Reviewerly/exHarmony huggingface dataset:
Integrated Solutions Energy Harvesting Systems

In this article the actual issue of the use of ambient energy for wireless sensor networks sensor supply and maintenance-free. A number of electronic components for the development of devices powered by the energy of the environment are considered. The main directions of development in the field of energy-saving devices.
Usability heuristics for the set‐top box and TV interfaces

Abstract Set‐top box (STB) is a special interface device that is connected to a TV and allows digital content to be received, decoded, and displayed on the TV. The new features added to STB&amp;TVs increase the complexity of the systems and lead to usability problems. This study proposes a set of usability heuristics and their corresponding criteria specific to STB&amp;TV interfaces that usability experts may use during the design and evaluation process. In this study, a total of 493 usability problems were collected from three sources: problems identified by software developers during the design and development phase, problems identified by three experts with cognitive walkthrough approach, and problems collected from customer complaints made to the service or call center. Then, each heuristics' checklist item was rated by the experts for how well they explain the usability problems, and the severity levels of the usability problems were determined. In the next step, through correspondence and cluster analyses, heuristics and the checklists were listed in descending order of severity. Furthermore, the proposed heuristic list was validated with three methods—user testing, expert judgment, and heuristic evaluation. As a result of this study, 16 usability heuristics were identified. Five of these heuristics—Visibility of system status (H4), Pleasurable and respectful interaction with the user (H13), Privacy (H14), Parental control (H15), and Easy access (H16) are related to catastrophic usability problems.
Simplified bacterial foraging optimization with quorum sensing for global optimization

Bacterial foraging optimization (BFO) has been exploited for function optimization, owing to its innovative ideas gleaned from the microbiological system. This paper first discusses its three crucial limitations: high computational cost, difficulty in parameter settings, and premature convergence. To alleviate the above problems, simplified BFO with quorum sensing (QS) is proposed. First, a novel computational framework is provided to reduce the computational complexity, leading to a simplified version. Second, the concept of “QS,” bacterial reciprocal behavior, is integrated into the simplified version by utilizing a new position updating equation coupled with a dynamic communication topology. Each bacterium adjusts its search trajectory based on both biased random walk and promising search directions provided by its communicatees. The communicatees are selected via a dynamic communication topology, where a rank-based communication strategy and two information mutation schemes are used for global exploration of the search space. Finally, a parameter automation strategy is introduced to promote the exploitation of promising regions. Further, the effectiveness and efficiency of the proposed algorithm are empirically confirmed on 30 benchmark functions, by comparing it with the four variants of BFO and four other advanced algorithms.
Adaptive Dendritic Cell-Deep Learning Approach for Industrial Prognosis Under Changing Conditions

Industrial prognosis refers to the prediction of failures of an industrial asset based on data collected by Internet of Things sensors. Prognostic models can experience the undesired effects of concept drift, namely, the presence of nonstationary phenomena that affects the data collected over time. Consequently, fault patterns learned from data become obsolete. To overcome this issue, contextual and operational changes must be detected and managed, triggering rapid model adaptation mechanisms. This article presents an adaptive learning approach based on a dendritic cell algorithm for drift detection and a deep neural network model that dynamically adapts to new operational conditions. A kernel density estimator with drift-based bandwidth is used to generate synthetic data for a faster adaptation, focusing on fine-tuning the lowest neural layers. Experimental results over a real-world industrial problem shed light on the outperforming behavior of the proposed approach when compared to other drift detectors and classification models.
ACTNET: End-to-End Learning of Feature Activations and Multi-stream Aggregation for Effective Instance Image Retrieval

We propose a novel CNN architecture called ACTNET for robust instance image retrieval from large-scale datasets. Our key innovation is a learnable activation layer designed to improve the signal-to-noise ratio of deep convolutional feature maps. Further, we introduce a controlled multi-stream aggregation, where complementary deep features from different convolutional layers are optimally transformed and balanced using our novel activation layers, before aggregation into a global descriptor. Importantly, the learnable parameters of our activation blocks are explicitly trained, together with the CNN parameters, in an end-to-end manner minimising triplet loss. This means that our network jointly learns the CNN filters and their optimal activation and aggregation for retrieval tasks. To our knowledge, this is the first time parametric functions have been used to control and learn optimal multi-stream aggregation. We conduct an in-depth experimental study on three non-linear activation functions: Sine-Hyperbolic, Exponential and modified Weibull, showing that while all bring significant gains the Weibull function performs best thanks to its ability to equalise strong activations. The results clearly demonstrate that our ACTNET architecture significantly enhances the discriminative power of deep features, improving significantly over the state-of-the-art retrieval results on all datasets.
DeepPoison: Feature Transfer Based Stealthy Poisoning Attack for DNNs

Deep neural networks are susceptible to poisoning attacks by purposely polluted training data with specific triggers. As existing episodes mainly focused on attack success rate with patch-based samples, defense algorithms can easily detect these poisoning samples. We propose DeepPoison, a novel adversarial network of one generator and two discriminators, to address this problem. Specifically, the generator automatically extracts the target class' hidden features and embeds them into benign training samples. One discriminator controls the ratio of the poisoning perturbation. The other discriminator works as the target model to testify the poisoning effects. The novelty of DeepPoison lies in that the generated poisoned training samples are indistinguishable from the benign ones by both defensive methods and manual visual inspection, and even benign test samples can achieve the attack. Extensive experiments have shown that DeepPoison can achieve a state-of-the-art attack success rate, as high as 91.74%, with only 7% poisoned samples on publicly available datasets LFW and CASIA. Furthermore, we have experimented with high-performance defense algorithms such as autodecoder defense and DBSCAN cluster detection and showed the resilience of DeepPoison.
Multi-thread hierarchical deep model for context-aware sentiment analysis

Real-time messaging and opinion sharing in social media websites have made them valuable sources of different kinds of information. This source provides the opportunity for doing different kinds of analysis. Sentiment analysis as one of the most important of these analyses gains increasing interests. However, the research in this field is still facing challenges. The mainstream of the sentiment analysis research on social media websites and microblogs just exploits the textual content of the posts. This makes the analysis hard because microblog posts are short and noisy. However, they have lots of contexts which can be exploited for sentiment analysis. In order to use the context as an auxiliary source, some recent papers use reply/retweet to model the context of the target post. We claim that multiple sequential contexts can be used jointly in a unified model. In this article, we propose a context-aware multi-thread hierarchical long short-term memory (MHLSTM) that jointly models different kinds of contexts, such as tweep, hashtag and reply besides the content of the target post. Experimental evaluations on a real-world Twitter data set demonstrate that our proposed model can outperform some strong baseline models by 28.39% in terms of relative error reduction.
Light-Weight Attention Semantic Segmentation Network for High-Resolution Remote Sensing Images

Semantic segmentation of high-resolution remote sensing (HRRS) images becomes more and more important at present. Popular approaches use deep learning to solve this task, which depends on a large amount of labeled data and powerful computing resources. When computing resources or the labeled data are insufficient, their performance will be severely degraded. To deal with this problem, we proposed a light-weight network with attention modules for semantic segmentation of HRRS images. The depth and width of the network are designed, which has a small number of parameters to ensure the efficiency of training. The network adopts an encoder-decoder architecture. The feature maps of different scales from the encoder are concatenated together after resizing to carry out multi-scale feature fusion. To capture the global semantic information from the context, the attention mechanism is employed in the decoder. With one GTX2080Ti GPU and only 15 MB parameters the model owns, our light-weight network has quality results evaluated on ISPRS Vaihingen Dataset with fewer parameters compared to other popular approaches.
Instance-Aware Remote Sensing Image Captioning with Cross-Hierarchy Attention

The spatial attention is a straightforward approach to enhance the performance for remote sensing image captioning. However, conventional spatial attention approaches consider only the attention distribution on one fixed coarse grid, resulting in the semantics of tiny objects can be easily ignored or disturbed during the visual feature extraction. Worse still, the fixed semantic level of conventional spatial attention limits the image understanding in different levels and perspectives, which is critical for tackling the huge diversity in remote sensing images. To address these issues, we propose a remote sensing image caption generator with instance-awareness and cross-hierarchy attention. 1) The instances awareness is achieved by introducing a multi-level feature architecture that contains the visual information of multi-level instance-possible regions and their surroundings. 2) Moreover, based on this multi-level feature extraction, a cross-hierarchy attention mechanism is proposed to prompt the decoder to dynamically focus on different semantic hierarchies and instances at each time step. The experimental results on public datasets demonstrate the superiority of proposed approach over existing methods.
FPGA Acceleration of ROS2-Based Reinforcement Learning Agents

Reinforcement learning agents have shown very good results in robot control and navigation tasks, allowing robots to learn how to interact with an environment appropriately in a model-free manner. However, real-world robot systems have strict latency, power, and cost constraints, thus requiring special hardware consideration for the demanding computations of neural networks. Furthermore, reinforcement learning networks should be able to interface efficiently with the various other robot components. To address these challenges, we propose a method for applying FPGA hardware accelerators to robotics reinforcement learning agents at the inference stage and seamlessly integrating the FPGA hardware module to the robot system by automatically wrapping it in a Robot Operating System 2 (ROS2) node. The proposed system is evaluated in three OpenAI gym control environments: Cartpole-v1, Acrobot-v1, and Pendulum-v0. In the evaluation, both quantized and non-quantized reinforcement learning neural networks are used, and the proposed FPGA system is observed to provide up to a 3.69x speed up and up to 52.7x better performance per watt when compared to an agent running on a ROS2 node on a modern CPU.
Image smoothing based on histogram equalized content-aware patches and direction-constrained sparse gradients

For better removing textures with larger gradients while preserving structural edges with smaller gradients, we propose an image smoothing method based on histogram equalized content-aware patches and direction-constrained sparse gradients. In order to better smooth the boundary concentration regions while maintaining the continuity of boundary pixels, a content-aware patching technology with boundary constraints is proposed. The irregular patches are represented by the smallest rectangular bounding boxes to reduce the computational complexity. Based on edge information, patches are divided into edge-patches and non-edge-patches. Histogram equalization is used to improve the edge gradient of patches with structural edge concentration, while the image decomposition is used to reduce the gradient of texture details. Taking the edge information as the smoothing factor, each patch is smoothed via direction-constrained sparse gradients. The entire image needs to be further smoothed to remove residual texture details. Experimental results show that new method has better visual effects in retaining structural edges and removing texture details, and has many applications, including edge detection, image abstraction, detail enhancement, and texture mapping.
Maximum Similarity Method for Image Mining

The paper discusses a new Image Mining approach to extracting and exploring relations in the image repositories. The proposed approach, called Maximum Similarity Method, is based on the identification of characteristic fragments in images by a set of predefined patterns. Such an identification is basically carried out as a comparison of the fragment intensity shape with the shapes of already registered patterns - precedents. Mathematically (statistically) such a comparison implies a selection of some measure of similarity and optimization (maximization) of that measure on a set of precedents. In the paper, basing on the principles of machine learning, a special type of similarity measure is proposed, and its reliability is discussed. In fact, this measure represents conditional probability distribution of the registered data - counts of a fragment tested when analogous data for the patterns are given. So, the search for the optimal precedent pattern that maximized the chosen similarity measure constitutes the proposed method.
Preamble-Based Packet Detection in Wi-Fi: A Deep Learning Approach

Wi-Fi systems based on the family of IEEE 802.11 standards that operate in unlicenced bands are the most popular wireless interfaces that use Listen Before Talk (LBT) methodology for channel access. Distinctive feature of majority of LBT-based systems is that the transmitters use preambles that precede the data to allow the receivers to acquire initial signal detection and synchronization. The first digital processing step at the receiver applied over the incoming discrete-time complex-baseband samples after analog-to-digital conversion is the packet detection step, i.e., the detection of the initial samples of each of the frames arriving within the incoming stream. Since the preambles usually contain repetitions of training symbols with good correlation properties, conventional digital receivers apply correlation-based methods for packet detection. Following the recent interest in data-based deep learning (DL) methods for physical layer signal processing, in this paper, we challenge the conventional methods with DL-based approach for Wi-Fi packet detection. Using one-dimensional Convolutional Neural Networks (1D-CNN), we present a detailed complexity vs performance analysis and comparison between conventional and DL-based Wi-Fi packet detection approaches.
Self-Management Skills as a Basis for the Effectiveness of e-Learning

The pandemic has forced universities to switch to e-Learning in force majeure mode. This served as a powerful impetus for the accelerated arming of universities with the resources that allow effective online and blended learning. In these conditions, it became possible to implement a learner-centered educational paradigm. However, this is hindered by the problem of the student’s lack of self-organization and self-motivation skills, as well as the lack of their ability to take responsibility for their own learning and development. This problem is gaining more and more theoretical and practical importance. The purpose of the work is to substantiate that courses such as self-management and time management contribute to the formation of competencies in the field of goal-setting, planning, self-organization, self-control and self-motivation, and therefore in the context of the implementation of learner-centered paradigm, they should serve as propaedeutics for е-Learning. For this purpose, the article provides a review of the content of the “Time Management” course in relation to the task of developing proactivity and the student’s ability to systematic and productive independent work.
Towards a New Approach to Identify WhatsApp Messages

Today traditional communication methods, such as SMS or phone calls, are used less often and are replaced by the use of chat applications. WhatsApp is one of the most popular chat applications nowadays. WhatsApp offers different ways of communicating, which include sending text messages and making phone calls. The implementation of encryption makes WhatsApp more challenging for law enforcement agencies to identify when a suspect is sending or receiving messages via this chat application. Most research in literature focused on the analysis of WhatsApp data by obtaining information from a physical device, such as a seized mobile device. However, it is not always possible to extract the data needed from a mobile device for the analysis of the WhatsApp data because of the encryption, or no devices have been seized yet. In addition, the current techniques for real time analysis of WhatsApp messages show that there is a high risk of detection by the suspect. Alternative methods are needed to understand the communication patterns of a suspect and criminal organizations. In this paper, we focused on identifying when a suspect is receiving or sending WhatsApp messages using only wiretap data. Therefore, no seized devices are needed. The pattern analysis has been used to identify patterns of data sent to and received from the WhatsApp servers. The identified patterns were tested against a large dataset created with different mobile devices to determine if the patterns are consistent. By using the technique described in this paper, investigators will obtain more information if and with whom a suspect is communicating.
Designing Physical Artifacts for Tangible Narratives: Lessons Learned from Letters to José

Digital tangible interfaces have been used to present or embed interactive stories for decades. While many researchers explored different ways of creating engaging interactive experiences, they focused more on the novelty of the interface and the new experience it brought for users, rather than the exploration of tangible interfaces as a language, or a medium, for storytelling. Through a visual journey of our creative explorations, this pictorial reflects upon the design and making of Letters to José, a tangible narrative built during a three-year practice-led research project. Located in the intersection of tangible interaction design and interactive storytelling, our work characterizes the relationships between the artifact, other artifacts, the body, and the space with respect to supporting the narrative experience. The outcome is an annotated visual typology of artifacts for storytelling in the context of tangible narrative, as a design category and toolbox for researchers and creators of tangible stories.
Multimodal Aggregation Approach for Memory Vision-Voice Indoor Navigation with Meta-Learning

Vision and voice are two vital keys for agents' interaction and learning. In this paper, we present a novel indoor navigation model called Memory Vision-Voice Indoor Navigation (MVV-IN), which receives voice commands and analyzes multimodal information of visual observation in order to enhance robots' environment understanding. We make use of single RGB images taken by a rst-view monocular camera. We also apply a self-attention mechanism to keep the agent focusing on key areas. Memory is important for the agent to avoid repeating certain tasks unnecessarily and in order for it to adapt adequately to new scenes, therefore, we make use of meta-learning. We have experimented with various functional features extracted from visual observation. Comparative experiments prove that our methods outperform state-of-the-art baselines.
Real World Performance of LTE Downlink in a Static Dense Urban Scenario - An Open Dataset

Open datasets with measurements in cellular mobile networks are rare. In this paper, we share 90 hours (over 600 000 samples with time-resolution of 500 milliseconds) of smartphone measurements conducted in a dense urban environment in the downlink of an operational LTE network. To capture, on the one hand, the short-term fluctuations of the measured key parameter indicators, and on the other hand, also time-of-day effects, we chose a static indoor setup. Despite the static configuration, the dataset offers a rich variability over time. The dataset can be appealing for machine learning applications (e.g., throughput prediction), simulations verification, or for educational purposes, as it contains detailed statistics throughout different LTE layers, which may help students to better comprehend the concepts of communication in LTE downlink.
Depth Estimation From a Single RGB Image Using Fine-Tuned Generative Adversarial Network

Estimating the depth map from a single RGB image is important to understand the nature of the terrain in robot navigation and has attracted considerable attention in the past decade. The existing approaches can accurately estimate the depth from a single RGB image, considering a highly structured environment. The problem becomes more challenging when the terrain is highly dynamic. We propose a fine-tuned generative adversarial network to estimate the depth map effectively for a given single RGB image. The proposed network is composed of a fine-tuned generator and a global discriminator. The encoder part of the generator takes input RGB images and depth maps and generates their joint distribution in the latent space. Subsequently, the decoder part of the generator decodes the depth map from the joint distribution. The discriminator takes real and fake pairs in three different configurations and then guides the generator to estimate the depth map from the given RGB image accordingly. Finally, we conducted extensive experiments with a highly dynamic environment dataset for verifying the effectiveness and feasibility of the proposed approach. The proposed approach could decode the depth map from the joint distribution more effectively and accurately than the existing approaches.