Reviewerly/exHarmony huggingface dataset:
Development of a Fuzzy-LQR and Fuzzy-LQG stability control for a double link rotary inverted pendulum

In this paper, a Fuzzy based Linear Quadratic Regulator (FLQR) and Linear Quadratic Gaussian (FLQG) controllers are developed for stability control of a Double Link Rotary Inverted Pendulum (DLRIP) system. The aim of this work is to study dynamic performance analysis of both FLQR and FLQG controllers and to compare them with the classical LQR and LQG controllers, respectively. A dynamic mechanical simulation model of the DLRIP was obtained using both the numerically SimMechanics toolbox in MATLAB and the analytically dynamic nonlinear equations. To determine the control performance of the controllers, Settling Time (Ts), Peak Overshoot (PO), Steady-State Error (Ess), and the total Root Mean Squared Errors (RMSEs) of the joint positions are computed. Furthermore, the dynamic responses of the controllers were compared based on robustness analysis under internal and external disturbances. To show the control performance of the controllers, several simulations were conducted. Based on the comparative results, the dynamic responses of both FLQR and FLQG controllers produce much better results than the dynamic responses of the classical LQR and LQG controllers, respectively. Moreover, the robustness results indicate that the FLQR and FLQG controllers under the internal and external disturbances were effective.
Performing predefined tasks using the human–robot interaction on speech recognition for an industrial robot

People who are not experts in robotics can easily implement complex robotic applications by using human–robot interaction (HRI). HRI systems require many complex operations such as robot control, image processing, natural speech recognition, and decision making. In this study, interactive control with an industrial robot was performed by using speech recognition software in the Turkish language. The collected voice data were converted to text data by using automatic speech recognition module based on deep neural networks (DNN). The proposed DNN (p-DNN) was compared to classic classification algorithms. Converted text data was improved in another module to select the process to be applied. According to selected process, position data were defined using image processing. The determined position information was sent to the robot using a fuzzy controller. The developed HRI system was implemented on a KUKA KR Agilus KR6 R900 sixx robot manipulator. The word accuracy rate of the p-DNN model was measured as 90.37%. The developed image processing module and fuzzy controller worked with minimal errors. The contribution of this study is that an industrial robot is easily programming using this software by people who are not experts in robotics and know Turkish.
A systematic review of Augmented Reality interfaces for collaborative industrial robots

Industry 4.0 is moving factories towards a high level of collaboration between human workers and industrial robots, with the aim of improving efficiency and productivity. Among other technologies, Augmented Reality (AR) is one of the most researched in recent years to provide novel user interfaces that could easily blend the real world with additional information. This literature review aims at identifying the main strengths and weaknesses of AR with industrial robots in human–robot collaborative scenarios. The term industrial robot is meant according to the ISO 8373:2012 definition. To this end, starting from 3734 initial works, 63 papers have been selected and analysed. The results suggest that AR technology shows its effectiveness also in this particular domain. With respect to traditional approaches, AR systems are faster and more appreciated by users. Nonetheless, the use of AR in human–robot collaborative scenarios is so cutting edge that not all the considered works have properly evaluated the proposed user interfaces. Future research should improve the qualitative evaluation in order to clearly point out both limitations and strengths of the proposed systems, involving also expert users in tests.
Low-rank tensor constrained co-regularized multi-view spectral clustering

Due to the efficiency of exploiting relationships and complex structures hidden in multi-views data, graph-oriented clustering methods have achieved remarkable progress in recent years. But most existing graph-based spectral methods still have the following demerits: (1) They regularize each view equally, which does not make sense in real applications. (2) By employing different norms, most existing methods calculate the error feature by feature, resulting in neglecting the spatial structure information and the complementary information. To tackle the aforementioned drawbacks, we propose an enhanced multi-view spectral clustering model. Our model characterizes the consistency among indicator matrices by minimizing our proposed weighted tensor nuclear norm, which explicitly exploits the salient different information between singular values of the matrix. Moreover, our model adaptively assigns a reasonable weight to each view, which helps improve robustness of the algorithm. Finally, the proposed tensor nuclear norm well exploits both high-order and complementary information, which helps mine the consistency between indicator matrices. Extensive experiments indicate the efficiency of our method.
Ensuring protocol compliance and data transparency in clinical trials using Blockchain smart contracts

Abstract Background Clinical Trials (CTs) help in testing and validating the safety and efficacy of newly discovered drugs on specific patient population cohorts. However, these trials usually experience many challenges, such as extensive time frames, high financial cost, regulatory and administrative barriers, and insufficient workforce. In addition, CTs face several data management challenges pertaining to protocol compliance, patient enrollment, transparency, traceability, data integrity, and selective reporting. Blockchain can potentially address such challenges because of its intrinsic features and properties. Although existing literature broadly discusses the applicability of blockchain-based solutions for CTs, only a few studies present their working proof-of-concept. Methods We propose a blockchain-based framework for CT data management, using Ethereum smart contracts, which employs IPFS as the file storage system to automate processes and information exchange among CT stakeholders. CT documents stored in the IPFS are difficult to tamper with as they are given unique cryptographic hashes. We present algorithms that capture various stages of CT data management. We develop the Ethereum smart contract using Remix IDE that is validated under different scenarios. Results The proposed framework results are advantageous to all stakeholders ensuring transparency, data integrity, and protocol compliance. Although the proposed solution is tested on the Ethereum blockchain platform, it can be deployed in private blockchain networks using their native smart contract technologies. We make our smart contract code publicly available on Github. Conclusions We conclude that the proposed framework can be highly effective in ensuring that the trial abides by the protocol and the functions are executed only by the stakeholders who are given permission. It also assures data integrity and promotes transparency and traceability of information among stakeholders.
Toward blockchain-based renewable energy microgrid design considering default risk and demand uncertainty

Renewable energy is a promising solution for addressing social and environmental issues related to the rapidly rising electric power consumption of manufacturers. Recently, a number of policies have been proposed by governments to stimulate the use of renewable energy by manufacturers. Financial incentives are considered as an efficient lever to increase the penetration of renewable energy. This study solves a blockchain-based renewable energy microgrid design problem, where the bank finances a loan to the power company to establish renewable generation units and the power company also offers a credit period for manufacturers to stimulate demand. Blockchain technology can be applied to mitigate default risk. The objective is to determine the number and location of renewable generation units, energy price in the time-of-use scheme, credit time to manufacturers, and blockchain implementation effort while maximizing the expected network profit. A solution method based on robust type-2 fuzzy programming was developed to solve the described problem. The application of the proposed approach was examined using an empirical case study. The results show that uncertain demand and default risk have a strong effect on the solution, and the total profit of the renewable energy microgrid is improved by approximately 1.73% using the proposed approach.
The role of residential rooftop photovoltaic in long-term energy and climate scenarios

The use of solar photovoltaic has strongly increased in the last decade. A significant part of this growth comes from home owners installing rooftop photovoltaic. Despite this key role, most long-term model-based scenarios do not consider decentralized supply of rooftop photovoltaic but concentrate on utility-scale photovoltaic instead. In this paper, we implement rooftop photovoltaic in the Integrated Assessment Model IMAGE to study its possible role in energy and climate scenarios. We first calculated the global technical and economic potential to derive regional cost-supply curves for rooftop photovoltaic. Next, we have added a new decision in the IMAGE model allowing household investment in rooftop photovoltaic based on the comparison of the whole-sale electricity price with the price of rooftop photovoltaic. The global suitable roof surface area was assessed at 36 billion m2, or 4.7 m2 capita−1, leading to a potential for rooftop photovoltaic of 8.3 PWh y−1, roughly 1.5 times the 2015 global residential electricity demand. In the baseline scenario, adding rooftop photovoltaic could lead to a 80–280% increased share of photovoltaic electricity production in 2050 (i.e. from 6% to 17% in total power production). This increase depends on regional characteristics that are essential to the deployment of rooftop photovoltaic: differences in social-economic and policy factors (capital costs, household income, and electricity prices) are considerably more important than physical factors, such as solar irradiance.
Distributed Multi-Sensor Fusion of PHD Filters With Different Sensor Fields of View

The paper addresses the problem of distributed multi-target tracking (MTT) in a network of sensors having different fields of view (FoVs). Probability hypothesis density (PHD) filters are running locally in every sensor for MTT. The weighted arithmetic average (WAA) fusion rule is employed to fuse the multiple local PHD densities due to its computational efficiency. First, we provide a theoretical analysis showing that the standard WAA fusion among sensors with different FoVs is unsuitable from the perspective of the principle of minimum discrimination information (PMDI). In fact, the information inconsistency among sensors due to the different FoVs unavoidably leads to an underestimation of the fused PHD. Then, motivated by the analysis, we devise two novel approaches to address the different FoV issue. The first approach accounts for the case where the sensor FoVs are known and time-invariant. The second one deals with the more complicated case where the actual sensor FoVs can be cropped due to unknown line of sight obstructions or sensors can obtain the information outside their current FoVs because of platform movement or information feedback. The essence of the proposed approaches is to perform the WAA in a more robust way by employing a set of state-dependent fusion weights which are computed online. The Gaussian mixture implementations of the proposed methods are also presented. Various simulation experiments, including a challenging tracking scenario involving six sensors with different FoVs and also random line of sight obstructions, are designed to demonstrate the efficacy of the proposed fusion approaches.
Determinants of mobile learning acceptance for STEM education in rural areas

Science, Technology, Engineering, and Mathematics (STEM) is faced with challenges, resulting in learners' poor performance especially in rural areas. Previous studies have shown that mobile learning can be used to alleviate the challenges faced in STEM education in rural areas. Despite the opportunities that mobile learning can bring to STEM education, very little is known about high school STEM learners', their teachers', and parents' acceptance of mobile learning, particularly in rural settings. This study proposed and used the High School's Acceptance of Mobile Learning Model to investigate the factors that predict rural high school STEM learners', their parents', and teachers' behavioural intention to use mobile learning for STEM learning. The High School's Acceptance of Mobile Learning Model is based on the Technology Acceptance Model (TAM). Stratified random sampling was used to select 550 survey participants. Partial least squares structural equation modeling was used to analyse data from 417 valid questionnaires. The proposed model explained 40.8% of the variance in learners', teachers', and parents' acceptance of mobile learning. The original TAM variables (perceived attitude, perceived usefulness, and perceived ease of use) had direct relationship with behavioural intention, and they also played mediating roles between the external variables and behavioural intention. Multigroup analysis results showed that, for parents and learners, three paths were significantly different. In contrast, all paths were not statistically significant different for learners and teachers. However, all the paths were significant in each group, meaning that High School's Acceptance of Mobile Learning Model can be used to predict acceptance of mobile learning for learners, parents, and teachers.
A Fourier transform based audio watermarking algorithm

In this paper; we propose two new substitution schemes for digital audio watermarking based on the Fourier transform. The integration of the watermark will be performed by combining the parity of the successive coefficients values; each variant will represent a different combination. In our experiments several variants of the Fourier transform are used (Discrete Fourier Transform, a Fractional Fourier Transform as well as a Quaternion Discrete Fourier Transform). For each transform, both variants of our watermarking scheme are applied and the results obtained show that our approach offers good imperceptibility and generates watermarked audio sample robust against various attacks with a high-quality watermark. However, using small audio file for our experiments considerably reduces the capacity of our approach in the frequency domain.
Real-time gun detection in CCTV: An open problem

Object detectors have improved in recent years, obtaining better results and faster inference time. However, small object detection is still a problem that has not yet a definitive solution. The autonomous weapons detection on Closed-circuit television (CCTV) has been studied recently, being extremely useful in the field of security, counter-terrorism, and risk mitigation. This article presents a new dataset obtained from a real CCTV installed in a university and the generation of synthetic images, to which Faster R-CNN was applied using Feature Pyramid Network with ResNet-50 resulting in a weapon detection model able to be used in quasi real-time CCTV (90 ms of inference time with an NVIDIA GeForce GTX-1080Ti card) improving the state of the art on weapon detection in a two stages training. In this work, an exhaustive experimental study of the detector with these datasets was performed, showing the impact of synthetic datasets on the training of weapons detection systems, as well as the main limitations that these systems present nowadays. The generated synthetic dataset and the real CCTV dataset are available to the whole research community.
Long-tail Session-based Recommendation

Session-based recommendation focuses on the prediction of user actions based on anonymous sessions and is a necessary method in the lack of user historical data. However, none of the existing session-based recommendation methods explicitly takes the long-tail recommendation into consideration, which plays an important role in improving the diversity of recommendation and producing the serendipity. As the distribution of items with long-tail is prevalent in session-based recommendation scenarios (e.g., e-commerce, music, and TV program recommendations), more attention should be put on the long-tail session-based recommendation. In this paper, we propose a novel network architecture, namely TailNet, to improve long-tail recommendation performance, while maintaining competitive accuracy performance compared with other methods. We start by classifying items into short-head (popular) and long-tail (niche) items based on click frequency. Then a novel preference mechanism is proposed and applied in TailNet to determine user preference between two types of items, so as to softly adjust and personalize recommendations. Extensive experiments on two real-world datasets verify the superiority of our method compared with state-of-the-art works.
On the integration of symbolic and sub-symbolic techniques for XAI: A survey

The more intelligent systems based on sub-symbolic techniques pervade our everyday lives, the less human can understand them. This is why symbolic approaches are getting more and more attention in the general effort to make AI interpretable, explainable, and trustable. Understanding the current state of the art of AI techniques integrating symbolic and sub-symbolic approaches is then of paramount importance, nowadays—in particular in the XAI perspective. This is why this paper provides an overview of the main symbolic/sub-symbolic integration techniques, focussing in particular on those targeting explainable AI systems.
A Deep Learning Approach for Human Activities Recognition From Multimodal Sensing Devices

Research in the recognition of human activities of daily living has significantly improved using deep learning techniques. Traditional human activity recognition techniques often use handcrafted features from heuristic processes from single sensing modality. The development of deep learning techniques has addressed most of these problems by the automatic feature extraction from multimodal sensing devices to recognise activities accurately. In this paper, we propose a deep learning multi-channel architecture using a combination of convolutional neural network (CNN) and Bidirectional long short-term memory (BLSTM). The advantage of this model is that the CNN layers perform direct mapping and abstract representation of raw sensor inputs for feature extraction at different resolutions. The BLSTM layer takes full advantage of the forward and backward sequences to improve the extracted features for activity recognition significantly. We evaluate the proposed model on two publicly available datasets. The experimental results show that the proposed model performed considerably better than our baseline models and other models using the same datasets. It also demonstrates the suitability of the proposed model on multimodal sensing devices for enhanced human activity recognition.
Local and Global Structure-Aware Entropy Regularized Mean Teacher Model for 3D Left Atrium Segmentation

Emerging self-ensembling methods have achieved promising semi-supervised segmentation performances on medical images through forcing consistent predictions of unannotated data under different perturbations. However, the consistency only penalizes on independent pixel-level predictions, making structure-level information of predictions not exploited in the learning procedure. In view of this, we propose a novel structure-aware entropy regularized mean teacher model to address the above limitation. Specifically, we firstly introduce the entropy minimization principle to the student network, thereby adjusting itself to produce high-confident predictions of unannotated images. Based on this, we design a local structural consistency loss to encourage the consistency of inter-voxel similarities within the same local region of predictions from teacher and student networks. To further capture local structural dependencies, we enforce the global structural consistency by matching the weighted self-information maps between two networks. In this way, our model can minimize the prediction uncertainty of unannotated images, and more importantly that it can capture local and global structural information and their complementarity. We evaluate the proposed method on a publicly available 3D left atrium MR image dataset. Experimental results demonstrate that our method achieves outstanding segmentation performances than the state-of-the-art approaches in scenes with limited annotated images.
Outlier-Resistant Recursive Filtering for Multisensor Multirate Networked Systems Under Weighted Try-Once-Discard Protocol

In this article, a new outlier-resistant recursive filtering problem (RF) is studied for a class of multisensor multirate networked systems under the weighted try-once-discard (WTOD) protocol. The sensors are sampled with a period that is different from the state updating period of the system. In order to lighten the communication burden and alleviate the network congestions, the WTOD protocol is implemented in the sensor-to-filter channel to schedule the order of the data transmission of the sensors. In the case of the measurement outliers, a saturation function is employed in the filter structure to constrain the innovations contaminated by the measurement outliers, thereby maintaining satisfactory filtering performance. By resorting to the solution to a matrix difference equation, an upper bound is first obtained on the covariance of the filtering error, and the gain matrix of the filter is then characterized to minimize the derived upper bound. Furthermore, the exponential boundedness of the filtering error dynamics is analyzed in the mean square sense. Finally, the usefulness of the proposed outlier-resistant RF scheme is verified by simulation examples.
Standing on the Shoulders of Giants: Hardware and Neural Architecture Co-Search With Hot Start

Hardware and neural architecture co-search that automatically generates artificial intelligence (AI) solutions from a given dataset are promising to promote AI democratization; however, the amount of time that is required by current co-search frameworks is in the order of hundreds of GPU hours for one target hardware. This inhibits the use of such frameworks on commodity hardware. The root cause of the low efficiency in existing co-search frameworks is the fact that they start from a “cold” state (i.e., search from scratch). In this article, we propose a novel framework, namely, HotNAS, that starts from a “hot” state based on a set of existing pretrained models (also known as model zoo) to avoid lengthy training time. As such, the search time can be reduced from 200 GPU hours to less than 3 GPU hours. In HotNAS, in addition to hardware design space and neural architecture search space, we further integrate a compression space to conduct model compressing during the co-search, which creates new opportunities to reduce latency, but also brings challenges. One of the key challenges is that all of the above search spaces are coupled with each other, e.g., compression may not work without hardware design support. To tackle this issue, HotNAS builds a chain of tools to design hardware to support compression, based on which a global optimizer is developed to automatically co-search all the involved search spaces. Experiments on ImageNet dataset and Xilinx FPGA show that, within the timing constraint of 5 ms, neural architectures generated by HotNAS can achieve up to 5.79% Top-1 and 3.97% Top-5 accuracy gain, compared with the existing ones.