Reviewerly/exHarmony huggingface dataset:
4F optical neural network acceleration: an architecture perspective

Low latency, high throughput inference on Convolution Neural Networks (CNNs) remains a challenge, especially for applications requiring large input or large kernel sizes. 4F optics provides a solution to potentially accelerate CNN inferences with Fourier optics and the well-known convolution theorem. However, existing 4F CNN accelerators suffer from various limitations that make the implementation of a multi-channel, multi-layer CNN not scalable or even impractical. In this paper, we discuss the limitations of 4F CNN accelerators including the positive sensor readout, intensity-only modulation and slow modulation frequency and methods to address them. We also propose the channel tiling method that can address an important throughput and precision bottleneck of high-speed, massively-parallel optical 4F computing systems, not requiring any additional optical hardware.
Blockchain-based P2P power trading mechanism for PV prosumer

Peer to peer (P2P) and blockchain are highly coupled in terms of decentralization and autonomous transactions. This paper designs a P2P power transaction mechanism based on block chain to meet the economic and security requirements of photovoltaic prosumer. Day-ahead and real-time P2P trading mechanism is designed. Fully open P2P trading mode and decentralized electricity price mechanism are adopted in day-ahead market, and P2P pool trading mode and electricity price mechanism based on supply/demand ratio are adopted in real-time market. In addition, smart contract rules based on blockchain Ethereum are designed to ensure safe operation and efficient intelligence on the blockchain platform. Finally, effectiveness and economy Prosumer P2P trading mechanism in IEEE 14-node simulation and verification of the design.
Efficient color image encryption algorithm based on 2D coupled chaos and multi-objective optimized S-box

Abstract In order to ensure the security of the encryption result and increase the speed of the encryption algorithm as much as possible, this paper proposes an efficient color image encryption algorithm based on 2D Sine-Logistic-Gaussian coupled chaos(2D-SLG chaos) and multi-objective optimized S-box. Firstly, this paper designs a 2D-SLG chaos to balance the complexity and computational cost of chaotic behavior. Secondly, this paper uses multi-objective particle swarm optimization to optimize the initial S-box to obtain an S-box with higher and balanced performance to perform the substitution operation. Finally, this paper uses the layer blending method to enhance the mutual influence between layers and uses the idea of linear congruence to generate permutation sequences to perform row and column shift operations. Experimental results show that the proposed encryption algorithm has excellent performance in key space, statistical analysis, key sensitivity, adjacent pixel correlation, differential attack, select plaintext attack and other security tests, and has high robustness and fast encryption speed. Therefore, the algorithm can meet the requirements of reliability and efficiency while ensuring strong security.
Modelling Co-located Team Communication from Voice Detection and Positioning Data in Healthcare Simulation

In co-located situations, team members use a combination of verbal and visual signals to communicate effectively, among which positional forms play a key role. The spatial patterns adopted by team members in terms of where in the physical space they are standing, and who their body is oriented to, can be key in analysing and increasing the quality of interaction during such face-to-face situations. In this paper, we model the students' communication based on spatial (positioning) and audio (voice detection) data captured from 92 students working in teams of four in the context of healthcare simulation. We extract non-verbal events (i.e., total speaking time, overlapped speech,and speech responses to team members and teachers) and investigate to what extent they can serve as meaningful indicators of students' performance according to teachers' learning intentions. The contribution of this paper to multimodal learning analytics includes: i) a generic method to semi-automatically model communication in a setting where students can freely move in the learning space; and ii) results from a mixed-methods analysis of non-verbal indicators of team communication with respect to teachers' learning design.
Efficient itinerary recommendation via personalized POI selection and pruning

Abstract Personalized itinerary recommendation has garnered wide research interests for their ubiquitous applications. Recommending personalized itineraries is complex because of the large number of points of interest (POI) to consider in order to construct an itinerary based on visitors’ interest and preference, time budget and uncertain queuing time. Previous studies typically aim to plan itineraries that maximize POI popularity, visitors’ interest and minimize queuing time. However, existing solutions may not reflect visitor preferences because when creating itineraries, they prefer to recommend POIs with short prior visiting periods. These recommendations can conflict with real-life scenarios as visitors typically spend less time at POIs that they do not enjoy, thus leading to the inclusion of unsuitable POIs. Moreover, constructing itineraries based on selected POIs is a challenging and time-consuming process. Existing approaches involve searching through a large number of non-optimal, duplicate itineraries that are time-consuming to review and generate. To address these issues, we propose an adaptive Monte Carlo tree search (MCTS)-based reinforcement learning algorithm EffiTourRec using an effective POI selection strategy by giving preference to POIs with long visiting times and short queuing times along with high POI popularity and visitor interest. In addition, to reduce non-optimal and duplicated itineraries generation, we propose an efficient MCTS search pruning technique to explore a smaller, more promising portion of solution space. Experiment results in real theme park datasets show clear advantages of our proposed method over baselines, where our method outperforms the current state-of-the-art by 20.89 to 52.32% in precision, 8.36 to 21.35% in F1-score and 40.00 to 67.64% in execution time.
Multi-view representation learning with Kolmogorov-Smirnov to predict default based on imbalanced and complex dataset

Existing solutions focus on improving overall accuracy for imbalanced and complex loan datasets, resulting in a lower precise recall for default samples. To embrace these challenges, based on peer-to-peer loan application information, we proposed a multi-view representation learning with Kolmogorov-Smirnov (KS) to effectively organize these complex data and predict default. Firstly, the features were automatically represented as multi-views based on their discreteness and correlation difference. Then, a corresponding multi-view deep neural network (MV-DNN) was developed to obtain knowledge in a multi-view way. Here, we firstly designed different view learning layers to obtain knowledge in corresponding views. Subsequently, to interact with the knowledge in different views, an information fusion layer was developed to fuse the acquired information. To face the challenge from imbalanced data distribution, the KS was set as evaluation metric to assist in training MV-DNN to improve the distinguishing ability for two classes of samples. The experimental results show compared with the MV-DNNs based on random and k-means multi-view strategies, and other advanced models, our method could provide optimal comprehensive performance and the most stable multi-view organizing results. Furthermore, we also verified the KS is the key component to assist the model in dealing with the imbalanced dataset.
An improved random bit-stuffing technique with a modified RSA algorithm for resisting attacks in information security (RBMRSA)

The recent innovations in network application and the internet have made data and network security the major role in data communication system development. Cryptography is one of the outstanding and powerful tools for ensuring data and network security. In cryptography, randomization of encrypted data increases the security level as well as the Computational Complexity of cryptographic algorithms involved. This research study provides encryption algorithms that bring confidentiality and integrity based on two algorithms. The encryption algorithms include a well-known RSA algorithm (1024 key length) with an enhanced bit insertion algorithm to enhance the security of RSA against different attacks. The security classical RSA has depreciated irrespective of the size of the key length due to the development in computing technology and hacking system. Due to these lapses, we have tried to improve on the contribution of the paper by enhancing the security of RSA against different attacks and also increasing diffusion degree without increasing the key length. The security analysis of the study was compared with classical RSA of 1024 key length using mathematical evaluation proofs, the experimental results generated were compared with classical RSA of 1024 key length using avalanche effect in (%) and computational complexity as performance evaluation metrics. The results show that RBMRSA is better than classical RSA in terms of security but at the cost of execution time.
Deep Gaussian processes for multiple instance learning: Application to CT intracranial hemorrhage detection

Intracranial hemorrhage (ICH) is a life-threatening emergency that can lead to brain damage or death, with high rates of mortality and morbidity. The fast and accurate detection of ICH is important for the patient to get an early and efficient treatment. To improve this diagnostic process, the application of Deep Learning (DL) models on head CT scans is an active area of research. Although promising results have been obtained, many of the proposed models require slice-level annotations by radiologists, which are costly and time-consuming.We formulate the ICH detection as a problem of Multiple Instance Learning (MIL) that allows training with only scan-level annotations. We develop a new probabilistic method based on Deep Gaussian Processes (DGP) that is able to train with this MIL setting and accurately predict ICH at both slice- and scan-level. The proposed DGPMIL model is able to capture complex feature relations by using multiple Gaussian Process (GP) layers, as we show experimentally.To highlight the advantages of DGPMIL in a general MIL setting, we first conduct several controlled experiments on the MNIST dataset. We show that multiple GP layers outperform one-layer GP models, especially for complex feature distributions. For ICH detection experiments, we use two public brain CT datasets (RSNA and CQ500). We first train a Convolutional Neural Network (CNN) with an attention mechanism to extract the image features, which are fed into our DGPMIL model to perform the final predictions. The results show that DGPMIL model outperforms VGPMIL as well as the attention-based CNN for MIL and other state-of-the-art methods for this problem. The best performing DGPMIL model reaches an AUC-ROC of 0.957 (resp. 0.909) and an AUC-PR of 0.961 (resp. 0.889) on the RSNA (resp. CQ500) dataset.The competitive performance at slice- and scan-level shows that DGPMIL model provides an accurate diagnosis on slices without the need for slice-level annotations by radiologists during training. As MIL is a common problem setting, our model can be applied to a broader range of other tasks, especially in medical image classification, where it can help the diagnostic process.
MoËT: Mixture of Expert Trees and its application to verifiable reinforcement learning

Rapid advancements in deep learning have led to many recent breakthroughs. While deep learning models achieve superior performance, often statistically better than humans, their adoption into safety-critical settings, such as healthcare or self-driving cars is hindered by their inability to provide safety guarantees or to expose the inner workings of the model in a human understandable form. We present MoËT, a novel model based on Mixture of Experts, consisting of decision tree experts and a generalized linear model gating function. Thanks to such gating function the model is more expressive than the standard decision tree. To support non-differentiable decision trees as experts, we formulate a novel training procedure. In addition, we introduce a hard thresholding version, MoËTh, in which predictions are made solely by a single expert chosen via the gating function. Thanks to that property, MoËTh allows each prediction to be easily decomposed into a set of logical rules in a form which can be easily verified. While MoËT is a general use model, we illustrate its power in the reinforcement learning setting. By training MoËT models using an imitation learning procedure on deep RL agents we outperform the previous state-of-the-art technique based on decision trees while preserving the verifiability of the models. Moreover, we show that MoËT can also be used in real-world supervised problems on which it outperforms other verifiable machine learning models.
Abnormal Data Detection and Identification Method of Distribution Internet of Things Monitoring Terminal Based on Spatiotemporal Correlation

As an important part of the ubiquitous power Internet of Things, the distribution Internet of Things can further improve the automation and informatization level of the distribution network. The reliability of the measurement data of the low-voltage terminal unit, as the sensing unit of the sensing layer of the distribution Internet of Things, has a great impact on the fault processing and advanced applications of the distribution Internet of Things. The self-check and the equipment working status monitoring of the main station of the low-voltage terminal unit struggle to identify the abnormality of measurement data. Aiming at this problem, an abnormal data detection and identification recognition method of a distribution Internet of Things monitoring terminal is proposed on the basis of spatiotemporal correlation. First, using the temporal correlation of monitoring terminal data, the proposed composite temporal series similarity measurement criterion is used to calculate the distance matrix between data, and the abnormal data detection is realized via combination with the improved DBSCAN algorithm. Then, using the spatial correlation of the data of the terminal unit, the geometric features of the spatial cross-correlation coefficient of the terminal nodes are extracted as the input of the cascaded fuzzy logic system to identify the abnormal source. Lastly, the effectiveness of the method is verified by a practical example.
TF-Unet:An automatic cardiac MRI image segmentation method

Personalized heart models are widely used to study the mechanisms of cardiac arrhythmias and have been used to guide clinical ablation of different types of arrhythmias in recent years. MRI images are now mostly used for model building. In cardiac modeling studies, the degree of segmentation of the heart image determines the success of subsequent 3D reconstructions. Therefore, a fully automated segmentation is needed. In this paper, we combine U-Net and Transformer as an alternative approach to perform powerful and fully automated segmentation of medical images. On the one hand, we use convolutional neural networks for feature extraction and spatial encoding of inputs to fully exploit the advantages of convolution in detail grasping; on the other hand, we use Transformer to add remote dependencies to high-level features and model features at different scales to fully exploit the advantages of Transformer. The results show that, the average dice coefficients for ACDC and Synapse datasets are 91.72 and 85.46%, respectively, and compared with Swin-Unet, the segmentation accuracy are improved by 1.72% for ACDC dataset and 6.33% for Synapse dataset.
SecVT: Securing the Vehicles of Tomorrow using Blockchain Technology

The Internet of Things (IoT) has developed from just an idea or concept to real-time market use-case. Automotive industry is one of the pioneers to adapt to the technology in its budding stage. They are not only focussing on the vehicle&#x0027;s internal features like the traditional self-driven vehicles but have developed a broader-field of view by focussing on communication with other vehicles. The exchange of large volume of data can pose a threat to the user&#x0027;s security. This calls for the need for implementation of multilevel cyber security countermeasures in order to prevent vulnerability to hacking. The use of blockchain has however been one the most useful and advanced technology developed to protect data, i.e., preserve user&#x0027;s personal information/ user&#x0027;s privacy (during communication). This paper mainly deals with the implementation of securing the information regarding next generation intelligent vehicles.
A Lightweight Object Detection Method in Aerial Images Based on Dense Feature Fusion Path Aggregation Network

In recent years, significant progress has been obtained in object detection using Convolutional Neural Networks (CNNs). However, owing to the particularity of Remote Sensing Images (RSIs), common object detection methods are not well suited for RSIs. Aiming at the difficulties in RSIs, this paper proposes an object detection method based on the Dense Feature Fusion Path Aggregation Network (DFF-PANet). Firstly, for better improving the detection performance of small and medium-sized instances, we propose Feature Reuse Module (FRM), which can integrate semantic and location information contained in feature maps; this module can reuse feature maps in the backbone to enhance the detection capability of small and medium-sized instances. After that, we design the DFF-PANet, which can help feature information extracted from the backbone to be fused more efficiently, and thus cope with the problem of external interference factors. We performed experiments on the Dataset of Object deTection in Aerial images (DOTA) dataset and the HRSC2016 dataset; the accuracy reached 71.5% mAP, which exceeds most object detectors of one-stage and two-stages at present. Meanwhile, the size of our model is only 9.2 M, which satisfies the requirement of being lightweight. The experimental results demonstrate that our method not only has better detection accuracy but also maintains high efficiency in RSIs.
Comparative study of YOLOv3 and YOLOv5's performances for real-time person detection

Deep learning algorithms have recently gained traction in smart cities and societies, such as in healthcare, surveillance, and in a variety of artificial intelligence-based real-life applications. Person detection is a big challenging task in modern computer vision applications. Thus, human appearances can be difficult to judge, as there are major differences in human postures and appearances. This paper discusses the performances of YOLO algorithms, especially YOLOv3 and YOLOv5 for person detection as a tool to enhance the security of public places in smart cities. To evaluate the performances, a challenging dataset called Penn-Fudan is used. Experimental results reveal that YOLOv3 outperforms YOLOv5 in terms of speed. However, YOLOv5 had the best recognition accuracy.
Development and validation of the e-learning satisfaction scale (eLSS)

This study was conducted to develop the e-Learning Satisfaction Scale (eLSS) and to examine its validity and reliability.
Population-based self-adaptive Generalised Masi Entropy for image segmentation: A novel representation

Image segmentation is an indispensable part of computer vision applications, and image thresholding is a popular one due to its simplicity and robustness. Generalised Masi entropy (GME) is an image thresholding method that exploits the additive/non-extensive information using entropic measure (r). r shows the measure of degree of extensibility and non-extensibility available in an image. From the literature, all research considered it as a fixed coefficient, while finding a proper value for r can enhance the efficacy of thresholding. This paper proposes a simple yet effective approach for adaptively finding a proper value for r without any background knowledge regarding the distribution of histogram. To this end, a new representation is proposed so that it can be used with any type of population-based metaheuristic (PBMH) algorithms. For the optimisation process, we use differential evolution (DE), as a representative. In addition, to further improve efficacy, we improve DE algorithm based on one-step k-means clustering, random-based sampling, Gaussian-based sampling, and opposition-based learning. Our extensive experiments compared to the most recent approaches on a set of benchmark images and in terms of several criteria clearly show that the proposed approach not only can find the proper value for r automatically but also it can improve the efficacy of GME-based image thresholding methods.
Coordination and Collaboration: How do Volunteer Moderators Work as a Team in Live Streaming Communities?

Volunteer moderators (mods) play significant roles in developing moderation standards and dealing with harmful content in their micro-communities. However, little work explores how volunteer mods work as a team. In line with prior work about understanding volunteer moderation, we interview 40 volunteer mods on Twitch - a leading live streaming platform. We identify how mods collaborate on tasks (off-streaming coordination and preparation, in-stream real-time collaboration, and relationship building both off-stream and in-stream to reinforce collaboration) and how mods contribute to moderation standards (collaboratively working on the community rulebook and individually shaping community norms). We uncover how volunteer mods work as an effective team. We also discuss how the affordances of multi-modal communication and informality of volunteer moderation contribute to task collaboration, standards development, and mod's roles and responsibilities.
Understanding, Addressing, and Analysing Digital Eye Strain in Virtual Reality Head-Mounted Displays

Digital eye strain (DES), caused by prolonged exposure to digital screens, stresses the visual system and negatively affects users’ well-being and productivity. While DES is well-studied in computer displays, its impact on users of virtual reality (VR) head-mounted displays (HMDs) is largely unexplored—despite that some of their key properties (e.g., the vergence-accommodation conflict) make VR-HMDs particularly prone. This work provides the first comprehensive investigation into DES in VR HMDs. We present results from a survey with 68 experienced users to understand DES symptoms in VR-HMDs. To help address DES, we investigate eye exercises resulting from survey answers and blue light filtering in three user studies (N = 71). Results demonstrate that eye exercises, but not blue light filtering, can effectively reduce DES. We conclude with an extensive analysis of the user studies and condense our findings in 10 key challenges that guide future work in this emerging research area.