, which restricts their practical application. Here, we propose a novel data augmentation pipeline based on local-aware image translation to achieve the representation mapping between cross-domain corresponding instances. Our pipeline contains three modules: Imaging Simulation System (ISS), Local-aware Progressive Image-to-Image Translation (LoPIT), and Image Harmonization (IH) modules. The ISS module generates simulated images with correct appearance and diverse features based on the input requirement information. To tackle the domain gap between simulated image and real-world image, we propose the Local-aware CycleGAN in the LoPIT module to achieve mapping based on local-aware learning and apply two sub-modules to progressively complete the remote sensing image global cross-domain translation. The IH module uses image harmonization technology to coordinate the visual appearance between the foreground and background to generate sufficient remote sensing ship images with precise representation, photorealistic style, and harmonious features. Moreover, we present a mixed dataset including real-world images and our synthetic images for remote sensing image fine-grained ship classification. Our dataset named RSSA-12 contains 12 categories of ship targets in 3831 images with high-quality annotated category labels, effectively alleviating the long-tail problem of existing datasets. Experimental results demonstrate that our progressive pipeline outperforms the state-of-the-art data augmentation method on the remote sensing fine-grained ship classification task.
Where Does Crowd-based Requirements Engineering End and Market Research Begin?

In recent years, market researchers have increasingly adopted automation, among other things to understand what users want from their product or service and how satisfied they are overall or with specific features or changes. Crowd-based Requirements Engineering (CrowdRE) is concerned with similar questions and employs requirements engineering (RE) methods to answer them. This suggests that the boundary between the fields of market research and CrowdRE has become blurred. This in turn raises the question: When is something still CrowdRE, and when has it become market research? This is an important question because it may require the community to rethink how industry might perceive CrowdRE in a wider context, and how future research should be framed. This problem statement paper explores this question along several real-world scenarios to trigger discussions in the community regarding possible solutions.
Cyclic and LCD Codes over a Finite Commutative Semi-local Ring

For an odd prime p, we obtain algebraic structure of cyclic codes of length n over a finite commutative non-chain semi-local ring $$\mathfrak {R}=\mathbb {F}_{p}[u,v,w]/\langle u^{2}-u,v^{2}-1,w^2-1,uv-vu,vw-wv,wu-uw\rangle $$ . These codes of length n can be viewed as principal ideals of the quotient ring $$\mathfrak {R}[x]/\langle x^n-1\rangle $$ . Here, a Gray map is defined to obtain p-ary quasi-cyclic codes of length 8n with index 8 as $$\mathbb {F}_p$$ -images of cyclic codes of length n over $$\mathfrak {R}$$ . Also, we present necessary and sufficient conditions for a cyclic code to be an LCD (linear complementary dual) code over $$\mathfrak {R}$$ . Moreover, it is shown that the Gray image of an LCD code of length n over $$\mathfrak {R}$$ is an LCD code of length 8n over $$\mathbb {F}_{p}$$ . Finally, a few non-trivial examples are given in support of our derived results.
Fuzzy Logic for Educational Purposes

Fuzzy logic is a popular logical system used in many applications in real life. The aim of this article is to briefly explain the basic concepts of fuzzy sets and fuzzy logic. We define an architecture of fuzzy logic systems and their parts. Using an example of an air-conditioning system we show how formal definitions of fuzzy logic are represented in an implementation.
Automatic Relation-aware Graph Network Proliferation

Graph neural architecture search has sparked much attention as Graph Neural Networks (GNNs) have shown powerful reasoning capability in many relational tasks. However, the currently used graph search space overem-phasizes learning node features and neglects mining hierarchical relational information. Moreover, due to diverse mechanisms in the message passing, the graph search space is much larger than that of CNNs. This hinders the straightforward application of classical search strategies for exploring complicated graph search space. We propose Automatic Relation-aware Graph Network Proliferation (ARGNP) for efficiently searching GNNs with a relation-guided message passing mechanism. Specifically, we first devise a novel dual relation-aware graph search space that comprises both node and relation learning operations. These operations can extract hierarchical node/relational information and provide anisotropic guidance for message passing on a graph. Second, analogous to cell proliferation, we design a network proliferation search paradigm to progressively determine the GNN architectures by iteratively performing network division and differentiation. The experiments on six datasets for four graph learning tasks demonstrate that GNNs produced by our method are superior to the current state-of-the-art hand-crafted and search-based GNNs. Codes are available at https://github.com/phython96/ARGNP.
Monitoring Cloud Services through Models at Runtime: A Case in an Ambient Assisted Living Environment.

Ambient Assisted Living (AAL) has become an important domain that provides software systems and services to support and improve people's daily quality of life.Yet, it has not achieved large market penetration.Existing works suggest that this is primarily due to not sufficiently addressing the quality requirements posed by healthcare organizations.In particular, there is a lack of comprehensive frameworks that allow the assessment of the quality of AAL systems.In previous work, we introduced a method for monitoring cloud services using models at runtime (MoS@RT), which allows the monitoring requirements or the metric operationalizations of these requirements to be changed at runtime without the modification of the underlying infrastructure.The method and its supporting infrastructure have been successfully applied in the monitoring of cloud services, but further evaluation in other domains is needed.In this paper, we report a case study on the use of MoS@RT to monitor cloud services in an AAL environment aimed at supporting the elderly's well-being.The results suggest that relevant quality attributes of AAL systems can be adequately monitored using MoS@RT and that the report generated by the monitoring infrastructure is useful for service providers and customers to help them ensure that cloud services meet the required levels of quality.
A time series-based dataset of open-source software evolution

Software evolution is the process of developing, maintaining, and updating software systems. It is known that the software systems tend to increase their complexity and size over their evolution to meet the demands required by the users. Due to this fact, researchers have increasingly carried out studies on software evolution to understand the systems' evolution pattern and propose techniques to overcome inherent problems in software evolution. Many of these works collect data but do not make them publicly available. Many datasets on software evolution are outdated, and/or are small, and some of them do not provide time series from software metrics. We propose an extensive software evolution dataset with temporal information about open-source Java systems. To build this dataset, we proposed a methodology of four steps: selecting the systems using a criterion, extracting and measuring their releases, and generating their time series. Our dataset contains time series of 46 software metrics extracted from 46 open-source Java systems, and we make it publicly available.
A Software Requirements Ecosystem: Linking Forum, Issue Tracker, and FAQs for Requirements Management

User feedback is an important resource in modern software development, often containing requirements that help address user concerns and desires for a software product. The feedback in online channels is a recent focus for software engineering researchers, with multiple studies proposing automatic analysis tools. In this work, we investigate the product forums of two large open source software projects. Through a quantitative analysis, we show that forum feedback is often manually linked to related issue tracker entries and product documentation. By linking feedback to their existing documentation, development teams enhance their understanding of known issues, and direct their users to known solutions. We discuss how the links between forum, issue tracker, and product documentation form a requirements ecosystem that has not been identified in the previous literature. We apply state-of-the-art deep-learning to automatically match forum posts with related issue tracker entries. Our approach identifies requirement matches with a mean average precision of 58.9% and hit ratio of 82.2%. Additionally, we apply deep-learning using an innovative clustering technique, achieving promising performance when matching forum posts to related product documentation. We discuss the possible applications of these automated techniques to support the flow of requirements between forum, issue tracker, and product documentation.
Smart Band for Senior Citizens

Senior citizens may be physically slow but deserve all the health care attention in their elderly ages. Fainting and being unmindful are two major concerns for senior citizens. Fall identification is a significant test in the public medical services area, particularly for the elderly people as the decrease in their actual wellness, and convenient and dependable observation is important to moderate the adverse consequences of falls. The main aim of this research is to develop a wearable device that monitors senior citizens and uses machine learning algorithms for detecting their falls and sending alerts to the caretaker. Our system provides boundary-less communication between the primary person and his caretaker. The models are trained and tested on the dataset generated by collecting various values from Accelerometer sensor for different kinds of actions [walking, falling, and seating]. Moreover, our system monitors the body parameters like temperature, Heartbeat, and SpO2 levels of the senior citizens and also provides the facility for the remainder system.