Reviewerly/exHarmony huggingface dataset:
Edge-assisted Puncturable Fine-grained Task Distribution for the IoT-oriented Crowdsensing

Internet of Things (IoT)-based mobile crowdsensing system has been increasingly widely deployed in data-driven smart city construction for its high efficiency, participation and flexibility. However, the mobile crowdsensing system may suffer unauthorized access to the task during the task distribution, thus imposing a threat to the city security and personal privacy. To deal with this threat, in this paper we propose an edge-assisted puncturable attribute-based access control scheme. This scheme provides efficient, one-to-many secure data sharing and delegates most of the computational overhead of resource-constrained terminal devices to the edge device. Then we design and integrate the Bloom filter into the proposed scheme to cope with the key exposure without key update. We provide rigorous security proof and the analysis of security properties. Theoretical comparison and experimental results demonstrate the feasibility and practicality of our scheme.
Finding Points on Elliptic Curves with Coppersmith’s Method

Several cryptosystems based on Elliptic Curve Cryptography such as ElGamal and KMOV process the message as a point $$M=(x_0,y_0)$$ of an elliptic curve with an equation of the form $$y^2=x^3+ax+b$$ over a finite field or a finite ring. In this paper, we present a method to find the small solutions of the former elliptic curve equation. Our method is based on Coppersmith's technique and enables one to find the solutions $$(x_0,y_0)$$ when $$|x_0|^3|y_0|^2$$ is smaller than the modulus.
Multi-sensor aided deep pose tracking

Accurate pose estimation of nearby objects is critical for robots to dynamically interact with their surroundings. The complexity of this task has led researchers to explore deep learning methods. Nowadays, many works have solely focused on developing complicated neural network architectures to estimate pose from a simple monocular camera. However, most of these methods struggle with inherent limitations of a single sensor system, like occlusion, which are commonly encountered in mobile robotics applications. Online, occlusion-robust pose estimation is extremely important in such cases, as mobility of a robot introduces major uncertainty that complicates manipulation. Hence, we present Multi-Sensor aided Deep Pose Tracking (MS-DPT), a framework for online object pose estimation to enable robust mobile manipulation. A Convolutional Neural Network (CNN) identifies key objects in an RGB-D image, from which object pose estimates are generated using a variant of the Iterative Closest Point (ICP) algorithm. An extended Kalman filter is used to fuse this pose estimate with onboard motion sensors to compensate for occlusion and robot motion during manipulation. This three stage method focuses on cohering different modalities and algorithms to improve the pose tracking stability and continuity in cases where the target object becomes heavily occluded by an obstacle or a mobile robot itself. The proposed approach accurately tracks textureless objects with high symmetries while operating at 10 FPS during experiments.
University social responsibility for the generation of ICT skills

In understanding the adaptation to emerging social needs, higher education institutions must make more flexible and develop an integration of information and communication technologies in training processes. The inclusion of the Information and Communication Technologies (ICT) in higher education institutions transforms the teacher's education and demand, their skills, and the orientation of their pedagogical practice of new knowledge, skills, and attitudes. Methodologically, documents and specialized bibliography are reviewed to assume the reflection and put forward arguments that describe some approaches for the innovation of educational practice and the transformation of the profile of the university professor in order to respond to the demands of today's society. It is concluded that it is necessary to apply a new conception of student-users, role changes in teachers, and administrative changes concerning communication systems, design, and distribution of teaching. All this implies, in turn, changes in the canons of teaching-learning towards a more flexible and technological model.
Technological Skills and Satisfaction for Post-pandemic Virtual Academic Continuity in Peruvian University Students, 2021

The study aimed to find out the relationship between technological skills, satisfaction and academic continuity of university students in a post-pandemic scenario. The study population consisted of 392 students from Peruvian universities and the exploratory factor analysis of the items was carried out for the adaptation of the 03 original instruments applied called online class satisfaction, scale of technological skills and academic continuity, as well as discriminant validity. of the theoretical model. According to the results found, the study hypotheses are verified, affirming the direct and significant relationship between technological skills and satisfaction, satisfaction and intention to continue, intention to continue and technological skills. In this way, the importance of the role of institutions to support the development of technological skills and satisfaction in order to ensure the retention of their students is highlighted.
Examination of Mammary Gland Evaluation by Using Deep Learning

High-density breasts are common among Japanese women, and thus, the detection rate of lesions is low, as they are obscured by the mammary glands during mammography screening. Therefore, attempts have been made to classify mammary gland concentration and present individuals with the risk of lesion obscuration; however, a large variability remains among doctors. In this study, we attempted to objectively classify mammary gland concentration by using deep learning. We examined the parameters of the learning image using a neural network console as a deep learning creation tool. As a result, a square image with a resolution of 64 × 64 and 100 learnings had the highest classification accuracy (88%). As the mammary gland concentration classification is a classification of the mammary gland of the entire breast, it is considered that the method by reduction has obtained a certain degree of accuracy. Next, training image data were expanded to obtain the subsequent classification accuracy. For data augmentation, the values of angle, brightness, and contrast were changed such that the image was similar to that before data augmentation. The resolution of 256 × 256 and network AlexNet were optimal, and 100% classification accuracy was obtained. In other words, the data augmentation—performed such that learning is equivalent in all classes—was considered effective. Conversely, because these are input images with similar features, they may not have various features. Therefore, verification using an unknown evaluation image will be required in the future.
Exploring Coarse-grained Pre-guided Attention to Assist Fine-grained Attention Reinforcement Learning Agents

Recently, people have applied the attention mechanism to deep reinforcement learning (DRL), which commits to helping agents focus on crucial factors to learn the task more effectively. However, there is still some margin between the current attention methods and natural human attention since evidence suggests that human attention can be pre-guided before they perform a task, allowing humans to quickly catch areas of important factors at the beginning of the task and then gradually refine fine-grained attention to learn the details during training. This allows humans to use their attention more efficiently. In this paper, we propose an attention method that mimics human attention for DRL in the Atari Games. The proposed method contains a fusion attention module, for which we build a simulated human coarse-grained pre-guided (SHCP) attention module to assist the original fine-grained attention of RL agents. The proposed SHCP attention module contains information about key objects for game tasks and is implemented as a coarse-grained attention region. The experimental results demonstrate that our method can quickly boost performance in the early stages and then outperform the current state-of-the-art fine-grained attention methods significantly in sample efficiency, just like human attention. Further analysis shows that, with fusion attention, agents can not only capture rich features of pre-guided attention but also extend to more improved features after training, which suggests the pre-guided attention signal acts as a good initializer. Therefore, we consider our work reveals a potential and promising direction that combines human attention signals to affect agents' behavior via attention mechanisms.
Motivating Learners in Multiorchestrator Mobile Edge Learning: A Stackelberg Game Approach

Mobile edge learning (MEL) is a learning paradigm that enables distributed training of machine learning (ML) models over heterogeneous edge devices (e.g., IoT devices). Multiorchestrator MEL refers to the coexistence of multiple learning tasks with different datasets, each of which being governed by an orchestrator to facilitate the distributed training process. In MEL, the training performance deteriorates without the availability of sufficient training data or computing resources. Therefore, it is crucial to motivate edge devices to become learners and offer their computing resources, and either offer their private data or receive the needed data from the orchestrator and participate in the training process of a learning task. In this work, we propose an incentive mechanism, where we formulate the orchestrators-learners’ interactions as a 2-round Stackelberg game to motivate the participation of the learners. In the first round, the learners decide which learning task to get engaged in, and then in the second round, the training parameters and the amount of data for training in case of participation such that their utility is maximized. We then study the training round analytically and derive the learners’ optimal strategy. Finally, numerical experiments have been conducted to evaluate the performance of the proposed incentive mechanism.
Retrograde Behavior Mitigation in Planner-Guided Robot Swarms

Using an automated planner to guide the behavior of a robot swarm is an effective way to control a decentralized multi-robotic system so that it can accomplish complex tasks. Although classical planning assumes that the actors are unitary agents performing atomic actions, the virtual agents in a planner-guided swarm are groups of individuals. When those individuals have differing beliefs about what step of the plan they are on, so-called retrograde behavior can occur, slowing or stopping progress and potentially causing catastrophic failure. We formally define this issue, explain several approaches to mitigate it, and report the results of experiments in simulation across three different swarm robotics scenarios. We determine that this problem can be solved by combining a plan analyzer with either offline programming changes or some form of additional online coordination, and present a decision tree for choosing the best mitigation method.
Emotion Recognition at Real-Time Applications using Meta-Learning

Emotion recognition is one of the important applications of computer vision and artificial intelligence for human computer interaction. ‘Surprise, Anger, Neutral, Fear, Sad, Happy, and Disgust’ are the seven basic emotions of humans. In this work, CMU MultiPIE database has been used to train and test the emotion recognition model. This model is then used to test the emotions of people in real-time in situations like interview, online classroom, office meetings, other human interactions etc. The emotions considered in this work are happy, surprise, angry, disgust and neutral. In interviews, a face-scanning algorithm helps to decide whether one deserves the job or not. Moving beyond recruitment, the same model can be used in schools to assess whether students are paying attention or to identify whether employees are paying attention in office meetings. It is required to develop a model that can detect emotions in videos with less number of images in the training set that works well for unknown samples. Meta-Learning provides a solution for this problem. The train database consists of front pose face images from CMU MultiPIE database. The test database contains various head pose images from CMU MultiPIE database. The proposed model provides an accuracy of 95% and 85% for training and testing respectively.
PMPNet: Pixel Movement Prediction Network for Monocular Depth Estimation in Dynamic Scenes

In this paper, we propose a novel method for monocular depth estimation in dynamic scenes. We first explore the arbitrariness of object’s movement trajectory in dynamic scenes theoretically. To overcome the arbitrariness, we use assume that points move along a straight line over short distances and then summarize it as a triangular constraint loss in two dimensional Euclidean space. This triangular loss function is used as part of our proposed pixel movement prediction network, PMPNet, to estimate a dense depth map from a single input image. To overcome the depth inconsistency problem around the edges, we propose a deformable support window module that learns features from different shapes of objects, making depth value more accurate around edge area. The proposed model is trained and tested on two outdoor datasets - KITTI and Make3D, as well as an indoor dataset - NYU Depth V2. The quantitative and qualitative results reported on these datasets demonstrate the success of our proposed model when compared against other approaches. Ablation study results on the KITTI dataset also validate the effectiveness of the proposed pixel movement prediction module as well as the deformable support window module.
Can We Really Trust Explanations? Evaluating the Stability of Feature Attribution Explanation Methods via Adversarial Attack

Explanations can increase the transparency of neural networks and make them more trustworthy. However, can we really trust explanations generated by the existing explanation methods? If the explanation methods are not stable enough, the credibility of the explanation will be greatly reduced. Previous studies seldom considered such an important issue. To this end, this paper proposes a new evaluation frame to evaluate the stability of current typical feature attribution explanation methods via textual adversarial attack. Our frame could generate adversarial examples with similar textual semantics. Such adversarial examples will make the original models have the same outputs, but make most current explanation methods deduce completely different explanations. Under this frame, we test five classical explanation methods and show their performance on several stability-related metrics. Experimental results show our evaluation is effective and could reveal the stability performance of existing explanation methods.
AR-HMD Multitask Viewing System Concept with a Supporting Handheld Viewport for Multiple Spatially-Anchored Workspaces

We propose a system concept for Augmented Reality Head-Mounted Display users, which supports multitask viewing with multiple vir-tual workspaces anchored in the real-world space. Although people encounter multitasking necessities frequently, the native AR HMD and existing interfaces lack measures to visualize multiple sets of spatially-anchored information in parallel. The system separately vi-sualizes two different sets of spatially-anchored information, one on each AR HMD and smartphone, enabling side-by-side multitasking on AR HMD without applying heavy load on users. We implemented a proof-of-concept prototype that allows side-by-side viewing of the two different virtual workspaces. The proposed concept shows promises of multitasking on AR HMD, and future research will de-velop the system to be fully functional and verified with user studies.
A Method for Analyzing Teacher Behavior in Classroom Based on the Long- and Short-Term Features of Pose Sequences

The analysis of teachers’ behaviors in classroom videos is helpful to objectively evaluate teaching, promote teaching reform, and improve teaching effectiveness. With the continuous introduction of deep learning models, there have been many analysis methods, but the recognition accuracy is not high enough. In this paper, we construct a new model combining convolutional neural network and recurrent neural network to learn and fuse short-term features with long-term features, and use the spatio-temporal information of videos to get the behavior categories. In our experiments, we collected teacher classroom videos in teaching scenarios to construct the dataset. The 2D pose heatmap sequences are extracted from the videos as model inputs, which are used to exclude the interference of environmental noise in the teaching scene. The experimental results show that this method can achieve higher recognition accuracy compared with the PoseR(2+1)D method on the teacher behavior dataset for teacher behavior analysis in teaching scenes.
Application Research in Computer Design System Based on 3D High-Definition Image Visual Communication

Aiming at the problems of blurred contours and incomplete overall distribution shape after 3D high-definition image registration, this paper proposes a multi-dimensional visual communication down-projection transformation image registration simulation. Matching points require the image to satisfy peripolar geometric constraints after correction. On the basis of the above, this paper uses 3D printing technology to obtain the internal structure of the image. This paper combines the stereo matching algorithm to simplify the huge visual communication image by using the grayscale of the pixel and the linear combination of its gradient as the similarity measure. Simulation experiments have fully verified the practicability and effectiveness of the method in this paper.
Kernel Extreme Learning Machine with Mixture Correntropy for Face Recognition

In this article, a robust kernel extreme learning machine (KELM) framework is designed using mixture correntropy for recognition of facial images. KELM is augmentation of ELM with kernel learning concept, has attained excellent performance in acknowledging numerous classification and regression problems. Due to random projection technique and no requisite of number of hidden neurons specified beforehand, KELM achieves better generalization performance than ELM. Since KELM is designed on MSE paradigm for Gaussian theory of noise, its efficiency may decrease for non-Gaussian cases. To enhance the learning speed and robustness of KELM, this work develops a new KELM framework with mixture correntropy (KELM-MXC) that opts mixture correntropy (MXC) as the optimization paradigm, instead of MSE. Experiments on face databases are performed to prove the effectiveness and comparison analogy with related state-of-the-art algorithms are reported to demonstrate the performance excellence of the presented algorithm.
An Automated Framework for Distributed Deep Learning–A Tool Demo

Split learning (SL) is a distributed deep-learning approach that enables individual data owners to train a shared model over their joint data without exchanging it with one another. SL has been the subject of much research in recent years, leading to the development of several versions for facilitating distributed learning. However, the majority of this work mainly focuses on optimizing the training process while largely ignoring the design and implementation of practical tool support. To fill this gap, we present our automated software framework for training deep neural networks from decentralized data based on our extended version of SL, termed Blind Learning. Specifically, we shed light on the underlying optimization algorithm, explain the design and implementation details of our framework, and present our preliminary evaluation results. We demonstrate that Blind Learning is 65% more computationally efficient than SL and can produce better performing models. Moreover, we show that running the same job in our framework is at least 4.5× faster than PySyft. Our goal is to spur the development of proper tool support for distributed deep learning.
An Intelligent Automated System for Porter Services in India

The major Idea of the Smart wearable railway porter Luggage System Project is to increase a railway porter tool that would be person-friendly. The task is used calculate the weight, time and distance, Generally, Staffs at Railway Stations price us randomly without understanding our weight of the bags or without understanding the space which he must carry, we advise an Effective Solution i.e. We Develop a transportable tool in which the person has to Enter the Load of the Luggage, Platform Number and we Use human Distance Tracker to Measure the distance primarily based totally on that we arrive at a cost which will become affordable for Everyone to Use which is all interconnected to a Mobile Application. Along with this system we introduced coronary heart charge sensor to degree the porter’s present day coronary heart charge, and the porter’s coronary heart charge may be uploaded to the cloud server the use of net of things.
SR-YOLO: Small Objects Detection Based on Super Resolution

Since the introduction of convolutional neural networks, object detection based on deep learning has made great progress and has been widely used in the industry. However, because the weak and small object contains too little information, the samples are rich in diversity, and there are different degrees of occlusion, the detection difficulty is too great, and the object detection has entered a bottleneck period of development. We firstly introduce a super-resolution network to solve the problem of small object pixel area being too small, and fuse the super-resolution generator with the object detection baseline model for collaborative training. In addition, in order to reinforce the weak feature of small objects, we design a convolution block based on the edge detection operator Sobel. Experiments show that proposed method achieves mAP50 improvement of 2.4% for all classes and 4.4% for the relatively weak pedestrian class on our dataset relative to the Yolov5s baseline model.
ReliefSD: Selecting Numerical Features for Fast Subgroup Discovery

Subgroup discovery (SD) identifies disproportionally distributed subsets from a dataset according to a target concept. Numerical features are often discretized before SD to avoid generating too many interval based patterns and aggravating the “pattern flooding” problem. However, early discretization greatly reduces the quality of subgroups. The addition of a few features, especially numerical features, often sharply prolongs the running time of SD, so removing irrelevant features may be a better choice. FSSD, a recently proposed non-discretization SD approach for numerical features, uses an empirical method to select a subset of features. Yet, the method ignores the labelling information, so it can not remove irrelevant features effectively. This paper analyses Relief based feature selection for SD, and suggests using interval based local subgroups to evaluate the discrimination ability of a feature. It presents ReliefSD, a novel feature selection method for SD by updating ReliefF. As interesting subgroups have many positive instances, ReliefSD only selects positive instances. Moreover, for each feature ReliefSD constructs a single feature based local subgroup whose boundary is defined by the randomly selected instance and its neighbouring positive instances. By evaluating the purity of the subgroups, ReliefSD iteratively estimates the importance of features. Experimental results on 10 UCI datasets suggest ReliefSD is the best in selecting feature subsets for FSSD when compared with the empirical method and ReliefF.