Reviewerly/exHarmony huggingface dataset:
Comparison of echo state network and feed-forward neural networks in electrical load forecasting for demand response programs

The electrical load forecasting is a fundamental technique for consumer load prediction for utilities. The accurate load forecasting is crucial to design Demand Response (DR) programs in the paradigm of smart grids. Artificial Neural Network (ANN) based techniques have been widely used in recent years and applied to predict the electric load with high accuracy to participate in DR programs for commercial, industrial and residential consumers. This research work is focused on the use and comparison of two ANN-based load forecasting techniques, i.e. Feed-Forward Neural Network (FFNN) and Echo State Network (ESN), on a dataset related to commercial buildings, in view of a possible DR program application. The results of both models are compared based on the load forecasting accuracy through experimental measurements and suitably defined metrics.
Integration of the waterfall model with ISO/IEC/IEEE 29148:2018 for the development of military defense system

Software development for military organizations follows standards and a set of guidelines established in the requirements engineering domain. However, these procedures are not always documented and it is difficult to reach understanding among stakeholders with varied backgrounds and interests. This article aims to obtain lessons learned through a case study of the development of a defense system for the Armed Forces of Brazil. From the validation of requirements specialist, a software development lifecycle was obtained that integrates the waterfall model and ISO/IEC/IEEE 29148 recommendations. The use of the model obtained facilitated the understanding of the processes that involved military teams, detailed specification of system requirements and success in the geoinformation based prototype.
SPADE 3: Supporting the New Generation of Multi-Agent Systems

Although intelligent agent-based systems have existed for several years, the progression in terms of real applications or their integration in the industry have not yet reached the expected levels.During the last two decades, many agent platforms have appeared with the aim of simplifying the development of multi-agent systems.Some of these platforms have been designed for general purposes, while others have been oriented towards specific domains.However, the lack of standards and the complexity associated with supporting such systems, among other difficulties, have hampered their generalised use.This article looks in depth at the current situation of existing agent platforms, trying to analyse their current shortcomings and their expected needs in the near future.The goal of the paper is to identify possible lines of work and some of the most crucial aspects to be considered in order to popularize the application of agent technology as a dynamic and flexible solution to current problems.Moreover, the paper presents SPADE 3, a new version of the SPADE middleware, which has been totally redesigned in order to conform to the identified challenges.Finally, a case study is proposed to illustrate how SPADE 3 is able to fulfill these challenges.
Quantitative sampling of atomic-scale electromagnetic waveforms

Tailored nanostructures can confine electromagnetic waveforms in extremely sub-wavelength volumes, opening new avenues in lightwave sensing and control down to sub-molecular resolution. Atomic light–matter interaction depends critically on the absolute strength and the precise time evolution of the near field, which may be strongly influenced by quantum-mechanical effects. However, measuring atom-scale field transients has remained out of reach. Here we introduce quantitative atomic-scale waveform sampling in lightwave scanning tunnelling microscopy to resolve a tip-confined near-field transient. Our parameter-free calibration employs a single-molecule switch as an atomic-scale voltage standard. Although salient features of the far-to-near-field transfer follow classical electrodynamics, we develop a comprehensive understanding of the atomic-scale waveforms with time-dependent density functional theory. The simulations validate our calibration and confirm that single-electron tunnelling ensures minimal back-action of the measurement process on the electromagnetic fields. Our observations access an uncharted domain of nano-opto-electronics where local quantum dynamics determine femtosecond atomic near fields. Ultrafast lightwave sampling based on scanning tunnelling microscopy is developed to resolve near fields with sub-picosecond time resolution and sub-nanometre spatial resolution. Parameter-free quantitative measurement is achieved by using a single-molecule switch.
Multi-Task Head Pose Estimation in-the-Wild

We present a deep learning-based multi-task approach for head pose estimation in images. We contribute with a network architecture and training strategy that harness the strong dependencies among face pose, alignment and visibility, to produce a top performing model for all three tasks. Our architecture is an encoder-decoder CNN with residual blocks and lateral skip connections. We show that the combination of head pose estimation and landmark-based face alignment significantly improve the performance of the former task. Further, the location of the pose task at the bottleneck layer, at the end of the encoder, and that of tasks depending on spatial information, such as visibility and alignment, in the final decoder layer, also contribute to increase the final performance. In the experiments conducted the proposed model outperforms the state-of-the-art in the face pose and visibility tasks. By including a final landmark regression step it also produces face alignment results on par with the state-of-the-art.
On the Security and Privacy Challenges of Virtual Assistants

Since the purchase of Siri by Apple, and its release with the iPhone 4S in 2011, virtual assistants (VAs) have grown in number and popularity. The sophisticated natural language processing and speech recognition employed by VAs enables users to interact with them conversationally, almost as they would with another human. To service user voice requests, VAs transmit large amounts of data to their vendors; these data are processed and stored in the Cloud. The potential data security and privacy issues involved in this process provided the motivation to examine the current state of the art in VA research. In this study, we identify peer-reviewed literature that focuses on security and privacy concerns surrounding these assistants, including current trends in addressing how voice assistants are vulnerable to malicious attacks and worries that the VA is recording without the user’s knowledge or consent. The findings show that not only are these worries manifold, but there is a gap in the current state of the art, and no current literature reviews on the topic exist. This review sheds light on future research directions, such as providing solutions to perform voice authentication without an external device, and the compliance of VAs with privacy regulations.
Accelerating the Search of Differential and Linear Characteristics with the SAT Method

The introduction of the automatic search boosts the cryptanalysis of symmetric-key primitives to some degree. However, the performance of the automatic search is not always satisfactory for the search of long trails or ciphers with large state sizes. Compared with the extensive attention on the enhancement for the search with the mixed integer linear programming (MILP) method, few works care for the acceleration of the automatic search with the Boolean satisfiability problem (SAT) or satisfiability modulo theories (SMT) method. This paper intends to fill this vacancy. Firstly, with the additional encoding variables of the sequential counter circuit for the original objective function in the standard SAT method, we put forward a new encoding method to convert the Matsui’s bounding conditions into Boolean formulas. This approach does not rely on new auxiliary variables and significantly reduces the consumption of clauses for integrating multiple bounding conditions into one SAT problem. Then, we evaluate the accelerating effect of the novel encoding method under different sets of bounding conditions. With the observations and experience in the tests, a strategy on how to create the sets of bounding conditions that probably achieve extraordinary advances is proposed. The new idea is applied to search for optimal differential and linear characteristics for multiple ciphers. For PRESENT, GIFT-64, RECTANGLE, LBlock, TWINE, and some versions in SIMON and SPECK families of block ciphers, we obtain the complete bounds (full rounds) on the number of active S-boxes, the differential probability, as well as the linear bias. The acceleration method is also employed to speed up the search of related-key differential trails for GIFT-64. Based on the newly identified 18-round distinguisher with probability 2−58, we launch a 26-round key-recovery attack with 260.96 chosen plaintexts. To our knowledge, this is the longest attack on GIFT-64. Lastly, we note that the attack result is far from threatening the security of GIFT-64 since the designers recommended users to double the number of rounds under the related-key attack setting.
Unified Confidence Estimation Networks for Robust Stereo Matching

We present a deep architecture that estimates a stereo confidence, which is essential for improving the accuracy of stereo matching algorithms. In contrast to existing methods based on deep convolutional neural networks (CNNs) that rely on only one of the matching cost volume or estimated disparity map, our network estimates the stereo confidence by using the two heterogeneous inputs simultaneously. Specifically, the matching probability volume is first computed from the matching cost volume with residual networks and a pooling module in a manner that yields greater robustness. The confidence is then estimated through a unified deep network that combines confidence features extracted both from the matching probability volume and its corresponding disparity. In addition, our method extracts the confidence features of the disparity map by applying multiple convolutional filters with varying sizes to an input disparity map. To learn our networks in a semi-supervised manner, we propose a novel loss function that use confident points to compute the image reconstruction loss. To validate the effectiveness of our method in a disparity post-processing step, we employ three post-processing approaches; cost modulation, ground control points-based propagation, and aggregated ground control points-based propagation. Experimental results demonstrate that our method outperforms state-of-the-art confidence estimation methods on various benchmarks.
Isolated sign language recognition using Convolutional Neural Network hand modelling and Hand Energy Image

This paper presents an isolated sign language recognition system that comprises of two main phases: hand tracking and hand representation. In the hand tracking phase, an annotated hand dataset is used to extract the hand patches to pre-train Convolutional Neural Network (CNN) hand models. The hand tracking is performed by the particle filter that combines hand motion and CNN pre-trained hand models into a joint likelihood observation model. The predicted hand position corresponds to the location of the particle with the highest joint likelihood. Based on the predicted hand position, a square hand region centered around the predicted position is segmented and serves as the input to the hand representation phase. In the hand representation phase, a compact hand representation is computed by averaging the segmented hand regions. The obtained hand representation is referred to as “Hand Energy Image (HEI)”. Quantitative and qualitative analysis show that the proposed hand tracking method is able to predict the hand positions that are closer to the ground truth. Similarly, the proposed HEI hand representation outperforms other methods in the isolated sign language recognition.
How to Leverage Hardness of Constant-Degree Expanding Polynomials over $$\mathbb {R}$$ R to build $$i\mathcal {O}$$ i O

In this work, we introduce and construct D-restricted Functional Encryption (FE) for any constant $$D \ge 3$$ , based only on the SXDH assumption over bilinear groups. This generalizes the notion of 3-restricted FE recently introduced and constructed by Ananth et al. (ePrint 2018) in the generic bilinear group model. A $$D=(d+2)$$ -restricted FE scheme is a secret key FE scheme that allows an encryptor to efficiently encrypt a message of the form $$M=(\varvec{x},\varvec{y},\varvec{z})$$ . Here, $$\varvec{x}\in \mathbb {F}_{\mathbf {p}}^{d\times n}$$ and $$\varvec{y},\varvec{z}\in \mathbb {F}_{\mathbf {p}}^n$$ . Function keys can be issued for a function $$f=\varSigma _{\varvec{I}= (i_1,..,i_d,j,k)}\ c_{\varvec{I}}\cdot \varvec{x}[1,i_1] \cdots \varvec{x}[d,i_d] \cdot \varvec{y}[j]\cdot \varvec{z}[k]$$ where the coefficients $$c_{\varvec{I}}\in \mathbb {F}_{\mathbf {p}}$$ . Knowing the function key and the ciphertext, one can learn $$f(\varvec{x},\varvec{y},\varvec{z})$$ , if this value is bounded in absolute value by some polynomial in the security parameter and n. The security requirement is that the ciphertext hides $$\varvec{y}$$ and $$\varvec{z}$$ , although it is not required to hide $$\varvec{x}$$ . Thus $$\varvec{x}$$ can be seen as a public attribute. D-restricted FE allows for useful evaluation of constant-degree polynomials, while only requiring the SXDH assumption over bilinear groups. As such, it is a powerful tool for leveraging hardness that exists in constant-degree expanding families of polynomials over $$\mathbb {R}$$ . In particular, we build upon the work of Ananth et al. to show how to build indistinguishability obfuscation ( $$i\mathcal {O}$$ ) assuming only SXDH over bilinear groups, LWE, and assumptions relating to weak pseudorandom properties of constant-degree expanding polynomials over $$\mathbb {R}$$ .
Collision-Free Autonomous Robot Navigation in Unknown Environments Utilizing PSO for Path Planning

Abstract The autonomous navigation of robots in unknown environments is a challenge since it needs the integration of a several subsystems to implement different functionality. It needs drawing a map of the environment, robot map localization, motion planning or path following, implementing the path in real-world, and many others; all have to be implemented simultaneously. Thus, the development of autonomous robot navigation (ARN) problem is essential for the growth of the robotics field of research. In this paper, we present a simulation of a swarm intelligence method is known as Particle Swarm Optimization (PSO) to develop an ARN system that can navigate in an unknown environment, reaching a pre-defined goal and become collision-free. The proposed system is built such that each subsystem manipulates a specific task which integrated to achieve the robot mission. PSO is used to optimize the robot path by providing several waypoints that minimize the robot traveling distance. The Gazebo simulator was used to test the response of the system under various envirvector representing a solution to the optimization problem.onmental conditions. The proposed ARN system maintained robust navigation and avoided the obstacles in different unknown environments. vector representing a solution to the optimization problem.
Classifier and Exemplar Synthesis for Zero-Shot Learning

Zero-shot learning (ZSL) enables solving a task without the need to see its examples. In this paper, we propose two ZSL frameworks that learn to synthesize parameters for novel unseen classes. First, we propose to cast the problem of ZSL as learning manifold embeddings from graphs composed of object classes, leading to a flexible approach that synthesizes “classifiers” for the unseen classes. Then, we define an auxiliary task of synthesizing “exemplars” for the unseen classes to be used as an automatic denoising mechanism for any existing ZSL approaches or as an effective ZSL model by itself. On five visual recognition benchmark datasets, we demonstrate the superior performances of our proposed frameworks in various scenarios of both conventional and generalized ZSL. Finally, we provide valuable insights through a series of empirical analyses, among which are a comparison of semantic representations on the full ImageNet benchmark as well as a comparison of metrics used in generalized ZSL. Our code and data are publicly available at https://github.com/pujols/Zero-shot-learning-journal.
Joint Training Framework for Text-to-Speech and Voice Conversion Using Multi-Source Tacotron and WaveNet

We investigated the training of a shared model for both text-to-speech (TTS) and voice conversion (VC) tasks. We propose using an extended model architecture of Tacotron, that is a multi-source sequence-to-sequence model with a dual attention mechanism as the shared model for both the TTS and VC tasks. This model can accomplish these two different tasks respectively according to the type of input. An end-to-end speech synthesis task is conducted when the model is given text as the input while a sequence-to-sequence voice conversion task is conducted when it is given the speech of a source speaker as the input. Waveform signals are generated by using WaveNet, which is conditioned by using a predicted mel-spectrogram. We propose jointly training a shared model as a decoder for a target speaker that supports multiple sources. Listening experiments show that our proposed multi-source encoder-decoder model can efficiently achieve both the TTS and VC tasks.
Attention-based recurrent neural network for influenza epidemic prediction

Abstract Background Influenza is an infectious respiratory disease that can cause serious public health hazard. Due to its huge threat to the society, precise real-time forecasting of influenza outbreaks is of great value to our public. Results In this paper, we propose a new deep neural network structure that forecasts a real-time influenza-like illness rate (ILI%) in Guangzhou, China. Long short-term memory (LSTM) neural networks is applied to precisely forecast accurateness due to the long-term attribute and diversity of influenza epidemic data. We devise a multi-channel LSTM neural network that can draw multiple information from different types of inputs. We also add attention mechanism to improve forecasting accuracy. By using this structure, we are able to deal with relationships between multiple inputs more appropriately. Our model fully consider the information in the data set, targetedly solving practical problems of the Guangzhou influenza epidemic forecasting. Conclusion We assess the performance of our model by comparing it with different neural network structures and other state-of-the-art methods. The experimental results indicate that our model has strong competitiveness and can provide effective real-time influenza epidemic forecasting.
Particle Swarm Optimization-Based Feature Weighting for Improving Intelligent Phishing Website Detection

Over the last few years, web phishing attacks have been constantly evolving causing customers to lose trust in e-commerce and online services. Various tools and systems based on a blacklist of phishing websites are applied to detect the phishing websites. Unfortunately, the fast evolution of technology has led to the born of more sophisticated methods when building websites to attract users. Thus, the latest and newly deployed phishing websites; for example, zero-day phishing websites, cannot be detected by using these blacklist-based approaches. Several recent research studies have been adopting machine learning techniques to identify phishing websites and utilizing them as an early alarm method to identify such threats. However, the important website features have been selected based on human experience or frequency analysis of website features in most of these approaches. In this paper, intelligent phishing website detection using particle swarm optimization-based feature weighting is proposed to enhance the detection of phishing websites. The proposed approach suggests utilizing particle swarm optimization (PSO) to weight various website features effectively to achieve higher accuracy when detecting phishing websites. In particular, the proposed PSO-based website feature weighting is used to differentiate between the various features in websites, based on how important they contribute towards recognizing the phishing from legitimate websites. The experimental results indicated that the proposed PSO-based feature weighting achieved outstanding improvements in terms of classification accuracy, true positive and negative rates, and false positive and negative rates of the machine learning models using only fewer websites features utilized in the detection of phishing websites.
When Agile Harms Learning and Innovation: (and What Can Be Done About It)

Originally developed for software development, Agile approaches are increasingly adopted by organizations that seek flexibility in the face of rapid change. However, little attention has been paid to the potential negative consequences of the implementation of Agile in large-scale settings. This article presents the results of a multi-site study of a multinational telecom company over five years during its implementation of Agile practices in the context of large-scale software development. The article points to six potential pitfalls of implementing such practices that may negatively influence individual learning, ideation, and exploitation capabilities. It offers advice on how to avoid these consequences in large, established firms.
Demonstration of Channel Multiplexing Quantum Communication Exploiting Entangled Sideband Modes

Channel multiplexing quantum communication based on exploiting continuous-variable entanglement of optical modes offers great potential to enhance channel capacity and save quantum resource. Here, we present a frequency-comb-type control scheme for simultaneously extracting a lot of entangled sideband modes with arbitrary frequency detuning from a squeezed state of light. We experimentally demonstrate fourfold channel multiplexing quantum dense coding communication by exploiting the extracted four pairs of entangled sideband modes. Due to high entanglement and wide frequency separation between each entangled pairs, these quantum channels have large channel capacity and the cross talking effect can be avoided. The achieved channel capacities have surpassed that of all classical and quantum communication under the same bandwidth published so far. The presented scheme can be extended to more channels if more entangled sideband modes are extracted.
Detection of Phishing Websites by Using Machine Learning-Based URL Analysis

In recent years, with the increasing use of mobile devices, there is a growing trend to move almost all real-world operations to the cyberworld. Although this makes easy our daily lives, it also brings many security breaches due to the anonymous structure of the Internet. Used antivirus programs and firewall systems can prevent most of the attacks. However, experienced attackers target on the weakness of the computer users by trying to phish them with bogus webpages. These pages imitate some popular banking, social media, e-commerce, etc. sites to steal some sensitive information such as, user-ids, passwords, bank account, credit card numbers, etc. Phishing detection is a challenging problem, and many different solutions are proposed in the market as a blacklist, rule-based detection, anomaly-based detection, etc. In the literature, it is seen that current works tend on the use of machine learning-based anomaly detection due to its dynamic structure, especially for catching the “zero-day” attacks. In this paper, we proposed a machine learning-based phishing detection system by using eight different algorithms to analyze the URLs, and three different datasets to compare the results with other works. The experimental results depict that the proposed models have an outstanding performance with a success rate.