Reviewerly/exHarmony huggingface dataset:
Enhancing Security of Mobile Cloud Computing by Trust- and Role-Based Access Control

The tremendous development in mobile technology attracts users’ attention. Thus, the users are shifting from traditional computational devices to smartphones and tablets, and because of that, mobile devices have anticipated most of the global IP traffic. However, mobile device’s resource-constrained behaviour cannot handle the heavy computational load. Mobile cloud computing (MCC) mitigates resource-constrained issues by enabling computing resources with minimal effort. However, providing security in MCC is an obstacle due to users’ uncertain and dynamic behaviour and the explosion of online computerized data. Providing security, confidentiality, and authentication is not enough in MCC; therefore, the users need authorization. Thus, the paper designs an access control mechanism by computing the trust based on the user’s uncertain behaviour. This mechanism mitigates the malicious actions caused by authenticated users. Performance results indicate that the access control mechanism accurately detects and mitigates malicious users from the MCC environment.
Asking the Proper Question: Adjusting Queries to Statistical Procedures Under Differential Privacy

We consider a dataset S held by an agency, and a vector query of interest, $$f(S) \in \mathbb {R}^k$$ , to be posed by an analyst, which contains the information required for some planned statistical inference. The agency will release an answer to the queries with noise that guarantees a given level of Differential Privacy using the well-known Gaussian noise addition mechanism. The analyst can choose to pose the original vector query f(S) or to transform the query and adjust it to improve the quality of inference of the planned statistical procedure, such as the volume of a confidence interval or the power of a given test of hypothesis. Previous transformation mechanisms that were studied focused on minimizing certain distance metrics between the original query and the one released without a specific statistical procedure in mind. Our analysis takes the Gaussian noise distribution into account, and it is non-asymptotic. In most of the literature that takes the noise distribution into account, a given query and a given statistic based on the query are considered and the statistic's asymptotic distribution is studied. In this paper we consider both non-random and random datasets, that is, samples, and our inference is on f(S) itself, or on parameters of the data generating process when S is a random sample. Our main contribution is in proving that different statistical procedures can be strictly improved by applying different specific transformations to queries, and in providing explicit transformations for different procedures in some natural situations.
When Machine Learning Models Leak: An Exploration of Synthetic Training Data

We investigate an attack on a machine learning classifier that predicts the propensity of a person or household to move (i.e., relocate) in the next two years. The attack assumes that the classifier has been made publically available and that the attacker has access to information about a certain number of target individuals. That attacker might also have information about another set of people to train an auxiliary classifier. We show that the attack is possible for target individuals independently of whether they were contained in the original training set of the classifier. However, the attack is somewhat less successful for individuals that were not contained in the original data. Based on this observation, we investigate whether training the classifier on a data set that is synthesized from the original training data, rather than using the original training data directly, would help to mitigate the effectiveness of the attack. Our experimental results show that it does not, leading us to conclude that new approaches to data synthesis must be developed if synthesized data is to resemble "unseen" individuals to an extent great enough to help to block machine learning model attacks.
Augmented reality mobile application to improve the astronomy teaching-learning process

It is during the primary education stage that children begin to awaken their interest in science and, in turn, have new mathematical, geographical, and scientific knowledge, which are the basis for understanding astronomical aspects. This research focuses on developing an Augmented Reality Mobile Application based on the Mobile-D methodology for the teaching-learning process of astronomy in 4th and 6th grade students. The random selection design of an experimental group applied to a sample of 60 students was used, subdivided into groups of 30 students each. Finally, it can be concluded that the use of an Augmented Reality mobile application for the teaching-learning process significantly influences elementary school students in the subject of astronomy.
Temporal Redundancy-Based Computation Reduction for 3D Convolutional Neural Networks

Making sense of human actions in video sequences has become an essential task in video surveillance applications. In such applications, 3D CNNs have become a prime choice due to their excellent performance. However, the performance advantage offered by these networks comes at a higher computational and memory cost. In this paper, a novel method is introduced to enhance temporal similarity at minimal accuracy degradation in input video sequences for 3D CNNs, as demonstrated through video benchmarks focusing on human action recognition. The proposed Temporal Similarity Tunnels (TST) method enhances temporal similarity in the feature maps of the initial and the subsequent frames, reducing computations in the convolutional layer of 3D CNNs. The proposed method achieves 46.5% (45%) computation reduction in the C3D network evaluated under the UCF101 (HMDB51) dataset, while maintaining an accuracy drop of <1 %. Similarly, the computation reduction in 3D-MobileNets v1 and v2 is 48% (38%) at an accuracy drop of 1 % (1.4%) for UCF10l dataset.
Enabling Energy-Efficient Inference for Self-Attention Mechanisms in Neural Networks

The study of specialized accelerators tailored for neural networks is becoming a promising topic in recent years. Such existing neural network accelerators are usually designed for convolutional neural networks (CNNs) or recurrent neural networks have been (RNNs), however, less attention has been paid to the attention mechanisms, which is an emerging neural network primitive with the ability to identify the relations within input entities. The self-attention-oriented models such as Transformer have achieved great performance on natural language processing, computer vision and machine translation. However, the self-attention mechanism has intrinsically expensive computational workloads, which increase quadratically with the number of input entities. Therefore, in this work, we propose an software-hardware co-design solution for energy-efficient self-attention inference. A prediction-based approximate self-attention mechanism is introduced to substantially reduce the runtime as well as power consumption, and then a specialized hardware architecture is designed to further increase the speedup. The design is implemented on a Xilinx XC7Z035 FPGA, and the results show that the energy efficiency is improved by 5.7x with less than 1% accuracy loss.
A Low-Storage Blockchain Framework Based on Incentive Pricing Strategies

Nowadays, blockchain bloat is an endangering issue caused by inefficient transaction storage mechanisms. Based on the Distributed File System (DFS), the blockchain network can reduce the local storage to solve the blockchain bloat problem. However, storing all blocks on DFS is not durable or scalable. Hence, classifying blocks into hot and cold was adopted in previous works. The blockchain nodes can reduce the time consumption and storage consumption by storing hot blocks locally. However, the previous works are not able to periodically check block integrity and do not provide a reward mechanism to encourage DFS system nodes to store blocks. We extend previous works based on the InterPlanetary File System (IPFS) and design an innovative scheme to incentivize IPFS nodes. The IPFS nodes are regulated with smart contracts and behave under the pricing strategy controls to increase profit. By adopting proof of retrievability, we guarantee the integrity of the blocks. Further, the redundant scheme extends our pricing strategy to improve the durability of our proposed framework. A load-balancing pricing strategy and a general pricing strategy are provided in the framework to reward the DFS nodes. Extensive experiments are presented to demonstrate that the latency and throughputs of our model are competitive, while still maintaining data integrity in the system. The additional increased throughput takes only 0.167% of that produced by the original Bitcoin and the upload latency takes only 6.67% of the mining time of the Bitcoin Mainnet. Furthermore, our load-balancing pricing strategy achieves the effectiveness to ensure the redundancy of blocks and reduces the overall storage consumption up to 97% using the load-balancing pricing strategy, compared to the non-load-balancing pricing strategy.
Monte Carlo Simulation with Experimental Research about Underwater Transmission and Imaging of Laser

Attenuation of the laser beam in underwater transmission and detection due to absorption and scattering results in a rapid reduction in energy and blurring of the image. By combining the bidirectional reflectivity distribution function (BRDF) with the Monte Carlo (MC) method, a full-link underwater imaging process model was established which comprehensively investigated the influence of water quality, transmission distance and target characteristics on imaging performance. In order to describe the transmission process of the light more accurately, by adding particles with both absorption and scattering functions in the medium, the Mie scattering theory was employed to simulate the real channel. Moreover, while setting the gate width, the pre-calibrated detector response curve was employed to build a corresponding relationship between the image grayscale and the detector collection energy, aiming to simulate the working mode of the detector in the experiment. In various imaging scenarios, the maximum relative errors between the simulated images and experimental results were within 30%, which proved the correctness of the imaging simulation model and the feasibility of the imaging MC (IMC) method to evaluate the quality of whole imaging process.
Towards Smart Home Data Interpretation Using Analogies to Natural Language Processing

Recent advances in the development of smart homes have led to the availability of a wide variety of devices providing a high level of convenience via gesture and speech control or fully automated operation. Many smart home appliances also address the aspects of safety and electricity savings by automatically powering themselves off after not being used for a while. However, many devices remain in a typical household that are not themselves “smart”, or are not primarily electric (such as heating systems). We address the savings aspect by identifying processes involving the use of multiple devices in the electrical flow data, as captured by a smart meter in a modern household, rather than focusing on a single appliance. Therefore, we introduce a novel approach to usage pattern analysis based on the idea that a pattern of device usages as a result of a resident's ‘routine’ (such as making breakfast) can be interpreted similarly to a natural language ‘sentence’; Natural Language Processing (NLP) algorithms can then be used for interpreting the residents' behavior. We introduce the notion of bag-oj-devices (BoD), derived from the bag-of-words model used in document classification. In an experiment, we show how we use this model to infer predictions about the inhabitants from device usage, such as the resident leaving for the day or just to fetch the newspaper.
IAMOT: Multi-object tracking with integrated heads and attention mechanism

Multi-object tracking is one of the most fundamental problems in computer vision with wide industrial applications. It involves the association of multiple targets across consecutive frames. The current trend has been gradually switched from tracking-by-detection paradigm to one-shot trackers. While special efforts have been made on anchor-free methods which turned out to be more suitable for the extraction of re-ID features (than anchor-based methods). However, most of the works used center points to represent the targets and regressed other properties, which lost global information and were unfavorable to ID switches. To address the above issues, we propose IAMOT, a simple yet effective network based on anchor-free architecture. Our network predicts the center point and derives the corner points of an object to obtain a refined bounding box through integrated heads. It also utilizes an additional attention module to weaken the occurrence of ID switches. Extensive experiments have been conducted on 4 mainstream datasets, where IAMOT exhibits superior performances by surpassing other state-of-the-art methods. Especially, we achieve 60.6% MOTA on MOT15, 75.8% MOTA on MOT16, 74.4% MOTA on MOT17 and 64.1% MOTA on MOT20, respectively.
Local Embedding Learning via Landmark-Based Dynamic Connections

Linear discriminant analysis (LDA) is one of the most effective and popular methods to reduce the dimensionality of data with Gaussian assumption. However, LDA cannot handle non-Gaussian data because the center point is incompetent to represent the distribution of data. Some existing methods based on graph embedding focus on exploring local structures via pairwise relationships of data for addressing the non-Gaussian issue. Due to massive pairwise relationships, the computational complexity is high as well as the locally optimal solution is hard to find. To address these issues, we propose a novel and efficient local embedding learning via landmark-based dynamic connections (LDC) in which we leverage several landmarks to represent different subclusters in the same class and establish the connections between each point and landmark. Furthermore, in order to explore the relationship of landmarks pairwise more precisely, the relationship between each point and their corresponding neighbor landmarks are found in the optimal subspace, rather than the original space, which can avoid the negative influence of the noises. We also propose an efficient iterative algorithm to deal with the proposed ratio minimization problem. Extensive experiments conducted on several real-world datasets have demonstrated the advantages of the proposed method.
Analyzing social media for measuring public attitudes toward controversies and their driving factors: a case study of migration

Among other ways of expressing opinions on media such as blogs, and forums, social media (such as Twitter) has become one of the most widely used channels by populations for expressing their opinions. With an increasing interest in the topic of migration in Europe, it is important to process and analyze these opinions. To this end, this study aims at measuring the public attitudes toward migration in terms of sentiments and hate speech from a large number of tweets crawled on the decisive topic of migration. This study introduces a knowledge base (KB) of anonymized migration-related annotated tweets termed as MigrationsKB (MGKB). The tweets from 2013 to July 2021 in the European countries that are hosts of immigrants are collected, pre-processed, and filtered using advanced topic modeling techniques. BERT-based entity linking and sentiment analysis, complemented by attention-based hate speech detection, are performed to annotate the curated tweets. Moreover, external databases are used to identify the potential social and economic factors causing negative public attitudes toward migration. The analysis aligns with the hypothesis that the countries with more migrants have fewer negative and hateful tweets. To further promote research in the interdisciplinary fields of social sciences and computer science, the outcomes are integrated into MGKB, which significantly extends the existing ontology to consider the public attitudes toward migrations and economic indicators. This study further discusses the use-cases and exploitation of MGKB. Finally, MGKB is made publicly available, fully supporting the FAIR principles.
An improved beetle antennae search path planning algorithm for vehicles

With the development of society, the application of mobile robots in industry and life is increasingly extensive, and the local path planning of mobile robots in unknown environments is a problem that needs to be solved. Aiming at the problem that the traditional beetle antennae search (BAS) algorithm can easily fall into local optimum and the optimization accuracy is low, we propose an improved beetle antennae search. It introduces a map safety threshold, the addition of virtual target points, and the smoothing of the path. Map safety threshold means extra space with obstacles at all times, improving path reliability by avoiding collisions. Adding virtual target points reduces situations where the vehicle gets stuck in local optima. The B-spline smoothing path reduces the original path's straight turns to improve the path's robustness. The effectiveness and superiority of the algorithm are verified by comparing and testing the existing path planning algorithms through simulation in different environments.
Nuclei segmentation of HE stained histopathological images based on feature global delivery connection network

The analysis of pathological images, such as cell counting and nuclear morphological measurement, is an essential part in clinical histopathology researches. Due to the diversity of uncertain cell boundaries after staining, automated nuclei segmentation of Hematoxylin-Eosin (HE) stained pathological images remains challenging. Although better performances could be achieved than most of classic image processing methods do, manual labeling is still necessary in a majority of current machine learning based segmentation strategies, which restricts further improvements of efficiency and accuracy. Aiming at the requirements of stable and efficient high-throughput pathological image analysis, an automated Feature Global Delivery Connection Network (FGDC-net) is proposed for nuclei segmentation of HE stained images. Firstly, training sample patches and their corresponding asymmetric labels are automatically generated based on a Full Mixup strategy from RGB to HSV color space. Secondly, in order to add connections between adjacent layers and achieve the purpose of feature selection, FGDC module is designed by removing the jumping connections between codecs commonly used in UNet-based image segmentation networks, which learns the relationships between channels in each layer and pass information selectively. Finally, a dynamic training strategy based on mixed loss is used to increase the generalization capability of the model by flexible epochs. The proposed improvements were verified by the ablation experiments on multiple open databases and own clinical meningioma dataset. Experimental results on multiple datasets showed that FGDC-net could effectively improve the segmentation performances of HE stained pathological images without manual interventions, and provide valuable references for clinical pathological analysis.
Evaluating Virtual Reality as a Tool for Empathic Modelling of Vision Impairment

Despite being situated in public spaces, public interactive displays (PIDs) are not always designed to be fully inclusive, particularly for people with vision impairments. Prototyping physical PIDs with accessible features can be time-consuming and there are ethical and safety barriers to navigate when recruiting appropriate participants for studies. While it is still important to represent this user group in the design process, empathic modelling can also be used to rapidly simulate some challenges people with disability might face when interacting with a system. Traditionally this method has been performed using physical props, however Virtual reality (VR) is a promising way to help amplify it due to its immersive nature. Despite this, its use as a tool for empathic design remains unexplored. Therefore, our work is aimed towards filling this gap through the evaluation of a VR prototype which simulates the experience of a visually impaired person interacting with a public interactive display. This work contributes design considerations for VR simulations to generate empathy towards people with vision impairment.
Directional Sensor Planning for Occlusion Avoidance

Directional sensors, such as video cameras, have become ubiquitous to many autonomous robots applications, such as monitoring and surveillance. The performance of these sensors and processing algorithms, however, may be hindered by the presence of objects that block visibility. This article presents a novel approach for planning the path of a mobile directional sensor deployed to observe multiple targets distributed in an environment populated with multiple obstacles and occlusions. Unlike existing art gallery or watchman’s route methods, the visibility theory and motion planners developed in this article account for both line-of-sight visibility and bounded field-of-view constraints, and can provide obstacle avoidance based on robot geometry and kinodynamic constraints. The computational complexity analysis and experiments on a camera-equipped drone demonstrate that, despite the challenging geometric characteristics of directional C-targets, the approach scales to real-world problems. Furthermore, when compared to algorithms inspired by traveling salesman and target coverage approaches, the directional visibility planners presented in this article are significantly more effective both at guaranteeing complete target visibility and at minimizing distance traveled.
Evolving Deep Multiple Kernel Learning Networks Through Genetic Algorithms

Today's Industrial Internet of Things (IIoT) have achieved excellent manufacturing efficiency and automation results by leveraging machine learning (ML) and deep learning (DL). However, trustworthiness of ML/DL brings significant challenges to IIoT. This article proposes an evolving deep multiple kernel learning network through genetic algorithm (KNGA). Our KNGA method uses genetic algorithm (GA) to find the best deep multiple kernel learning structure, including the weights and the topology of the model. Compared with the current well-known models, KNGA has advantages in three aspects: 1) It can achieve good results without using many samples during model training; 2) the model can evolve in the process of training, including self-growth, and self-pruning; and 3) its trustworthiness and reliability can be guaranteed. Moreover, the whole model ensures excellent performance and requires manual adjustment of only a few parameters. Extensive experiments on the UCI, KEEL, Caltech256, and MNIST datasets demonstrate the effectiveness and trustworthiness of the proposed method.