Reviewerly/exHarmony huggingface dataset:
Human-machine symbiosis: A multivariate perspective for physically coupled human-machine systems

The notion of symbiosis has been increasingly mentioned in research on physically coupled human-machine systems. Yet, a uniform specification on which aspects constitute human-machine symbiosis is missing. By combining the expertise of different disciplines, we elaborate on a multivariate perspective of symbiosis as the highest form of interaction in physically coupled human-machine systems, characterized by a oneness of the human and the machine. Four dimensions are considered: Task, interaction, performance, and experience. First, human and machine accomplish a common objective by completing tasks conceptualized on a decomposition, a decision and an action level (task dimension). Second, each partner possesses an internal representation of the oneness they form, including the partner's inner states (e.g. experiences) and their joint influence on the environment. This representation constitutes the "symbiotic understanding" between both partners, being the basis of a joint and highly coordinated action (interaction dimension). Third, the symbiotic interaction leads to synergetic effects regarding the complementary strengths of the partners, resulting in a higher overall performance (performance dimension). Fourth, symbiotic systems specifically change the user's experiences, like flow, acceptance, sense of agency, and embodiment (experience dimension). Our multivariate perspective allows a clear description of symbiotic human-machine systems and helps to bridge barriers between different disciplines.
Privacy preservation of cloud data in business application enabled by multi-objective red deer-bird swarm algorithm

Decision-making is one of the important deals in knowledge transfer and it can be useful for the multi-source domains. Meanwhile, the existing knowledge transfer schemes do not use privacy-preserving techniques for preserving security. This can be a problem for critical domains like financial market forecasting as the misuse of security can lead to legal and financial implications. In recent years, cloud services have revolutionized various technological applications. Cloud computing has become more popular with digital technologies as it provides uninterrupted services like transmission, storage, and intensive computing of data. The architecture of the cloud is also cost-efficient. Besides, various promising services from the cloud, some challenges need to be addressed to secure the privacy of the cloud users as millions of users access its services. Privacy preservation is an important aspect in the field of data mining, and the necessity of securing important data in the cloud from hackers is on the rise. Privacy-preserving data mining algorithms have been analyzed over recent years to provide sufficient solutions for securing the privacy of the data in the cloud. This paper plans to introduce a new hybrid meta-heuristic concept for developing a privacy preservation strategy towards business data under the cloud sector. The main objective of this paper is to design a new hybrid red deer-bird swarm algorithm (RD-BSA) to ensure higher convergence and since the use of control parameters over the solution generation is minimized. The proposed privacy preservation scheme on three financial databases is evaluated with the performance against the existing privacy preservation schemes. Different analyses like statistical, key sensitivity, Known-Plaintext Attack (KPA), and Chosen-Plaintext Attack (CPA) are used for evaluating the efficiency of the algorithm. The comparative analysis of the proposed model over the conventional models demonstrates its effective performance via diverse analysis.
Research on the transaction mode and mechanism of grid-side shared energy storage market based on blockchain

Energy storage has high application value in the power system, especially in the field of auxiliary services, but the transaction mechanism and process are not yet perfect. Considering the advantages of security and transparency of blockchain technology, this article combines blockchain with energy storage auxiliary services and proposes a blockchain-based grid-side shared energy storage market transaction model and mechanism. This article first introduces the concept and application status of blockchain, and compares the advantages of blockchain technology applied to power transactions. Combined with the existing policies and market rules, the research on the participation of energy storage in auxiliary services was carried out, and the market mechanism for the participation of energy storage in peak and frequency modulation auxiliary services was designed. Finally, a blockchain-based energy storage market-oriented transaction model and mechanism is proposed to improve the flexibility of the power system and promote clean energy consumption.
An insulator self-blast detection method based on YOLOv4 with aerial images

Due to long-term exposure to the natural environment, insulators are prone to self-blast, threatening the safety and reliability of transmission lines. Because of the different sizes of the insulator self-blast area and the complicated background, it is inevitable that the missed and false detections occur. To solve this problem, this paper proposes a deep neural network called Mina-Net (Multi-Layer INformation Fusion and Attention Mechanism Network). Mina-Net is based on YOLOv4. First, the shallow feature map with more detailed texture information is fused into the feature pyramid. Then, an improved SENet is applied to recalibrate the features of different levels in the channel direction. The experimental results on the actual dataset show that Mina-Net has increased the average precision by 4.78% compared with the YOLOv4.
Multi-Stream Interaction Networks for Human Action Recognition

Skeleton-based human action recognition has received extensive attention due to its efficiency and robustness to complex backgrounds. Though the human skeleton can accurately capture the dynamics of human poses, it fails to recognize human actions induced by the interaction between human and objects, making it is of great importance to further explore the interaction between the human and objects for human action recognition. In this paper, we devise the multi-stream interaction networks (MSIN), to simultaneously explore the dynamics of human skeleton, objects, and the interaction between human and objects. Specifically, apart from the traditional human skeleton stream, 1) the second stream explores the dynamics of object appearance from the objects surrounding the human body joints; and 2) the third stream captures the dynamics of object position in regard to the distance between the object and different human body joints. Experimental results on three popular skeleton-based human action recognition datasets, NTU RGB + D, NTU RGB + D 120, and SYSU, demonstrate the effectiveness of the proposed method, especially for recognizing the human actions with human-object interactions.
Quantum dot technology for quantum repeaters: from entangled photon generation toward the integration with quantum memories

Abstract The realization of a functional quantum repeater is one of the major research goals in long-distance quantum communication. Among the different approaches that are being followed, the one relying on quantum memories interfaced with deterministic quantum emitters is considered as one of the most promising solutions. In this work, we focus on the hardware to implement memory-based quantum-repeater schemes that rely on semiconductor quantum dots (QDs) for the generation of polarization entangled photons. Going through the most relevant figures of merit related to efficiency of the photon source, we select significant developments in fabrication, processing and tuning techniques aimed at combining high degree of entanglement with on-demand pair generation, with a special focus on the progress achieved in the representative case of the GaAs system. We proceed to offer a perspective on integration with quantum memories, both highlighting preliminary works on natural–artificial atomic interfaces and commenting a wide choice of currently available and potentially viable memory solutions in terms of wavelength, bandwidth and noise-requirements. To complete the overview, we also present recent implementations of entanglement-based quantum communication protocols with QDs and highlight the next challenges ahead for the implementation of practical quantum networks.
Attributes Guided Feature Learning for Vehicle Re-Identification

Vehicle Re-ID has recently attracted enthusiastic attention due to its potential applications in smart city and urban surveillance. However, it suffers from large intra-class variation caused by view variations and illumination changes, and inter-class similarity especially for different identities with a similar appearance. To handle these issues, in this paper, we propose a novel deep network architecture, which guided by meaningful attributes including camera views, vehicle types and colors for vehicle Re-ID. In particular, our network is end-to-end trained and contains three subnetworks of deep features embedded by the corresponding attributes. For network training, we annotate the view labels on the VeRi-776 dataset. Note that one can directly adopt the pre-trained view (as well as type and color) subnetwork on the other datasets with only ID information, which demonstrates the generalization of our model. Extensive experiments on the benchmark datasets VeRi-776 and VehicleID suggest that the proposed approach achieves the promising performance and yields to a new state-of-the-art for vehicle Re-ID.
A new target tracking filter based on deep learning

At present, current filters can basically solve the filtering problem in target tracking, but there are still many problems such as too many filtering variants, too many filtering forms, loosely coupled with the target motion model, and so on. To solve the above problems, we carry out cross-application research of artificial intelligence theory and methods in the field of tracking filters. We firstly analyze the computation graphs of typical α-β and Kalman. Through analysis, it is concluded that α-β and Kalman have the same computation structures analogous to a typical recurrent neural network and can be considered as a kind of recurrent neural network with constrained weights. Then, given this and considering that a recurrent neural network has the recognition capability for target motion patterns, a new filter is developed in a unified neural network architecture and specifically constructed using feedforward neural network, recurrent neural network, and attention mechanism. And the unified tracking filter proposed in this paper can generate three aspects of unity: a unified target motion model, an adaptive filter method, and an overall track filtering framework. Finally, Simulation results show that the proposed filter is effective and useful, of which the overall performance is superior to those of compared filters.
A Novel Learning Strategy for the Trade-Off Between Accuracy and Computational Cost: A Touch Modalities Classification Case Study

Wearable systems require resource-constrained embedded devices for the elaboration of the sensed data. These devices have to host energy-efficient artificial intelligence (AI) algorithms to output information to a human user. In this regard, the single-layer feed-forward neural networks (SLFNNs) proved to be very effective for deployment on very low-end devices. SLFNNs showed promising results when the goal was to balance the trade-off between accuracy and predictor complexity. Nevertheless, the elaboration system, which consists of data pre-processing and features extraction stages, affects the resource occupancy, computational cost, and also eventual generalization performance. In this paper, we propose a learning strategy based on the out-of-samples technique that leads to finding the configurations of the elaboration unit and the parameters of the predictor that best balance the generalization accuracy and the computational cost of the whole system. An intensive study over the touch modalities classification problem shows that the best solution in terms of the only accuracy is achieved by the SLFNNs, outperforming existing algorithms. Moreover, the learning procedure selects the configuration of the processing and features extraction stages, and the parameters of the predictor that present the best trade-off between generalization performance and computational cost.
Sinergym

We introduce Sinergym, an open-source building simulation and control framework for training reinforcement learning agents. The proposed framework is compatible with EnergyPlus models and allows to implement Python-based controllers, facilitating reproducibility of experiments and generalization to multiple scenarios. A comparison between Sinergym and other existing libraries for building control is included. We describe its design and main functionalities, such as offering a diverse set of environments with different buildings, weather types and action spaces. The provided examples show the usage of the framework for benchmarking reinforcement learning methods for building control.
Review of light field technologies

Light fields are vector functions that map the geometry of light rays to the corresponding plenoptic attributes. They describe the holographic information of scenes by representing the amount of light flowing in every direction through every point in space. The physical concept of light fields was first proposed in 1936, and light fields are becoming increasingly important in the field of computer graphics, especially with the fast growth of computing capacity as well as network bandwidth. In this article, light field imaging is reviewed from the following aspects with an emphasis on the achievements of the past five years: (1) depth estimation, (2) content editing, (3) image quality, (4) scene reconstruction and view synthesis, and (5) industrial products because the technologies of lights fields also intersect with industrial applications. State-of-the-art research has focused on light field acquisition, manipulation, and display. In addition, the research has extended from the laboratory to industry. According to these achievements and challenges, in the near future, the applications of light fields could offer more portability, accessibility, compatibility, and ability to visualize the world.
Variational Abnormal Behavior Detection With Motion Consistency

Abnormal crowd behavior detection has recently attracted increasing attention due to its wide applications in computer vision research areas. However, it is still an extremely challenging task due to the great variability of abnormal behavior coupled with huge ambiguity and uncertainty of video contents. To tackle these challenges, we propose a new probabilistic framework named variational abnormal behavior detection (VABD), which can detect abnormal crowd behavior in video sequences. We make three major contributions: (1) We develop a new probabilistic latent variable model that combines the strengths of the U-Net and conditional variational auto-encoder, which also are the backbone of our model; (2) We propose a motion loss based on an optical flow network to impose the motion consistency of generated video frames and input video frames; (3) We embed a Wasserstein generative adversarial network at the end of the backbone network to enhance the framework performance. VABD can accurately discriminate abnormal video frames from video sequences. Experimental results on UCSD, CUHK Avenue, IITB-Corridor, and ShanghaiTech datasets show that VABD outperforms the state-of-the-art algorithms on abnormal crowd behavior detection. Without data augmentation, our VABD achieves 72.24% in terms of AUC on IITB-Corridor, which surpasses the state-of-the-art methods by nearly 5%.
Modeling the Continuance Intention to Use Automated Writing Evaluation Among Chinese EFL Learners

Despite the growing attention being paid to the use of Automated Writing Evaluation (AWE) in China, it is still uncertain what factors lie behind EFL (English-as-a-foreign-language) learners’ continuance intention to use it. To this end, by adding two external factors (i.e., computer self-efficacy and perceived ease of use) to the expectation confirmation model (ECM), we surveyed 345 Chinese EFL learners and tested a number of proposed hypotheses using their response data. Data were analyzed using descriptive statistics and structural equation modeling (SEM). Results demonstrated that four factors directly influenced EFL learners’ continuance intention to use AWE, of which perceived ease of use was the most significant factor. Furthermore, confirmation was the most important factor impacting on EFL learners’ satisfaction and perceived ease of use of AWE. Perceived ease of use of AWE played an important role in influencing EFL learners’ perceived usefulness of AWE. Implications regarding the findings were also discussed.
Multibranch Attentive Gated ResNet for Short-Term Spatio-Temporal Solar Irradiance Forecasting

The increasing penetration of solar generation into power grids has promoted the need for accurate and reliable short-term solar irradiance forecasting. This article introduces a novel multibranch attentive gated recurrent residual network (ResAttGRU) consisting of multiple branches of residual networks, gated recurrent units (GRUs), and the attention mechanism. The proposed multibranch ResAttGRU is capable of modeling data at various resolutions, extracting hierarchical features, and capturing short- and long-term dependencies. Moreover, this network also presents a strong multitimescale representative within the proposed architecture, while GRUs can exploit temporal information at less computational cost than the popular long short-term memory (LSTM). The novelty of the proposed architecture is to employ multiple convolutional-based branches with different filter lengths to learn multitimescale features jointly, accelerate the learning process, and reduce overfitting by leveraging shared representations as the auxiliary information. This study also compares the multibranch ResAttGRU networks with state-of-the-art deep learning methods using 18 years of NSRDB data at 12 solar sites. Finally, the proposed multibranch ResAttGRU requires 7.1&#x0025; fewer parameters than multibranch residual LSTM while achieving similar average RMSE, MAE, and R-squared values.
What’s up with Requirements Engineering for Artificial Intelligence Systems?

In traditional approaches to building software systems (that do not include an Artificial Intelligent (AI) or Machine Learning (ML) component), Requirements Engineering (RE) activities are well-established and researched. However, building software systems with one or more AI components may depend heavily on data with limited or no insight into the system’s workings. Therefore, engineering such systems poses significant new challenges to RE. Our search showed that literature has focused on using AI to manage RE activities, with limited research on RE for AI (RE4AI). Our study’s main objective was to investigate current approaches in writing requirements for AI/ML systems, identify available tools and techniques used to model requirements, and find existing challenges and limitations. We performed a Systematic Literature Review (SLR) of current RE4AI methods and identified 27 primary studies. Using these studies, we analysed the key tools and techniques used to specify and model requirements and found several challenges and limitations of existing RE4AI practices. We further provide recommendations for future research, based on our analysis of the primary studies and mapping to industry guidelines in Google PAIR). The SLR findings highlighted that present RE applications were not adaptive to manage most AI/ML systems and emphasised the need to provide new techniques and tools to support RE4AI.
Improved Opposition-Based Particle Swarm Optimization Algorithm for Global Optimization

Particle Swarm Optimization (PSO) has been widely used to solve various types of optimization problems. An efficient algorithm must have symmetry of information between participating entities. Enhancing algorithm efficiency relative to the symmetric concept is a critical challenge in the field of information security. PSO also becomes trapped into local optima similarly to other nature-inspired algorithms. The literature depicts that in order to solve pre-mature convergence for PSO algorithms, researchers have adopted various parameters such as population initialization and inertia weight that can provide excellent results with respect to real world problems. This study proposed two newly improved variants of PSO termed Threefry with opposition-based PSO ranked inertia weight (ORIW-PSO-TF) and Philox with opposition-based PSO ranked inertia weight (ORIW-PSO-P) (ORIW-PSO-P). In the proposed variants, we incorporated three novel modifications: (1) pseudo-random sequence Threefry and Philox utilization for the initialization of population; (2) increased population diversity opposition-based learning is used; and (3) a novel introduction of opposition-based rank-based inertia weight to amplify the execution of standard PSO for the acceleration of the convergence speed. The proposed variants are examined on sixteen bench mark test functions and compared with conventional approaches. Similarly, statistical tests are also applied on the simulation results in order to obtain an accurate level of significance. Both proposed variants show highest performance on the stated benchmark functions over the standard approaches. In addition to this, the proposed variants ORIW-PSO-P and ORIW-PSO-P have been examined with respect to training of the artificial neural network (ANN). We have performed experiments using fifteen benchmark datasets obtained and applied from the repository of UCI. Simulation results have shown that the training of an ANN with ORIW-PSO-P and ORIW-PSO-P algorithms provides the best results than compared to traditional methodologies. All the observations from our simulations conclude that the proposed ASOA is superior to conventional optimizers. In addition, the results of our study predict how the proposed opposition-based method profoundly impacts diversity and convergence.
Classification of histopathological whole slide images based on multiple weighted semi-supervised domain adaptation

Deep learning has become more important in histopathological images classification for computer-aided cancer diagnosis. However, accurate histopathological image classification based on deep network relies on lots of labeled images, while the expert annotation of whole slide images (WSIs) is time-consuming and laborious. Therefore, how to obtain good classification results with limited labeled samples is still a major challenging task. To overcome the above difficulty, a deep transferred semi-supervised domain adaptation model (HisNet-SSDA) is proposed for classification of histopathological WSIs. Semi-supervised domain adaptation transfers knowledge from a label-rich source domain to a partially labeled target domain. First, a transferred pre-trained network HisNet is designed for high-level feature extraction of the randomly sampled patches from the source and target domains. Then the features of the two domains are aligned through semi-supervised domain adaptation utilizing a multiple weighted loss functions criterion which contains a novel manifold regularization term. The predicted probabilities of sampled patches are aggregated for the image-level classification. Classification results evaluated on two colon cancer datasets demonstrate the remarkable performance of the proposed method (accuracy: 94.32%±0.49%, sensitivity: 94.59%±0.46%, specificity: 94.06%±0.27% and accuracy: 91.92%±0.32%, sensitivity: 92.01%±0.47%, specificity: 91.83%±0.23%), which indicate that the proposed method can be an effective tool for WSIs classification in clinical practice.