Reviewerly/exHarmony huggingface dataset:
Requirements elicitation methods based on interviews in comparison: A family of experiments

There are several methods to elicit requirements through interviews between an end-user and a team of software developers. The choice of the best method in this context is usually on subjective developers' preferences instead of objective reasons. There is a lack of empirical evaluations of methods to elicit requirements that help developers to choose the most suitable one. This paper designs and conducts a family of experiments to compare three methods to elicit requirements: Unstructured Interviews, where there is no specific protocol or artifacts; Joint Application Design (JAD), where each member of the development team has a specific role; Paper Prototyping, where developers contrast the requirements with the end-user through prototypes. The experiment is a between-subjects design with next response variables: number of requirements, time, diversity, completeness, quality and performance. The experiment consists of a maximum of 4 rounds of interviews between students that play the role of developers and an instructor that plays the role of client. Subjects had to elaborate a requirements specification document as results of the interviews. We recruited 167 subjects in 4 replications in 3 years. Subjects were gathered in development teams of 6 developers at most, and each team was an experimental unit. We found some significant differences. Paper Prototyping yields the best results to elicit as many requirements as possible, JAD requires the highest time to report the requirements and the least overlapping, and Unstructured Interviews yields the highest overlapping and the lowest time to report the requirements. Paper Prototyping is the most suitable for eliciting functional requirements, JAD is the most suitable for non-functional requirements and to avoid overlapping, Unstructured Interviews is the fastest but with poor quality in the results.
Perceiving online public opinion: The impact of Facebook opinion cues, opinion climate congruency, and source credibility on speaking out

The novel affordances and unique features on social media have transformed the way people assess public opinion. Drawing on the spiral of silence (SOS) theory, this study examines the roles that user-generated comments (UGCs) and aggregated user representations (AURs), represented by reaction emojis, play in shaping perceptions of the opinion climate. It also investigates how features on the platform trigger perceptions of source credibility to influence willingness to speak out. Results from a 3 (opinion cues: UGCs-only vs AURs-only vs UGCs and AURs) × 2 (opinion climate congruency: congruent vs incongruent) × 2 (source credibility: high vs low) between-subjects experimental design revealed that audiences perceived reaction emojis to reflect public opinion. Source credibility was found to affect willingness to speak out and moderate opinion climate congruency. The findings extend the SOS theory by highlighting the significant role that novel affordances have on SOS components operating online. Implications of the findings were discussed.
Influence Function for Unbiased Recommendation

Recommender system is one of the most successful machine learning technologies for commerce. However, it can reinforce the closed feedback loop problem, where the recommender system generates items to users, then the further recommendation model is trained with the data that users' feedback to the items. Such self-reinforcing pattern can cause data bias problems. There are several debiasing methods, inverse-propensity-scoring (IPS) is a practical one for industry product. Since it is relatively easy to reweight training samples, and ameliorate the distribution shift problem. However,because of deterministic policy problem and confoundings in real-world data, it is hard to predict propensity score accurately. Inspired by the sample reweight work for robust deep learning, we propose a novel influence function based method for recommendation modeling, and analyze how the influence function corrects the bias. In the experiments, our proposed method achieves better performance against the state-of-the-art approaches.
METAL: Minimum Effort Temporal Activity Localization in Untrimmed Videos

Existing Temporal Activity Localization (TAL) methods largely adopt strong supervision for model training, which requires (1) vast amounts of untrimmed videos per each activity category and (2) accurate segment-level boundary annotations (start time and end time) for every instance. This poses a critical restriction to the current methods in practical scenarios where not only segment-level annotations are expensive to obtain, but many activity categories are also rare and unobserved during training. Therefore, Can we learn a TAL model under weak supervision that can localize unseen activity classes? To address this scenario, we define a novel example-based TAL problem called Minimum Effort Temporal Activity Localization (METAL): Given only a few examples, the goal is to find the occurrences of semantically-related segments in an untrimmed video sequence while model training is only supervised by the video-level annotation. Towards this objective, we propose a novel Similarity Pyramid Network (SPN) that adopts the few-shot learning technique of Relation Network and directly encodes hierarchical multi-scale correlations, which we learn by optimizing two complimentary loss functions in an end-to-end manner. We evaluate the SPN on the THUMOS'14 and ActivityNet datasets, of which we rearrange the videos to fit the METAL setup. Results show that our SPN achieves performance superior or competitive to state-of-the-art approaches with stronger supervision.
Wavelet Synthesis Net for Disparity Estimation to Synthesize DSLR Calibre Bokeh Effect on Smartphones

Modern smartphone cameras can match traditional DSLR cameras in many areas thanks to the introduction of camera arrays and multi-frame processing. Among all types of DSLR effects, the narrow depth of field (DoF) or so called bokeh probably arouses most interest. Today's smartphones try to overcome the physical lens and sensor limitations by introducing computational methods that utilize a depth map to synthesize the narrow DoF effect from all-in-focus images. However, a high quality depth map remains to be the key differentiator between computational bokeh and DSLR optical bokeh. Empowered by a novel wavelet synthesis network architecture, we have greatly narrowed the gap between DSLR and smartphone camera in terms of the bokeh more than ever before. We describe three key Modern smartphone cameras can match traditional digital single lens reflex (DSLR) cameras in many areas thanks to the introduction of camera arrays and multi-frame processing. Among all types of DSLR effects, the narrow depth of field (DoF) or so called bokeh probably arouses most interest. Today's smartphones try to overcome the physical lens and sensor limitations by introducing computational methods that utilize a depth map to synthesize the narrow DoF effect from all-in-focus images. However, a high quality depth map remains to be the key differentiator between computational bokeh and DSLR optical bokeh. Empowered by a novel wavelet synthesis network architecture, we have narrowed the gap between DSLR and smartphone camera in terms of bokeh more than ever before. We describe three key enablers of our bokeh solution: a synthetic graphics engine to generate training data with precisely prescribed characteristics that match the real smartphone captures, a novel wavelet synthesis neural network (WSN) architecture to produce unprecedented high definition disparity map promptly on smartphones, and a new evaluation metric to quantify the quality of the disparity map for real images from the bokeh rendering perspective. Experimental results show that the disparity map produced from our neural network achieves much better accuracy than the other state-of-the-art CNN based algorithms. Combining the high resolution disparity map with our rendering algorithm, we demonstrate visually superior bokeh pictures compared with existing top rated flagship smartphones listed on the DXOMARK mobiles.
Automating GDPR Compliance using Policy Integrated Blockchain

Data Protection regulations, like GDPR, mandate security controls to secure Personal Identifiable Information (PII) of the users which they share with service providers.With the volume of shared data reaching exascale proportions, it is challenging to ensure GDPR compliance in real time.We propose a novel approach that integrates GDPR Ontology with Blockchain to facilitate real time automated data compliance.Our framework ensures data operation is allowed only when validated by data privacy policies in compliance with privacy rules in GDPR.When a valid transaction takes place the PII data is automatically stored off-chain in a database.Our system, built using Semantic Web and Ethereum Blockchain, includes an access-control system that enforces data privacy policy when data is shared with third parties.
Accurate Light Field Depth Estimation via an Occlusion-Aware Network

Depth estimation is a fundamental problem for light field based applications. Although recent learning-based methods have proven to be effective for light field depth estimation, they still have troubles when handling occlusion regions. In this paper, by leveraging the explicitly learned occlusion map, we propose an occlusion-aware network, which is capable of estimating accurate depth maps with sharp edges. Our main idea is to separate the depth estimation on non-occlusion and occlusion regions, as they contain different properties with respect to the light field structure, i.e., obeying and violating the angular photo consistency constraint. To this end, three modules are involved in our network: the occlusion region detection network (ORDNet), the coarse depth estimation network (CDENet), and the refined depth estimation network (RDENet). Specifically, ORDNet predicts the occlusion map as a mask, while under the guidance of the resulting occlusion map, CDENet and REDNet focus on the depth estimation on non-occlusion and occlusion areas, respectively. Experimental results show that our method achieves better performance on 4D light field benchmark, especially in occlusion regions, when compared with current state-of-the-art light-field depth estimation algorithms.
Multi-Connectivity and Edge Computing for Ultra-Low-Latency Lifelike Virtual Reality

We explore a novel multi-user mobile VR system for streaming scalable 8K360^° video at high reliability and immersion fidelity, and low interactive latency, via a synergistic integration of scalable 360^° tiling, dual-band millimeter wave (mmWave) and Wi-Fi transmission, and edge computing. High rate directed mmWave links are studied to send VR viewport-specific high-quality enhancement layers of the 360^° content to the individual users, while Wi-Fi broadcast of the base layer of the entire 360^° panorama is sent to all users, to augment the system's reliability. The viewport-specific enhancement layers can comprise compressed and raw 360^° tiles, decoded first at the edge server. We explore the joint optimization of the mmWave access point to user association, the choice of 360^° tiles to be transmitted decompressed, the allocation of mmWave data rate across the compressed tiles in a viewport-specific enhancement layer, and the allocation of computing resources at the edge server and user devices. Our objective is to maximize the minimum delivered VR immersion fidelity across all users, given transmission, latency, and computing constraints. We demonstrate that our framework can enable a significant improvement in immersion fidelity (8dB to 10 dB) and spatial resolution (8Kvs. 4K), over MPEG-DASH that uses Wi-Fi transmission only. We also show that an increasing number of raw 360^° tiles are sent, as the mmWave link rate or the edge server/user computing power increase, exploring rigorously here the fundamental interplay between computing and communication capabilities, end-to-end system latency, and delivered VR immersion fidelity.
Corpora as open educational resources for language teaching

Abstract Corpora, large electronic collections of texts, have been used in language teaching for several decades. Also known as Data‐Driven Learning (DDL), this method has been gaining popularity because empirical research has consistently shown its effectiveness for learning. However, corpora are still underutilized, especially with learners of languages other than English, at lower proficiency levels, and in non‐university contexts. This is regrettable because DDL has a great potential for developing modular flipped content, especially for hybrid, remote, and online courses. This article first provides an overview of DDL applications and findings of empirical research. Next, it outlines obstacles to wider DDL implementation as well as available and possible solutions. Corpus user guides and exercise collections tied to specific corpora are discussed as one promising direction, and an example of such new open educational resources for teaching German is presented. The article concludes with a discussion of implications and future directions.
Combining OC-SVMs With LSTM for Detecting Anomalies in Telemetry Data With Irregular Intervals

To ensure the safety and stability of spacecrafts of which thousands of telemetry parameters are monitored, fast and accurate response to anomalies or potential hazards is very important and challenging. This task becomes more difficult when the obtained telemetry data are sampled at irregular intervals. Long Short-Term Memory networks (LSTM), as time series prediction models, have been applied to satellite anomaly detection and show a promising prospect. However, the anomaly detection method merely based on LSTM does not show a stable performance: when the prediction performance of LSTM is not satisfying, the performance of subsequent anomaly detection will be affected, and the impact is augmented when the telemetry data are of irregular intervals. In order to solve these problems, time intervals are introduced into the LSTM model directly. Besides that, a novel anomaly detection method, Detecting Anomalies using LSTM and Ensembled One-Class Support Vector Machines (DALEO) is proposed to further improve the performance of anomaly detection. In DALEO, multiple One-Class Support Vector Machines are used to obtain the ensemble outputs of high precision and high recall respectively. These ensemble outputs are integrated into the two stages of the anomaly detection method with LSTM in a novel way. Extensive empirical studies on real-world datasets of satellites and space shuttles demonstrate that DALEO improves the performance of anomaly detection significantly when dealing with telemetry data with irregular intervals.
On the relational autonomy of materials: entanglements in maker literacies research

Abstract This article addresses key components of posthumanism and maker literacies by reporting on empirical data from two makerspace research sites. Using posthuman methodologies, we suggest practical considerations of the relational autonomy of materials through entanglements between humans, non‐humans and more‐than‐humans in makerspace classroom settings. We propose answers to the following research questions: How do materials manifest their relational autonomy in makerspaces? How could the relational autonomy of materials impact maker literacies pedagogy? With this article, our contribution warrants researchers to think about the unpredictability of maker work through posthuman methodologies and how maker projects can help speak against the failing student rhetoric in literacy education.
Twitter Sentiment Analysis Using Machine Learning For Product Evaluation

Twitter, a micro-running a blog website, is a massive repository of public opinions expressed in the direction of numerous humans, offerings, companies, merchandise, etc. Sentiment evaluation is the system of analyzing one's public evaluations. Sentiment analysis whilst combined with twitter offers beneficial insights into what's expressed on Twitter. The big availability of online evaluations and postings in social media gives invaluable feedback for groups to make better knowledgeable choices in guidance their marketing techniques towards user's pastimes and alternatives. Sentiment evaluation is, therefore, vital for determining the general public's opinion toward selected services or products. This paper emphasizes the different techniques utilized for classifying the product critiques (which can be within the form of tweets) according to critiques expressed in tweets to analyze whether or not the massive behavior is positive, negative or neutral and use of that analysis for the evaluation of product market. Data used in this look at our online product critiques gathered from twitter and used to rank the satisfactory classifier for sentiments.
Beyond User Embedding Matrix

Modeling large scale and rare-interaction users are the two major challenges in recommender systems, which derives big gaps between researches and applications. Facing to millions or even billions of users, it is hard to store and leverage personalized preferences with a user embedding matrix in real scenarios. And many researches pay attention to users with rich histories, while users with only one or several interactions are the biggest part in real systems. Previous studies make efforts to handle one of the above issues but rarely tackle efficiency and cold-start problems together.
Video Recommendation with Multi-gate Mixture of Experts Soft Actor Critic

In this paper, we propose a reinforcement learning based large scale multi-objective ranking system for optimizing short-video recommendation on an industrial video sharing platform. Multiple competing ranking objective and implicit selection bias in user feedback are the main challenges in real-world platform. In order to address those challenges, we integrate multi-gate mixture of experts and soft actor critic into the ranking system. We demonstrated that our proposed framework can greatly reduce the loss function compared with systems only based on single strategies.
Learning mixtures of linear regressions in subexponential time via Fourier moments

We consider the problem of learning a mixture of linear regressions (MLRs). An MLR is specified by k nonnegative mixing weights p 1, …, p k summing to 1, and k unknown regressors w 1,...,w k ∈ℝ d . A sample from the MLR is drawn by sampling i with probability p i , then outputting (x, y) where y = ⟨ x, w i ⟩ + η, where η∼(0,ς2) for noise rate ς. Mixtures of linear regressions are a popular generative model and have been studied extensively in machine learning and theoretical computer science. However, all previous algorithms for learning the parameters of an MLR require running time and sample complexity scaling exponentially with k.
Passive- and not active-risk tendencies predict cyber security behavior

Vulnerabilities to online cyber-related crime are typically the result of poor decisions on the part of users. To date, research on risk-taking behavior applied to cyber-security situations has concentrated mainly on the risks that stem from active behavioral choices (e.g., opening an attachment from an unknown sender). However, risk may result from the failure to implement an action (e.g., not strengthening a password). These two types of risk have been differentiated and termed active- and passive-risk behaviors. We conducted two studies (Study 1 and Study 2) that examine how self-reported active- and passive-risk behaviors predict cyber-security behavioral intentions. In Study 3, we examine how active and passive risk relate to actual cyber-security behavior. The results show that cyber-security behavioral intentions and actual cyber behaviors are significantly correlated with self-reported individual differences in passive-risk behavior but not in active-risk behavior. We discuss the theoretical and practical implications of these findings.
MVL: Multi-View Learning for News Recommendation

In this paper, we propose a Multi-View Learning (MVL) framework for news recommendation which uses both the content view and the user-news interaction graph view. In the content view, we use a news encoder to learn news representations from different information like titles, bodies and categories. We obtain representation of user from his/her browsed news conditioned on the candidate news article to be recommended. In the graph-view, we propose to use a graph neural network to capture the user-news, user-user and news-news relatedness in the user-news bipartite graphs by modeling the interactions between different users and news. In addition, we propose to incorporate attention mechanism into the graph neural network to model the importance of these interactions for more informative representation learning of user and news. Experiments on a real world dataset validate the effectiveness of MVL.
Medicine Expenditure Prediction via a Variance- Based Generative Adversarial Network

Machine learning (ML) offers a wide range of techniques to predict medicine expenditures using historical expenditures data as well as other healthcare variables. For example, researchers have developed multilayer perceptron (MLP), long short-term memory (LSTM), and convolutional neural network (CNN) models for predicting healthcare outcomes. However, recently proposed generative approaches (e.g., generative adversarial networks; GANs) are yet to be explored for time-series prediction of medicine-related expenditures. The primary objective of this research was to develop and test a generative adversarial network model (called “variance-based GAN or V-GAN”) that specifically minimizes the difference in variance between model and actual data during model training. For our model development, we used patient expenditure data of a popular pain medication in the US. In the V-GAN model, we used an LSTM model as a generator network and a CNN model or an MLP model as a discriminator network. The V-GAN model's performance was compared with other GAN variants and ML models proposed in prior research such as linear regression (LR), gradient boosting regression (GBR), MLP, and LSTM. Results revealed that the V-GAN model using an LSTM generator and a CNN discriminator outperformed other GAN-based prediction models, as well as the LR, GBR, MLP, and LSTM models in correctly predicting medicine expenditures of patients. Through this research, we highlight the utility of developing GAN-based architectures involving variance minimization for predicting patient-related expenditures in the healthcare domain.
A Mnemonic Kalman Filter for Non-Linear Systems With Extensive Temporal Dependencies

Analytic dynamic models for target estimation are often approximations of the potentially complex behaviour of the object of interest. Its true motion might depend on hundreds of parameters and can involve long-term temporal correlation. However, conventional models keep the degrees of freedom low and they usually assume the Markov property to reduce computational complexity. In particular, the Kalman Filter assumes prior and posterior Gaussian densities and is hence restricted to linear transition functions which are often insufficient to reflect the behaviour of a real object. In this letter, a Mnemonic Kalman Filter is introduced which overcomes the Markov property and the linearity restriction by learning to predict a full transition probability density with Long Short-Term Memory networks.