Reviewerly/exHarmony huggingface dataset:
Towards a Systematic Study of Values in SE: Tools for Industry and Education

This paper reports on the development of ongoing research into the systematic study of human values in software engineering (SE). Firstly, we outline the rationale for this work, highlighting the significance of human values in SE and the need for both a more structured approach to their study and tools to support their articulation in the SE domain. We then explain our theoretical framework, which draws on social psychology research on values. Secondly, we introduce tools designed in line with this framework and the context of their use so far. Our tools adopt a variety of research methods, including structured mixed-method techniques, such as the 'Values Q-Sort' (V-QS), and more open design-based methods, such as the 'Values Probes'. Given the multi-faceted nature of the topic, we argue that a combination of qualitative, quantitative and design thinking techniques is a necessary step for the study of human values in SE; and that these methods should be both a) robust and replicable, and b) creative and provocative. Finally, we highlight the potential applications of our tools in SE industry and education.
A personalized Virtual Reality Experience for Relaxation Therapy

Virtual Reality (VR) has the potential to change not only to the way we consume and perceive entertainment but also to improve other important areas of society. One sector that is starting to benefit from the advantages of VR is the treatment of stress related mental illnesses. VR is able to bring relaxation therapy to the next level in which solutions can be scalable (without the need for real-time dedicated professionals) and personalized. This paper presents VRelax, a personalized VR relaxation therapy approach. By means of semantic methodologies and online learning techniques, VRelax provides a personalized, relaxing virtual environment to the user.
Designing the Impression of Social Agents' Real-time Interruption Handling

Human interaction partners can deal with interruptions and then resume the interaction. This ability should be emulated by social agents. How fast interruptions are handled might influence the overall impression of an agent. In this paper, we present the results of a user study on how a human dialog partner perceives the be- havior of a virtual agent handling verbal user interruptions with different reaction times. The study goes beyond typical perception experiments by preserving the real-time interaction experience. For the evaluation, we rely on a parametrizable parallelized computa- tional model that represents dialog flow, overlap detection, conflict recognition, and conflict handling in real-time. The evaluation re- sults show that the timing of the agent's interruption handling in interactive human-agent dialogues is related to different interper- sonal attitudes.
Using scenarios to forecast outcomes of a refugee crisis

The Syrian civil war has led to millions of Syrians fleeing the country, and has resulted in a humanitarian crisis. By considering how such socio-political events may unfold, scenarios can lead to informed forecasts that can be used for decision-making. We examined the relationship between scenarios and forecasts in the context of the Syrian refugee crisis. Forty Turkish students who had been trained to use a brainstorming technique generated scenarios that might follow within six months of the Turkish government banning Syrian refugees from entering the country. The participants generated 3–6 scenarios. Over half were rated as ‘high’ quality in terms of completeness, relevance/pertinence, plausibility, coherence, and transparency (order effects). The scenario quality was unaffected by the scenario quantity. Even though no forecasts were requested, the participants’ first scenarios contained 0–17 forecasts. The mean forecast accuracy was 45%, and this was unaffected by the forecast quantity. Therefore, brainstorming can offer a simple and quick way of generating scenarios and forecasts that can potentially help decision-makers to tackle humanitarian crises.
From natural language text to rules: knowledge acquisition from formal documents for aircraft assembly

(2019). From natural language text to rules: knowledge acquisition from formal documents for aircraft assembly. Journal of Engineering Design: Vol. 30, Ontological Engineering for Supporting Semantic Reasoning in Design, pp. 417-444.
Impact of Various Motion Interpolation Algorithms on 360° Video QoE

In our study, we compare the impact of various motion interpolation (MI) algorithms on 360° video Quality of Experience (QoE). For doing so, we conducted a subjective test with 12 video expert viewers, while a pair comparison test method was used. We interpolated four different 20 s long 30 fps 360° source contents to the native 90 Hz refresh rate of popular Head-Mounted Displays using three different MI algorithms. Subsequently, we compared these 90 fps videos against each other to investigate the influence on the QoE. Regarding the algorithms, we found out that ffmpeg blend does not lead to a significant improvement of QoE, while MCI and butterflow do so. Additionally, we concluded that for 360° videos containing fast and sudden movements, MCI should be preferred over butterflow, while butterflow is more suitable for slow and medium motion videos. While comparing the time needed for rendering the 90 fps interpolated videos, ffmpeg blend is the fastest, while MCI and butterflow need much more time.
Improving the Security of the DRS Scheme with Uniformly Chosen Random Noise

At PKC 2008, Plantard et al. published a theoretical framework for a lattice-based signature scheme. Recently, after ten years, a new signature scheme dubbed as the Diagonal Reduction Signature (DRS) scheme was presented in the NIST PQC Standardization as a concrete instantiation of the initial work. Unfortunately, the initial submission was challenged by Yu and Ducas using the structure that is present on the secret key noise. In this paper, we are proposing a new method to generate random noise in the DRS scheme to elimite the aforementioned attack, and all subsequent potential variants.
A Low-Cost Search-and-Rescue Drone for Near Real-Time Detection of Missing Persons

In this work, an unmanned aerial system is implemented to search an outdoor area for an injured or missing person (subject) without requiring a connection to a ground operator or control station. The system detects subjects using exclusively on-board hardware as it traverses a predefined search path, with each implementation envisioned as a single element of a larger swarm of identical search drones. Imagery is streamed from a camera to an Odroid single-board computer, which prepares the data for inference by a Neural Compute Stick vision accelerator. A single-class TinyYolo network, trained on the Okutama-Action dataset and an original Albatross dataset, is utilized to detect subjects in the prepared frames. The detection apparatus is mounted on a drone and field tests validate the system feasibility and efficacy.
Skeleton‐based action recognition with JRR‐GCN

A novel joints relation-reasoning, graph convolutional network (JRR-GCN) is proposed to solve the problem of skeleton-based action recognition (SAR). Different from the conventional spatial convolutional network-based methods, the adjacency matrices of JRR-GCN is reasoned by joints relation-reasoning network (JRR) automatically, which results in generating a more realistic representation of skeleton topology and yields better adjacency matrices for every sample. JRR is trained with the reinforcement learning with a novel state-action mapping scheme. Extensive experiments are conducted on two public SAR datasets, NTU-RGB+D and kinetics. Also the obtained results demonstrate the effectiveness of JRR-GCN comparing with the state-of-the-art SAR methods.
Vertical and Horizontal Data Partitioning for Classifier Ensemble Learning

Multiple classifier systems have proven superiority over individual ones to solve classification tasks. One of the main issues in those solution relies in data size, when the amount of data to be analyzed becomes huge. In this paper, the performance of ensemble system to succeed by using only portions of the available data is analyzed. For this, extensive experimentation with homogeneous ensemble systems trained with 50% of data and 50% of features is performed, using bagging sampling schema. Simple and weighted majority voting schemes are implemented to combine the classifier outputs. Experimental results including 25 datasets show the benefit of using multiple classifiers trained on limited data. The ensemble size and the accuracy obtained with individual model trained over the entire dataset is compared.
How Deep Is a Virtual Reality Experience? Virtual Environments, Emotions and Physiological Measures

Previous studies have shown a relationship between virtual environments and subjectively evaluated emotions. The present study intends to understand if an emotion evaluated subjectively is related to an objective measurement like heart rate. In the study, a design between subjects with two virtual environments was used. A sample of 58 participants participated in this study, in two immersive virtual environments: Helix® (a roller coaster experience, n = 36); and Yana® (experience of sunset/sunrise, n = 22). Subjective and objective measures (heart rate) were measured. The results are consistent with previous studies and our hypotheses. As for the subjective measures it is concluded that these measures were related to physiological measures, so the emotional response to an event in virtual reality is not only an interpretation of the situation but also a physiological phenomenon.
Addressing Vocabulary Gap in E-commerce Search

E-commerce customers express their purchase intents in several ways, some of which may use a different vocabulary than that of the product catalog. For example, the intent for "women maternity gown" is often expressed with the query, "ladies pregnancy dress". Search engines typically suffer from poor performance on such queries because of low overlap between query terms and specifications of the desired products. Past work has referred to these queries as vocabulary gap queries. In our experiments, we show that our technique significantly outperforms strong baselines and also show its real-world effectiveness with an online A/B experiment.
Analysis of Paddy Productivity Using NDVI and K-means Clustering in Cibarusah Jaya, Bekasi Regency

Abstract Information about rice productivity is one of the references for government to maintain food availability. With remote sensing technology, rice productivity can be known faster. This research was conducted using UAV (Unmanned Aerial Vehicle) and Sentinel-2 Satellite. Sentinel-2 NDVI which has a low resolution with high resolution UAV images, both variables have similarity values and regression reaches 0.8. NDVI are grouped into 8 classes using k-means clustering based on the similarity of the waveforms of each data retrieval point. Based on characteristic of k-means classes, field which has earlier planting times and the location closer to the water source, allowing a higher paddy productivity. Further analysis was also carried out to get the best period to estimate paddy productivity using Sentinel-2 imagery. Sentinel-2 was chosen because it has a distance between data as far as 5 days, allowing it to be more accurate. The best time is obtained at 63 DAP (Days After Planting), which is when NDVI reaches its maximum state. The estimation model of rice productivity based on UAV has a high coefficient of determination compared to Sentinel-2 so that the relationship between maximum NDVI UAV and rice productivity is better than Sentinel-2.
To Be or Not to Be? Which Is the Case with Robots in the Hotel Industry?

Recognizing that in the future robots will play an important role in the operation of tourist units, there is a need for a first record of existing research and bibliography. The ultimate aim of this bibliographic study is to record the current situation and then make proposals for future research due to a lack of relevant primary data. It should be noted that the relevant literature is particularly limited, with references being mainly from 2017 to 2018, focusing on Ivanov’s research, but with limited empirical data and the lack of publications in high-quality scientific journals. For this reason, this study proposes two research cases to be explored in a future survey. The first concerns the potential increase in customer satisfaction from the use of robots and the second the effect of using robots on job satisfaction of hotel workers.
Quadrature-Based Nonlinear Joint Probabilistic Data Association Filter

This paper deals with the coupled problem of data association and nonlinear estimation for enhanced Space Situational Awareness (SSA) applications. High-order quadrature methods are used to implement a joint probabilistic data association (JPDA) filter to associate multiple measurements to multiple targets at any given point of time. It is shown that the use of higher-order quadrature methods achieves greater accuracy and stability. In particular, the recently developed conjugate unscented transformation is used to estimate the association probabilities and state estimates. Numerical simulations are used to illustrate the performance of the JPDA filter for SSA applications.
A Medical Image Retrieval System in PACS Environment for Clinical Decision Making

Abstract Image retrieval plays a major role in an integrated healthcare environment for various purposes, such as computer-aided diagnosis, medical education, Tele-surgeries, evidence-based medicine, and many more. In the retrieval system, two kinds of approaches are mainly followed: Text-Based Image Retrieval (TBIR) and Content-Based Image Retrieval (CBIR). The former approach requires lot of human effort and time, also subject to human perception. The latter approach pays greater attention to global and local information, such as the color, shape, region, and texture of an image. The major drawback of CBIR is its inability to distinguish the characteristics of heterogeneous medical images. Considering the limitations of both approaches, an integrated framework is proposed, based on the distinct characteristics of TBIR and CBIR. In both approaches, the related feature descriptors of images and terms of documents are extracted. In order to reduce the irrelevant features in CBIR, a modified ant colony optimization with a relevance feedback mechanism is incorporated. In this proposed framework, the performance of CBIR, TBIR, and their fusion (combination of TBIR and CBIR) is analyzed and the precision of each approach is 78.8%, 85.9%, and 94.8%, respectively.
Enabling Privacy-Preserving Sharing of Cyber Threat Information in the Cloud

Network threats often come from multiple sources and affect a variety of domains. Collaborative sharing and analysis of Cyber Threat Information (CTI) can greatly improve the prediction and prevention of cyber-attacks. However, CTI data containing sensitive and confidential information can cause privacy exposure and disclose security risks, which will deter organisations from sharing their CTI data. To address these concerns, the consortium of the EU H2020 project entitled Collaborative and Confidential Information Sharing and Analysis for Cyber Protection (C3ISP) has designed and implemented a framework (i.e. C3ISP Framework) as a service for cyber threat management. This paper focuses on the design and development of an API Gateway, which provides a bridge between end-users and their data sources, and the C3ISP Framework. It facilitates end-users to retrieve their CTI data, regulate data sharing agreements in order to sanitise the data, share the data with privacy-preserving means, and invoke collaborative analysis for attack prediction and prevention. In this paper, we report on the implementation of the API Gateway and experiments performed. The results of these experiments show the efficiency of our gateway design, and the benefits for the end-users who use it to access the C3ISP Framework.
Admission Test Modelling of State Islamic College in Indonesia Using Local Linear for Bivariate Longitudinal Data

Abstract The main challenge in the development of education is improving the quality of education and equitable education. Improving the quality of education is needed in order to create high-quality human resources. Equitable education carried out also still has problems with the uneven quality of education in each region. The aim of this research is to determine the effect of public senior high school status on the admission test scores, i.e., scholastic aptitude test score and Islamic test score of state Islamic colleges in Indonesia. In this research we use a local linear estimator to estimate nonparametric regression model for longitudinal data, and then apply it to the data of admission test scores of state islamic colleges in Indonesia based on the percentage of public senior high school. The result shows that the pattern of the scholastic aptitude test and Islamic test based on the percentage of public senior high school of state Islamic colleges in western Indonesia is different from that in eastern Indonesia. The result of this research can be used by the government to make policies so that the quality of state Islamic colleges will be better by increassing the admission test scores.
Verification-Led Smart Contracts

Turing complete smart contract formalisms (e.g. Solidity) are conceptually appealing, but leave the door open to the problems of verifying completely arbitrary code, a task which can be of arbitrarily high complexity or can be undecidable. We argue that a more structured approach, in which smart contract families are designed ab initio with efficient verifiability in mind, provide a much more practical way forward. We emphasis that the boundary between on-chain and off-chain information, which must always be determined in an application specific manner, is crucial in determining the practicability of smart contract verification. We discuss the role of refinement technologies in breaking down the complexity of smart contract verification, and illustrate the argument using the Event-B formal modelling framework and Solidity as implementation vehicle.
Preliminary Results on Ancient Cham Glyph Recognition from Cham Inscription images

This paper presents an original work on ancient Cham glyph which is a language of Cham people in the Southeast Asia from the 6th to 15th century. Unfortunately it is in danger of being destroyed by times as well as of being ignored when the specialists of ancient Cham disappear. Motivated by this fact, we contribute to build a corpus of ancient Cham glyph that contains of 1607 images of Cham inscriptions. These images have been carefully pre-processed then annotated into 37 classes by a specialist/historian in ancient Cham. This digitized version of Cham glyph could be stored anywhere as long as wanted and available to wide audience for studying and exploration. As the corpus is the first introduced world wide, no work on automatic Cham recognition has been considered. We then investigate computer vision and machine learning techniques to show how effective a machine learning technique could be for this case study on a still limited amount of data. The best recognition F1-score is 86.3% with features extracted from GoogleNet and K-NN classifier, showing very promising performance.
Power-Constrained Image Contrast Enhancement Through Sparse Representation by Joint Mixed-Norm Regularization

Power-constrained image contrast enhancement is a fundamental step for improving the battery life of modern consumer devices with embedded emissive-display panels, such as organic light-emitting diodes (OLEDs). The conventional power-constrained image contrast enhancement in OLED displays is typically performed using histogram-relevant priors or heuristic curve-fitted techniques. This results in underexposure effects or color-tone changes in the reconstructed image. Therefore, this paper proposes a novel power-constrained sparse representation model by joint l2, 1, γ, and isotropic total variation norm (called mixed-norm) regularized sparse coding to simultaneously improve power saving and the perceptible visual quality of the OLED displays. The qualitative and quantitative experiments demonstrate that the proposed technique noticeably outperforms the state-of-the-art power-constrained contrastenhancement techniques.
What is happening in the city? A case study for user-centred geovisualisation design

For citizens, being aware of what is happening in their urban surroundings becomes challenging as more information from diverse sources becomes available. In this paper, we describe our user-centered approach of designing an interactive tool making use of urban data visualisations to facilitate people’s decisions about social and cultural events. After gathering the needs of urban actors through formative user studies, we identified beneficial data types and collected a variety of data sets from publicly accessible online sources. For the aim of enabling casual exploration of events in the city, we designed a set of geovisualisation prototypes and designed a variety of evaluative user studies based on established geovisualisation techniques. The main aim here is to enable casual exploration of events in the city more than the intended search for specific events. We developed two prototypes that make use of two different geovisualisations to represent events: Prototype A uses common location markers, and prototype B uses a novel glyph design to visualize more types of data at a glance. We share the lessons learned from the results of our study, which will inform the design of geographical data visualisations for citizens.
Empirical evaluation of deep learning models for sentiment analysis

The availability of computing resources and generation of large scale data emanating from Artificial Intelligence, Internet of Things and social media platforms have resulted into resurgence of deep learning technology. Deep learning architectures have been successfully adopted to solve the problems arising in variety of domains such as computer vision, information retrieval, robotics, and natural language processing, etc. Due to inherent ability of deep architectures to extract hierarchical structures from complex multimedia data, they have been widely used for the tasks of classification, regression and prediction. Motivated by the same, this paper addresses the problem of identifying the subjective information from text documents and predicting the sentiments at sentence level using deep feedforward neural network with global average pooling and long short term memory model with dense layers. The experimentation details state that both models are on par and provide good accuracy on the benchmarked dataset of sentiment classification.
Machine Learning at the Patent Office: Lessons for Patents and Administrative Law

The empirical data indicate that a relatively small increment of additional USPTO investment in prior art search at the initial examination stage could be a cost-effective mechanism for improving accuracy in the patent system. This contribution argues that machine learning provides a promising arena for such investment. Notably, the use of machine learning in patent examination does not raise the same potent concerns about individual rights and discrimination that it raises in other areas of administrative and judicial process. That said, even an apparently easy case like prior art search at the USPTO poses challenges. The most important generalizable challenge relates to explainability. The USPTO has stressed transparency to the general public as necessary for achieving adequate explainability. However, at least in contexts like prior art search, adequate explainability does not require full transparency. Moreover, full transparency would chill provision of private sector expertise and would be susceptible to gaming.