Reviewerly/exHarmony huggingface dataset:
Passel: Improved Scalability and Efficiency of Distributed SVM using a Cacheless PGAS Migrating Thread Architecture

Stochastic Gradient Descent (SGD) is a valuable algorithm for large-scale machine learning, but has proven difficult to parallelize on conventional architectures because of communication and memory access issues. The HogWild series of mixed logically distributed and physically multi-threaded algorithms overcomes these issues for problems with sparse characteristics by using multiple local model vectors with asynchronous atomic updates. While this approach has proven effective for several reported examples, there are others, especially very sparse cases, that do not scale as well. This paper discusses an SGD Support Vector Machine (SVM) on a cacheless migrating thread architecture using the Hogwild algorithms as a framework. Our implementations on this novel architecture achieved superior hardware efficiency and scalability over that of a conventional cluster using MPI. Furthermore these improvements were gained using naive data partitioning techniques and hardware with substantially less compute capability than that present in conventional systems.
Deep active reinforcement learning for privacy preserve data mining in 5G environments

Frequent pattern mining (FIM) identifies the most important patterns in data sets. However, due to the huge and high-dimensional nature of transactional data, classical pattern mining techniques suffer from the limitations of dimensions and data annotations. Recently, data mining while preserving privacy is considered as an important research area. Information privacy is a tradeoff that must be considered when using data. Through many years, privacy-preserving data mining (PPDM) made use of methods that are mostly based on heuristics. The operation of deletion was used to hide the sensitive information in PPDM. In this study, we used deep active learning to protect private and sensitive information. This paper combines entropy-based active learning with an attention-based approach to effectively hide sensitive patterns. The constructed models are then validated using high-dimensional transactional data with attention-based and active learning methods in a reinforcement environment. The results show that the proposed model can support and improve the effectiveness of decision-making by increasing the number of training instances through the use of a pooling technique and an entropy uncertainty measure. The proposed paradigm can achieve data sanitization by the hiding sensitive items and avoiding to hide the non-sensitive items. The model outperforms greedy, genetic, and particle swarm optimization approaches.
Question type and answer related keywords aware question generation

The traditional end-to-end Neural Question Generation (NQG) models tend to generate generic and bland questions, as there are two obscure points: 1) the modifications of the answer in the context can be used as the clues to the answer mentioned in the question, while they are generally not unique and can be used independently for generating diverse questions; 2) the same question content can also be asked in diverse ways, which depends on personal preference in practice. The above-mentioned two points are indeed two variables to conduct question generation, but they are not annotated in the original dataset and are thus ignored by the traditional end-to-end models. In this paper we propose a framework that clarifies those two points through two sub-modules to better conduct question generation. We take experiments based on the GPT-2 model and the SQuAD dataset, and prove that our framework can improve the performance measured by similarity metrics, while it also provides appropriate alternatives for controllable diversity enhancement.
Integrating Context and Intention for Optimal Semantic Web Service Composition Using AI Planning

This paper deals with an approach for the dynamic composition of web service (WS) based on the user’s intention and context. Our idea to implement this approach is based on the combination of artificial intelligence (AI) planning techniques and the semantic aspect describing the web service that will be composed. To this end, we will transform the web service composition problem into an AI planning problem. First, conversion of web service semantically described in OWL and OWL-S to an alternative problem and domain description specified in the planning domain description language (PDDL) is made. Then, a planner (AI Planner) using the Genetic Algorithm (GA) features will be invoked, by adapting the PDDL planning language actions to the GA rules, in order to generate the most optimal composition plan (sequence of PDDL actions), which solves the conceived service composition problem, having a goal to achieve (the intention) according to the problem domain description (context).
A Practical and Efficient Node Blind SignCryption Scheme for the IoT Device Network

In recent years, Internet of Things (IoT for short) research has become one of the top ten most popular research topics. IoT devices also embed many sensing chips for detecting physical signals from the outside environment. In the wireless sensing network (WSN for short), a human can wear several IoT devices around her/his body such as a smart watch, smart band, smart glasses, etc. These IoT devices can collect analog environment data around the user’s body and store these data into memory after data processing. Thus far, we have discovered that some IoT devices have resource limitations such as power shortages or insufficient memory for data computation and preservation. An IoT device such as a smart band attempts to upload a user’s body information to the cloud server by adopting the public-key crypto-system to generate the corresponding cipher-text and related signature for concrete data security; in this situation, the computation time increases linearly and the device can run out of memory, which is inconvenient for users. For this reason, we consider that, if the smart IoT device can perform encryption and signature simultaneously, it can save significant resources for the execution of other applications. As a result, our approach is to design an efficient, practical, and lightweight, blind sign-cryption (SC for short) scheme for IoT device usage. Not only can our methodology offer the sensed data privacy protection efficiently, but it is also fit for the above application scenario with limited resource conditions such as battery shortage or less memory space in the IoT device network.
Low-latency head-tracking for Augmented Reality

Since the early days, latency in Augmented Reality has been known and has become one of the most popular research topics in AR. There are two types of AR technology, mobile AR and Head-Mounted Device (HMD). Our research considers HMDs because they require a higher precision when the real world is augmented with virtual objects. The delay between head movement and representation of the corresponding movement in the HMD should be minimized to increase the performance of AR. Our approach aims to reduce the latency by adding an Inertial Measurement Unit (IMU) sensor to the HMD, equipped with a camera to enable the tracking on an open-hardware 3D-printed AR headset. The IMU has been used for the user&#x0027;s head position with low latency. However, the IMU is connected by USB to a the computer that oerfirm the rendering, and we are there conducting experiments to determine the delay between the IMU and frames rendered by the computer with and without game engines. The research concludes that the delay we got from our study between IMU and a computer is 65.73 ms on average, and a little higher when we used a game engine, 87.60 ms on average.
Automatic Face Mask Detection Using Deep Learning

COVID-19 shook the entire world with its highly infectious transmission and death rate. As per CDC guidelines, wearing a mask can effectively reduce the spread of COVID-19 and create a protective barrier against the virus until the efficacy of currently available vaccines reaches 100% and the majority of people get vaccinated. Wearing masks is highly recommended almost everywhere, in schools, stores, movies, etc. to prevent the spread of this virus, however, monitoring people to see whether they wear masks is not an easy task. As a result, different face mask detection models were proposed. In this paper, we introduce a face mask detection model using deep learning. We have taken into consideration three different categories to train our model, Mask, No Mask, and Incorrect Mask. The proposed model has achieved 96% accuracy.
Optimization study on installation angle optimization of vacuum tube collector in high altitude towns in Western Sichuan

Western Sichuan is rich in solar energy resources due to its high altitude, large temperature difference between day and night and low temperature all year round. Vacuum tube collectors have been widely used in high altitude towns in western Sichuan due to their advantages of simple structure, cold resistance and frost resistance. Accurately designing the installation inclination angle of solar collectors is an important condition to improve the utilization rate of solar energy resources. Therefore, this paper selected 6 representative high-altitude towns in western Sichuan to analyse the influence of installation angle on various solar radiation energy collected by vacuum tube collector, the characteristics of the optimal installation angle and the influencing factors of the optimal installation angle. The results show that the optimum installation angle of vacuum tube collector is mainly determined by direct radiation and scattered radiation. For the vacuum tube collector located due south, except for the influence of local latitude, the ratio Ra of scattered radiation to total radiation has a significant effect on the annual optimal inclination angle. In this paper, the optimum installation angle and recommended application range of annual installation angle for vacuum tube collectors in 6 towns are obtained.
Aircraft Type Recognition in Multi-view Optical Images Using Coarse-to-Fine Framework

Aircraft type recognition in multi-view optical images is a challenging task. The imaging perspective is vulnerable to aircraft posture, flying height, and other factors. Such issues often cause the large intra-class difference within the captured scenes. In this work, we propose a novel coarse-to-fine aircraft type recognition framework in multi-view optical images, which consists of two main parts: instance segmentation model and contour template matching model. In the coarse stage, a contour-based instance segmentation model is applied to extract the aircraft contour in coordinate pointset form. Then inner-distance shape context (IDSC) is adopted as contour feature descriptor for contour template matching. In the fine stage, we design a novel matching evaluation criterion to select the optimal matching template. Experimental results show that the proposed approach achieves the top1 accuracy of 71.3% and top3 of 90.8% on the established aircraft type testing set.
Secure and Flexible Key Protected Identity Framework for Mobile Devices

Mobile or IOT based applications are emerging rapidly across the globe and there is a massive digital transformation happening within each country. It is a need of an hour to improve and protect digital identity during online transactions through handheld devices. This paper proposes a Mobile ID solution based on Mobile-originated PKI without the need for the actual identity card or a card reader. The solution proposed focuses on security, privacy, and usability using open standards which will protect Personally Identifiable Information (PII) over handheld devices. The proposed mobile ID solution has better cost-efficacy and privacy than today’s scenario. It also explicates the Mobile ID solution with established secure identity among users, authorities, other organizations of public, and private sectors.
Wheelchair driving strategies: A comparison between standard joystick and gaze-based control

This paper aims to evaluate and compare the driving performances achieved with a power wheelchair using a standard joystick versus a novel gaze-based technology. The gaze-based interface, called RoboEYE, involves a novel paradigm of computer interaction that handles the receipt of information from an eye tracker, using it as a continuous input for wheelchair navigation. A pool of 36 subjects has tested both technologies in a circuit designed considering the Wheelchair Skill Test. The experimental analysis involved evaluations of specific metrics of motion and the submission of questionnaires to collect required information about perceived feelings and mental workload. The joystick proved to be the best driving interface. It turned out to be more accurate and efficient than the gaze-based solution. However, the latter achieved only small differences in driving kinematics. These differences can be considered negligible from an operational point of view, offering a driving experience similar to that achievable with the joystick. Testers reported no particular stress, fatigue, or frustration when switching from one interface to another. These elements suggest that the proposed gaze-based solution is an appropriate alternative for a technology transition driven by a pathological change in the user's condition.
Monitoring and evaluation of people in indoors and outdoors using deep learning

The monitoring of people in health centers and geriatric homes is performed by rehabilitation professionals who manually evaluate the surveillance cameras for identifying one person’s position, and his physical condition. However, this task is tedious and demands the full attention of the rehabilitation staff because patients with neurological conditions need special care or in some cases the 24/7 monitoring. On the other hand, the use of artificial intelligence in the detection of objects and people through images or videos has presented a great performance. This article presents a methodology based on deep learning for the detection and monitoring of people in closed and open environments using video. The proposed method is non-invasive, low-cost, and evaluates the physical activity and inactivity of people in real-time. Preliminary results in public databases present outstanding results in the monitoring and estimation of caloric expenditure in people in indoor and outdoor spaces.
Comparison of K- N earest Neighbor (K -NN) and Naïve Bayes Algorithm for Sentiment Analysis on Google Play Store Textual Reviews

Fintech Lending/Peer-to-Peer Lending in its application has developed very rapidly. As an agricultural country, Indonesia is undoubtedly a market asset for the banking world to improve its services, especially in the capital. IGrow, as part of a crowdfunding company registered with the Indonesian Financial Services Authority (OJK), has taken an essential role in the field of agricultural capital. I Grow has a Fintech platform on the Google Play Store that has mixed reviews and opinions among users. Various opinions and reviews from users will undoubtedly have a positive and negative impact on the development of the business. The purpose of this study is to provide sentiment analysis of the IGROW platform on the Google Play Store. The method used in this study uses the K-Nearest Neighbor (K-NN) Algorithm and the Naive Bayes classification algorithm. The study results show that the accuracy, precision, and recall values of K-NN and Naive Bayes classification are (73.85%, 76.60%, 85.71 %) and (75.38%, 80.95%, 80.95%). The Naive Bayes classification produces slightly better predictions than K-NN by getting slightly better accuracy and precision values, but K-NN gets better values on recall.
Hyperledger Fabric Blockchain Framework: Efficient Solution for Academic Certificate Decentralized Repository

During the past few decades, the number of forgery of government-issued certificates has increased dramatically, causing considerable economic and reputational damage to the test centers involved. Traditional verification methods involve the use of e-mail, telephone, and proxy for background checks. This may require more cost, safety and time. Blockchain technology can solve this problem. Blockchain based solutions have underlying features like security, tamper proof, transparency and consensus. In addition, the data history can be managed on a blockchain network that can be accessed anytime and anywhere. The purpose of this article is to analyze blockchain technology and establish a permissioned blockchain-based academic certificate verification system HLFeCERT for issuing and verifying certificates using the SHA256 hash algorithm and digital watermarking technology for encryption and decryption of academic certificates to prevent tampering. This means that students do not need to share all their certificates publically, but only share whichever they want to. This is made possible using private blockchain.
Collision-Line Counting Method Using DeepSORT to Count Pedestrian Flow Density and Hungary Algorithm

With the great success of deep learning in the field of machine vision, crowd density statistics based on machine vision have become more and more important in today's world. In order to calculate the density of people flow in a certain area, this paper combines the YOLOv4 objective detection algorithm and the DeepSORT multi-objective tracking algorithm, and uses the Kalman filter algorithm and the Hungarian algorithm for trajectory prediction and data association. And it is proposed to carry out density statistics through the Collision-line counting method. This method uses the ID information and trajectory information of pedestrians in the objective tracking process to count the number of people and directions passing through a certain set line segment, and indirectly count the density of pedestrian flow in the corresponding area. This method can also be used in conjunction with multiple cameras at multiple entrances and exits in a certain area, and has the characteristics of flexible operation and high precision. In addition, this method can also be used to detect traffic flow.
State of art on object detection solution applied to COVID 19’s spreading prevention

In order to effectively prevent the spread of COVID19, people from different parts of the world were supposed to be wearing face masks after the WHO put it as a primordial instruction to stop its propagation. Researchers from different backgrounds gathered their efforts to ensure the respect of wearing face mask, namely AI field researchers. In this research, we are interested on the AI applications that were done from the beginning of the pandemic to prevent the COVID 19 contamination, especially those related to the mask wearing detection. The detection of wearing mask is classified as a computer vision problem, more specifically, an object detection one. Besides, with the evolution of the computational power and the availability of huge number of datasets, deep learning models using image and video processing techniques were proposed in order to detect people transgressing the wearing mask rule. In this paper we introduce a literature review of object detection, a case study of this problem which consists in the wearing mask detection, the related works as well as the different proposed solutions, and the suggested general pipeline for the treatment of this problem.
Tracking the Spawning of Group with Poisson multi-Bernoulli mixture Filter

In group target tracking, spawning is an important event of group target motion. This paper proposes a Poisson multi-Bernoulli mixture (PMBM) filter based on group target spawning. The algorithm models the target extension by random matrix and predicts spawning event according to multiple hypothesis structure. The missed detection can be reduced due to the priori information given by spawned subgroup targets. The simulation shows that the proposed algorithm could improve the tracking performance for group spawning event.
SemFSR: An Unsupervised Face SR with Semantic Features for Multiple Degradations

Face Super-Resolution (FSR) field has witnessed significant progress with the development of deep learning, which is also widely applied in high-level vision tasks as the preprocessing step. Recently, FSR methods are exploring to utilize facial priors in the reconstruction of High-Resolution (HR) faces. However, facial priors directly extracted from Low-Resolution (LR) faces are less accurate or even unavailable. Meanwhile, the generalization ability of FSR methods can be further improved across different degradations, e.g., manual interpolated degradations, and real-world degradation with noise and blur. To tackle these problems, we propose a coarse-to-fine unsupervised FSR method based on semantic features called SemFSR under multiple degradations. The SemFSR contains the Degradation Stage and the Generation Stage. The Degradation Stage learns to generate degraded LR faces interfered with noise and blur. At the Generation Stage, we firstly reconstruct the "Coarse-SR" face from the degraded LR face for more accurate semantic features. Then we further propose the Channel Attention Block with Semantic features (CAB-S) and Semantic Loss to reconstruct the "Fine-SR" face. Both quantitative and qualitative experiments demonstrate the superiority of our SemFSR when encountering multiple degraded LR faces, compared with the state-of-the-art supervised and unsupervised FSR methods.
Real-time unstructured road detection based on CNN and Gibbs energy function

Road detection algorithms with high robustness as well as timeliness are the basis for developing intelligent assisted driving systems. To improve the robustness as well as the timeliness of unstructured road detection, a new algorithm is proposed in this paper. First, for the first frame in the video, the homography matrix H is estimated based on the improved random sample consensus (RANSAC) algorithm for different regions in the image, and the features of H are automatically extracted using convolutional neural network (CNN), which in turn enables road detection. Secondly, in order to improve the rate of subsequent similar frame detection, the color as well as texture features of the road are extracted from the detection results of the first frame, and the corresponding Gaussian mixture models (GMMs) are constructed based on Orchard-Bouman, and then the Gibbs energy function is used to achieve road detection in subsequent frames. Finally, the above algorithm is verified in a real unstructured road scene, and the experimental results show that the algorithm is 98.4% accurate and can process 58 frames per second with 1024×960 pixels.
Acceleration of DNN-Based Video Object Detection Using Temporal Dependency of the Object Size

Many studies have been proposed to improve the accuracy of Deep Neural Network based object detection. Some of them bring an increase in computation, which is a problem in tasks such as autonomous driving that requires high accuracy and low latency. Feature Pyramid Network (FPN) is a structure commonly used in improving the accuracy of object detection. However, it slows down the inference speed because of the high computation. To accelerate the inference while maintaining the accuracy of FPN, this paper proposes dynamic acceleration of object detection with FPN using temporal dependency of object sizes in a video. We modify FPN to have faster inference speed when targeting certain object sizes. By using the previous object sizes, the target object size is determined. The modified FPN is used in a dynamic manner, which speeds up the inference. In this method, we achieve 20.9% faster inference at the cost of a 0.06 mAP drop on the ImageNet VID validation dataset.