Reviewerly/exHarmony huggingface dataset:
Contextual Attention Refinement Network for Real-Time Semantic Segmentation

Recently, significant progress has been made in pixel-level semantic segmentation using deep neural networks. However, for the current semantic segmentation methods, it is still challenging to achieve the balance between segmentation accuracy and computational cost. To address this issue, we propose the Contextual Attention Refinement Network (CARNet). In this method, we construct the Contextual Attention Refinement Module (CARModule), which learns an attention vector to guide the fusion of low-level and high-level features for obtaining higher segmentation accuracy. The CARModule is lightweight and can be directly equipped with different types of network structures. To better optimize the network, we additionally consider the semantic information, and further introduce the Semantic Context Loss (SCLoss) into the overall loss function. In the experiments, we validate the effectiveness and efficiency of our method on several public datasets for semantic segmentation. The results show that our method achieves a good balance on accuracy and computational costs.
Performance Evaluation of Deep Recurrent Neural Networks Architectures: Application to PV Power Forecasting

Smart grid systems require an accurate energy prediction from renewable sources to ensure high sustainability and power quality. For PV plants, a precise estimation of the generated PV power is crucial for the reduction of the production/demand unbalance. This essential need comes from the high variability of weather parameters during the PV electricity generation. Long Short Term Memory (LSTM) and Gated Recurrent Unit (GRU) recurrent neural networks proved their high efficiency in forecasting applications. Thus, this paper proposes a comprehensive evaluation of the LSTM and GRU techniques for PV power estimation in the medium/long horizon. The evaluation is based on a fair assessment of the aforementioned architectures for one week and more than three months (98 days) periods.
Travel Recommendation via Fusing Multi-Auxiliary Information into Matrix Factorization

As an e-commerce feature, the personalized recommendation is invariably highly-valued by both consumers and merchants. The e-tourism has become one of the hottest industries with the adoption of recommendation systems. Several lines of evidence have confirmed the travel-product recommendation is quite different from traditional recommendations. Travel products are usually browsed and purchased relatively infrequently compared with other traditional products (e.g., books and food), which gives rise to the extreme sparsity of travel data. Meanwhile, the choice of a suitable travel product is affected by an army of factors such as departure, destination, and financial and time budgets. To address these challenging problems, in this article, we propose a Probabilistic Matrix Factorization with Multi-Auxiliary Information (PMF-MAI) model in the context of the travel-product recommendation. In particular, PMF-MAI is able to fuse the probabilistic matrix factorization on the user-item interaction matrix with the linear regression on a suite of features constructed by the multiple auxiliary information. In order to fit the sparse data, PMF-MAI is built by a whole-data based learning approach that utilizes unobserved data to increase the coupling between probabilistic matrix factorization and linear regression. Extensive experiments are conducted on a real-world dataset provided by a large tourism e-commerce company. PMF-MAI shows an overwhelming superiority over all competitive baselines on the recommendation performance. Also, the importance of features is examined to reveal the crucial auxiliary information having a great impact on the adoption of travel products.
Online semi-supervised learning with learning vector quantization

Online semi-supervised learning (OSSL) is a learning paradigm simulating human learning, in which the data appear in a sequential manner with a mixture of both labeled and unlabeled samples. Despite the recent advances, there are still many unsolved problems in this area. In this paper, we propose a novel OSSL method based on learning vector quantization (LVQ). LVQ classifiers, which represent the data of each class by a set of prototypes, have found their usage in a wide range of pattern recognition problems and can be naturally adapted to the online scenario by updating the prototypes with stochastic gradient optimization. However, most of the existing LVQ algorithms were designed for supervised classification. To extract useful information from unlabeled data, we propose two simple and computationally efficient methods based on clustering assumption. To be specific, we use the maximum conditional likelihood criterion for updating prototypes when data sample is labeled, and the Gaussian mixture clustering criterion or neural gas clustering criterion for adjusting prototypes when data sample is unlabeled. These two criteria are utilized alternatively according to the availability of label information to make full use of both supervised and unsupervised data to boost the performance. By extensive experiments, we show that the proposed method exhibits higher accuracy compared with the baseline methods and graph-based methods and is much more efficient than graph-based methods in both training and test time.
A Convolutional Attention Residual Network for Stereo Matching

Deep learning based on convolutional neural network (CNN) has been successfully applied to stereo matching, which has achieved greater improvement in speed and accuracy compared with traditional methods.However, existing CNN-based stereo matching frameworks frequently encounter two problems.First, the existing stereo matching network has a large number of parameters, which results in too long matching running time since excessive network width and excessive number of convolution kernels.Second, in some areas where reflection, refraction and fine structure may lead to ill-posed problems, the disparity estimation errors can be occurred.In this paper, we proposed a lightweight network, convolution attention residual network (CAR-Net), which can balance the real-time matching and matching accuracy for stereo matching.Besides, a multi-scale residual network called CBAM-ResNeXt, which combines attention, was proposed for features extraction.With an aim is to simplify the parameters of the network model by reducing the size of filters and to extract the semantic features such as categories and locations from the image through convolutional block attention module (CBAM).Here, the CBAM consists of channel attention module and spatial attention module, where the semantic information of the feature map can be fully maintained after the parameters were simplified.Moreover, we proposed a dimension-extended 3D-CBAM, which is connected to 3DCNN for cost aggregation.By combining these two sub-modules of attention, the network is guided to selectively focus on the foreground or background regions, so as to improve the disparity accuracy in the ill-posed regions.The experimental results showed that our proposed method generated high accuracy and optimized the velocity compared to the state-of-the-art benchmark on KITTI 2012, KITTI 2015 and Scene Flow.
A Blockchain-Based Access Control Scheme for Smart Grids

At present, the access control schemes in the power grid are centralized. In the centralized system, the data of the network sensor nodes is transmitted by centralized nodes, and the data itself may be illegally tamped with or lost, which can lead to reduced system reliability. For this feature, we apply blockchain technology to the design of access control schemes. In this paper, we propose a blockchain-based access control scheme that is suitable for multiple scenarios in the smart grid. Our access control scheme is based on an identity-based combined encryption, signature and signcryption scheme. In addition, we design a consensus algorithm in the power system for the consortium blockchain architecture to solve the key escrow problem of the untrusted third parties. Our scheme also ensures the confidentiality, integrity, authentication and non-repudiation of the data. Compared with the existing work, our scheme can use the same key pair to encrypt, sign and signcrypt the message, which has lower computation and communication costs in multiple scenarios of smart grids.
Infrared Pedestrian Detection with Converted Temperature Map

Infrared pedestrian detection aims to detect persons in outdoor thermal images. It shows a unique advantage in dark environment or bad weather compared to daytime visible images (the RGB image). Most current methods treat infrared detection the same way as with visible images, e.g. regarding the infrared image as a special gray-scale visible image. In this paper, we tackle this problem with more emphasis on the underlying temperature information in infrared images. We build an image-temperature transformation formula based upon infrared image formation theory, which can convert infrared image into temperature map with the prior of pedestrian pixel-temperature value. The whole detection process follows a two-stage manner. In the first stage, we use a common detector which treats the infrared image as the gray-scale visible image to provide primary detection results and a pedestrian position prior (the highest-confidence pedestrian detection box in each image). In the second stage, we convert infrared images into corresponding temperature maps and train a temperature net for detection. The final results consist of both the primary detection and the temperature net outputs, detecting pedestrians with characteristics in both image and temperature domain. We show that the converted temperature image is less affected by environmental factors, and that its detector shows amazing complementary ability with the primary detector. We carry out extensive experiments and analysis on two public infrared datasets, the OTCBVS dataset and the FLIR dataset; and demonstrate the effectiveness of incorporating temperature maps.
Nanoscale Coherent Light Source

A laser is composed of an optical resonator and a gain medium. When stimulated emission dominates mirror losses, the emitted light becomes coherent. We propose a new class of coherent light sources based on wavelength sized regular structures of quantum emitters whose eigenmodes form high-$Q$ resonators. Incoherent pumping of few atoms induces light emission with spatial and temporal coherence. We show that an atomic nanoring with a single gain atom at the center behaves like a thresholdless laser, featuring a narrow linewidth. Symmetric subradiant excitations provide optimal operating conditions.
Federated Authorization over Access to Personal Data for Decentralized Identity Management

The digital identity problem is a complex one in large part because it involves personal data, the algorithms which compute insights on the data, and the management of the identifiers that are linked to personal data. The reality of today is that personal data of an individual is distributed throughout the Internet, in both private and public institutions, and increasingly also on the user's devices. In order to empower individuals to have a say in who has access to their personal data and to enable individuals to make use of their data for their own purposes, a coherent and scalable federated authorization architecture is required as a fundamental component of the decentralized identity solution. This federation must allow an individual to easily manage access policies, and to grant and retract consent for access to data distributed across multiple repositories. This article describes the User Managed Access architecture and protocols that provide the foundation for scalable federated authorization.
Digital Transformation of Public Administration Through Blockchain Technology

Proper understanding of blockchain technology is one of key importance for decision-makers and staff in public administration sectors, as it helps them decide whether this approach can be of practical use in the realisation of their statutory mission. Blockchain technology is often perceived as a failsafe and unbreakable system with potential to transform many segments of the economy. Blockchain solutions have already been employed with success as basis for digital transactions in such areas as electricity market, trade, cryptocurrencies, stock trading, etc. Their application potential is also actively explored in other sectors of the economy, such as banking, insurance, and public administration. Blockchain technology can be approached not only as an innovative solution, but also as a tool for effective creation of novel management practices and models of operation in various types of organizations and institutions. The contribution of the chapter is an evaluation of potential uses and conditions for the effective application of the blockchain technology in the public administration sector. The study is constructed on the fundament of literature studies, empirical observations, case study analyses and synthetic evaluations, with the aim of revealing the potential applications of the blockchain technology and highlighting the challenges and possible directions of blockchain research in the public sector.
Object Captioning and Retrieval with Natural Language

We address the problem of jointly learning vision and language to understand the object in a fine-grained manner. The key idea of our approach is the use of object descriptions to provide the detailed understanding of an object. Based on this idea, we propose two new architectures to solve two related problems: object captioning and natural language-based object retrieval. The goal of the object captioning task is to simultaneously detect the object and generate its associated description, while in the object retrieval task, the goal is to localize an object given an input query. We demonstrate that both problems can be solved effectively using hybrid end-to-end CNN-LSTM networks. The experimental results on our new challenging dataset show that our methods outperform recent methods by a fair margin, while providing a detailed understanding of the object and having fast inference time. The source code will be made available.
Interpretation and modeling of emotions in the management of autonomous robots using a control paradigm based on a scheduling variable

The paper presents a technical introduction to psychological theories of emotions. It highlights a usable idea implemented in a number of recently developed computational systems of emotions, and the hypothesis that emotion can play the role of a scheduling variable in controlling autonomous robots. In the main part of this study, we outline our own computational system of emotion – xEmotion – designed as a key structural element in the developed target device, being an Intelligent System of Decision-making (ISD) for autonomous and robotic units. The ISD system has a cognitive architecture based on the principles of human psychology. The main purpose of building such a system is to prepare a framework for autonomous units used in system engineering (Kowalczuk and Czubenko, 2011; Czubenko et al., 2015). In particular, ISD is based on the concepts of cognitive psychology (in information processing) and motivation theory, which includes the system of needs (for decision-making). The xEmotion subsystem, however, focuses on modeling an alternative approach based on emotion. The xEmotion implementation covers aspects of somatic, appraisal and evolutionary theories of emotions using fuzzy sets. In this article, we also illustrate the core emotional behavior of the ISD system using simulation. The first application is a user interface for identifying emotions and predicting human behavior. The second is an eSailor simulation, which illustrates the possible behavior of the xEmotion subsystem. The last is an xDriver simulation experiment, which is to prove the validity of the concept of using emotion-based systems, according to the SVC principle. In summary, we also discuss other possible applications of the xEmotion system.
The effect of classical driving field on the spectrum of a qubit and entanglement swapping inside dissipative cavities

In this paper, we study the effect of the classical driving field on the spontaneous emission spectrum of a qubit embedded in a dissipative cavity. Furthermore, we monitor the entanglement dynamics of the driven qubit with its radiative decay under the action of the classical field. Afterward, we carry out an investigation on the possibility of entanglement swapping between two such driven qubit systems. The swapping will be feasible with the aid of a Bell state measurement performing on the photons leaving the cavities. It is demonstrated that the classical driving field has a beneficial effect on the prolonging of the swapped entanglement.
DynExit: A Dynamic Early-Exit Strategy for Deep Residual Networks

Early-exit is a kind of technique to terminate a pre-specified computation at an early stage depending on the input samples and has been introduced to reduce energy consumption for Deep Neural Networks (DNNs). Previous early-exit approaches suffered from the burden of manually tuning early-exit loss-weights to find a good trade-off between complexity reduction and system accuracy. In this work, we first propose DynExit, a dynamic loss-weight modification strategy for ResNets, which adaptively modifies the ratio of different exit branches and searches for a proper spot for both accuracy and cost. Then, an efficient hardware unit for early-exit branches is developed, which can be easily integrated to existing hardware architectures of DNNs to reduce average computing latency and energy cost. Experimental results show that the proposed DynExit strategy can reduce up to 43.6% FLOPS compared to the state-of-the-arts approaches. On the other hand, it is able to achieve 1.2% accuracy improvement over the existing end-to-end fixed loss-weight training scheme with comparable computation reduction ratio. The proposed hardware architecture for DynExit is evaluated on the platform of Xilinx Zynq-7000 ZC706 development board. Synthesis results demonstrate that the architecture can achieve high speed with low hardware complexity. To the best of our knowledge, this is the first hardware implementation for early-exit techniques used for DNNs in open literature.
SDNLog-Foren: Ensuring the Integrity and Tamper Resistance of Log Files for SDN Forensics using Blockchain

Despite bringing many benefits of global network configuration and control, Software Defined Networking (SDN) also presents potential challenges for both digital forensics and cybersecurity. In fact, there are various attacks targeting a range of vulnerabilities on vital elements of this paradigm such as controller, Northbound and Southbound interfaces. In addition to solutions of security enhancement, it is important to build mechanisms for digital forensics in SDN which provide the ability to investigate and evaluate the security of the whole network system. It should provide features of identifying, collecting and analyzing log files and detailed information about network devices and their traffic. However, upon penetrating a machine or device, hackers can edit, even delete log files to remove the evidences about their presence and actions in the system. In this case, securing log files with fine-grained access control in proper storage without any modification plays a crucial role in digital forensics and cybersecurity. This work proposes a blockchain-based approach to improve the security of log management in SDN for network forensics, called SDNLog-Foren. This model is also evaluated with different experiments to prove that it can help organizations keep sensitive log data of their network system in a secure way regardless of being compromised at some different components of SDN.
Image copy‐move forgery detection algorithm based on ORB and novel similarity metric

Image forgery poses a serious threat in electric power, medicine and other fields. Relevant departments need to pay a great price to identify the authenticity of the image. For traditional copy-move forgery image detection, the existing methods have at least two problems: low robustness and poor matching caused by a low number of feature points. Here, a novel similarity metric combining cosine and Jaccard is proposed to improve feature matching, which combines with oriented features from accelerated segment test and rotated binary robust independent elementary features (ORB) feature extraction to realise effective and fast image forgery detection. First, the image is divided into overlapping blocks, and ORB is used to extract the feature points of each image block to obtain the text information. Second, the novel similarity metric is used to calculate similarity and match the text. Finally, two image blocks with the highest similarity are located. The experimental results show that, on the one hand, ORB can greatly lessen detection time. On the other hand, the novel similarity metric can improve the poor matching caused by the small number of feature points. Combining the two methods can exhibit high robustness to translation, rotation, noise, illumination and JPEG compression.
Decoding surface code with a distributed neural network–based decoder

Abstract There has been a rise in decoding quantum error correction codes with neural network–based decoders, due to the good decoding performance achieved and adaptability to any noise model. However, the main challenge is scalability to larger code distances due to an exponential increase of the error syndrome space. Note that successfully decoding the surface code under realistic noise assumptions will limit the size of the code to less than 100 qubits with current neural network–based decoders. Such a problem can be tackled by a distributed way of decoding, similar to the renormalization group (RG) decoders. In this paper, we introduce a decoding algorithm that combines the concept of RG decoding and neural network–based decoders. We tested the decoding performance under depolarizing noise with noiseless error syndrome measurements for the rotated surface code and compared against the blossom algorithm and a neural network–based decoder. We show that a similar level of decoding performance can be achieved between all tested decoders while providing a solution to the scalability issues of neural network–based decoders.
Conversational Agents for Recipe Recommendation

As technology improves, the use of conversational agents to help users solve information seeking tasks is becoming ever more prevalent. To date we know little about how people behave with such systems, particularly in diverse contexts and for different tasks, their specific needs or how best to support these. By employing a Wizard of Oz (WoZ) methodology and developing a conversational framework, in this work we study how participants (n=28) interact with such a system in an attempt to solve recipe recommendation tasks. Our results are mostly encouraging for the future development of conversational agents in this context, however, they also provide insights into the complexities of building such a system that could convincingly engage with users in productive, human-like conversations.