Reviewerly/exHarmony huggingface dataset:
Scoping the potential usefulness of seasonal climate forecasts for solar power management

Solar photovoltaic energy is widespread worldwide and particularly in Europe, which became in 2016 the first region in the world to pass the 100 GW of installed capacity. As with all the renewable energy sources, for an effective management of solar power, it is essential to have reliable and accurate information about weather/climate conditions that affect the production of electricity. Operations in the solar energy industry are normally based on daily (or intra-daily) forecasts. Nevertheless, information about the incoming months can be relevant to support and inform operational and maintenance activities. This paper discusses a methodology to assess whether a seasonal climate forecast can provide a useful prediction for a specific sector, in this paper the European solar power industry. After evaluating the quality of the forecasts in providing probabilistic information for solar radiation, we describe how to assess their potential usefulness for a generic user by proposing an approach that takes into account not only their accuracy but also other potentially relevant factors. This approach is called index of opportunity and is then illustrated by presenting an example for the European solar power sector. The index of opportunity provides indications about where and when seasonal climate forecasts can benefit the decision-making in the photovoltaic sector. Even more importantly, it suggests an approach on how to evaluate their usefulness for the user's decision-making. This approach has the advantage of not limiting the definition of the usefulness only to the quality of the forecasts but rather considering, in an explicit way, all the factors that must be combined with the forecast's quality to define what is useful or not for the user.
A Hybridization of Sine Cosine Algorithm with Steady State Genetic Algorithm for Engineering Design Problems

Sine Cosine Algorithm (SCA), a newly proposed optimization approach, has gained the interest of researchers to solve the optimization problems in different fields due to its efficiency and simplicity. As well as a genetic algorithm (GA) has proved its robustness in solving a large variety of complex optimization problems. In this paper, a hybridization of SCA with steady state genetic algorithm (SSGA) is proposed to solve engineering design problems. This approach integrates the merits of exploration capability of SCA and exploitation capability of SSGA to avoid exposure to early convergence, speed up the search process and quick the convergence to best results in a reasonable time. The proposed approach incorporates concepts from SSGA and SCA and generates individuals in a new generation by crossover and mutation operations of SSGA and also by mechanisms of SCA. Efficiency of the proposed algorithm is evaluated using two complex engineering design problems to verify its validity and reliability. Results show that the proposed approach has superior performance compared to other optimizations techniques.
Exploiting flower constancy in flower pollination algorithm: improved biotic flower pollination algorithm and its experimental evaluation

Recent growth of metaheuristic search strategies has brought a huge progress in the domain of computational optimization. The breakthrough started since the well-known Particle Swarm Optimization algorithm had been introduced and examined. Optimization technique presented in this contribution mimics the process of flower pollination. It is build on the foundation of the first technique of this kind—known as Flower Pollination Algorithm (FPA). In this paper, its simplified and improved version, obtained after extensive performance testing, is presented. It is based on only one natural phenomena—called flower constancy—the natural mechanism allowing pollen carrying insects to remember the positions of the best pollen sources. Modified FPA, named as Biotic Flower Pollination Algorithm (BFPA) and relying solely on biotic pollinators, outperforms original FPA, which itself proved to be very effective approach. The paper first presents a short description of original FPA and the changes leading to Biotic Flower Pollination Algorithm. It also discusses performance of the modified algorithm on a full set of CEC17 benchmark functions. Furthermore, in that aspect, the comparison between BFPA and other optimization algorithms is also given. Finally, brief exemplary application of modified algorithm in the field of probabilistic modeling, related to physics and engineering, is also presented.
Parameter Uncertainty for End-to-end Speech Recognition

Recent work on neural networks with probabilistic parameters has shown that parameter uncertainty improves network regularization. Parameter-specific signal-to-noise ratio (SNR) levels derived from parameter distributions were further found to have high correlations with task importance. However, most of these studies focus on tasks other than automatic speech recognition (ASR). This work investigates end-to-end models with probabilistic parameters for ASR. We demonstrate that probabilistic networks outperform conventional deterministic networks in pruning and domain adaptation experiments carried out on the Wall Street Journal and CHiME-4 datasets. We use parameter-specific SNR information to select parameters for pruning and to condition the parameter updates during adaptation. Experimental results further show that networks with lower SNR parameters (1) tolerate increased sparsity levels during parameter pruning and (2) reduce catastrophic forgetting during domain adaptation.
Revisiting the Class Imbalance Issue in Software Defect Prediction

Software defect prediction is related to the testing area of software industry. Several methods have been developed for the prediction of bugs in software source codes. The objective of this study is to find the inconsistency of performance between imbalances and balance data set and to find the distinction of performance between single classifier and aggregate classifier (voting). In this investigation, eight publicly available data sets have collected, also seven algorithms and hard voting are used for finding precision, recall and F-1 score to predict software defect. In these collected data, two sets are almost balanced. For this investigation, these balanced data sets have converted into imbalanced sets as average non-defective and defective ratio of the other 6 data sets. The experiment result shows that performance of the two balanced data sets is lower than other six sets. After conversion of two data sets, the performance has increased as like as other six data sets. Another observation is the performance metric that shows the results of precision, recall and F1-score for voting are 0.92, 0.84 and 0.87 respectively, which are better than other single classifier. This study has been able to shows that- imbalance of non-defective and defective classes have a big impact on software defect prediction and the voting is the best performer among the classifiers.
Synthesis of Fuzzy Controller Based on Simple PID Controller

This article will present the nonlinear mathematical model of quadrotor, then this model will be linearized around more than one operational point to cover all nonlinear system dynamics while fulfilling different movements. PID controller was developed with considering of control signals constraints. The data of closed loops PID of quadrotor including error and control signals will be analyzed to derive the most appropriate input and output linguistic variables and rule base that describe the behavior, more precisely the dynamics of the system. The proposed method of analysis will define the main parameters of fuzzy controller and help avoiding the complexity of contributing experts' knowledge which is either difficult to give full description neither readily exists in reach of the designer. This will lead to design a fuzzy controller that is supposed to maintain the performance of basic PID controller and to be immune to uncertainty of the model coefficients.
On the calibration and applicability of global solar radiation models based on temperature extremities in India

Abstract In the present work, global solar radiation models based on the difference of monthly‐average daily ambient temperature extremes are calibrated for country‐wide application in India. Solar radiation measurements obtained from the Indian Meteorological Department for 22 locations of India for a duration of 15 years (1986–2000) were used. Solar radiation models based on temperature extremes were reviewed and 33 forms were considered for analysis. The models were derived using all the collected data by fitting a model form and extracting the coefficients with the help of DataFit® software. The calibrated models were analyzed and compared using statistical tools and ranked using Global Performance Indicator (GPI) where a higher value represented a model's better performance. Then, the models were arranged using GPI and it was found that model form 33 (fourth order polynomial) resulted in best estimation capability with a GPI of 0.0763. Further, Model 23, Model 28, Model 24, and Model 26 were also among the top models with GPI values of 0.0655, 0.0587, 0.0478, and 0.0426, respectively. Best ranking models were also used to obtain estimates for representative sites in regions with different climate characteristics within India and it was found that the models performed reasonably well.
A Hierarchical Cooperative Mission Planning Mechanism for Multiple Unmanned Aerial Vehicles

In this paper, the cooperative multi-task online mission planning for multiple Unmanned Aerial Vehicles (UAVs) is studied. Firstly, the dynamics of unmanned aerial vehicles and the mission planning problem are studied. Secondly, a hierarchical mechanism is proposed to deal with the complex multi-UAV multi-task mission planning problem. In the first stage, the flight paths of UAVs are generated by the Dubins curve and B-spline mixed method, which are defined as “CBC)” curves, where “C” stands for circular arc and “B” stands for B-spline segment. In the second stage, the task assignment problem is solved as multi-base multi-traveling salesman problem, in which the “CBC” flight paths are used to estimate the trajectory costs. In the third stage, the flight trajectories of UAVs are generated by using Gaussian pseudospectral method (GPM). Thirdly, to improve the computational efficiency, the continuous and differential initial trajectories are generated based on the “CBC” flight paths. Finally, numerical simulations are presented to demonstrate the proposed approach, the designed initial solution search algorithm is compared with existing methods. These results indicate that the proposed hierarchical mission planning method can produce satisfactory mission planning results efficiently.
Decoupling Category-wise Independence and Relevance with Self-attention for Multi-label Image Classification

Multi-label image classification has achieved remarkable progress thanks to deep convolutional neural networks (CNNs). In this paper, we propose a Decouple Network (DecoupleNet) which is an end-to-end CNN-based framework able to trade off class-level feature independence and relevance during training. The proposed DecoupleNet is able to decouple category-wise independence and relevance with image-level supervision. We design a category-wise space-to-depth module with a spatial pooling strategy to exploit more meaningful convolutional features. They are integrated with class-wise correlated information which is automatically learned via a new self-attention mechanism. We conduct extensive experiments on two large-scale benchmarks: the MS-COCO and the NUS-WIDE, where the proposed DecoupleNet obtains impressive performance compared favorably against the state-of-the-art methods on multi-label image classification.
Evolutionary Computation Techniques for Constructing SAT-Based Attacks in Algebraic Cryptanalysis

In this paper we present the results on applying evolutionary computation techniques to construction of several cryptographic attacks. In particular, SAT-based guess-and-determine attacks studied in the context of algebraic cryptanalysis. Each of these attacks is built upon some set of Boolean variables, thus it can be specified by a Boolean vector. We use two general evolutionary strategies to find an optimal vector: (1+1)-EA and GA. Based on these strategies parallel algorithms (based on modern SAT-solvers) for solving the problem of minimization of a special pseudo-Boolean function are implemented. This function is a fitness function used to evaluate the runtime of a guess-and-determine attack. We compare the efficiency of (1+1)-EA and GA with the algorithm from the Tabu search class, that was earlier used to solve related problems. Our GA-based solution showed the best results on a number of test instances, namely, cryptanalysis problems of several stream ciphers (cryptographic keystream generators).
Flexible, Efficient, and Secure Access Delegation in Cloud Computing

The convenience of the cloud-assisted Internet of Things has led to the need for improved protections for the large volumes of data collected from devices around the world and stored on cloud-based servers. Proxy re-encryption (PRE) has been presented as a suitable mechanism for secure transmission and sharing of files within the cloud. However, existing PRE schemes do not support unidirectional data transformation, fine-grained controls, multiple hops, and identity-based encryption simultaneously. To solve these problems, we propose a unidirectional multi-hop identity based-conditional PRE scheme that meets all of the above requirements. Our proposal has the additional benefits of a constant ciphertext size, non-interactivity, and collusion resistance. We also prove that our scheme is secure against adaptive identity chosen-ciphertext attacks in the standard model.
Transfer Learning Using Raw Waveform Sincnet for Robust Speaker Diarization

Speaker diarization tells who spoke and when? in an audio stream. SincNet is a recently developed novel convolutional neural network (CNN) architecture where the first layer consists of parameterized sinc filters. Unlike conventional CNNs, SincNet take raw speech waveform as input. This paper leverages SincNet in vanilla transfer learning (VTL) setup. Out-domain data is used for training SincNet-VTL to perform frame-level speaker classification. Trained SincNet-VTL is later utilized as feature extractor for in-domain data. We investigated pooling (max, avg) strategies for deriving utterance-level embedding using frame-level features extracted from trained network. These utterance/segment level embedding are adopted as speaker models during clustering stage in diarization pipeline. We compared the proposed SincNet-VTL embedding with baseline i-vector features. We evaluated our approaches on two corpora, CRSS-PLTL and AMI. Results show the efficacy of trained SincNet-VTL for speaker-discriminative embedding even when trained on small amount of data. Proposed features achieved relative DER improvements of 19.12% and 52.07% for CRSS-PLTL and AMI data, respectively over baseline i-vectors.
The Use of Mobile Technologies by Immigrant Adolescents in Coping With the New Language and With Their Formal Studies

In light of the extensive use of mobile technologies by adolescents, the chapter examined the usability of mobile technologies for new immigrant adolescents in supporting their absorption process in the new country, acquiring the new language, and coping with their studies. The study also sought to determine what the adolescents' most meaningful experiences were that mobile technologies made possible in the course of their absorption in the new country. Participating in the study were 125 new immigrant adolescents, who have resided in Israel between 6 and 12 months. This is a qualitative study. The study included (1) interviews before completing an open questionnaire, (2) completion of an open-question questionnaire, and (3) personal interviews that took place after the questionnaire was completed. Participants reported that thanks to mobile technologies they were able to integrate into the daily life of their new environment. Translation software, databases, various applications, and social network groups have helped satisfy their needs, assisted in real time with difficulties they encountered, and helped create a sense of social connection and belonging.
Toward Drone Privacy via Regulating Altitude and Payload

The commercial drone market is booming. However ubiquitous drones have raised great concern about people’s privacy. Computer vision algorithms may disclose details of human activities on the ground from the videos taken by drones. We may mitigate the threats from adversarial computer vision by exploring the limitations of computer vision techniques. In this paper, we propose to mitigate the drone security and privacy threats through regulating the drones altitude and its on-board camera capability. We systematically study the factors that can affect the effectiveness of drone attacks given the camera specification, drone altitude and drone dynamics. We validate our theoretical analysis by evaluating two scenarios, password inference and racial recognition. It can be observed that regulating drone altitude and camera capability shall preserve human privacy on the ground.
Building Resource Auto-scaler with Functional-Link Neural Network and Adaptive Bacterial Foraging Optimization

In this paper, we present a novel intelligent proactive auto-scaling solution for cloud resource provisioning systems. The solution composes of an improvement variant of functional-link neural network and adaptive bacterial foraging optimization with life-cycle and social learning for proactive resource utilization forecasting as a part of our auto-scaler module. We also propose several mechanisms for processing simultaneously different resource metrics for the system. This enables our auto-scaler to explore hidden relationships between various metrics and thus help make more realistic for scaling decisions. In our system, a decision module is developed based on the cloud Service-Level Agreement (SLA) violation evaluation. We use Google trace dataset to evaluate the proposed solution well as the decision module introduced in this work. The gained experiment results demonstrate that our system is feasible to work in real situations with good performance.
CAN: Contextual Aggregating Network for Semantic Segmentation

Fully convolutional neural networks (FCNs) have shown great success in dense estimation tasks. One key pillar of such progress is mining multi-scale context cues from features in different convolutional layers. This paper introduces contextual aggregating network(CAN), a generic convolutional feature ensembling framework for semantic segmentation. Our framework first captures multi-scale contextual clues by concatenating multi-level feature representation, which carries both coarse semantics and fine details. Then it adaptively integrates stacked features to perform dense pixel estimation. The proposed CAN is trainable end-to-end, and allows us to fully investigate multi-scale context information embedded in images. The experiments show the promising results of our method on PASCAL VOC 2012 and Cityscapes dataset.
Low-Light Underwater Image Enhancement for Deep-Sea Tripod

To monitor the sedimentary process and morphological evolution in the South China Sea, free-ascending deep-sea tripod (FDT) has been developed. This FDT was equipped with a deep-sea camera and landed on the sea floor at a depth of 2100 m. Although the FDT was equipped with an artificial light, the battery capacity limited the duration and intensity of light. Therefore, enhancing such low-illumination images to obtain clear visual effects is an important advancement for analyzing the geological evolution process. In this paper, an adaptive bright-color channel-based low-light underwater image-enhancement method and a denoising method are proposed to enhance such images and remove noise and artifacts. The experimental results demonstrated that the proposed method outperformed state-of-the-art methods.
Lightweight Implementations of NIST P-256 and SM2 ECC on 8-bit Resource-Constraint Embedded Device

Elliptic Curve Cryptography (ECC) now is one of the most important approach to instantiate asymmetric encryption and signature schemes, which has been extensively exploited to protect the security of cyber-physical systems. With the advent of the Internet of Things (IoT), a great deal of constrained devices may require software implementations of ECC operations. Under this circumstances, the SM2, a set of public key cryptographic algorithms based on elliptic curves published by Chinese Commercial Cryptography Administration Office, was standardized at ISO in 2017 to enhance the cyber-security. However, few research works on the implementation of SM2 for constrained devices have been conducted. In this work, we fill this gap and propose our efficient, secure, and compact implementation of scalar multiplication on a 256-bit elliptic curve recommended by the SM2, as well as a comparison implementation of scalar multiplication on the same bit-length elliptic curve recommended by NIST. We re-design some existent techniques to fit the low-end IoT platform, namely 8-bit AVR processors, and our implementations evaluated on the desired platform show that the SM2 algorithms have competitive efficiency and security with NIST, which would work well to secure the IoT world.
Optimized Quantization in Distributed Graph Signal Processing

Distributed graph signal processing methods require that the graph nodes communicate by exchanging messages. These messages have a finite precision in a realistic network, which may necessitate to implement quantization. Quantization, in turn, generates errors in the distributed processing tasks, compared to perfect settings. This paper proposes a novel method to minimize the quantization error without compromising the communication costs by bounding the exchanged messages along with allocating a limited bit budget through the network in an optimized way. In particular, the quantization adapts to the network topology and message importance in the iterative distributed processing algorithm. Our results show that the proposed method is efficient in minimizing the quantization error and that it outperforms baseline algorithms when the bit budget is limited.