Reviewerly/exHarmony huggingface dataset:
Multi-objective design of grid-tied solar photovoltaics for commercial flat rooftops using particle swarm optimization algorithm

Flat roof solar PV design is an important and challenging process as it requires various parameters to be considered (such as weather, location, shading, regulations, purpose of use, etc.), and standard guidelines for building a solar PV system are not currently available. An even more challenging task is to improve the design of a solar PV system in order to optimize energy generation in a cost-effective manner, particularly in high-latitudes. In the present research, in order to determine optimal system size, parameters optimization should be carried out. The primary focus of this study is to develop an optimized system which maximizes generating capacity while maintaining cost-effectiveness of the solar PV system prior to the system implementation. Hence, a multi-objective optimization model is developed. The solar PV system design process is improved by optimizing the tilt angle and the inter-row spacing of solar PV modules. An objective function is developed based on the total solar energy generation per PV system and energy generation per PV module in order to improve cost effectiveness. The goal is to maximize these two outputs of the objective function. The particle swarm optimization (PSO) method is used to accomplish the optimization. Given that the two objectives are different and both have equal and opposite effects on the other, modifications are made to the PSO. Finally, the outputs of the optimization model are compared and validated with a case study. The model presented in this paper will assist users in decision making based on the technical output and financial aspects of the PV system.
DMS: Dynamic Model Scaling for Quality-Aware Deep Learning Inference in Mobile and Embedded Devices

Recently, deep learning has brought revolutions to many mobile and embedded systems that interact with the physical world using continuous video streams.Although there have been significant efforts to reduce the computational overheads of deep learning inference in such systems, previous approaches have focused on delivering 'best-effort' performance, resulting in unpredictable performance under variable environments.In this paper, we propose a runtime control method, called DMS (Dynamic Model Scaling), that enables dynamic resource-accuracy trade-offs to support various QoS requirements of deep learning applications.In DMS, the resource demands of deep learning inference can be controlled by adaptive pruning of computation-intensive convolution filters.DMS avoids irregularity of pruned models by reorganizing filters according to their importance so that varying number of filters can be applied efficiently.Since DMS's pruning method incurs no runtime overhead and preserves the full capacity of original deep learning models, DMS can tailor the models at runtime for concurrent deep learning applications with their respective resource-accuracy trade-offs.We demonstrate the viability of DMS by implementing a prototype.The evaluation results demonstrate that, if properly coordinated with system level resource managers, DMS can support highly robust and efficient inference performance against unpredictable workloads.
Improved Sparse Pinball Twin SVM

In this paper, we propose an improved version of sparse pinball twin support vector machine (SPTSVM) [1], called improved sparse pinball twin support vector machine (ISPTSVM). SPTSVM implements empirical risk minimization principle and the matrices appearing in the formulation of SPTSVM are positive semi-definite. Here, we reformulate the primal problems of SPTSVM by introducing extra regularization term to the objective function of SPTSVM. Unlike SPTSVM, structural risk minimization (SRM) principle is implemented in the proposed ISPTSVM which embodies the marrow of statistical learning theory. Also, the matrices that appear in the dual formulation of the proposed ISPTSVM are positive definite. Results computed on multiple UCI benchmark datasets clearly indicate the effectiveness and applicability of the proposed ISPTSVM compared to pinball support vector machine (Pin-SVM), twin bounded support vector machine (TBSVM) and SPTSVM.
Randomized sampling-based trajectory optimization for UAVs to satisfy linear temporal logic specifications

We address the problem of optimal trajectory planning for a fixed wing- or quadrotor aircraft to satisfy linear temporal logic (LTL) specifications on its motion. To this end, we propose a randomized sampling-based motion planning algorithm with probabilistic guarantees of completeness and optimality. Similar to the well-known rapidly-exploring random tree (RRT⁎) algorithm, the proposed algorithm incrementally generates a tree in which edges are associated with vehicle control inputs. Random samples are taken from multiple copies of the state space, where each copy is uniquely associated with a state of the Büchi automaton that accepts the given LTL specification. To achieve significant reductions in computation time, we propose a sampling heuristic that provides a bias for growing the tree structure. This sampling heuristic preserves the completeness and optimality properties of the RRT⁎ algorithm. We provide numerical simulation results of the application of the proposed algorithm to show up to approximately 80% reductions in computation time achieved due to this sampling heuristic.
Blowfish–Secured Audio Steganography

Data security has become essential to provide secure communication that is free of attacks. A combination of steganography and cryptography provides very secure communication, where steganography hides the secret data and cryptography scrambles the secret data into an unreadable form. This paper proposes a message security scheme that applies audio steganography and cryptography to provide a secure communication by embedding a secret message inside an audio file. The Blowfish algorithm is used for cryptography and the LSB coding technique is used for steganography. The LSB coding alternates between the left channel and the right channel respectively. The proposed scheme is evaluated in terms of the mean square error (MSE), peak signal to noise ratio (PSNR), a hearing test and a comparison between the waveform plots of the cover and stego audios. The results prove that the proposed scheme exhibits very good performance. In addition, it is found to surpass other proposed schemes from the literature with much higher PSNR values.
Service discovery techniques in Internet of Things: a survey

Internet of Things (IoT) aims to minimize human intervention and to adapt the behavior of systems to the user's needs. Achieving this vision involves the abstraction of IoT objects resources as services and the composition of these services. A major obstacle to the use of services is the discovery and selection of these services. Due to the excessive number of connected objects searching and discovering specific services offered by these objects is becoming a challenging task. The use of traditional solution of service discovery is non-practical for IoT due to its scale and complexity. Enabling an easy and seamless discovery of these IoT services is crucial for the success of IoT. Resource discovery in IoT has captured much attention in both academia and industry. Therefore, many architectures and frameworks have been proposed to provide solutions for its issues. In this paper, we present a state-of-the-art study of service discovery methods in IoT systems, classifying them according to their search approaches and providing a comparison of the selected works. Equally, we identify and discuss key challenges and future research directions that will support the success of IoT.
Multimodal feature learning and fusion on B-mode ultrasonography and sonoelastography using point-wise gated deep networks for prostate cancer diagnosis

B-mode ultrasonography and sonoelastography are used in the clinical diagnosis of prostate cancer (PCa). A combination of the two ultrasound (US) modalities using computer aid may be helpful for improving the diagnostic performance. A technique for computer-aided diagnosis (CAD) of PCa is presented based on multimodal US. Firstly, quantitative features are extracted from both B-mode US images and sonoelastograms, including intensity statistics, regional percentile features, gray-level co-occurrence matrix (GLCM) texture features and binary texture features. Secondly, a deep network named PGBM-RBM2 is proposed to learn and fuse multimodal features, which is composed of the point-wise gated Boltzmann machine (PGBM) and two layers of the restricted Boltzmann machines (RBMs). Finally, the support vector machine (SVM) is used for prostatic disease classification. Experimental evaluation was conducted on 313 multimodal US images of the prostate from 103 patients with prostatic diseases (47 malignant and 56 benign). Under five-fold cross-validation, the classification sensitivity, specificity, accuracy, Youden's index and area under the receiver operating characteristic (ROC) curve with the PGBM-RBM2 were 87.0%, 88.8%, 87.9%, 75.8% and 0.851, respectively. The results demonstrate that multimodal feature learning and fusion using the PGBM-RBM2 can assist in the diagnosis of PCa. This deep network is expected to be useful in the clinical diagnosis of PCa.
Deep ensemble network using distance maps and body part features for skeleton based action recognition

Human action recognition is a hot research topic in the field of computer vision. The availability of low cost depth sensors in the market made the extraction of reliable skeleton maps of human objects easier. This paper proposes three subnets, referred to as SNet, TNet, and BodyNet to capture diverse spatio-temporal dynamics for action recognition task. Specifically, SNet is used to capture pose dynamics from the distance maps in the spatial domain. The second subnet (TNet) captures the temporal dynamics along the sequence. The third net (BodyNet) extracts distinct features from the fine-grained body parts in the temporal domain. With the motivation of ensemble learning, a hybrid network, referred to as HNet, is modeled using two subnets (TNet and BodyNet) to capture robust temporal dynamics. Finally, SNet and HNet are fused as one ensemble network for action classification task. Our method achieves competitive results on three widely used datasets: UTD MHAD, UT Kinect and NTU RGB+D.
Real-Time Detection and Recognition of Multiple Moving Objects for Aerial Surveillance

Detection of moving objects by unmanned aerial vehicles (UAVs) is an important application in the aerial transportation system. However, there are many problems to be handled such as high-frequency jitter from UAVs, small size objects, low-quality images, computation time reduction, and detection correctness. This paper considers the problem of the detection and recognition of moving objects in a sequence of images captured from a UAV. A new and efficient technique is proposed to achieve the above objective in real time and in real environment. First, the feature points between two successive frames are found for estimating the camera movement to stabilize sequence of images. Then, region of interest (ROI) of the objects are detected as the moving object candidate (foreground). Furthermore, static and dynamic objects are classified based on the most motion vectors that occur in the foreground and background. Based on the experiment results, the proposed method achieves a precision rate of 94% and the computation time of 47.08 frames per second (fps). In comparison to other methods, the performance of the proposed method surpasses those of existing methods.
Visible and Near-Infrared Reflectance Spectroscopy

Visible/near-infrared (VNIR) reflectance spectra are used in laboratory, field, and airborne studies to characterize geologic materials. This chapter covers the region 0.3–5 µm and describes the species responsible for the absorption of radiation at specific wavelengths that create spectral features used to identify minerals, rocks, and other geologic materials. Fe contributes greatly to VNIR spectral signatures, producing features near 1 and 2 µm for Fe2+ in spectra of pyroxene and glass, while a broad, strong band from ~0.9 to 1.3 µm is characteristic of Fe2+ in olivine, carbonate, and many sulfates; a weak band near 1.2 µm is due to Fe2+ in feldspar; and bands near 0.6 and 0.9 µm arise from Fe3+ in ferric oxides/hydroxides. Water bands occur near 0.96, 1.15, 1.4, 1.9, and 2.9 µm, depending on the mineral structure, while structural OH bands occur near 1.4, 2.1–2.5, and 2.7 µm. Additional features are observed for carbonates, nitrates, sulfates, phosphates, chlorides, and perchlorates. The spectral signatures of geologic samples are also affected by how photons interact with particles in the sample. Factors such as grain size, coatings and mixtures influence the reflectance, transmittance, and absorption of photons at grain boundaries and contribute to the VNIR spectral properties of geologic materials.
The blockchain and how it can influence conceptions of the self

Blockchain technologies are rapidly being developed and tested in a broad range of business and governmental settings. Their unique cryptographic characteristics and configurations enable users of these systems to transact directly and anonymously. The data these users generate are timestamped and immutable. In open blockhains, individual users take responsibility for managing and protecting their own data and for ensuring the reliability of the parties with whom they transact. The socio-material characteristics of these systems will influence user attitudes and behaviors in ways that are profound and difficult to predict. Outcomes have not yet been researched, and the academy has adopted a stance of technological determinism despite the fact that implicit assumptions about outcomes are literally coded in as these systems are developed. We envision potential impacts that may result from self-sovereign ownership of data including: commoditization of the self and relationships with others, the need to police personal data and reputation, and new perceptions of time and history that result from transaction sequentialization and permanence. Further research on the societal impacts of blockchain technologies is needed as these systems become ubiquitous.
Cryptographic Attribute-Based Access Control (ABAC) for Secure Decision Making of Dynamic Policy With Multiauthority Attribute Tokens

This article aims to establish a cryptographic solution to improve security and reliability of the National Institute of Standards and Technology's attribute-based access control (ABAC) model. By breaking down the existing structure of attribute-based encryption, we propose a new cryptographic ABAC (C-ABAC) framework with dynamic policy authorization and real-time attribute credentials. Moreover, a practical C-ABAC construction is proposed to support provable policy decision making and verifiable attribute Tokens among multiple distributed authorities. In this construction, we develop a concrete approach of generating a cryptographic policy from access control markup language. We also prove that attribute Token has existential unforgeability under chosen-attribute and chosen-nonce attacks, and the cryptographic policy is existentially unforgeable under chosen-object attack. In addition, our C-ABAC construction provides semantic security against chosen-plaintext attack with Token and policy queries under the extended general Diffie-Hellman exponent assumption. Finally, we evaluate the performance of the C-ABAC system according to complexity analysis and experimental results. The results show that the C-ABAC system is reliable and easy to implement.
Automatic dialect identification system for Kannada language using single and ensemble SVM algorithms

In this paper, an automatic dialect identification (ADI) system is proposed by extracting spectral and prosodic features for Kannada language. A new dialect dataset is collected from native speakers of Kannada language (A Dravidian language). This dataset includes five distinct dialects of Kannada language representing five geographical regions of Karnataka state. Investigation of the significance of spectral and prosodic variations on five Kannada dialects is carried out. Mel-frequency cepstral coefficients (MFCCs), spectral flux, and entropy are used as representatives of spectral features. Besides, pitch and energy features are extracted as representatives of prosodic parameters for identification of dialects. These raw feature vectors are further processed to get a new derived feature vectors by using statistical processing. In this paper, a single classifier based multi-class support vector machine (SVM) and multiple classifier based ensemble SVM (ESVM) techniques are employed for classification of dialects. The effectiveness and performance evaluation of the explored features are carried out on newly collected Kannada speech corpus, with five Kannada dialects and internationally known standard Intonation Variation in English (IViE) dataset with nine British English dialects. Experimental results have demonstrated that the derived feature vectors performs better when compared to raw feature vectors. However, ESVM technique has demonstrated better performance over a single SVM. Spectral and prosodic features have resulted individually with the dialect recognition performance of 83.12% and 44.52% respectively. Further, the complementary nature of both spectral and prosodic features is evaluated by combining both feature vectors for dialect recognition. However, an increase in dialect recognition performance of about 86.25% is observed. This indicates the existence of complementary dialect specific evidence with spectral and prosodic features. The experiments conducted on standard IViE corpus have shown a higher recognition rate of 91.38% using ESVM. Proposed ADI systems with derived features have shown better performance over the state-of-the-art i-vector feature based systems on both datasets.
Automated Recommendation of Software Refactorings Based on Feature Requests

During software evolution, developers often receive new requirements expressed as feature requests. To implement the requested features, developers have to perform necessary modifications (refactorings) to prepare for new adaptation that accommodates the new requirements. Software refactoring is a well-known technique that has been extensively used to improve software quality such as maintainability and extensibility. However, it is often challenging to determine which kind of refactorings should be applied. Consequently, several approaches based on various heuristics have been proposed to recommend refactorings. However, there is still lack of automated support to recommend refactorings given a feature request. To this end, in this paper, we propose a novel approach that recommends refactorings based on the history of the previously requested features and applied refactorings. First, we exploit the stateof-the-art refactoring detection tools to identify the previous refactorings applied to implement the past feature requests. Second, we train a machine classifier with the history data of the feature requests and refactorings applied on the commits that implemented the corresponding feature requests. The machine classifier is then used to predict refactorings for new feature requests. We evaluate the proposed approach on the dataset of 43 open source Java projects and the results suggest that the proposed approach can accurately recommend refactorings (average precision 73%).
The sociocognitive dimension of hate speech in readers’ comments on Serbian news websites

Comment sections on news websites bear no immediate threat of sanctions and users feel free to express open criticism or insult. Other than causing offense and undermining the dignity of readers with a different opinion, a rather dangerous outcome of hate speech is that it affects the sociocognitive interface between discourse and society in the sense that it has a negative impact on the readers’ cognitive notions of information, beliefs and knowledge. Based on the analytical approach proposed in Sociocognitive Discourse Studies, the study examines 939 comments posted in the comment section of the Serbian news website Politika. Besides confirming that hate speech in the readers’ comments is a rather frequent means of establishing a communicative Common Ground among news readers, the analysis shows that hate speech is a reflection of the negative impact of news on the sociocognitive interface between discourse and society. The research also demonstrates that Sociocognitive Discourse Studies may be applied to the analysis of readers’ comments as a specific type of written discourse that reflects the mental models news readers create when exposed to media discourse.
CHIP: Channel-Wise Disentangled Interpretation of Deep Convolutional Neural Networks

With the increasing popularity of deep convolutional neural networks (DCNNs), in addition to achieving high accuracy, it becomes increasingly important to explain how DCNNs make their decisions. In this article, we propose a CHannel-wise disentangled InterPretation (CHIP) model for visual interpretations of DCNN predictions. The proposed model distills the class-discriminative importance of channels in DCNN by utilizing sparse regularization. We first introduce network perturbation to learn the CHIP model. The proposed model is capable to not only distill the global perspective knowledge from networks but also present class-discriminative visual interpretations for the predictions of networks. It is noteworthy that the CHIP model is able to interpret different layers of networks without retraining. By combining the distilled interpretation knowledge at different layers, we further propose the Refined CHIP visual interpretation that is both high-resolution and class-discriminative. Based on qualitative and quantitative experiments on different data sets and networks, the proposed model provides promising visual interpretations for network predictions in an image classification task compared with the existing visual interpretation methods. The proposed model also outperforms the related approaches in the ILSVRC 2015 weakly supervised localization task.
Deep Pyramid Generative Adversarial Network With Local and Nonlocal Similarity Features for Natural Motion Image Deblurring

It is of great importance to capture long-range dependency in image deblurring based on deep learning. Existing methods often capture long-range dependency by a large receptive field, which contributes by deep stacks of local convolutional operations. Therefore, it restricts network representation ability and causes unpleasant artifacts of restored images. In this paper, we propose a deep pyramid generative adversarial network with local and nonlocal similarity features, called LNL-PGAN, for natural motion image deblurring. First, we propose a nonlocal feature block as an essential component of the pyramid generator for obtaining nonlocal similarity features at multiple levels. Second, we design a local feature block as another essential component to make a great balance between local and nonlocal similarity features. The local and nonlocal feature blocks capture meaningful short-range and long-range dependencies in the pyramid generator to increase network representation ability. Third, we design a multiscale generative adversarial loss to preserve edge details and facilitate sharp edge prediction of restored images, and we introduce a multistage training strategy to facilitate network training, which can further improve the quality of the restored image. Extensive experimental results demonstrate that the proposed method yields superior performance against state-of-the-art methods on natural motion image deblurring in terms of visual quality and objective index, and it can be used as a unified network for single and dynamic motion image deblurring.