Reviewerly/exHarmony huggingface dataset:
Pairwise-Comparison-Based Rank Learning for Benchmarking Image Restoration Algorithms

Image restoration has attracted substantial attention recently and many image restoration algorithms have been proposed for restoring latent clear images from degraded images. However, determining how to objectively evaluate the performances of these algorithms remains an open problem, which may hinder the further development of advanced image restoration techniques. Most image restoration-quality metrics are designed for specific restoration applications; hence, their generalization ability is limited. For benchmarking image restoration algorithms, the ranking of restored images that are generated via various algorithms, is the most heavily considered factor. Inspired by this, this paper presents a pairwise-comparison-based rank learning framework for benchmarking the performances of image restoration algorithms, which focuses on the relative quality ranking of restored images. Under the proposed framework, we further propose a general image restoration quality metric by integrating quality-aware features in both the spatial and frequency domains. The proposed metric exhibits good generalization performance, and it is applicable to various restoration applications. The results of extensive experiments that were conducted on eight public databases of five restoration scenarios demonstrate the superior performance of the proposed method over the existing quality metrics. Moreover, the proposed framework is used to improve the existing quality metrics for benchmarking image restoration algorithms and highly encouraging results are obtained.
A Progression Model of Software Engineering Goals, Challenges, and Practices in Start-Ups

Context: Software start-ups are emerging as suppliers of innovation and software-intensive products. However, traditional software engineering practices are not evaluated in the context, nor adopted to goals and challenges of start-ups. As a result, there is insufficient support for software engineering in the start-up context. Objective: We aim to collect data related to engineering goals, challenges, and practices in start-up companies to ascertain trends and patterns characterizing engineering work in start-ups. Such data allows researchers to understand better how goals and challenges are related to practices. This understanding can then inform future studies aimed at designing solutions addressing those goals and challenges. Besides, these trends and patterns can be useful for practitioners to make more informed decisions in their engineering practice. Method: We use a case survey method to gather first-hand, in-depth experiences from a large sample of software start-ups. We use open coding and cross-case analysis to describe and identify patterns, and corroborate the findings with statistical analysis. Results: We analyze 84 start-up cases and identify 16 goals, 9 challenges, and 16 engineering practices that are common among start-ups. We have mapped these goals, challenges, and practices to start-up life-cycle stages (inception, stabilization, growth, and maturity). Thus, creating the progression model guiding software engineering efforts in start-ups. Conclusions: We conclude that start-ups to a large extent face the same challenges and use the same practices as established companies. However, the primary software engineering challenge in start-ups is to evolve multiple process areas at once, with a little margin for serious errors.
A novel online supervised hyperparameter tuning procedure applied to cross-company software effort estimation

Software effort estimation is an online supervised learning problem, where new training projects may become available over time. In this scenario, the Cross-Company (CC) approach Dycom can drastically reduce the number of Within-Company (WC) projects needed for training, saving their collection cost. However, Dycom requires CC projects to be split into subsets. Both the number and composition of such subsets can affect Dycom's predictive performance. Even though clustering methods could be used to automatically create CC subsets, there are no procedures for automatically tuning the number of clusters over time in online supervised scenarios. This paper proposes the first procedure for that. An investigation of Dycom using six clustering methods and three automated tuning procedures is performed, to check whether clustering with automated tuning can create well performing CC splits. A case study with the ISBSG Repository shows that the proposed tuning procedure in combination with a simple threshold-based clustering method is the most successful in enabling Dycom to drastically reduce (by a factor of 10) the number of required WC training projects, while maintaining (or even improving) predictive performance in comparison with a corresponding WC model. A detailed analysis is provided to understand the conditions under which this approach does or does not work well. Overall, the proposed online supervised tuning procedure was generally successful in enabling a very simple threshold-based clustering approach to obtain the most competitive Dycom results. This demonstrates the value of automatically tuning hyperparameters over time in a supervised way.
Discovering natural language commands in multimodal interfaces

Discovering what to say and how to say it remains a challenge for users of multimodal interfaces supporting speech input. Users end up "guessing" commands that a system might support, often leading to interpretation errors and frustration. One solution to this problem is to display contextually relevant command examples as users interact with a system. The challenge, however, is deciding when, how, and which examples to recommend. In this work, we describe an approach for generating and ranking natural language command examples in multimodal interfaces. We demonstrate the approach using a prototype touch- and speech-based image editing tool. We experiment with augmentations of the UI to understand when and how to present command examples. Through an online user study, we evaluate these alternatives and find that in-situ command suggestions promote discovery and encourage the use of speech input.
Investigation of the vectored thruster AUVs based on 3SPS-S parallel manipulator

As an extremely significant tool, autonomous underwater vehicles (AUVs) obtain corresponding development which is widely used in the oceanographic survey, military applications and ocean investigation. However, it is rather hard to fulfill missions about ocean exploration in suspended status or at slow speeds for traditional AUVs, due to the effect of the control surfaces trends to decline or even invalid completely in this condition. To overcome the limitation mentioned above, a torpedo-shaped AUV with vectored thrust ducted propeller is presented in this paper, in which the vector thruster is designed based on a 3SPS-S parallel manipulator. The 3SPS-S parallel manipulator, which has merits of compact structure, high reliability, high precision and fast response, is employed for thrust vectoring control mechanism. Additionally, the kinematics and dynamics model of the thrust-vectoring mechanism is constructed, and the MATLAB simulation results show the designed vectored thruster have great application superiority and potential for AUV. Finally, a control scheme of the vectored thruster is designed after considering the case study. The main idea of this paper lies in describing a novel design of the vectored thruster AUV based on 3SPS-S parallel manipulator, which can complete the mission at zero or slow forward speeds.
PILOT: Practical Privacy-Preserving Indoor Localization Using OuTsourcing

In the last decade, we observed a constantly growing number of Location-Based Services (LBSs) used in indoor environments, such as for targeted advertising in shopping malls or finding nearby friends.Although privacy-preserving LBSs were addressed in the literature, there was a lack of attention to the problem of enhancing privacy of indoor localization, i.e., the process of obtaining the users' locations indoors and, thus, a prerequisite for any indoor LBS.In this work we present PILOT, the first practically efficient solution for Privacy-Preserving Indoor Localization (PPIL) that was obtained by a synergy of the research areas indoor localization and applied cryptography.We design, implement, and evaluate protocols for Wi-Fi fingerprint-based PPIL that rely on 4 different distance metrics.To save energy and network bandwidth for the mobile end devices in PPIL, we securely outsource the computations to two non-colluding semi-honest parties.Our solution mixes different secure two-party computation protocols and we design size-and depth-optimized circuits for PPIL.We construct efficient circuit building blocks that are of independent interest: Single Instruction Multiple Data (SIMD) capable oblivious access to an array with low circuit depth and selection of the k-Nearest Neighbors with small circuit size.Additionally, we reduce Received Signal Strength (RSS) values from 8 bits to 4 bits without any significant accuracy reduction.Our most efficient PPIL protocol is 553x faster than that of Li et al. (INFOCOM'14) and 500x faster than that of Ziegeldorf et al. (WiSec'14).Our implementation on commodity hardware has practical run-times of less than 1 second even for the most accurate distance metrics that we consider, and it can process more than half a million PPIL queries per day.
Estimating Gaussian mixture models in the local neighbourhood of embedded word vectors for query performance prediction

The study of query performance prediction (QPP) in information retrieval (IR) aims to predict retrieval effectiveness. The specificity of the underlying information need of a query often determines how effectively can a search engine retrieve relevant documents at top ranks. The presence of ambiguous terms makes a query less specific to the sought information need, which in turn may degrade IR effectiveness. In this paper, we propose a novel word embedding based pre-retrieval feature which measures the ambiguity of each query term by estimating how many 'senses' each word is associated with. Assuming each sense roughly corresponds to a Gaussian mixture component, our proposed generative model first estimates a Gaussian mixture model (GMM) from the word vectors that are most similar to the given query terms. We then use the posterior probabilities of generating the query terms themselves from this estimated GMM in order to quantify the ambiguity of the query. Previous studies have shown that post-retrieval QPP approaches often outperform pre-retrieval ones because they use additional information from the top ranked documents. To achieve the best of both worlds, we formalize a linear combination of our proposed GMM based pre-retrieval predictor with NQC, a state-of-the-art post-retrieval QPP. Our experiments on the TREC benchmark news and web collections demonstrate that our proposed hybrid QPP approach (in linear combination with NQC) significantly outperforms a range of other existing pre-retrieval approaches in combination with NQC used as baselines.
Assembling translations from multi-engine machine translation outputs

In this paper, we present a hybrid architecture for developing a system combination model that works in three layers to achieve better translated outputs. In the first layer, we have various machine translation models (i.e. Neural Machine Translation (NMT), Statistical Machine Translation (SMT), etc.). In the second layer, the outputs of these models are combined to leverage the advantages of both the systems (i.e SMT and NMT systems) by using the statistical approach and neural-based approach. But each approach has some advantages and limitations. So, instead of selecting an individual combined system’s output as the final one, we apply these outputs in the final layer to produce the target output by assigning appropriate preferences to SMT based and neural-based combinations. Though there are some techniques for system combination but no such approach exists which uses preferences from various system combination models (statistical and neural) for the purpose of better assembling. Empirical results show improved performance in the terms of translation accuracy. Our experiments on two benchmark datasets of English–Hindi and Hindi–English pairs show that the proposed model performs significantly better than the participating models. Apparently, the efficacy of proposed model is significantly better than the state-of-the art machine translation combination systems (6.10 and 4.69 BLEU points for English-to-Hindi, and Hindi-to-English, respectively).
Toward a Distributed Carbon Ledger for Carbon Emissions Trading and Accounting for Corporate Carbon Management

ABSTRACT Greenhouse gas (GHG) emissions control requires coordinated efforts and collaboration at all levels of governmental bodies, non-for-profit organizations, and private sectors. However, the target is difficult to achieve due to challenges arising from conflicts of interest and lack of trust between stakeholders. Thus, we propose a distributed carbon ledger (DCL) system using blockchain technology. Our analysis suggests that the adoption of DCL not only strengthens the corporate accounting system for carbon asset management but also fits within existing market-based emissions trading schemes (ETSs). Blockchain-enabled DCL allows the integration of national emission trading schemes (ETSs) and corporate carbon asset management into a synthetic single mechanism. JEL Classifications: M41; O44.
LGSA: Hybrid Task Scheduling in Multi Objective Functionality in Cloud Computing Environment

Cloud computing turns to be a big shift from the conventional perception of the IT resources. It is a transpiring computing technology that is increasingly stabling itself as the promising future of distributed on-demand computing. The processes comprised in it are the ones that act as a vital backbone and which strengthen the entire stream of cloud computing as a whole. In specific, Task scheduling is the one such phenomena that enhances the cloud computing in terms of performance. Hence task scheduling that is considered as a predominant one amidst others is what this paper comprises all about. Maximizing the profit via assigning the whole task to the virtual machine is what the problem of scheduling deals with. Although there prevails many more ways to resolve this problem, this paper explores one such solution that consumes lesser number of resources, having lower cost and much importantly consuming lesser energy. By making a profound research regarding this approach of scheduling so as to represent the multi-objective function, both lion optimization algorithm and gravitational search algorithm are hybridized. In spite of having certain drawbacks which could be avoided although, the brighter side relies the merits of making use of both lion search and gravitational search algorithm. There could be many means of measurement for computing the performance of the algorithm. The different algorithms that aid to depict the comparable study encompasses gravitational search algorithm, genetic algorithm and lion, particle swarm optimization. The experimental results serve as the evident for depicting the bitterness of our proposed algorithm compared to the prevailing approaches. As an unexplored path may seem trivial but is effective so does the betterment of our lion approach.
Low frequency frame-wise normalization over constant-Q transform for playback speech detection

The playback speech contains information from the environment, playback and recorder used. This work focuses on proposal of a novel normalization scheme, namely, low frequency frame-wise normalization (LFFN) as one of the modules in feature extraction process that is hypothesized to help in capturing the artifacts from the playback speech. It is based on low frequency bin processing that is performed frame-wise and hence its name. The constant-Q transform (CQT) based features are found to provide the benchmark results for detection of spoofing attacks. In this work, LFFN is combined with CQT to extract two new features from octave and linear power spectra, respectively. The first one is obtained by CQT, LFFN and octave segmentation that is referred to as constant-Q normalization segmentation coefficients (CQNSC). The latter uses conventional constant-Q cepstral coefficient (CQCC) and LFFN to obtain constant-Q normalization cepstral coefficients (CQNCC). The studies are performed on ASVspoof 2017 version 2.0 corpus that is designed for studying playback speech detection. The experimental results show the effectiveness of proposed LFFN with CQT based features. We obtain equal error rate of 10.63% and 10.31% for CQNSC and CQNCC features on the evaluation set of ASVspoof 2017 version 2.0 corpus, respectively.
Image Haze Removal Using Airlight White Correction, Local Light Filter, and Aerial Perspective Prior

Light is scattered and absorbed when travelling through atmosphere particles, leading to visibility attenuation for images captured, especially in hazy scenes. In addition, hazy images may suffer from color distortion caused by haze or sandstorm, resulting in a poor visual quality. In order to effectively enhance visibility and correct possible color casts for such images, we propose a new image dehazing algorithm based on an improved haze optical model, which consists of three modules: airlight white correction (AWC), local light filter (LLF), and aerial perspective prior (APP). In the proposed algorithm, the AWC module detects and corrects possible color cast, the LLF module downplays non-hazy bright pixels (e.g., headlight and white objects) for more accurate airlight estimation, and the APP module uses the minimum/maximum channel and their difference for scene transmission estimation. The experimental results demonstrate that the proposed method outperforms other state-of-the-art dehazing methods in three ways: 1) our results have better visual quality; 2) our method performs the best in terms of color restoration; and 3) our method is very efficient at removing haze and color casts.
FgGAN: A Cascaded Unpaired Learning for Background Estimation and Foreground Segmentation

The moving object segmentation (MOS) in videos with bad weather, irregular motion of objects, camera jitter, shadow and dynamic background scenarios is still an open problem for computer vision applications. To address these issues, in this paper, we propose an approach named as Foreground Generative Adversarial Network (FgGAN) with the recent concepts of generative adversarial network (GAN) and unpaired training for background estimation and foreground segmentation. To the best of our knowledge, this is the first paper with the concept of GAN-based unpaired learning for MOS. Initially, video-wise background is estimated using GAN-based unpaired learning network (network-I). Then, to extract the motion information related to foreground, motion saliency is estimated using estimated background and current video frame. Further, estimated motion saliency is given as input to the GANbased unpaired learning network (network-II) for foreground segmentation. To examine the effectiveness of proposed FgGAN (cascaded networks I and II), the challenging video categories like dynamic background, bad weather, intermittent object motion and shadow are collected from ChangeDetection.net-2014 [26] database. The segmentation accuracy is observed qualitatively and quantitatively in terms of F-measure and percentage of wrong classification (PWC) and compared with the existing state-of-the-art methods. From experimental results, it is evident that the proposed FgGAN shows significant improvement in terms of F-measure and PWC as compared to the existing stateof-the-art methods for MOS.
Gender Classification Using Machine Learning with Multi-Feature Method

Nowadays gender classification is a very challenging task in a real-time application based on face recognition. The demand for real-time application based on gender classification will increase in the future. Bag of Words (Bow), Scale Invariant Fourier Transform (SIFT) and K-means clustering are used for feature extractors and classification. This state of art methodology gives more efficient result on different standard datasets. This research proposed a new algorithm for automatic live Gender Recognition (GR) using Support Vector Machine (SVM) is used for classification. The implementation of result work tested on FEI, Live Images and SCIEN database for GR. The detection rate has reached up to 98% in FEI dataset, 94% in Live/Own dataset and 91% SCIEN dataset respectively. This proposed state of art methodology is compared with the previous techniques and achieved better results which will help in the development of real-time identification systems.
EMIF Catalogue: A collaborative platform for sharing and reusing biomedical data

The collaboration and knowledge exchange between researchers are often hindered by the nonexistence of accurate information about which databases may support research studies. Even though a considerable amount of patient health information does exist, it is usually distributed and hidden in many institutions. The goal of this project is to provide, for any research community, a holistic view of biomedical datasets of interests, from which researchers can explore several distinct levels of granularity.We developed a community-centered approach to facilitate data sharing while ensuring privacy. A dynamic schema allows exposing any metadata model about existing repositories. The framework was developed following a modular plugin-based architecture that facilitates the integration of internal and external tools.The EMIF Catalogue, a web platform for sharing and reusing biomedical data. Through this system, data custodians can publish and share different levels of information, while the researchers can search for databases that fulfill research requirements.The EMIF Catalogue currently fosters several distinct research communities, with different levels of data governance, combining, for instance, data available in pan-European EHR and Alzheimer cohorts. This portal is publicly available at https://emif-catalogue.eu.
Robust Visual Detection and Tracking Strategies for Autonomous Aerial Refueling of UAVs

In this paper, we propose a robust detection and tracking strategy for autonomous aerial refueling of unmanned aerial vehicles. The proposed framework includes two modules: a faster deep-learning-based detector (DLD) and a more accurate reinforcement-learning-based tracker (RLT). In the detection stage, the DLD achieves faster speed by combining the efficient MoblieNet with the you only look once framework. In the tracking stage, RLT is proposed to obtain target's position accurately and fastly by performing hierarchically positioning and adjusting target bounding box according to the reinforcement learning. The precision of drogue object tracking is 98.7%, which is obviously higher than the other comparison methods. The speed of our network can achieve 15 frames/s on GPU Titan X. The experimental results validate the effectiveness and robustness of the proposed framework.
Tier Application in Multi-Cloud Databases to Improve Security and Service Availability

The need for data outsourcing or database as a service (DaaS) is extremely important for any organization. In addition, data storage or data retrieval cost high especially for small companies. This chapter aims to study the various challenges in cloud computing communication issue. The chapter on multi-tiering a cloud emphasizes security enhancement due to the application of tiers in the cloud such that services availability is also improved. Cloud computing is a recent network trend for advanced communication systems. A lot of research led to the advancements in the areas of newer modes but multi-tiering a cloud is still an embarking issue for the researchers.