Reviewerly/exHarmony huggingface dataset:
A Selective Cross-Substitution Technique for Encrypting Color Images Using Chaos, DNA Rules and SHA-512

An innovative approach of selective encryption for color images is proposed that utilizes SHA-512 hash of plain image to modify initial conditions and control parameters of 1-Dimensional (1D) chaotic maps. The three channels (red, green and blue) of a color image are combined into 1D array and permute using sorted index of a pseudo-random sequence. The 1D permuted array is split into three sub-arrays, DNA encoding is applied on every pixel of each channel chaotically and then separate each DNA encoded channel into its Least Significant Bits (LSB) and Most Significant Bits (MSB). The substitution is carried out in two phases using addition and exclusive-or operations on MSB of each channel only. In 1 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">st</sup> phase, the DNA addition operation is applied on chaotically selected MSB of a pixel of one channel to LSB part of a pixel of other channel in a twisted fashioned named cross-substitution. The translated DNA bases from pseudo-random numbers are exclusive-or with cross substituted output to surge the complexity. The second substitution phase is accomplished by combining MSB part of a channel with randomly selecting LSB at pixel level. The novel algorithm is highly suitable for real time applications as it requires single round of permutation/substitution which can resist all possible statistical and differential attacks. The simulation results and analysis show that the proposed technique has the best quality output and highly efficient.
A Multi-Objective Optimization Scheme for Job Scheduling in Sustainable Cloud Data Centers

For a number of years, due to an exponential increase in the demand for an eco-friendly environment, there has been a rapid increase in the green city revolution across the globe. Subsequently, load shifting of major energy consumers from conventional power grids to renewable energy sources (RES) has become inevitable. Towards this end, cloud data centers (DCs) have emerged as significant consumers of energy that solely rely on power grids to fuel their day-to-day operations. Nevertheless, their energy consumption has increased significantly which in turn has substantially raised the global carbon footprint rate. These challenges can be best addressed by the judicious utilization of RES which have well established advantages like reduced operational costs and carbon emissions. Keeping in view of the above facts, the ultimate goal of the proposed work is to design a comprehensive workload classification; and job scheduling and Vitual machine placement architecture for cloud DCs powered by RES and power grids. For this, a multi-objective optimization scheme is proposed which operates in two phases. In phase I, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">a random forest-based wrapper scheme</i> known as Boruta, is used for relevant feature set selection for the incoming workload. This is followed by classification of the workload using a locality sensitive hashing-based support vector machines approach. In phase II, a multi-objective optimization problem for job scheduling and VM placement is formulated with respect to parameters such as service level agreement (SLA), energy cost, carbon footprint rate (CFR), and availability of RES. It is further solved using an enhanced heuristic approach based on a greedy strategy. Our experimental evaluations show an average improvement of approximately 31 percent in energy utilization, 28 percent in energy cost, and 36 percent in CFR, with a slight degradation in SLA assurance (about 2 percent) compared with the existing schemes.
A Highly-Portable True Random Number Generator Based on Coherent Sampling

True Random Number Generators (TRNGs) are indispensable in modern cryptosystems. Unfortunately, in order to guarantee high entropy of the generated numbers, many TRNG designs require a complex implementation procedure, often involving manual placement and routing. In this work, we introduce a dynamic calibration mechanism for the Coherent Sampling Ring Oscillator based TRNG (COSO-TRNG) enabling easy integration of the entropy source into complex systems. The TRNG setup procedure automatically selects a configuration that guarantees the security requirements. In the experiments, we show that the proposed mechanism is capable of assuring correct TRNG operation even when an automatic placement is carried out and when the design is ported to another FPGA family. We generated random bits on both a Xilinx Spartan 6 and a Microsemi SmartFusion2 implementation that, without post processing, passed AIS-31 statistical tests at a throughput of 3.30 Mbit/s and 1.47 Mbit/s respectively.
Pop-Net: Encoder-Dual Decoder for Semantic Segmentation and Single-View Height Estimation

The single-view semantic 3D challenge in 2019 Data Fusion Contest is to predict both semantic labels and normalized digital surface model (nDSM) for urban scenes from single-view satellite images. We propose a novel pyramid on pyramid network (Pop-Net) based on Encoder-Dual Decoder framework to end-to-end multi-task learning. The encoder is a deformable ResNet-101 backbone network. Two feature pyramid networks, as decoders, are responsible for semantic segmentation and height estimation, respectively. Semantic information is crucial to estimate height. Therefore, regression pyramid on the semantic pyramid is introduced to leverage semantic features to help height estimation. To deal with outliers in heights, we leverage anchor-based regression and smooth L1 loss for optimization to obtain more robust height estimation. Without bells and whistles, our single model entry achieves 77.78% mIoU and 53.40% mIoU-3 on test set, ranking 2nd in the Single-view Semantic 3D Challenge of the 2019 IEEE GRSS Data Fusion Contest. The code is available at https://github.com/Z-Zheng/PopNet.
ICT in education: Benefits, Challenges and New directions

The presented work gives an overview of the state of the art of ICT in education, at the end of the second decade of the 21st century. Current and needed policies, benefits, challenges and risks in the use of ICT are explained. A clear distinction between using ICT as a supportive tool from using ICT as a pedagogical means in education is the main driving force of this work. For this reason, the cognitive, pedagogical and affective benefits of using ICT for teaching and learning are projected to specific, also advanced technologies available today and their role is analyzed through examples. Finally, a critical review towards the current technological, research and pedagogical aspects of ICT in the educational procedure is provided, and the main directions that efforts should be taken towards are discussed.
Low-Rank and Locality Constrained Self-Attention for Sequence Modeling

Self-attention mechanism becomes more and more popular in natural language processing (NLP) applications. Recent studies show the Transformer architecture which relies mainly on the attention mechanism achieves much success on large datasets. But a raised problem is its generalization ability is weaker than CNN and RNN on many moderate-sized datasets. We think the reason can be attributed to its unsuitable inductive bias of the self-attention structure. In this paper, we regard the self-attention as matrix decomposition problem and propose an improved self-attention module by introducing two linguistic constraints: low-rank and locality. We further develop the low-rank attention and band attention to parameterize the self-attention mechanism under the low-rank and locality constraints. Experiments on several real NLP tasks show our model outperforms the vanilla Transformer and other self-attention models on moderate size datasets. Additionally, evaluation on a synthetic task gives us a more detailed understanding of working mechanisms of different architectures.
Formal knowledge model for online social network forensics

Currently, examining social media networks is an integral part of most investigations. However, getting a clear view of the events relevant to the incident from a large set of data, such as social media, is a challenging task. Automation of the forensic and analysis process is the only solution to manage large data sets and get useful information. However, automation in digital forensics is a technical issue with legal implications. The legal system accepts only those automated processes that are reproducible, explainable, and rigorously testable. Therefore, automated forensic processes must be based on formal theories, which are rare in digital forensics. This article explains a theoretical and formal knowledge model for forensic automation on online social networks. This model consists of an event-based knowledge model, which provides theoretical concepts that can assist in the construction and interpretation of the events related to the incident under investigation. The proposed model is implemented through an ontology to provide semantically rich and formal representation to the concepts. This article also describes the feasibility of legally acceptable automated analysis operators, based on a formal theory, for online social network forensics.
Brain tumor segmentation approach based on the extreme learning machine and significantly fast and robust fuzzy C-means clustering algorithms running on Raspberry Pi hardware

Automatic decision support systems have gained importance in health sector in recent years. In parallel with recent developments in the fields of artificial intelligence and image processing, embedded systems are also used in decision support systems for tumor diagnosis. Extreme learning machine (ELM), is a recently developed, quick and efficient algorithm which can quickly and flawlessly diagnose tumors using machine learning techniques. Similarly, significantly fast and robust fuzzy C-means clustering algorithm (FRFCM) is a novel and fast algorithm which can display a high performance. In the present study, a brain tumor segmentation approach is proposed based on extreme learning machine and significantly fast and robust fuzzy C-means clustering algorithms (BTS-ELM-FRFCM) running on Raspberry Pi (PRI) hardware. The present study mainly aims to introduce a new segmentation system hardware containing new algorithms and offering a high level of accuracy the health sector. PRI’s are useful mobile devices due to their cost-effectiveness and satisfying hardware. 3200 training images were used to train ELM in the present study. 20 pieces of MRI images were used for testing process. Figure of merid (FOM), Jaccard similarity coefficient (JSC) and Dice indexes were used in order to evaluate the performance of the proposed approach. In addition, the proposed method was compared with brain tumor segmentation based on support vector machine (BTS-SVM), brain tumor segmentation based on fuzzy C-means (BTS-FCM) and brain tumor segmentation based on self-organizing maps and k-means (BTS-SOM). The statistical analysis on FOM, JSC and Dice results obtained using four different approaches indicated that BTS-ELM-FRFCM displayed the highest performance. Thus, it can be concluded that the embedded system designed in the present study can perform brain tumor segmentation with a high accuracy rate.
Delayed labelling evaluation for data streams

Abstract A large portion of the stream mining studies on classification rely on the availability of true labels immediately after making predictions. This approach is well exemplified by the test-then-train evaluation, where predictions immediately precede true label arrival. However, in many real scenarios, labels arrive with non-negligible latency. This raises the question of how to evaluate classifiers trained in such circumstances. This question is of particular importance when stream mining models are expected to refine their predictions between acquiring instance data and receiving its true label. In this work, we propose a novel evaluation methodology for data streams when verification latency takes place, namely continuous re-evaluation. It is applied to reference data streams and it is used to differentiate between stream mining techniques in terms of their ability to refine predictions based on newly arriving instances. Our study points out, discusses and shows empirically the importance of considering the delay of instance labels when evaluating classifiers for data streams.
ANFIS with natural language processing and gray relational analysis based cloud computing framework for real time energy efficient resource allocation

As one of the most commonly used types of application, command line applications have aroused extensive attention. Predicting command line application workload and providing reasonable resource scheduling strategy can effectively improve resource utilization and performance of VMs. This paper provided a novel scheme with two-level hybrid adaptive model to predict VMs load based on the strong regularity of command line applications, and elastically configured the CPU and memory resource for VMs. Instead of time series prediction, we extracted and analyzed the feature attributes of command line applications by natural language processing (NLP) technology, and used the gray relational analysis (GRA) to implement attribute reductions. Then a two-level hybrid adaptive model was designed to predict the VM load efficiently and accurately, including CPU and memory load. Bayesian algorithm was used for classification to select the applications that increase the CPU of a VM by more than 5%, then giving the CPU and memory load prediction of VMs by aANFIS model. Extensive experiments demonstrated that the elastic allocation strategy can improve the VMs performance and resource utilization.
Phase reconstruction from amplitude spectrograms based on directional-statistics deep neural networks

This paper presents a deep neural network (DNN)-based phase reconstruction method from amplitude spectrograms. In speech processing, an amplitude spectrogram is often used for processing, and the corresponding phases are reconstructed from the amplitude spectrogram by using the Griffin-Lim method. However, the Griffin-Lim method causes unnatural artifacts in synthetic speech. To solve this problem, we propose the directional-statistics DNNs for predicting phases from the amplitude spectrograms. We first propose the von Mises distribution DNN, which is a generative model having the von Mises distribution and models histograms of a periodic variable. We extend it for modeling group delay that has a stronger connection to the amplitude spectrograms. Furthermore, we generalize the group-delay modeling and propose another DNN called the sine-skewed generalized cardioid distribution DNN for modeling asymmetric histograms such as a group delay. Results from objective and subjective evaluations indicate that (1) our von Mises distribution DNN can predict group delay more accurately than predicting phases, (2) our DNN works as better initialization of the Griffin-Lim method, (3) the phase reconstruction methods based on our von Mises distribution DNN achieve better speech quality than the conventional Griffin-Lim method, and (4) our sine-skewed generalized cardioid distribution DNN models the group delay more accurately than our von Mises distribution DNN.
Electronic-Digital Smart Contracts: Modernization of Legal Tools for Foreign Economic Activity

The article explores the possibilities of improving the methods of managing foreign economic activity and increasing its efficiency with the help of smart contracts. The advantages of the blockchain technology and the legal contradictions associated with the use of smart contracts in the practice of economic entities are analyzed. The methodological basis consists of the authors’ materialistic, positivistic worldview, general scientific methods of cognition, a number of special legal methods. The purpose of the study is to search for improvements in the legal support of electronic interaction of participants in foreign economic activity, registration of facts of property relations between them, which can be executed by smart contracts. As a result of the study, it was substantiated that a smart contract can neutralize many civil law problems, including questions about applicable law, judicial jurisdiction, verification of counterparty powers. Based on the analysis, it was substantiated the conclusion that neither at the domestic (national) level of legislation, nor at the international level within the framework of universal or regional agreements, a coherent systemic matter of legal norms regulating the conditions, procedure and limits of use of smart contracts was created.
Separable Convolutional LSTMs for Faster Video Segmentation

Semantic Segmentation is an important module for autonomous robots such as self-driving cars. The advantage of video segmentation approaches compared to single image segmentation is that temporal image information is considered, and their performance increases due to this. Hence, single image segmentation approaches are extended by recurrent units such as convolutional LSTM (convLSTM) cells, which are placed at suitable positions in the basic network architecture. However, a major critique of video segmentation approaches based on recurrent neural networks is their large parameter count and their computational complexity, and so, their inference time of one video frame takes up to 66 percent longer than their basic version. Inspired by the success of the spatial and depthwise separable convolutional neural networks, we generalize these techniques for convLSTMs in this work, so that the number of parameters and the required FLOPs are reduced significantly. Experiments on different datasets show that the segmentation approaches using the proposed, modified convLSTM cells achieve similar or slightly worse accuracy, but are up to 15 percent faster on a GPU than the ones using the standard convLSTM cells. Furthermore, a new evaluation metric is introduced, which measures the amount of flickering pixels in the segmented video sequence.
On-line object detection: a robotics challenge

Object detection is a fundamental ability for robots interacting within an environment. While stunningly effective, state-of-the-art deep learning methods require huge amounts of labeled images and hours of training which does not favour such scenarios. This work presents a novel pipeline resulting from integrating (Maiettini et al. in 2017 IEEE-RAS 17th international conference on humanoid robotics (Humanoids), 2017) and (Maiettini et al. in 2018 IEEE/RSJ international conference on intelligent robots and systems (IROS), 2018), which naturally trains a robot to detect novel objects in few seconds. Moreover, we report on an extended empirical evaluation of the learning method, justifying that the proposed hybrid architecture is key in leveraging powerful deep representations while maintaining fast training time of large scale Kernel methods. We validate our approach on the Pascal VOC benchmark (Everingham et al. in Int J Comput Vis 88(2): 303–338, 2010), and on a challenging robotic scenario (iCubWorld Transformations (Pasquale et al. in Rob Auton Syst 112:260–281, 2019). We address real world use-cases and show how to tune the method for different speed/accuracy trades-off. Lastly, we discuss limitations and directions for future development.
Encryption-based steganography of images by multiobjective whale optimal pixel selection

Information security involves hiding secret images, text, audio or video files within any other files that can be used to preserve the secret data from being theft by the third party. Recent works of the literature in steganography have the threat of secret information being retrieved by an anonymous user. Some techniques do not support some image formats for embedding. To overcome the disadvantages associated with the existing algorithms, we proposed encryption-based steganography. Encryption algorithms play vital roles to protect original data from unauthorized access. In this paper, the cover image is separated into RGB components separately. Multilevel discrete wavelet transform (DWT) transformation is applied over the transformed image components. Optimal pixels are selected using Multiobjective Whale Optimization. The secret image is split into RGB components. Encryption using Data Encryption Standard, Advanced Encryption Standard and Signcryption algorithms is performed over R, G and B separated components, respectively. The encrypted image is embedded into the selected pixel point of cover image. Inverse DWT is applied to retrieve the stego image. The results are analyzed to prove low error rate with high secured secret image.
Moving Cast Shadows Segmentation Using Illumination Invariant Feature

This paper presents an effective framework for removing moving cast shadows. Taking the reflection property of object surface for shadow regions under static and fixed scenes, an approximation estimation strategy of bidirectional reflectance distribution function as illumination invariant feature is proposed. It is valid for different types of shadow scenes. In this paper, we propose a new multiple ratios-based technique to justify shadow type for each frame: intensity ratio, area ratio and edge ratio of shadow regions are introduced. According to shadow types, several specified strategies are designed. For weak shadows, multiple features fusion strategy is employed, including color constancy, texture consistency and illumination invariant. For strong shadows, illumination invariant is utilized to detect the umbra and color constancy is utilized to detect the penumbra. Moreover, a suite of shadow direction features is firstly proposed to identify penumbra. The proposed approach is verified in fourteen video sequences varying from weak to strong shadows. The experimental results demonstrate the effectiveness and robustness of the proposed method for both indoor and outdoor scenes compared with some state-of-the-art approaches.
Task-Independent Computational Abilities of Semiconductor Lasers with Delayed Optical Feedback for Reservoir Computing

Reservoir computing has rekindled neuromorphic computing in photonics. One of the simplest technological implementations of reservoir computing consists of a semiconductor laser with delayed optical feedback. In this delay-based scheme, virtual nodes are distributed in time with a certain node distance and form a time-multiplexed network. The information processing performance of a semiconductor laser-based reservoir computing (RC) system is usually analysed by way of testing the laser-based reservoir computer on specific benchmark tasks. In this work, we will illustrate the optimal performance of the system on a chaotic time-series prediction benchmark. However, the goal is to analyse the reservoir’s performance in a task-independent way. This is done by calculating the computational capacity, a measure for the total number of independent calculations that the system can handle. We focus on the dependence of the computational capacity on the specifics of the masking procedure. We find that the computational capacity depends strongly on the virtual node distance with an optimal node spacing of 30 ps. In addition, we show that the computational capacity can be further increased by allowing for a well chosen mismatch between delay and input data sample time.