Reviewerly/exHarmony huggingface dataset:
Understanding practices and needs of researchers in human state modeling by passive mobile sensing

Passive mobile sensing for the purpose of human state modeling is a fast-growing area. It has been applied to solve a wide range of behavior-related problems, including physical and mental health monitoring, affective computing, activity recognition, routine modeling, etc. However, in spite of the emerging literature that has investigated a wide range of application scenarios, there is little work focusing on the lessons learned by researchers, and on guidance for researchers to this approach. How do researchers conduct these types of research studies? Is there any established common practice when applying mobile sensing across different application areas? What are the pain points and needs that they frequently encounter? Answering these questions is an important step in the maturing of this growing sub-field of ubiquitous computing, and can benefit a wide range of audiences. It can serve to educate researchers who have growing interests in this area but have little to no previous experience. Intermediate researchers may also find the results interesting and helpful for reference to improve their skills. Moreover, it can further shed light on the design guidelines for a future toolkit that could facilitate research processes being used. In this paper, we fill this gap and answer these questions by conducting semi-structured interviews with ten experienced researchers from four countries to understand their practices and pain points when conducting their research. Our results reveal a common pipeline that researchers have adopted, and identify major challenges that do not appear in published work but that researchers often encounter. Based on the results of our interviews, we discuss practical suggestions for novice researchers and high-level design principles for a toolkit that can accelerate passive mobile sensing research.
Towards higher order fairness functionals for smooth path planning

Smoothness of mobile and vehicle navigation has become relevant to ensure the safety and the comfortability of riding. The robotics community has been able to render smooth trajectories in mobile robots by using non-linear optimization approaches and well-known fairness metrics considering the curvature variations along the path. In this paper, we introduce the possibility of computing smooth paths from observed mobile robot trajectories from higher order non-linear fairness functionals. Our approach is potential to enable the generation of simple and computationally-efficient path planning smoothing for navigation in mobile robots.
IDE support for cloud-based static analyses

Integrating static analyses into continuous integration (CI) or continuous delivery (CD) has become the best practice for assuring code quality and security. Static Application Security Testing (SAST) tools fit well into CI/CD, because CI/CD allows time for deep static analyses on large code bases and prevents vulnerabilities in the early stages of the development lifecycle. In CI/CD, the SAST tools usually run in the cloud and provide findings via a web interface. Recent studies show that developers prefer seeing the findings of these tools directly in their IDEs. Most tools with IDE integration run lightweight static analyses and can give feedback at coding time, but SAST tools used in CI/CD take longer to run and usually are not able to do so. Can developers interact directly with a cloud-based SAST tool that is typically used in CI/CD through their IDE? We investigated if such a mechanism can integrate cloud-based SAST tools better into a developers’ workflow than web-based solutions. We interviewed developers to understand their expectations from an IDE solution. Guided by these interviews, we implemented an IDE prototype for an existing cloud-based SAST tool. With a usability test using this prototype, we found that the IDE solution promoted more frequent tool interactions. In particular, developers performed code scans three times more often. This indicates better integration of the cloud-based SAST tool into developers’ workflow. Furthermore, while our study did not show statistically significant improvement on developers’ code-fixing performance, it did show a promising reduction in time for fixing vulnerable code.
Provision of a Recommender Model for Blockchain-Based IoT with Deep Reinforcement Learning

With developments in human societies and the information and communication technology, the Internet of Things (IoT) has penetrated various aspects of daily life and different industries. The newly emerging blockchain technology has become a viable solution to the IoT security due to its inherent characteristics such as distribution, security, immutability, and traceability. However, integrating the IoT with the blockchain technology faces certain challenges such as latency, throughput, device power limitation, and scalability. Recent studies have focused on the role of artificial intelligence methods in improving the IoT performance in a blockchain. According to their results, there are only a few effects on the improvement of IoT-based performance with limited power. This study proposes a conceptual model to improve the blockchain throughput in IoT-based devices with limited power through deep reinforcement learning. This model benefits from a recommender agent based on deep reinforcement learning in the mobile edge computing layer to improve the throughput and select the right mining method.
A novel approach to unsupervised pattern discovery in speech using Convolutional Neural Network

In this paper, a novel approach to unsupervised pattern discovery for speech signals is proposed. Recently, we introduced an image processing method (IPM) that extracts the desired keywords present in a pair of speech utterances. This method performs well in detecting true positives but, at the same time it also detects higher number of false positives. Therefore, this paper aims to reduce the detection of false positives and improve the accuracy of the pattern discovery task. In the proposed work, we use the Convolutional Neural Network (CNN) as a binary classifier to detect the true and false keyword match candidates. A new frame histogram technique is introduced to generate sufficient training samples from IPM to train the CNN. The trained CNN model classifies the matched patterns into true and false classes and identifies the pairs of speech documents that contain the same keyword. The proposed method is evaluated on the Hindi as well as Bengali speech databases. The results are compared with state-of-the-art methods. The detected matched pairs of speech utterances are grouped into broader domain clusters using Newman’s clustering algorithm. These clusters are useful for speech retrieval tasks.
End-To-End Neural Coreference Resolution Revisited: A Simple Yet Effective Baseline

Since the first end-to-end neural coreference resolution model was introduced, many extensions to the model have been proposed, ranging from using higher-order inference to directly optimizing evaluation metrics using reinforcement learning. Despite improving the coreference resolution performance by a large margin, these extensions add substantial extra complexity to the original model. Motivated by this observation and the recent advances in pre-trained Transformer language models, we propose a simple yet effective baseline for coreference resolution. Even though our model is a simplified version of the original neural coreference resolution model, it achieves impressive performance, outperforming all recent extended works on the public English OntoNotes benchmark. Our work provides evidence for the necessity of carefully justifying the complexity of existing or newly proposed models, as introducing a conceptual or practical simplification to an existing model can still yield competitive results.
A Pipeline for Hand 2-D Keypoint Localization Using Unpaired Image to Image Translation

Hand pose estimation is getting a lot of attention in many areas such as Human-Computer Interaction and Sign Language Recognition. A fundamental step to accurately estimate the hand pose involves detecting and localizing fingertips in an image. Despite the progress of 2-D hand pose estimation in recent studies, accurate and robust detection and localization of fingertips still remains a challenging task due to low resolution of a fingertip in images and varying lightning condition.
Self-service Data Classification Using Interactive Visualization and Interpretable Machine Learning

Machine learning algorithms often produce models considered as complex black-box models by both end users and developers. Such algorithms fail to explain the model in terms of the domain they are designed for. The proposed Iterative Visual Logical Classifier (IVLC) is an interpretable machine learning algorithm that allows end users to design a model with more confidence and without compromising the accuracy. Such technique is especially helpful for tasks like cancer diagnostics with high cost of errors. With the proposed interactive and lossless multidimensional visualization, end users can identify the pattern and make explainable decisions, which is not possible in black box machine learning methodologies. The interpretable IVLC algorithm is supported by the Interactive Shifted Paired Coordinates Software System (SPCVis), which is a lossless multidimensional data visualization system. The interactivity provides flexibility to the end user to perform data classification as self-service without a machine learning expert. Interactive pattern discovery is challenging for data with hundreds of dimensions/features. To overcome this problem, this chapter proposes an automated classification approach combined with new Coordinate Order Optimizer (COO) algorithm and a Genetic algorithm. The COO algorithm automatically generates the coordinate pair sequences that best represent the data separation and the genetic algorithm optimizes the IVLC algorithm by automatically generating the areas for data classification. The feasibility of the approach is shown by experiments on benchmark datasets covering both interactive and automated processes.
Learning Context-Aware Convolutional Filters for Implicit Discourse Relation Classification

Implicit discourse relation classification (IDRC) is considered the most difficult component of shallow discourse parsing as the relation prediction in the absence of necessary clues requires a deep understanding of the context information of the sentences. Convolutional neural networks (CNNs) have emerged as an important encoding block for sentences in natural language processing (NLP). CNNs use a specific set of filters for the inputs which may lead to the partial coverage of contextual clues. Furthermore, conventional CNNs may not allow the initial communication between the sentences which is a crucial step for IDRC. We present an adaptive convolution approach for IDRC that utilizes context aware filters for the convolution operation. The goal is to abstract the context of sentences in the filters and let them interact with sentence representations, i.e. learning the representations through learned filters. Our model acts as a cross questioning agent by generating filters from one argument and convolving them with the other for the IDRC task. This process is analogous to the attention mechanism because both methods aim at abstracting contextual information. Different from the attention mechanism, our approach directly encodes the contextual representations in the form of filters and allows the initial communication between arguments during encoding. Furthermore, the adaptive convolution can also work alongside the attention mechanism to enhance the representational ability of the adaptive CNN encoder. Experiments on PDTB 2.0 and CDTB datasets show that our approach outperforms all the baselines by a fair margin and achieves excellent results.
Blockchain in Food Traceability: A Systematic Literature Review

Blockchain technology can be used in the food sector to improve food quality, product recalls, inventory tracking, demand response, traceability, transparency, consumer trust, and mitigating recall costs. Growing interest in Blockchain food traceability technology i.e., to track any food, feed, food-producing animals, or substances used for consumption through all stages of production, processing, and distribution necessitates a systematic literature review. The objective of this systematic literature review (SLR) was to identify the advantages of using Blockchain in food supply chains through studies on Blockchain implementation for food traceability. This study investigated 14 primary recent studies published between 2017 and 2020 from web sources and digital libraries. According to the findings, 21% of papers were published in 2017 and 43% in 2020, indicating that the use of Blockchain technology in food traceability is gaining traction over time. The 14 Blockchain systems studied in this SLR used four different Blockchain platforms: 8 out 14 used Ethereum. Blockchain-based traceability systems provided numerous traceability features, such as complete farm-to-fork traceability, recoding of every single transaction, digital tracking, decentralised file systems, visualisation methods for intuitively displaying risks, and the ability to reconstruct the product's history up to the origin for the purpose of quality verification.
Dynamic and reliable subtask tracker with general schatten <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" altimg="si1.svg"><mml:mi>p</mml:mi></mml:math>-norm regularization

Some multi-task trackers adopt an inaccurate shrink strategy to treat different rank components equally. Thus, their flexibility is vulnerable to some tracking challenges. To resolve this problem, we propose a spatial-aware reliable multi-subtask tracker via weighted Schatten p-norm regularization (SLRT-W), which dynamically chooses the suitable and reliable subset of the whole subtasks for tracking. Its major merits not only assign the flexible weights to different subtask rank components depending on their tracking contribution, but also preserve consistent spatial layout structure and correspondence of layered multi-subtask. Specifically, multiple layered subtasks correspond to different target subregions, they are cooperative and complement. A weighted Schatten p-norm is introduced to adaptively shrink different multi-subtask rank components, and emphasize important components as reliable ones. Then, a structured hyper-graph regularized term simultaneously exploits the intrinsic geometry correspondence among multiple layers of subtasks, and spatial layout structure inside each layer. We devise an alternatively generalized iterated shrinkage method to optimize the multi-subtask Schatten p-norm minimization. Finally, a robust decision-evaluation strategy is developed to choose the reliable multi-subtask tracking combination. Encouraging results on some challenging benchmarks demonstrate the proposed tracker performs favorably in robustness and accuracy, against some state-of-the-art trackers.
Enhancing online business sector: digital trust formation process

The paper reviews Georgia's digital trust formation process and underlines the most important challenges for the online business sector. This research can be generalized for most developing countries where the digital transition process is on the agenda, especially for countries that have recently emerged from economic-political transition. Georgia has completed the transition from a centrally planned economic system (the heritage of being part of the Soviet Union) to a free market economy. Although trust in institutions is improving gradually, mistrust in business processes is still a great challenge for local business sectors. A wide-reaching lockdown caused by the 2020-2021 COVID-19 pandemic pushed both firms and individuals further towards online activity worldwide. In this regard, trust has become the key determinant in facilitating electronic transactions. Technological advances are providing dozens of tools to improve customer satisfaction and trust, which should lead to customer loyalty. On the other hand, lack of digital skills and digital security problems raises digital untrust concerns. Therefore, trust research in the digital economy is becoming more actual for academic or business studies. Digital trust is a multidimensional factor that is under the influence of digital and physical operations. In Georgia, businesses have to overcome obstacles built-in people's minds as a heritage of the Soviet Union. Georgia was a part of the Soviet Union for 70 years, which critically degraded the trust of both formal and informal institutions. Therefore, building digital trust in business is getting more complex as it includes traditional trust problems and digital challenges too. The main research purpose in this paper is to present the digital trust forming process in Georgia to reveal the major problems. This study involved the trust stack model in analysing three different stages of trust formation in the Georgian digital economy (trust in the idea, trust in the platform, trust in the individual). The research identified challenges and steps which should be carried out soon. Research academic findings and methodological approaches can be used to analyze other developing countries whose economies are influenced by the digital transformation process.
A Survey and Future Perspectives of Hybrid Deep Learning Models for Text Classification

The advances in deep learning (DL) models have proven to achieve outstanding results in text classification tasks. This success is due to DL models’ potential to reach high accuracy with less need for engineered features. Despite their popularity, DL models have their strengths and weaknesses in their learning capacity, depending on the task. Researchers in the recent past have proposed various hybrid models to compensate for these weaknesses. This study presents a performance analysis of hybrid DL models compared to stand-alone DL models on various text classification tasks. Various research articles published between 2015 and 2020 on text classification using hybrid DL models were selected from leading computer science and engineering journals and analyzed. The findings suggest that hybrid DL models can better capture a syntactic representation of text, extract multiple feature maps, and effectively improve text classification results. The study also presents an improved awareness of different hybrid DL architectures in the field of text classification.
Element graph-augmented abstractive summarization for legal public opinion news with graph transformer

Automatic summarization for legal public opinion news has been an attractive research problem in recent years. Compared with the open-domain, summarization for legal public opinion news has two essential constraints: (1) the key information (e.g., the case elements) of the news should be summarized; (2) the factual errors should be avoided in the generated summary. To address these challenges, the summarizer should learn a structured representation of the news (event plan), making it better to understand the event information implied in the news. This paper proposes a novel element graph-augmented abstractive summarization model, which first constructs the structural graph by extracting elements from the source document and then produces graph representation via graph transformer network. Finally, the structural representation is taken as an essential complementary component of the conventional sequence-to-sequence model to guide the decoding process simultaneously. Furthermore, the pre-trained language model is introduced to enhance the sequential and structural encoder, which further promotes the summarization model’s performance. For evaluation, we build a large-scale legal public opinion news (LPO-news) corpus. Experimental results on LPO-news and another news-oriented CNN/Daily mail dataset show that our model significantly outperforms other baselines in terms of both ROUGE scores and Bert scores. We also perform a human evaluation to demonstrate our model’s effectiveness by evaluating the generated summary using several subjective metrics.
Anomaly Detection for Injection Molding Using Probabilistic Deep Learning

Data collection from industrial devices is becoming more popular with the advent of technologies and trends such as the Industrial Internet of Things (IIoT) and Industry 4.0. We propose a deep learning-based approach to detect anomalies in real-time from multivariate time series data and interpret the detected anomalies' root causes. We apply the method to real-world industrial data collected from two injection molding machines. Additionally, we evaluate the method using artificially generated multivariate time series data. We compare the performance of the method to five baseline algorithms from the literature. Our results indicate that the method can detect anomalies in the injection molding machine data and interpret the root causes of the detected anomalies with high performance. Similarly, the method works well on the artificially generated multivariate time series data, demonstrating that the method is also applicable to other multivariate time series data problems.
A Regional Day-Ahead Rooftop Photovoltaic Generation Forecasting Model Considering Unauthorized Photovoltaic Installation

Rooftop photovoltaic (PV) systems are usually behind the meter and invisible to utilities and retailers and, thus, their power generation is not monitored. If a number of rooftop PV systems are installed, it transforms the net load pattern in power systems. Moreover, not only generation but also PV capacity information is invisible due to unauthorized PV installations, causing inaccuracies in regional PV generation forecasting. This study proposes a regional rooftop PV generation forecasting methodology by adding unauthorized PV capacity estimation. PV capacity estimation consists of two steps: detection of unauthorized PV generation and estimation capacity of detected PV. Finally, regional rooftop PV generation is predicted by considering unauthorized PV capacity through the support vector regression (SVR) and upscaling method. The results from a case study show that compared with estimation without unauthorized PV capacity, the proposed methodology reduces the normalized root mean square error (nRMSE) by 5.41% and the normalized mean absolute error (nMAE) by 2.95%, It can be concluded that regional rooftop PV generation forecasting accuracy is improved.
Robustness of AutoML for Time Series Forecasting in Sensor Networks

Sensor data collection in IoT networks is sensitive to malfunction of sensors and communications. Hence, it is important that models using the data work in a reasonable way even when there are some, potentially temporary, problems. In this paper, we investigate the robustness of AutoML systems for time series forecasting in sensor networks, using temperature data as example. We experiment with different AutoML systems and study how the resulting models tolerate faults in their input data. The analyzed AutoML systems are Microsoft's Azure AutoML, Intel's Analytics Zoo AutoML, and Facebook's Prophet. As a result, we rank AutoML systems based on their performance with respect to data faults and their severity. In addition, we show how the AutoML generated models differ given the data fault type.