Reviewerly/exHarmony huggingface dataset:
Deep Atrous Spatial Features-Based Supervised Foreground Detection Algorithm for Industrial Surveillance Systems

Camera-based surveillance systems largely perform an intrusion detection task for sensitive areas. The task may seem trivial but is quite challenging due to environmental changes and object behaviors such as those due to night-time, sunlight, IR camera, camouflage, and static foreground objects, etc. Convolutional neural network based algorithms have shown promise in dealing with these challenges. However, they are exclusively focused on accuracy. This article proposes an efficient supervised foreground detection (SFDNet) algorithm based on atrous deep spatial features. The features are extracted using atrous convolution kernels to enlarge the field-of-view of a kernel mask, thereby encoding rich context features without increasing the number of parameters. The network further benefits from a residual dense block strategy that mixes the mid and high-level features to retain the foreground information lost in low-resolution high-level features. The extracted features are expanded using a novel pyramid upsampling network. The feature maps are upsampled using bilinear interpolation and pass through a 3x3 convolutional kernel. The expanded feature maps are concatenated with the corresponding mid and low-level feature maps from an atrous feature extractor to further refine the expanded feature maps. The SFDNet showed better performance than high-ranked foreground detection algorithms on the three standard databases. The testing demo can be found at https://drive.google.com/file/d/1z_zEj9Yp7GZeM2gSIwYKvSzQlxMAiarw/view?usp=sharing.
Incorruptible Auditing: Blockchain-Powered Graph Database Management

In modern and interconnected world, information is accumulatively stored digitally, making the process of exchanging, gathering and querying the information much easier. Continuously, it has introduced new challenges about how to ensure its consistency and reliability due to the sheer volume of data. A blockchain-based information system can provide an incorruptible record of history, enabling better auditing and data management practices. The paper describes how to combine an Exonum blockchain and a Neo4j graph database into a system that can provide a verifiable audit trail of data integrity and its modifications for information stored in a graph database.
CA-GAN: Weakly Supervised Color Aware GAN for Controllable Makeup Transfer

While existing makeup style transfer models perform an image synthesis whose results cannot be explicitly controlled, the ability to modify makeup color continuously is a desirable property for virtual try-on applications. We propose a new formulation for the makeup style transfer task, with the objective to learn a color controllable makeup style synthesis. We introduce CA-GAN, a generative model that learns to modify the color of specific objects (e.g. lips or eyes) in the image to an arbitrary target color while preserving background. Since color labels are rare and costly to acquire, our method leverages weakly supervised learning for conditional GANs. This enables to learn a controllable synthesis of complex objects, and only requires a weak proxy of the image attribute that we desire to modify. Finally, we present for the first time a quantitative analysis of makeup style transfer and color control performance.
Principals between exploitation and exploration: Results of a nationwide study on ambidexterity of school leaders

Both organizational and management research suggest that schools and their leaders need to be ambidextrous to secure prosperity and long-term survival in dynamic environments characterized by competition and innovation. In this context, ambidexterity refers to the ability to simultaneously pursue exploitation and exploration and thus to deliver efficiency, control and incremental improvements while embracing flexibility, autonomy and discontinuous innovation. Using a unique, randomized and representative data set of N = 405 principals, we present findings on principals’ exploitation and exploration. The results indicate: (a) that principals engage far more often in exploitative than in explorative activities; (b) that exploitative activities in schools are executed at the expense of explorative activities; and (c) that explorative and ambidextrous activities of principals are positively associated with the (perceived) competition between schools. The study brings a novel perspective to educational research and demonstrates that applying the concept of ambidexterity has the potential to further our understanding of effective educational leadership and management.
Monte Carlo-Based Performance Analysis for Underwater Continuous-Variable Quantum Key Distribution

There is a growing interest in the security of underwater communication with the increasing demand for undersea exploration. In view of the complex composition and special optical properties of seawater, this paper deals with a performance analysis for continuous-variable quantum key distribution (CVQKD) over an underwater link. In particular, we focus on analyzing the channel transmittance and detection efficiency based on Monte Carlo simulation for different water types, link distances and transceiver parameters. A comparison between the transmittance obtained by simple Beer’s law and Monte Carlo simulation reveals that the transmittance of underwater link may be severely underestimated in the previous underwater CVQKD research. The effect of the receiver aperture and field of view (FOV) on detection efficiency under different water types is further evaluated based on Monte Carlo. Simulation results show that the transmission distance of the underwater CVQKD system obtained by Monte Carlo simulation in pure sea water, clear ocean water and coastal ocean water is larger than that obtained by Beer’s law, while the key rate of the system in all types of water is smaller than that obtained by Beer’s law because the size and FOV of the receiver aperture are taken into account. By considering the practical system parameters, this paper establishes a comprehensive model for evaluating the security of underwater CVQKD systems with different system configurations.
Block-Phasor: A Decentralized Blockchain Framework to Enhance Security of Synchrophasor

The synchrophasor technology has been changing the wide-area monitoring system (WAMS) of power grids; as it facilitates real-time grid situational awareness, observability and state estimation, and online stability assessment. However, the measurement samples inside the substations, or in phasor data concentrators (PDCs) can become a target for cyber-attacks, such as false data injection and data tampering attacks. Moreover, the synchrophasors are vulnerable to a single point of failure. Blockchain, the main technology behind the bitcoin, can simultaneously ensure reliability, security, and integrity with decentralization properties in a peer-to-peer system. Therefore, to mitigate the issues in the synchrophasor network, in this paper, we propose a blockchain-based synchrophasor network protection framework to enhance the self-defensive capability of its monitoring system. Our proposed model can mitigate data manipulation attacks from malicious entities by introducing bloom filter based fast phasor data identity validation, and Merkle tree-based fast data verification. Finally, the blockchain consensus can help to keep the consistency in the system and eliminate malicious entities from manipulating the overall security of the protection framework. Our experimental results show that the proposed system is scalable and fast in terms of synchrophasor data verification.
Hybrid safe–strong rules for efficient optimization in lasso-type problems

The lasso model has been widely used for model selection in data mining, machine learning, and high-dimensional statistical analysis. However, with the ultrahigh-dimensional, large-scale data sets now collected in many real-world applications, it is important to develop algorithms to solve the lasso that efficiently scale up to problems of this size. Discarding features from certain steps of the algorithm is a powerful technique for increasing efficiency and addressing the Big Data challenge. This paper proposes a family of hybrid safe–strong rules (HSSR) which incorporate safe screening rules into the sequential strong rule (SSR) to remove unnecessary computational burden. Two instances of HSSR are presented, SSR-Dome and SSR-BEDPP, for the standard lasso problem. SSR-BEDPP is further extended to the elastic net and group lasso problems to demonstrate the generalizability of the hybrid screening idea. Extensive numerical experiments with synthetic and real data sets are conducted for both the standard lasso and the group lasso problems. Results show that the proposed hybrid rules can substantially outperform existing state-of-the-art rules.
On shortest Dubins path via a circular boundary

The paper characterizes the shortest bounded-curvature paths from an initial configuration (a location and a heading orientation), via the boundary of an intermediate circle, to a target configuration. Such paths are fundamentally required in motion planning of a Dubins vehicle that has to avoid entering certain forbidden regions and when addressing the Dubins Traveling Salesman Problem with Neighborhoods. By using Pontryagin's maximum principle and analyzing the necessary conditions for state inequality constraints, the geometric properties of the shortest bounded-curvature paths are established. These geometric properties not only allow restricting the shortest bounded-curvature path within a sufficient family of 26 candidates but also enable us to devise a completely analytic solution for finding the candidate path. As a consequence, the shortest bounded-curvature path can be computed in a constant time by checking the path lengths of these 26 candidates. Finally, numerical examples validate the developments in the paper and highlight its importance in addressing some Dubins Traveling Salesman Problems with Neighborhoods.
A memory network based end-to-end personalized task-oriented dialogue generation

Building a personalized task-oriented dialogue system is an important but challenging task. Significant success has been achieved in the template selection responses. However, preparing a massive response template is time-consuming and human-labor intensive. In this paper, we propose an end-to-end framework based on memory networks for response generation in a personalized task-oriented dialogue system. Our model consists of three parts: a retrieval module, a memory encoder network and a memory decoder network. Retrieval module employs the user utterances and user attributes to collect relevant responses from other users. Memory encoder is trained with textual features to obtain dialogue representation. Memory decoder is composed of an RNN and a rule-memory network for response generation. Experiments on the benchmark dataset show that our model achieves better performance than strong baselines in personalized task-oriented dialogue generation.
Semantic-rebased cross-modal hashing for scalable unsupervised text-visual retrieval

Recently, learning-based cross-modal hashing has gained increasing research interests for its low computation complexity and memory requirement. Among existing cross-modal techniques, supervised algorithms can gain better performance. However, due to the cost of acquiring labeled data, unsupervised methods become our choice when faced with large scale unlabeled web images. The label-free nature of unsupervised cross-modal hashing hinders models from exploiting the exact semantic data similarity. Existing research typically simulates the semantics by a heuristic geometric prior in the original feature space with pseudo labels or traditional dense graph structures. However, this introduces heavy bias into the model as the original features are not fully representing the underlying multi-view data relations, and these two structures may face with issues like interference noise or high sensitivity to cluster number. To address the problem above, in this paper, we propose a novel unsupervised sparse-graph based hashing method called Semantic-Rebased Cross-modal Hashing (SRCH). A novel ‘Set-and-Rebase’ process is defined to initialize and update the cross-modal similarity graph of training data. In particular, we set the graph according to the intra-modal feature geometric basis and then alternately rebase it to update the edges within according to the hashing results. We develop an alternating optimization routine to rebase the graph and train the hashing auto-encoders with closed-form solutions so that the overall framework is efficiently trained. Our experimental results on benchmarked datasets demonstrate the superiority of our model against state-of-the-art algorithms.
Use of Images Augmentation and Implementation of Doubly Stochastic Models for Improving Accuracy of Recognition Algorithms Based on Convolutional Neural Networks

The article proposes methods for increasing the efficiency of pattern recognition in images using convolutional neural networks in conditions of insufficient reference images. It is proposed to use various kinds of image transformations to generate new images and increase the training sample volume. In particular, methods of scaling, adding noise, blurring, etc. are proposed. In addition to standard image transformations, it is supposed to increase the database by presenting patterns using a doubly stochastic model. In addition, the need for the use of regularization in training is noted. The recognition results are compared for two convolutional neural networks that differ only in the number of neurons for different initial data. It is shown that the highest recognition accuracy is provided by a network trained on a large number of augmentations in combination with images generated by a doubly stochastic model.
SDT: An integrated model for open-world knowledge graph reasoning

Knowledge graphs (KGs) have a wide range of applications, such as recommender systems, relation extraction, and intelligent question answering systems. However, existing KGs are far from complete. Knowledge graph reasoning (KGR) has been studied to complete KGs by inferring missing entities or relations. But most previous methods require that all entities should be seen during training, which is impractical for real-world KGs with new entities emerging daily. In this paper, we address the open-world KGR task: how to perform reasoning when entities are not observed at training time. The description-embodied knowledge representation learning (DKRL) model attempts to study the open-world KGR task. We find that DKRL does not consider hierarchical type information contained in entities when learning entities and relations representations, which results in poor performance. To address this problem, we propose a novel model, SDT, that incorporates the structural information, entity descriptions, and hierarchical type information of entities into a unified framework to learn more representative embeddings for KGs. Specifically, for entity descriptions, we explore continuous bag-of-words and convolutional neural networks models to encode the semantics of entity representations. For hierarchical types, we utilize a recursive hierarchy encoder and a weighted hierarchy encoder to construct the projection matrices of hierarchical types. We evaluate the SDT model on both open-world and closed-world reasoning tasks, including entity prediction and relation prediction. Experimental results on large-scale datasets show that SDT achieves a lower mean rank and higher [email protected] than the baseline methods.
Detection of Computer Graphics Using Attention-Based Dual-Branch Convolutional Neural Network from Fused Color Components

With the development of 3D rendering techniques, people can create photorealistic computer graphics (CG) easily with the advanced software, which is of great benefit to the video game and film industries. On the other hand, the abuse of CGs has threatened the integrity and authenticity of digital images. In the last decade, several detection methods of CGs have been proposed successfully. However, existing methods cannot provide reliable detection results for CGs with the small patch size and post-processing operations. To overcome the above-mentioned limitation, we proposed an attention-based dual-branch convolutional neural network (AD-CNN) to extract robust representations from fused color components. In pre-processing, raw RGB components and their blurred version with Gaussian low-pass filter are stacked together in channel-wise as the input for the AD-CNN, which aims to help the network learn more generalized patterns. The proposed AD-CNN starts with a dual-branch structure where two branches work in parallel and have the identical shallow CNN architecture, except that the first convolutional layer in each branch has various kernel sizes to exploit low-level forensics traces in multi-scale. The output features from each branch are jointly optimized by the attention-based fusion module which can assign the asymmetric weights to different branches automatically. Finally, the fused feature is fed into the following fully-connected layers to obtain final detection results. Comparative and self-analysis experiments have demonstrated the better detection capability and robustness of the proposed detection compared with other state-of-the-art methods under various experimental settings, especially for image patch with the small size and post-processing operations.
Reducing efforts of software engineering systematic literature reviews updates using text classification

Systematic Literature Reviews (SLRs) are frequently used to synthesize evidence in Software Engineering (SE), however replicating and keeping SLRs up-to-date is a major challenge. The activity of studies selection in SLR is labor intensive due to the large number of studies that must be analyzed. Different approaches have been investigated to support SLR processes, such as: Visual Text Mining or Text Classification. But acquiring the initial dataset is time-consuming and labor intensive. In this work, we proposed and evaluated the use of Text Classification to support the studies selection activity of new evidences to update SLRs in SE. We applied Text Classification techniques to investigate how effective and how much effort could be spared during the studies selection phase of an SLR update. Considering the SLRs update scenario, the studies analyzed in the primary SLR could be used as a classified dataset to train Supervised Machine Learning algorithms. We conducted an experiment with 8 Software Engineering SLRs. In the experiments, we investigated the use of multiple preprocessing and feature extraction tasks such as tokenization, stop words removal, word lemmatization, TF-IDF (Term-Frequency/Inverse-Document-Frequency) with Decision Tree and Support Vector Machines as classification algorithms. Furthermore, we configured the classifier activation threshold for maximizing Recall, hence reducing the number of Missed selected studies. The techniques accuracies were measured and the results achieved on average a F-Score of 0.92 and 62% of exclusion rate when varying the activation threshold of the classifiers, with a 4% average number of Missed selected studies. Both the Exclusion rate and number of Missed selected studies were significantly different when compared to classifier which did not use the configuration of the activation threshold. The results showed the potential of the techniques in reducing the effort required of SLRs updates.
Context-Aware and Energy-Aware Video Streaming on Smartphones

High quality video streaming for mobile devices implies high energy consumption due to the transmitted data and the variation of wireless signals. As an example, transmissions in mobile scenarios (e.g., inside a moving bus) consumes more energy for devices than when accessing from a static environment (e.g., at home). The QoE for the user does not substantially increase when watching high bitrate videos in a vibrating environment (i.e., a moving vehicle), as the context, in this case vehicle’s vibration, affects the perceived QoE. To address this problem, we propose to save energy by considering the context (environment) of video streaming. To model the impact of context, we exploit the embedded accelerometer in smartphones to record the vibration level during video streaming. Based on quality assessment experiments, we collect traces and model the impact of video bitrate and vibration level on QoE, and model the impact of video bitrate and signal strength on power consumption. Based on the QoE model and the power model, we formulate the context-aware and energy-aware video streaming problem as an optimization problem. We present an optimal algorithm which can maximize QoE and minimize energy. Since the optimal algorithm requires perfect knowledge of future tasks, we propose an online bitrate selection algorithm. To further improve the performance of the online algorithm, we propose a crowdsourcing based bitrate selection algorithm. Through real measurements and trace-driven simulations, we demonstrate that the proposed algorithms can significantly outperform existing approaches when considering both energy and QoE.
Low resource end-to-end spoken language understanding with capsule networks

Designing a Spoken Language Understanding (SLU) system for command-and-control applications is challenging. Both Automatic Speech Recognition and Natural Language Understanding are language and application dependent to a great extent. Even with a lot of design effort, users often still have to know what to say to the system for it to do what they want. We propose to use an end-to-end SLU system that maps speech directly to semantics and that can be trained by the user through demonstrations. The user can teach the system a new command by uttering the command and subsequently demonstrating its meaning through an alternative interface. The system will learn the mapping from the spoken command to the task. The dependency on the user also allows different languages and non-standard or impaired speech as valid inputs. Teaching the system requires effort from the user, so it is crucial that the system learns quickly. In this paper we propose to use capsule networks for this task, which are believed to be data efficient. We discuss two architectures for using capsule networks. We analyse their performance and compare them with two baseline systems, one based on Non-negative Matrix Factorisation (NMF) which has been successful for this task and one encoder-decoder approach. We show that in most cases the capsule network performs better than the baseline systems. Furthermore, we demonstrate the versatility of the architecture by inferring speaker identity and the user’s word choice through multitask learning.