Reviewerly/exHarmony huggingface dataset:
Inspector gadget

As machine learning for images becomes democratized in the Software 2.0 era, one of the serious bottlenecks is securing enough labeled data for training. This problem is especially critical in a manufacturing setting where smart factories rely on machine learning for product quality control by analyzing industrial images. Such images are typically large and may only need to be partially analyzed where only a small portion is problematic (e.g., identifying defects on a surface). Since manual labeling these images is expensive, weak supervision is an attractive alternative where the idea is to generate weak labels that are not perfect, but can be produced at scale. Data programming is a recent paradigm in this category where it uses human knowledge in the form of labeling functions and combines them into a generative model. Data programming has been successful in applications based on text or structured data and can also be applied to images usually if one can find a way to convert them into structured data. In this work, we expand the horizon of data programming by directly applying it to images without this conversion, which is a common scenario for industrial applications. We propose Inspector Gadget, an image labeling system that combines crowdsourcing, data augmentation, and data programming to produce weak labels at scale for image classification. We perform experiments on real industrial image datasets and show that Inspector Gadget obtains better performance than other weak-labeling techniques: Snuba, GOGGLES, and self-learning baselines using convolutional neural networks (CNNs) without pre-training.
TRUAA—Earthquake Early Warning System for Israel: Implementation and Current Status

Abstract Following a governmental decision to build a national earthquake early warning system (EEWS) named TRUAA, the Geological Survey of Israel has upgraded the national Israeli Seismic Network with more than 100 stations countrywide. The stations are spread mainly along the main hazardous fault systems of the Dead Sea and Carmel-Zfira, which potentially may produce Mw 7.5 earthquakes. Currently the system is shifting from the deployment phase into a testing phase in which the earthquake point-source integrated code (EPIC) EEW algorithm is used. During the deployment phase, real-time performance of the EEW algorithm has steadily improved, with alert delays (span between origin time and EEW alert time) reduced down to 3 s in some cases. We present an overview of TRUAA, the performance of EPIC during the deployment phase and during playbacks of historic events, as well as our suggested alert approach for Israel.
A First Step Towards a Streaming Linked Data Life-Cycle

Alongside with the ongoing initiative of FAIR data management, the problem of handling Streaming Linked Data (SLD) is relevant as never before. The Web is changing to tame Data Velocity and fulfill the needs of a new generation of Web applications. New protocols (e.g. WebSockets and Server-Sent Events) emerge to grant continuous and reactive data access. Under the Stream Reasoning initiative, the Semantic Web community has been actively working on query languages, engines, and vocabularies to address the scientific and technical challenges of taming Data Velocity without neglecting Data Variety. Nevertheless, a set of guidelines that showcase how to reuse existing resources to produce and consume streams on the Web is still missing. In this paper, we walk through the life-cycle of streaming linked data. We discuss the challenges of applying FAIR principles when publishing data streams. Moreover, we contextualise the usage of prominent Semantic Web resources, i.e., (i) TripleWave, R2RML/RML, VoCaLS, RSP-QL. We apply the guidelines to three representative examples of real-world Web streams: DBpedia Live changes, Wikimedia EventStreams, and the Global Database of Events, Language and Tone (GDELT). Last but not least, we open-sourced our code at https://w3id.org/webstreams .
Genetic algorithm to improve Back Propagation Neural Network ship track prediction

Abstract This paper studies a method that uses genetic algorithm to simultaneously optimize the number and weights of Back Propagation (BP) neural network neurons to predict the ship’s trajectory, so as to accurately predict the ship’s trajectory. The trajectory of the ship can be predicted by the neural network, but the selection of the setting parameters of the neural network requires rich experience and a large amount of attempts. In this study, the genetic algorithm (GA) is used to optimize the structure and weights of the neural network at the same time, avoiding the manual setting of parameters and improving the accuracy of the neural network prediction.
Functional Suitability Measurement Based on ISO/IEC 25010 for e-Commerce Website

The intense competition between e-Commerce makes every e-Commerce has a way to be able to compete in the market, one of which is to improve the quality of e-Commerce websites. Therefore, research is needed to find out the quality of the web with the buyer's intention on the E-commerce web by evaluating it. There are various standard software evaluation models used such as ISO / IEC 25010. This paper, the ISO/IEC 25010 to assess the functional suitability of various Indonesian e-Commerce websites, namely Wadezig!, Screamous, 347 Clothing, Thanksinsomnia and Bloods, which were determined through Bukalapak customer reviews. Functional suitability has three calculated attributes, namely completeness, correctness, and appropriateness. From the measurements that have been made, the functional suitability can help e-Commerce websites to improve the quality of their website, especially in terms of functional suitability so as to provide comfort and good service to customers. This evaluation can also be used as a reference for developing e-Commerce websites to be better.
Deep Learning Models for Representing Out-of-Vocabulary Words

Communication has become increasingly dynamic with the popularization of social networks and applications that allow people to express themselves and communicate instantly. In this scenario, distributed representation models have their quality impacted by new words that appear frequently or that are derived from spelling errors. These words that are unknown by the models, known as out-of-vocabulary (OOV) words, need to be properly handled to not degrade the quality of the natural language processing (NLP) applications, which depend on the appropriate vector representation of the texts. To better understand this problem and finding the best techniques to handle OOV words, in this study, we present a comprehensive performance evaluation of deep learning models for representing OOV words. We performed an intrinsic evaluation using a benchmark dataset and an extrinsic evaluation using different NLP tasks: text categorization, named entity recognition, and part-of-speech tagging. Although the results indicated that the best technique for handling OOV words is different for each task, Comick, a deep learning method that infers the embedding based on the context and the morphological structure of the OOV word, obtained promising results.
Detecting unusual input to neural networks

Abstract Evaluating a neural network on an input that differs markedly from the training data might cause erratic and flawed predictions. We study a method that judges the unusualness of an input by evaluating its informative content compared to the learned parameters. This technique can be used to judge whether a network is suitable for processing a certain input and to raise a red flag that unexpected behavior might lie ahead. We compare our approach to various methods for uncertainty evaluation from the literature for various datasets and scenarios. Specifically, we introduce a simple, effective method that allows to directly compare the output of such metrics for single input points even if these metrics live on different scales.
VTT: Long-term Visual Tracking with Transformers

Long-term visual tracking is a challenging problem. State-of-the-art long-term trackers, e.g., GlobalTrack, utilize region proposal networks (RPNs) to generate target proposals. However, the performance of the trackers is affected by occlusions and large scale or ratio variations. To address these issues, in this paper, we are the first to propose a novel architecture with transformers for long-term visual tracking. Specifically, the proposed Visual Tracking Transformer (VTT) utilizes a transformer encoder-decoder architecture for aggregating global information to deal with occlusion and large scale or ratio variation. Furthermore, it also shows better discriminative power against instance-level distractors without the need for extra labeling and hard-sample mining. We conduct extensive experiments on three large-scale long-term tracking datasets and have achieved state-of-the-art performance.
Environment-induced sudden change of coherence in quantum systems

Manipulating dynamical evolution is an important task in quantum information. The sudden death phenomenon (SDP), which is predicted to be unattainable beyond entanglement [T. Yu and J. H. Eberly, Sudden death of entanglement, Science 323, 598 (2009)], would be an especially surprising feature in quantum coherence. In this paper, we modulate the spatial modes of photons and build a spatially inclined channel using a series of specially made quartz plate pairs. Within this channel, sudden changes in coherence occur in open-quantum systems. We, then, describe the SDP within a general theoretical framework of sudden quantum-coherence changes and by defining the $n\mathrm{th}$-order sudden coherence change. Moreover, by adding non-Markovian noise, we find that coherence can rebirth after a sudden death. We further compare the sudden change features in coherence and quantum entanglement. We find that both coherence and entanglement perform identical dynamical processes upon Bell diagonal states, although their dynamical characteristics change when the initial state is partially entangled. Our results provide insights into the sudden change phenomena of quantum correlations beyond entanglement along with intriguing perspectives on quantum coherence dynamics.
Brain Segmentation using Adaptive Thresholding, K-Means Clustering and Mathematical Morphology in MRI Data

Nowadays, many methods have been applied for brain segmentation on MRI data. This paper proposes a new method for brain segmentation using Adaptive Thresholding, K-Means Clustering, and Morphological Mathematics in MRI data. The adaptive threshold was chosen because the adaptive threshold method will vary across images to suit various lighting conditions and background changes. We segment the corpus callosum. This experiment shows that with the Adaptive Thresholding, K-Means Clustering, and Mathematical Morphology to segment the corpus callosum produces the highest Dice Similarity Coefficient (DSC) value of 0.757.
PrintARface: Supporting the Exploration of Cyber-Physical Systems through Augmented Reality

The increasing functionalities and close integration of hardware and software of modern cyber-physical systems present users with distinct challenges in applying and, especially, appropriating those systems within their practices. Existing approaches to design for appropriation and the development of sociable technologies that might support users seeking to understand how to make such technologies work in a specific practice, often lack appropriate user interfaces to explain the internal and environment-related behavior of a technology. By taking the example of 3D printing, we examine how augmented reality can be used as a novel human–machine interface to ease the way for hardware-related appropriation support. Within this paper we designed, implemented and evaluated a prototype called PrintARface, that extends a physical 3D printer by incorporating virtual components. Reflections upon the evaluation of our prototype are used to provide insights that foster the development of hardware-related appropriation support by encompassing augmented reality-based human–machine interfaces.
User Attitudes On Direct-to-Consumer Genetic Testing

Advances in biotechnology now allow users to obtain their genetic information, including ancestry and predisposition to various diseases and health issues, with relative ease. With these new commercial services come a host of privacy concerns with respect to data sharing and access. User data is being sold to third parties, including pharmaceutical and biotechnology companies, and may be accessed by law enforcement in accordance with proper legal procedures. Moreover, many users of these services go on to deposit the data they obtain into online, public repositories that are fully accessible to anyone with an internet connection. The full extent of the risks they face may not be apparent to users. This paper reports on a semistructured interview study ( <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$n=24$</tex> ) examining user concerns regarding these tests, what information they believe they are revealing, and what they think companies are doing with their data. We find that users are concerned with privacy, and understand at a basic level the nature of the data they are revealing. However, their privacy concerns are often insufficient to deter them from taking such a test, and many have difficulty grasping some of the implications of sharing their genetic information with commercial entities.
Score Prediction Algorithm Combining Deep Learning and Matrix Factorization in Sensor Cloud Systems

In this era of exponential growth in the scale of data, information overload has become an urgent problem, and the use of increasingly flexible sensor cloud systems (SCS) for data collection has become a mainstream trend. Recommendation algorithms can search massive data sets to uncover information that meets the needs of users based on their interests. To improve the accuracy of recommendation scoring, this article proposes a score prediction algorithm that combines deep learning and matrix factorization. To address the problem of sparse scoring data, our study employs a sensor cloud system to collect data information, preprocesses the collected information, and then uses a deep learning model combined with explicit and implicit feedback to generate recommendations. The proposed algorithm, MF-NeuRec, combines fusion matrix decomposition and the NeuRec model score prediction algorithm. The algorithm employs user-based and item-based NeuRec algorithms to extract the feature vectors of users and items under implicit feedback data. The obtained user and item feature vectors are integrated in a certain ratio through the use of matrix decomposition under the display feedback data. The user and item feature vectors obtained by the algorithm are merged and analyzed to predict how users will rate items. Experiments demonstrate that the algorithm can improve the accuracy of recommendations.
Voice-Based AI in Call Center Customer Service: Evidence from a Field Experiment

Voice-based artificial intelligence (AI) systems have been recently deployed to replace traditional interactive voice response (IVR) systems in call center customer service. However, there is little evidence that sheds light on how the implementation of AI systems impacts customer behavior, as well as AI systems’ effects on call center customer service performance. By leveraging the proprietary data obtained from a natural field experiment in a large telecommunication company, we examine how the introduction of a voice-based AI system affects call length, customers’ demand for human service, and customer complaints in call center customer service. We find that the implementation of the AI system temporarily increases the duration of machine service and customers’ demand for human service; however, it persistently reduces customer complaints. Furthermore, our results reveal interesting heterogeneity in the effectiveness of the voice-based AI system. For relatively simple service requests, the AI system reduces customer complaints for both experienced and inexperienced customers. However, for complex requests, customers appear to learn from the prior experience of interacting with the AI system, which leads to fewer complaints. Moreover, the AI-based system has a significantly larger effect on reducing customer complaints for older and female customers as well as for customers who have had extensive experience using the IVR system. Finally, we find that the speech-recognition failures in customer-AI interactions lead to increases in customers’ demand for human service and customer complaints. The results from this study provide implications for the implementation of an AI system in call center operations.
Company employee quality evaluation model based on BP neural network

With the development of science and technology and the continuous improvement of people’s living standards, the traditional staff quality evaluation can no longer meet the needs of production and life, and the BP neural network has also appeared many shortcomings in practical applications. This article mainly studies the company’s employee quality evaluation model based on BP neural network. This article first collects and preprocesses employees’ usual performance data, and then predicts their corresponding quality scores based on BP neural network. And use MATLAB software to simulate the constructed prediction model, and finally develop a complete set of employee performance data prediction system based on this model, so as to achieve the purpose of employee quality evaluation. The experimental data in this paper shows that the average relative error of model training output tends to be stable. After the 40th iteration of training, the average relative error of model training can reach 0.0128. After the prediction model training was completed, 15 sets of verification samples were used to verify the model. The verification results found that the average relative error of the model converged, so the model did not overfit. Experimental results show that although BP neural network has two excellent functions of adaptive and nonlinear approximation, it can solve the complex nonlinear relationship between normal performance and overall performance. But BP neural network still has its own inevitable shortcomings in some aspects. For example the redundancy between the employee scoring sample data; the problem that the input variable dimensionality is too high, which leads to the low efficiency of the model; the fuzzy neural network is easy to fall into the local optimum and it is difficult to find the global optimum.
Comparative Case Study of Plan-Driven and Agile Approaches in Student Computing Projects

Plan first, execute second” and “Adapt to change as you iterate” are two paradigms that are well known in software development, as they represent disparate schools of project management: traditional and agile. Despite a long-lasting debate on prominence of each, little comparative research has been reported, particularly in the context of academic projects, where all too often a no-process approach still prevails. This paper presents an experiment that was carried out during the Spring semester of the 2018-2019 academic year at two engineering schools in Europe in order to assess the impacts of a given development method on the success of student computing undertakings. A structured, metric-based assessment scheme was applied to reveal patterns associated with the use of Plan-driven, Iterative and no-process approaches on product quality, team productivity and teamwork quality facets.
COVID-19 Therapy Target Discovery with Context-Aware Literature Mining

The abundance of literature related to the widespread COVID-19 pandemic is beyond manual inspection of a single expert. Development of systems, capable of automatically processing tens of thousands of scientific publications with the aim to enrich existing empirical evidence with literature-based associations is challenging and relevant. We propose a system for contextualization of empirical expression data by approximating relations between entities, for which representations were learned from one of the largest COVID-19-related literature corpora. In order to exploit a larger scientific context by transfer learning, we propose a novel embedding generation technique that leverages SciBERT language model pretrained on a large multi-domain corpus of scientific publications and fine-tuned for domain adaptation on the CORD-19 dataset. The conducted manual evaluation by the medical expert and the quantitative evaluation based on therapy targets identified in the related work suggest that the proposed method can be successfully employed for COVID-19 therapy target discovery and that it outperforms the baseline FastText method by a large margin.
P6: A Declarative Language for Integrating Machine Learning in Visual Analytics

We present P6, a declarative language for building high performance visual analytics systems through its support for specifying and integrating machine learning and interactive visualization methods. As data analysis methods based on machine learning and artificial intelligence continue to advance, a visual analytics solution can leverage these methods for better exploiting large and complex data. However, integrating machine learning methods with interactive visual analysis is challenging. Existing declarative programming libraries and toolkits for visualization lack support for coupling machine learning methods. By providing a declarative language for visual analytics, P6 can empower more developers to create visual analytics applications that combine machine learning and visualization methods for data analysis and problem solving. Through a variety of example applications, we demonstrate P6's capabilities and show the benefits of using declarative specifications to build visual analytics systems. We also identify and discuss the research opportunities and challenges for declarative visual analytics.
A Parallel Education Based Intelligent Tutoring Systems Framework

although online education has improved efficiency of learners' access to high-quality educational resources, real-time interaction between instructors and learners have not yet been achieved in online personalized learning. Intelligent Tutoring Systems (ITS) provides a feasible way to realize realtime personalized learning guidance and resource recommendations by applying AI to capture and analyze online learners' characteristics and behaviors. In this paper, reviews and trends of ITS are discussed and three challenges of ITS research & development are pointed out: learner model, guidance mechanism and human-computer interaction mechanism. In order to address these issues, parallel intelligence theory is introduced and a parallel intelligence education based ITS framework is proposed.
PhishOut: Effective Phishing Detection Using Selected Features

Phishing emails are the first step for many of today's attacks. They come with a simple hyperlink, request for action or a full replica of an existing service or website. The goal is generally to trick the user to voluntarily give away his sensitive information such as login credentials. Many approaches and applications have been proposed and developed to catch and filter phishing emails. However, the problem still lacks a complete and comprehensive solution. In this paper, we apply knowledge discovery principles from data cleansing, integration, selection, aggregation, data mining to knowledge extraction. We study the feature effectiveness based on Information Gain and contribute two new features to the literature. We compare six machine-learning approaches to detect phishing based on a small number of carefully chosen features. We calculate false positives, false negatives, mean absolute error, recall, precision and F-measure and achieve very low false positive and negative rates. Naive Bayes has the least true positives rate and overall Neural Networks holds the most promise for accurate phishing detection with accuracy of 99.4%.