Reviewerly/exHarmony huggingface dataset:
Dark Channel: The Devil is in the Details

Dark channel prior is a highly valued theory mostly used in the area of haze removal. It reveals the wide existence of pixels with very small channel. However, the definition is in a decorated way that relies on the concept of dark channel. The dark channel works like an image domain wherein non-sky regions of clear outdoor images are close to zero. In this letter we show that, such a concept is unnecessary and misleading, and can be improved by using a more basic concept named dark pixel. A novel transmission estimation method based on finding dark pixels is proposed. The method is concise and easy to understand. In comparisons with state-of-the-art methods, our method shows significant improvements on both transmission estimation and haze removal results.
The impact of result diversification on search behaviour and performance

Result diversification aims to provide searchers with a broader view of a given topic while attempting to maximise the chances of retrieving relevant material. Diversifying results also aims to reduce search bias by increasing the coverage over different aspects of the topic. As such, searchers should learn more about the given topic in general. Despite diversification algorithms being introduced over two decades ago, little research has explicitly examined their impact on search behaviour and performance in the context of Interactive Information Retrieval (IIR). In this paper, we explore the impact of diversification when searchers undertake complex search tasks that require learning about different aspects of a topic (aspectual retrieval). We hypothesise that by diversifying search results, searchers will be exposed to a greater number of aspects. In turn, this will maximise their coverage of the topic (and thus reduce possible search bias). As a consequence, diversification should lead to performance benefits, regardless of the task, but how does diversification affect search behaviours and search satisfaction? Based on Information Foraging Theory (IFT), we infer two hypotheses regarding search behaviours due to diversification, namely that (i) it will lead to searchers examining fewer documents per query, and (ii) it will also mean searchers will issue more queries overall. To this end, we performed a within-subjects user study using the TREC AQUAINT collection with 51 participants, examining the differences in search performance and behaviour when using (i) a non-diversified system (BM25) versus (ii) a diversified system (BM25 + xQuAD) when the search task is either (a) ad-hoc or (b) aspectual. Our results show a number of notable findings in terms of search behaviour: participants on the diversified system issued more queries and examined fewer documents per query when performing the aspectual search task. Furthermore, we showed that when using the diversified system, participants were: more successful in marking relevant documents, and obtained a greater awareness of the topics (i.e. identified relevant documents containing more novel aspects). These findings show that search behaviour is influenced by diversification and task complexity. They also motivate further research into complex search tasks such as aspectual retrieval—and how diversity can play an important role in improving the search experience, by providing greater coverage of a topic and mitigating potential bias in search results.
VMAF Oriented Perceptual Optimization for Video Coding

In the light of low costs and automatic assessment, objective visual quality metrics enjoy many important applications such as perceptual coding. Recently multiple metrics obtain further improvement by means of machine learning. However, due to the absence of specific formulas, it's often hard to incorporate learning based metrics into video coding. In this paper, taking the state-of-the-art learning based metric VMAF for example, we propose a method of perceptual coding in an inferential manner for learning based metrics. The rate distortion optimization is adapted during coding as well. Experimental results show that compared with conventional methods, the proposed method can achieve obvious bitrate saving under HEVC coding.
Analysis of Mining Performance Based on Mathmatical Approach of PoW

Blockchain technology has been developed for cryptocurrency systems. Bitcoin, the most famous cryptocurrency type, guarantees a safe consensus using the Proof of Work (PoW) process. The PoW scheme is currently operating as a basic consensus algorithm for blockchain systems that are used widely in various industries. However, the PoW algorithm lacks in support of mathematical analysis, which hinders discoveries of potential weaknesses and flaws in the blockchain system. Therefore, in this paper, the correlation between the key parameters (i.e., TPS, difficulty, miners) in a blockchain system are analyzed based on mathematical analysis.
Observation of short-term variations in the clay minerals ratio after the 2015 Chile great earthquake $$(8.3M_{\mathrm{w}})$$ using Landsat 8 OLI data

In this paper, we explore the potential use of available free Landsat sensor data to investigate the short-term variations (STV) in the clay mineral ratio (CMR) following the 2015 Chile great moment magnitude earthquake ( $$8.3M_{\mathrm{w}})$$ . The present investigation was carried out in the absence of ground observation data. Landsat 8 Operational Land Imager (OLI)-based multi-temporal imageries of before, after and non-earthquakes were used to derive the above parameter by applying the band ratio approach of bands 6 and 7, where the before and after imageries were compared with non-seismic event images as well as for validation. For the temporal automatic lineament data extraction and final lineament mapping, band 8 (panchromatic) was used by applying the LINE algorithm technique of PCI Geomatica, and ArcGIS 10.5 software, respectively. All these derived products finally interact with the regional geology, fault line and lineament systems. The results reveal that CMR can easily identify the STV at temporal scales before and after the earthquake, while both are normal during non-earthquake time. However, this variation was observed in all three buffer zones (i.e., 50, 100 and 150 km buffer) and highly pronounced especially in the fault adjoining areas. Therefore, we found this research to be effective and could be used as an alternative method for future earthquake studies.
How Are Agile Release Trains Formed in Practice? A Case Study in a Large Financial Corporation

The Scaled Agile Framework (SAFe) is currently the most widely adopted framework for scaling agile in the software intensive industry. Despite this, there exists very little scientific research on the transformation process, as well as on the challenges and success factors of using SAFe in large-scale organizations. To start filling in this research gap, we conducted a case study by investigating the formation of agile release trains and the related challenges in a large financial organization adopting SAFe. We conducted 24 interviews with 27 interviewees, after which we analyzed the transcribed interviews using open and axial coding. The SAFe transformation started by forming a pilot train with teams that already had experience in agile practices. The success of the pilot led to the launching of new release trains. The forming of new agile release trains was challenging due to politics, difficulties in identifying the value streams, and the avoidance of a radical restructuring of the organization. These challenges led to opting for an organic way of transformation. Management organized several workshops to identify stakeholders for the second train. This was followed by team members choosing their teams based on skills and interests. The last two trains were formed using Lego workshops. The most significant challenges after forming the release trains at the case organization were struggles with existing projects and challenges due to inter-train dependencies.
Emerging Paradigms in Transform-Based Medical Image Compression for Telemedicine Environment

With the widely dispersed populations, efficient medical services have scarcely touched the lives of the people in rural areas. However, the exponential increase in the growth of Information and Communication Technology has made the medical community to widen their clinical reach through telemedicine. Despite the fact that telemedicine bridges the urban–rural health divide, the millions of medical images generated each year represent an enormous challenge for telemedicine. Handling the colossal volume of image datasets increases the need for efficient image compression methods. Thus, developing a compression method that results in reduction of redundancies in images as well as preserving the vital information is one of the critical challenges to be addressed in telemedicine. This chapter investigates the performance of different transforms for the compression of medical images. For this purpose, the author make use of a paradigm of transforms consists of Ripplet, Bandelet, Radon, Contourlet, Curvelet, Discrete Cosine Transform, Haar Wavelet and Geometric Transforms. All the transforms are encoded using SPIHT (Set Partitioning in Hierarchical Trees) encoder to provide better and quality compression. A detailed analysis on the performances offered by the aforementioned transforms on a set of MRI and CT images is carried out in terms of PSNR, SSIM, Computational time and Compression ratio. Experimental results prove that significant improvements on compression performance are achieved through transform-based methods. Additionally, an extensive subjective evaluation is performed on the visual quality of the compressed images. The subjective assessment signifies that the transform-based compression methods maintain the diagnostic visual quality of medical images and offer better representation of edges and curves. This chapter enables the reader to understand the significance of transform-based medical image compression for telemedicine applications.
Compression‐based steganography

Summary Conventional privacy‐enforcement mechanisms, such as encryption‐based ones, are frequently used to prevent third‐party eavesdroppers to intercept confidential information exchanged between two or more parties. However, the use of such mechanisms can be perceivable and it alerts the involved intercepting entities that could devote some effort in trying to remove the protection, eg, by cracking the encryption keys used or by exploiting the vulnerabilities of the technological solution used to protect the data. Sometimes, from the security point of view, avoiding to draw the attention or suspect to intermediate intercepting entities, may be better than protecting a data in a conventional manner. In such direction, one of the most effective approaches is hiding the secret information to be exchanged inside other data, through steganographic techniques. In this work, we exploit, for this specific purpose, the hierarchical structure of a compressed archive, as well as the algorithms and parameters used to create and maintain such archive. It is important to point out that, by doing this, the secret information is in no way semantically related to the contents of the compressed archive. This can be extremely useful in many cloud‐based situations where several confidential data is moved across multiple independent data center, which are under the control of different and not always fully trusted authorities. The effectiveness of this proposal has been assessed by using a properly designed and implemented prototype, where extensive tests have been performed within the context of a proof‐of‐concept.
Hyperspectral image reconstruction using Multi-colour and Time-multiplexed LED illumination

The rapidly rising industrial interest in hyperspectral imaging (HSI) has generated an increased demand for cost effective HSI devices. We are demonstrating a mobile and low-cost multispectral imaging system, enabled by time-multiplexed RGB Light Emitting Diodes (LED) illumination, which operates at video framerate. Critically, a deep Multi-Layer Perceptron (MLP) with HSI prior in the spectral range of 400–950 nm is trained to reconstruct HSI data. We incorporate regularisation and dropout to compensate for overfitting in the largely ill-posed problem of reconstructing the HSI data. The MLP is characterised by a relatively simple design and low computational burden. Experimental results on 51 objects of various references and naturally occurring materials show the effectiveness of this approach in terms of reconstruction error and classification accuracy. We were also able to show that utilising additional colour channels to the three R, G and B channels adds increased value to the reconstruction.
Vision Sensor Based Action Recognition for Improving Efficiency and Quality Under the Environment of Industry 4.0

In the environment of industry 4.0, human beings are still an important influencing factor of efficiency and quality which are the core of product life cycle management. Hence, monitoring and analyzing humans’ actions are essential. This paper proposes a vision sensor based method to evaluate the accuracy of operators’ actions. Each action of operators is recognized in real time by a Convolutional Neural Network (CNN) based classification model in which hierarchical clustering is introduced to minimize the effects of action uncertainty. Warnings are triggered when incorrect actions occur in real time and applications of action analysis of workers on a reducer assembling line show the effectiveness of the proposed method. The research is expected to provide a guidance for operators to correct their actions to reduce the cost of quality defects and improve the efficiency of workforce.
An App that Changes Mentalities about Mobile Learning—The EduPARK Augmented Reality Activity

The public usually associates mobile devices to distraction and learning disruption, and they are not frequently used in formal education. Additionally, games and parks are both associated with play and leisure time, and not to learn. This study shows that the combination of mobiles, games, and parks can promote authentic learning and contributes to changing conventional mentalities. The study is framed by the EduPARK project that created an innovative app for authentic learning, supported by mobile and augmented reality (AR) technologies, for game-based approaches in a green park. A case study of the EduPARK strategy’s educational value, according to 86 Basic Education undergraduate students, was conducted. The participants experienced the app in the park and presented their opinion about: (i) mobile learning; (ii) the app’s usability; and (iii) the impact of the educational strategy in terms of factors, such as intrinsic motivation and authentic learning. Data collection included a survey and document collection of student reflections. Data were subjected to descriptive statistics, System Usability score computing, and content analysis. Students considered that the EduPARK strategy has educational value, particularly regarding content learning and motivation. From this study emerged seven supporting pillars that constitute a set of guidelines for future development of mobile game-based learning.
Integrating Multi-level Tag Recommendation with External Knowledge Bases for Automatic Question Answering

We focus on using natural language unstructured textual Knowledge Bases (KBs) to answer questions from community-based Question-and-Answer (Q8A) websites. We propose a novel framework that integrates multi-level tag recommendation with external KBs to retrieve the most relevant KB articles to answer user posted questions. Different from many existing efforts that primarily rely on the Q8A sites’ own historical data (e.g., user answers), retrieving answers from authoritative external KBs (e.g., online programming documentation repositories) has the potential to provide rich information to help users better understand the problem, acquire the knowledge, and hence avoid asking similar questions in future. The proposed multi-level tag recommendation best leverages the rich tag information by first categorizing them into different semantic levels based on their usage frequencies. A post-tag co-clustering model, augmented by a two-step tag recommender, is used to predict tags at different levels for a given user posted question. A KB article retrieval component leverages the recommended multi-level tags to select the appropriate KBs and search/rank the matching articles thereof. We conduct extensive experiments using real-world data from a Q8A site and multiple external KBs to demonstrate the effectiveness of the proposed question-answering framework.
Multi-person 3D pose estimation from 3D cloud data using 3D convolutional neural networks

Human pose estimation is considered one of the major challenges in the field of Computer Vision, playing an integral role in a large variety of technology domains. While, in the last few years, there has been an increased number of research approaches towards CNN-based 2D human pose estimation from RGB images, respective work on CNN-based 3D human pose estimation from depth/3D data has been rather limited, with current approaches failing to outperform earlier methods, partially due to the utilization of depth maps as simple 2D single-channel images, instead of an actual 3D world representation. In order to overcome this limitation, and taking into consideration recent advances in 3D detection tasks of similar nature, we propose a novel fully-convolutional, detection-based 3D-CNN architecture for 3D human pose estimation from 3D data. The architecture follows the sequential network architecture paradigm, generating per-voxel likelihood maps for each human joint, from a 3D voxel-grid input, and is extended, through a bottom-up approach, towards multi-person 3D pose estimation, allowing the algorithm to simultaneously estimate multiple human poses, without its runtime complexity being affected by the number of people within the scene. The proposed multi-person architecture, which is the first within the scope of 3D human pose estimation, is comparatively evaluated on three single person public datasets, achieving state-of-the-art performance, as well as on a public multi-person dataset achieving high recognition accuracy.
A Logic-Based Question Answering System for Cultural Heritage

Question Answering (QA) systems attempt to find direct answers to user questions posed in natural language. This work presents a QA system for the closed domain of Cultural Heritage. Our solution gradually transforms input questions into queries that are executed on a CIDOC-compliant ontological knowledge base. Questions are processed by means of a rule-based syntactic classification module running an Answer Set Programming system. The proposed solution is being integrated into a fully-fledged commercial system developed within the PIUCULTURA project, funded by the Italian Ministry for Economic Development.
Evolution of Scrum Transcending Business Domains and the Future of Agile Project Management

Abstract The growing popularity of Agile management methods has led to their application to a number of areas inside, but also outside, the software development domain. However, despite many practitioner reports, research exploring whether and how Agile practices have been applied in domains beyond software development has been rather patchy with little empirical evidence. What is there behind the hype and how can other domains learn from it? To address this gap in the research, we present the findings of our study on the application of Agile management practices in other domains, including an outlook towards a potential expansion enabling Business Agility. To practitioners, this study presents examples of Scrum applied outside the software development domain including concrete practices applied. To researchers, it presents an empirical starting point for further exploration.
A Meta-Ensemble Classifier Approach: Random Rotation Forest

Ensemble learning is a popular and intensively studied field in machine learning and pattern recognition to increase the performance of the classification. Random forest is so important for giving fast and effective results. On the other hand, Rotation Forest can get better performance than Random Forest. In this study, we present a meta-ensemble classifier, called Random Rotation Forest to utilize and combine the advantages of two classifiers (e.g. Rotation Forest and Random Forest). In the experimental studies, we use three base learners (namely, J48, REPTree, and Random Forest) and two meta-learners (namely, Bagging and Rotation Forest) for ensemble classification on five datasets in UCI Machine Learning Repository. The experimental results indicate that Random Rotation Forest gives promising results according to base learners and bagging ensemble approaches in terms of accuracy rates, AUC, precision and recall values. Our method can be used for image/pattern recognition and machine learning problems.
Energy‐efficient quantum‐inspired stochastic Q‐HypE algorithm for batch‐of‐stochastic‐tasks on heterogeneous DVFS‐enabled processors

Summary Scheduling on dynamic voltage and frequency scaling enabled processors to determine the Pareto‐optimal solutions with optimized makespan and energy consumption demands faster multi‐objective scheduling algorithms. In general, the problem of multi‐objective optimization, ie, finding the Pareto‐optimal solutions to optimize two or more QoS parameters, has been proven to be an NP‐complete problem. In this work, we propose a novel energy‐efficient quantum‐inspired stochastic Q‐HypE algorithm to schedule the batch‐of‐stochastic‐tasks (BoT) on DVFS‐enabled processors with the aim to optimize the makespan of BoT as well as the energy consumption of processors. The stochastic processing times of tasks are drawn from independent probability distributions. The proposed Q‐HypE algorithm evolves from combined characteristics of quantum computing and a hypervolume based multi‐objective optimization HypE algorithm. The proposed Q‐HypE algorithm simultaneously minimizes the makespan and energy consumption of the Pareto‐optimal solutions whereas the dynamics of quantum computing accelerate the process of HypE to further minimize the overheads of hypervolume estimation. Experimental results reveal the effectiveness of the proposed Q‐HypE algorithm both in terms of the number and quality of solutions offered.
Proposing a novel method for improving the performance of collaborative filtering systems regarding the priority of similar users

Recommender systems (RS) are an efficient and useful tool for personalizing services and providing efficient recommendations to users in different applications. One of the most successful techniques used in Recommender Systems is Collaborative Filtering (CF) which uses the rating matrix to find users with similar interests as the active user. The issue which usually arises with this method is the sparsity of the rating matrix which affects the process of finding similar users and the quality of the recommendations greatly. In this paper, a new method has been provided to increase the efficiency of the system against sparse data domains. The basis of the proposed method is extracting preference patterns from the rating matrix in the way that for each active user, a three-level tree of neighboring users is constructed. The active user is situated in the root of the tree, direct neighbors of the active user in the second level and indirect neighbors of the active user are situated in the third level. Then the similarity level of the active user with its direct and indirect neighbors is calculated. Finally the calculated similarity value is used in the process of predicting the ratings. This factor affects the quality of the ratings given by the neighbors. Results of the experiments on Movielens and Jester datasets indicate that in most cases, the proposed method provides better results than other widely utilized methods.