Reviewerly/exHarmony huggingface dataset:
An Improved Vibe Algorithm Based on Adaptive Thresholding and the Deep Learning-Driven Frame Difference Method

Foreground detection is the main way to identify regions of interest. The detection effectiveness determines the accuracy of subsequent behavior analysis. In order to enhance the detection effect and optimize the problems of low accuracy, this paper proposes an improved Vibe algorithm combining the frame difference method and adaptive thresholding. First, we adopt a shallow convolutional layer of VGG16 to extract the lower-level features of the image. Features images with high correlation are fused into a new image. Second, adaptive factors based on the spatio-temporal domain are introduced to divide the foreground and background. Finally, we construct an inter-frame average speed value to measure the moving speed of the foreground, which solves the mismatch problem between background change rate and model update rate. Experimental results show that our algorithm can effectively solve the drawback of the traditional method and prevent the background model from being contaminated. It suppresses the generation of ghosting, significantly improves detection accuracy, and reduces the false detection rate.
Path Planning for the Rapid Reconfiguration of a Multi-Robot Formation Using an Integrated Algorithm

Path planning is crucial in the scheduling and motion planning of multiple robots. However, solving multi-robot path-planning problems efficiently and quickly is challenging due to their high complexity and long computational time, especially when dealing with many robots. This paper presents a unified mathematical model and algorithm for the path planning of multiple robots moving from one formation to another in an area with obstacles. The problem was initially simplified by constructing a cost matrix, and then the route planning was achieved by integrating an elite enhanced multi-population genetic algorithm and an ant colony algorithm. The performance of the proposed planning method was verified through numerical simulations in various scenarios. The findings indicate that this method exhibits high computational efficiency and yields a minimal overall path distance when addressing the path-planning problem of a multi-robot formation reconstruction. As a result, it holds promising potential for the path-planning problem of a multi-robot formation reconstruction.
LHKV: A Key-Value Data Collection Mechanism Under Local Differential Privacy

Local differential privacy (LDP) is an emerging technology used to protect privacy. Users are required to locally perturb their raw data under the framework of LDP, before they are transmitted to the server. This technology can be applied to various data types, including key-value data. However, in existing LDP mechanisms for key-value data, it is difficult to balance data utility and communication costs, particularly when the domain of keys is large. In this paper we propose a local-hashing-based mechanism called LHKV for collecting key-value data. LHKV can maintain high utility and keep the end-to-end communication costs low. We provide theoretical proof that LHKV satisfies $$\epsilon $$ -LDP and analyze the variances of frequency and mean estimations. Moreover, we employ Fast Local Hashing to accelerate the aggregation and estimation process, which significantly reduces computation costs. We also conduct experiments to demonstrate that, in comparison with the existing mechanisms, LHKV can effectively reduce communication costs without sacrificing utility while ensuring the same LDP guarantees.
Secure Computation Protocol of Text Similarity against Malicious Attacks for Text Classification in Deep-Learning Technology

With the development of deep learning, the demand for similarity matching between texts in text classification is becoming increasingly high. How to match texts quickly under the premise of keeping private information secure has become a research hotspot. However, most existing protocols currently have full set limitations, and the applicability of these methods is limited when the data size is large and scattered. Therefore, this paper applies the secure vector calculation method for text similarity matching in the case of data without any complete set constraints, and it designs a secure computation protocol of text similarity (SCTS) based on the semi-honest model. At the same time, elliptic-curve cryptography technology is used to greatly improve the execution efficiency of the protocol. In addition, we also analyzed the possibility of the malicious behavior of participants in the semi-honest-model protocol, and further designed an SCTS protocol suitable for the malicious model using the cut-and-choose and zero-knowledge-proof methods. By proposing a security mechanism, this protocol aims to provide a reliable and secure computing solution that can effectively prevent malicious attacks and interference. Finally, through the analysis of the efficiencies of the existing protocols, the efficiencies of the protocols under the malicious model are further verified, and the practical value for text classification in deep learning is demonstrated.
Kernel Block Diagonal Representation Subspace Clustering with Similarity Preservation

Subspace clustering methods based on the low-rank and sparse model are effective strategies for high-dimensional data clustering. However, most existing low-rank and sparse methods with self-expression can only deal with linear structure data effectively, but they cannot handle data with complex nonlinear structure well. Although kernel subspace clustering methods can efficiently deal with nonlinear structure data, some similarity information between samples may be lost when the original data are reconstructed in the kernel space. Moreover, these kernel subspace clustering methods may not obtain an affinity matrix with an optimal block diagonal structure. In this paper, we propose a novel subspace clustering method termed kernel block diagonal representation subspace clustering with similarity preservation (KBDSP). KBDSP contains three contributions: (1) an affinity matrix with block diagonal structure is generated by introducing a block diagonal representation term; (2) a similarity-preserving regularizer is constructed and embedded into our model by minimizing the discrepancy between inner products of original data and inner products of reconstructed data in the kernel space, which better preserve the similarity information between original data; (3) the KBDSP model is proposed by integrating the block diagonal representation term and similarity-preserving regularizer into the kernel self-expressing frame. The optimization of our proposed model is solved efficiently by utilizing the alternating direction method of multipliers (ADMM). Experimental results on nine datasets demonstrate the effectiveness of the proposed method.
Analyzing the Emotions That News Agencies Express towards Candidates during Electoral Campaigns: 2018 Brazilian Presidential Election as a Case of Study

Since online social networks play an increasingly important role in the final voting decision of each individual, political parties and candidates are changing the way of doing politics and campaigning, increasing their digital presence. In this paper, we propose a methodology to analyze and measure the emotions that news agencies express on social media towards candidates and apply it to the 2018 Brazilian elections. The presented method is based on a sentiment analysis and emotion mining by means of machine learning and Natural Language Processing approaches such as Naïve Bayes classification and Stemming calculation. We found that if doing basic sentiment detection, nearly all posts are neutral. However, when we analyze emotions, following Ekman’s six basic emotions, we do not find neutrality but clear and identifiable emotions. Next, we present and discuss the associative patterns between news agencies and presidential candidates. Finally, since the candidate that captured the highest and most negative attention emerged victorious in the elections, we discuss the potential importance of having a social media presence, regardless of generating positive or negative emotions.
Block pruning residual networks using Multi-Armed Bandits

ABSTRACTRecently, deep neural networks have been adopted in many application domains showing impressive performances that sometimes even exceed those of humans. However, the considerable size of deep learning models and their computational cost limits their use in mobile and embedded systems. Pruning is an important technique that has been used in the literature to compress a trained model by removing some of its elements (weights, neurons, filters, etc.) that do not notably contribute to its performance. In this paper, we propose a new block pruning method for residual networks that removes holes layers of the network based on the Multi-Armed Bandits (MAB) framework. The proposed method takes advantage of the existence of multiple trainable paths in residual models to identify and prune the less important residual blocks for inference. To evaluate our approach, we conducted extensive experiments using three different MAB algorithms, namely ε-greedy, Win Stay Lose Shift (WSLS), and the Upper Confidence Bound (UCB). The obtained results on different datasets show that our block pruning method can considerably reduce the model size and FLOPs with little to no impact on overall accuracy. We conducted a comparative analysis of our method with three existing techniques to demonstrate that our approach is highly competitive in terms of accuracy, while significantly reducing both model size and FLOPs. Moreover, our experimental results demonstrate that when our block pruning method is combined with a filter-level pruning technique, it leads to even greater reductions in model size and FLOPs while maintaining a relatively high accuracy.KEYWORDS: Deep learningNeural network compressionMulti-armed bandits Disclosure statementNo potential conflict of interest was reported by the author(s).Notes1. The code available online for BlockDrop(Wu et al., Citation2018), HRank (Lin et al., Citation2020), and CHIP (Sui et al., Citation2021) does not include ResNet152. Also, the available code for BlockDrop(Wu et al., Citation2018) does not include ResNet110 with the ImageNet dataset
Recognition and Measurement of Crypto-Assets from the Perspective of Retail Holders

The Markets in Crypto-Assets (MiCa) Regulation of the European Union is the first comprehensive piece of legislation that seeks to protect the interests of investors in the crypto-assets sector. Although the market value of crypto-assets is significant at world level, there is a lack of clear regulatory guidelines regarding the recognition, measurement, and presentation of crypto-assets in the financial statements of investors. Considering that not all digital assets are the same, retail holders need to take into account the characteristics, rights, and obligations associated with the crypto-assets they purchase to determine the appropriate accounting method. Therefore, the research question of the present article is: Which are the main types of crypto-assets and how should they be recognized and measured in the financial statements of investors and holders? We perform a review of the accounting policies and options, relying on relevant regulations, standards, regulatory drafts, legal and academic papers, recommendations of market regulators, crypto-asset white papers, industry opinions, and media articles. There are different accounting treatments that can be applied, depending on the legal and technological aspects of each class of crypto-assets. Based on a critical discussion of accounting policies and options, our research has implications for accounting professionals, but also for standard setters, who are urged to provide clear guidelines. Identifying the key economic characteristics of each asset and determining the most appropriate way to recognize these characteristics in the financial statements are crucial for the development of a functional and trustworthy market in crypto-assets.
Federated Learning for Clients’ Data Privacy Assurance in Food Service Industry

The food service industry must ensure that service facilities are free of foodborne pathogens hosted by organic residues and biofilms. Foodborne diseases put customers at risk and compromise the reputations of service providers. Fluorescence imaging, empowered by state-of-the-art artificial intelligence (AI) algorithms, can detect invisible residues. However, using AI requires large datasets that are most effective when collected from actual users, raising concerns about data privacy and possible leakage of sensitive information. In this study, we employed a decentralized privacy-preserving technology to address client data privacy issues. When federated learning (FL) is used, there is no need for data sharing across clients or data centralization on a server. We used FL and a new fluorescence imaging technology and applied two deep learning models, MobileNetv3 and DeepLabv3+, to identify and segment invisible residues on food preparation equipment and surfaces. We used FedML as our FL framework and Fedavg as the aggregation algorithm. The model achieved training and testing accuracies of 95.83% and 94.94% for classification between clean and contamination frames, respectively, and resulted in intersection over union (IoU) scores of 91.23% and 89.45% for training and testing, respectively, of segmentation of the contaminated areas. The results demonstrated that using federated learning combined with fluorescence imaging and deep learning algorithms can improve the performance of cleanliness auditing systems while assuring client data privacy.
Global Guided Cross-Modal Cross-Scale Network for RGB-D Salient Object Detection

RGB-D saliency detection aims to accurately localize salient regions using the complementary information of a depth map. Global contexts carried by the deep layer are key to salient objection detection, but they are diluted when transferred to shallower layers. Besides, depth maps may contain misleading information due to the depth sensors. To tackle these issues, in this paper, we propose a new cross-modal cross-scale network for RGB-D salient object detection, where the global context information provides global guidance to boost performance in complex scenarios. First, we introduce a global guided cross-modal and cross-scale module named G2CMCSM to realize global guided cross-modal cross-scale fusion. Then, we employ feature refinement modules for progressive refinement in a coarse-to-fine manner. In addition, we adopt a hybrid loss function to supervise the training of G2CMCSNet over different scales. With all these modules working together, G2CMCSNet effectively enhances both salient object details and salient object localization. Extensive experiments on challenging benchmark datasets demonstrate that our G2CMCSNet outperforms existing state-of-the-art methods.
Influential Topographic Factor Identification of Soil Heavy Metals Using GeoDetector: The Effects of DEM Resolution and Pollution Sources

Heavy metals (HMs) entering soils are redistributed by natural processes such as surface runoff and soil erosion. These natural processes are characterized by topographic factors (TFs, e.g., Topographic Wetness Index (TWI), Total Catchment Area (TCA), Slope, and Aspect), which are commonly quantified by a digital elevation model (DEM) of a certain spatial resolution. Nevertheless, few studies have examined how DEM resolutions affect the detection of influential TFs of soil HMs. In this study, we first applied the GeoDetector method to explore the coupling between the eight TFs and the concentrations of eight soil HMs under a gradient of DEM resolutions. We found that the important sorting of eight TFs on one HM at different resolutions is inconsistent. For example, for Hg, TWI emerged to be the dominant factor among the eight TFs at 90 m resolution, whereas TCA took the lead at 3000 m resolution. Moreover, the results strongly deny the existence of an optimal resolution (OR) among the HMs for any specific topographical factor. We further applied a source apportionment model (Positive Matrix Factorization—PMF) to explore the effects of five identified pollution sources and the underlying environmental processes on the inconsistent ORs. The main reason for such OR inconsistency is that each HM may be released from various sources and subsequently undergo environmental processes that are topographically modulated at different spatial scales. The main reason for such OR inconsistency is that each HM may have various sources and subsequent environmental processes that happen at different spatial scales. Moreover, each TF could simultaneously reflect different transport and transformation processes. Therefore, the apparent OR for one metal is jointly composed of the preferences of all the sources it contains; thus, it cannot be determined by the OR preferences of a single source alone. Based on the composition and intensity of pollution sources, we propose three possible strategies for a more robust GeoDetector analysis. The findings reported here provide new insights into the proper use of GeoDetector for selecting the appropriate DEM resolutions when identifying influential environmental factors.
An Optimized and Stable CF<sup>2</sup> Mobile Robot Motion Planning Approach and Adaptation for Moving Targets

AbstractIn this paper, the mobile robot motion planning approach (PSO-CF2-mt: PSO-CF2 for moving targets) is improved to track a moving target following a smooth path. PSO-CF2-mt was tested previously in various static and dynamic environments and proved its capacity to track moving targets whatever the form of the target’s trajectory. The problem with PSO-CF2-mt that we want to face in this paper is the stability of the robot’s motion. It is tackled by limiting the angular velocity of the robot. The angular speed limit is PSO selected and the variance of the angular speed is added to the fitness function as a third objective. This new version of PSO-CF2-mt is called OS-CF2-mt (Optimized Stable CF2-mt). Simulation results prove the capacity of OS-CF2-mt in ensuring stable travels for mobile robots following short, secure and smooth paths when tracking moving targets whether the environment is static or dynamic and whatever the form of the target’s trajectory.Keywords: Force Field (F2)Mobile robotMotion planningMoving targetoptimizationPSOvariance and dynamic environments Disclosure statementNo potential conflict of interest was reported by the author(s).
VibHead: An Authentication Scheme for Smart Headsets through Vibration

Recent years have witnessed the fast penetration of Virtual Reality (VR) and Augmented Reality (AR) systems into our daily life, the security and privacy issues of the VR/AR applications have been attracting considerable attention. Most VR/AR systems adopt head-mounted devices (i.e., smart headsets) to interact with users and the devices usually store the users’ private data. Hence, authentication schemes are desired for the head-mounted devices. Traditional knowledge-based authentication schemes for general personal devices have been proved vulnerable to shoulder-surfing attacks, especially considering the headsets may block the sight of the users. Although the robustness of the knowledge-based authentication can be improved by designing complicated secret codes in virtual space, this approach induces a compromise of usability. Another choice is to leverage the users’ biometrics; however, it either relies on highly advanced equipments which may not always be available in commercial headsets or introduce heavy cognitive load to users. In this paper, we propose a vibration-based authentication scheme, VibHead, for smart headsets. Since the propagation of vibration signals through human heads presents unique patterns for different individuals, VibHead employs a CNN-based model to classify registered legitimate users based the features extracted from the vibration signals. We also design a two-step authentication scheme where the above user classifiers are utilized to distinguish the legitimate user from illegitimate ones. We implement VibHead on a Microsoft HoloLens equipped with a linear motor and an IMU sensor which are commonly used in off-the-shelf personal smart devices. According to the results of our extensive experiments, with short vibration signals (≤ 1 s ), VibHead has an outstanding authentication accuracy; both FAR and FRR are around \(5\% \) .
Out of Coverage Area: Tribes and Digital Exclusion in North-east India

Digital space is fast transforming the way we communicate, building social relations, a sense of entertainment, business, political campaign and an arena of exclusion for certain social groups. The COVID-19 pandemic exposes the deep digital divide and social inequality among different social groups in India. Digital exclusion is the new horizon of social exclusion. This paper charts out where tribes in North-east India stand in terms of digital infrastructure/space. The paper argues that digitalising welfare schemes/programmes and e-governance without adequate digital/IT infrastructure further marginalised and excluded tribes from development processes leading to disempowerment.
Partially Typed Multiparty Sessions

A multiparty session formalises a set of concurrent communicating participants. We propose a type system for multiparty sessions where some communications between participants can be ignored. This allows us to type some sessions with global types representing interesting protocols, which have no type in the standard type systems. Our type system enjoys Subject Reduction, Session Fidelity and "partial" Lock-freedom. The last property ensures the absence of locks for participants with non ignored communications. A sound and complete type inference algorithm is also discussed.
A composition–decomposition based federated learning

Abstract Federated learning has been shown to be efficient for training a global model without needing to collect all data from multiple entities to the centralized server. However, the model performance, communication traffic, and data privacy and security are still the focus of federated learning after it has been developed. In this paper, a composition–decomposition based federated learning, denoted as CD-FL, is proposed. In the CD-FL approach, the global model, composed of K sub-models with the same framework, will be decomposed and broadcast to all clients. Each client will randomly choose a sub-model, update its parameters using its own dataset, and upload this sub-model to the server. All sub-models, including the sub-models before and after updating, will be clustered into K clusters to form the global model of the next round. Experimental results on Fashion-MNIST, CIFAR-10, EMNIST, and Tiny-IMAGENET datasets show the efficiency of the model performance and communication traffic of the proposed method.