Reviewerly/exHarmony huggingface dataset:
Garbage Detection and Classification using Faster-RCNN with Inception-V2

Street garbage monitoring is important for ensuring a clean and healthy civic environment. In this context, automatic detection and classification of waste/litter objects on the streets is necessary for proper garbage disposal. Specifically, such detection and classification of waste objects could be utilized in developing automated waste sorting applications. This paper attempts to build a Faster R-CNN based predictive model for automatic classification of ten different types of waste/litter objects. Two pre-trained networks namely Inception-V2 and ResNet-101 are investigated as backbone networks for feature extraction. The performance of the proposed model is also compared with two baselines namely RFCN (region-based fully convolutional network) and SSD (single shot multibox detector). It is observed that the Faster R-CNN configured with InceptionV2 achieves the highest mAP (mean average precision) of 92%.
Selective Federated Learning for On-Road Services in Internet-of-Vehicles

The Internet-of-Vehicles (IoV) can make driving safer and bring more services to smart vehicle (SV) users. Specif-ically, with IoV, the road service provider (RSP) can collaborate with SVs to provide high-accurate on-road information-based services by implementing federated learning (FL). Nonetheless, SVs&#x0027; activities are very diverse in IoV networks, e.g., some SVs move frequently while other SVs are occasionally disconnected from the network. Consequently, obtaining information from all SVs for the learning process is costly and impractical. Furthermore, the quality-of-information (QoI) obtained by SVs also dramatically varies. That makes the learning process from all SVs simultaneously even worse when some SVs have low QoI. In this paper, we propose a novel selective FL approach for an IoV network to address these issues. Particularly, we first develop an SV selection method to determine a set of active SVs based on their location significance. In this case, we adopt a K-means algorithm to classify significant and insignificant areas where the SVs are located according to the areas&#x0027; average annual daily flow of vehicles. From the set of SVs in the significant areas, we select the best SVs for the FL execution based on the SVs&#x0027; QoI at each learning round. Through simulation results using a real-world on-road dataset, we observe that our proposed approach can converge to the FL results even with only 10&#x0025; of active SVs in the network. Moreover, our results reveal that the RSP can optimize on-road services with faster convergence up to 63&#x0025; compared with other baseline FL methods.
Evolutionary Adversarial Attacks on Payment Systems

Credit card fraud detection is arguably the most critical use case of machine learning for any payment system. Deep neural networks and tree-based classifiers can provide state-of-the-art performance for fraud classification. However, we try to emphasize that these models have serious vulnerabilities that need to be addressed. Studies show that it is possible to fool machine learning models with curated input samples known as adversarial examples. Attackers can use these examples to deceive the fraud classifiers deployed by institutions, causing considerable financial harm. We feel that the literature on adversarial examples for fraud detection systems has been limited to simpler datasets. In this paper, we use two large publicly available datasets for credit card fraud detection to benchmark the performance of some conventional machine learning models and compare the effectiveness of different black-box attacks on the best-performing model. Lastly, we introduce a novel gradient-free approach to black-box attacks, which uses evolution-based specialized perturbations to create attacks (ESPA). We show that the new method requires far fewer queries than other black-box attack methods like Zeroth Order optimization, Boundary Attack, and HopSkipJump, and can leverage the information gained from previously successful attacks.
Dual-Masking Framework against Two-Sided Model Attacks in Federated Learning

With the popularity of AIoT (Artificial Intelligence of Things) services, we can foresee that smart end devices will generate tremendous user data at the edge. In particular, it is critical to address how to properly distill knowledge from the edge network in a communication-efficient and privacy-preserving manner. Federated learning (FL), one of the promising machine learning frameworks, ensures data privacy by allowing end devices to collaboratively train a shared model without exposing raw data to an aggregation server. However, due to its distributed nature, the framework is vulnerable to two major threats: the Model Inversion Attacks and the Model Poisoning Attacks. An abnormal aggregator or malicious end devices may probably launch these attacks in the training phase. The former leaks sensitive information by reversing the model weights to users&#x0027; raw data. Still, the latter can break the model security and mislead the global model to wrong inference results. Unfortunately, the existing research has not tackled such two-sided model attacks that occurred concurrently in FL. Therefore, in this paper, we propose a dual-masking federated learning (DMFL) framework that advocates partial weights uploading in the aggregation process and applies two kinds of masks on both the end device and the aggregator sides. Based on the benchmark data for image classification, our experimental results show that the proposed DMFL framework outperforms other baselines, confirming that it can successfully preserve weights privacy and protect model security for AIoT.
Fast and accurate control of gates for quantum hypercomputation based on coherent domains of water

Abstract Quantum electrodynamics (QED) predicts that, under suitable conditions for temperature and density, liquid water can be described as a two-phases system composed by a coherent and a non-coherent phase. The first one is characterized by the formation of macroscopic regions, called “;coherent domains” (CDs), in which all the molecules are phase - correlated among them and with a self-trapped electromagnetic field that leaks out from the CDs in the form of an evanescent tail. This feature, along with the possibility for the CDs to have an excited energy spectrum, allow them to interact each other through quantum tunneling of virtual photons. We have already shown how to exploit this dynamics to realize quantum hypercomputation by using a set of universal quantum gates made of interacting water CDs. In this paper we discuss how to accurately and fast control the macroscopic quantum states of such water coherent domains, by using electromagnetic potentials to modify the phase of their wavefunctions. Finally, a possible experimental set-up designed to this aim, based on the electric version of the Aharonov-Bohm effect, is discussed.
Multimodal Topic Modeling by Exploring Characteristics of Short Text Social Media

Millions of people post images and texts to express their feelings and point of views on social media everyday, especially on the short text social media such as Twitter or Weibo. As the images can provide important supplementary information for the text, many multimodal topic models have been developed to mine the topics from the multimodal social media content. We summarize three fundamental characteristics of the short text multimodal social media. The first is that the text of a short social media document generally belong to only one topic. The second is that the attached images can be relevant to multiple topics due to the rich information expressed in the images. The last is that although in most cases, text and images in social media posts are relevant, it should be noted that in a small number of cases, text and pictures are not relevant. However, most of the current multimodal topic models fail to model the these characteristics, and thus may produce low-quality topics. Based on these characteristics, we propose an unsupervised multimodal topic model SMMTM to model the short text multimodal social media documents. In the SMMTM model, only one topic is sampled for the the text while an image can belong to different topics. The correlation of the topics between the text and the images in a document are also formulated in an appropriate way. The experiments on three short text social media datasets with four evaluation metrics show the advantages of our model over the existing models.
Unimodal Face Classification with Multimodal Training

Face recognition is a crucial task in various multimedia applications such as security check, credential access and motion sensing games. However, the task is challenging when an input face is noisy (e.g. poor-condition RGB image) or lacks certain information (e.g. 3D face without color). In this work, we propose a Multimodal Training Unimodal Test (MTUT) framework for robust face classification, which exploits the cross-modality relationship during training and applies it as a complementary of the imperfect single modality input during testing. Technically, during training, the framework (1) builds both intra-modality and cross-modality autoencoders with the aid of facial attributes to learn latent embeddings as multimodal descriptors, (2) proposes a novel multimodal embedding divergence loss to align the heterogeneous features from different modalities, which also adaptively avoids the useless modality (if any) from confusing the model. This way, the learned autoencoders can generate robust embeddings in single-modality face classification on test stage. We evaluate our framework in two face classification datasets and two kinds of testing input: (1) poor-condition image and (2) point cloud or 3D face mesh, when both 2D and 3D modalities are available for training. We experimentally show that our MTUT framework consistently outperforms ten baselines on 2D and 3D settings of both datasets <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> Code can be found in the following url: https://github.com/wbteng9526/mtut_fr.
Deriving mapping functions to tie anthropometric measurements to body mass index via interpretable machine learning

This paper aims at leveraging recent advancements in interpretable machine learning to better understand how anthropometric measurements can be tied to body mass index (BMI). Two objectives are of interest to this work, the first is to develop a properly validated interpretable machine learning (ML) ensemble capable of accurately predicting BMI, and the second is to derive a mapping function (i.e., a ML-based expression) that can describe the relationship between BMI and anthropometric measurements. This paper analyzes a historical database published by Penrose et al. (1985) containing thirteen body circumference measurements for 252 men. Four ML algorithms are then blended into an ensemble to examine the collected anthropometric measurements, namely, Extreme Gradient Boosted Trees, Light Gradient Boosted Trees, Random Forest, and Keras Slim Residual Network. The ensemble was then augmented with the SHAP (SHapley Additive exPlanations) and partial dependence plot techniques to understand the effect of each anthropometric measurement on BMI and the interaction between these anthropometric measurements. The proposed ensemble not only can comprehend the relation between anthropometric measurements and BMI index and derive a new non-parametric expression to predict BMI, but can also be used to interpret such relation and help us understand the logic driving ML’s predictions. Further, the interpretability analysis reveals that the main anthropometric measurements influencing BMI are the chest, abdomen, and hip circumference. Finally, clinicians and researchers are encouraged to leverage interpretability ML tools in their works instead of those of “black-box” nature.
Robustness Against Adversarial Attacks Using Dimensionality

Adversarial attacks have been a major deterrent towards the adoption of machine learning models reliably in many safety-critical applications. Structured perturbations introduced to test samples can fool high performing trained neural networks and force errors. We note that one of the key causes that facilitate adversarial attacks, is the curse of dimensionality or the behaviour of the high dimensional spaces. In this paper, we propose a machine learning infrastructure that is robust against adversarial attacks. The proposed pipeline uses a parallel pathway to carry out the primary machine learning task as well as identify adversarial samples. We use dimension reduction techniques to project adversarial samples into lower dimensions, thus eliminating adversarial perturbations. The end-to-end system has been tested to provide robustness, through different choices of neural architectures (VGG-19 [1], Inception ResNet v2 [2]), modes of adversarial attacks (FGSM [3] and PGD [4]) and multiple datasets (MNIST [5], CIFAR-10 and CIFAR-100 [6]). The overall accuracy of the end-to-end system was consistently found to be within a range of 1–2% (2%–4% in worst case scenarios) of what the accuracy would have been without the presence of any adversarial examples. We also show that this robustness is delivered without much compromise in time consumed for inference per sample.
Individual Attribute and Cascade Influence Capability-Based Privacy Protection Method in Social Networks

Users can obtain intelligent services by sharing information in social networks. Big data technologies can discover underlying benefits from this information. However, stringent security concern is raised at the same time. The public data can be utilized by adversaries, which will bring dire consequences. In this paper, the influence maximization problem is investigated in a privacy protection environment, which aims to find a subset of secure users that can make the spread of influence maximization and privacy disclosure minimization. At first, in order to estimate the risk level for each user, a Bayesian-based individual privacy risk evaluation model is proposed to rank the individual risk levels. Secondly, as the aim is to measure the influence capability for each user, a cascade influence capability evaluation model is designed to rank the friend influence capability levels. Finally, based on these two factors, a privacy protection method is designed for solving the influence maximization with attack constraint problem. In addition, the comparison experiments show that our method can achieve the goal of influence maximization and privacy disclosure minimization efficiently.
Digital migrants and digital diaspora: new problems and trends of international migration

The “Digital migrant”, as a concept, is used to refer to a migrant who realises his information needs by information digital technologies. This give rise to new trends in international migration processes, among which the global informatisation can be highlighted. This is expressed in the mass representation of migrants in social networks, in virtual network migrant communities’ creation. The digital diaspora being an electronic platform is the basis for migrants’ access to online public content, serves as an information base for the migrants’ digital adaptation and their virtual identity’s formation, which is becoming more and more transnational. The article proposes an instrumental definition of “digital migrant” concept, reveals factors of digital ethnic national community’s functioning, features of migrants’ social adaptation in digital diaspora, new types of interpersonal communication in digital diaspora, new trends and problems of digital migration, and new types of migrants’ identity and of digital virtual identity
Augmenting Personalized Question Recommendation with Hierarchical Information for Online Test Platform

Personalized question recommendation for students is an important research topic in the field of smart education. Current studies depend on collaborative filtering based, cognitive diagnosis based, or cognitive diagnosis based on collaborative filtering methods. However, the above methods can only model the knowledge state for a single student and the common features of similar students while ignoring students’ flat and hierarchical information. To solve the problems above, we propose an augmenting personalized question recommendation method(APQR) which combines flat and hierarchical information. Firstly, we propose a framework to capture student and question hierarchical information jointly. Secondly, we propose a cognitive diagnostic method that uses flat and hierarchical information to model students’ proficiency on each question. Finally, we recommend questions based on students’ performance by using probabilistic matrix factorization combined with students’ proficiency. We apply APQR to personalized question recommendation to demonstrate the performance improvement via an online test platform dataset. The promising results show that the proposed APQR can recommend questions to students effectively.
Design of a Robot Vision System for Microconvex Recognition

The goals of Industry 4.0 are to achieve a higher level of operational efficiency and productivity, as well as a higher level of automatization. Also, the measurement at the nano-level on the surface of the target object by a machine has been considered for automation, but there are problems such as the need for high cost and a large amount of time for measurement. In this paper, we propose a robot vision system based on an intelligent algorithm for recognizing microconvexes on arbitrary surfaces. The proposed system is inexpensive, make quick measurement and is capable of autonomously recognizing microconvexes to improve the efficiency of production processes.
Characteristics of optical flow from aerial thermal imaging, “thermal flow”

Abstract This study explores the utility of optical flow calculated from thermal imaging cameras, “thermal flow,” mounted on an aircraft for localization in day and night conditions. Our sensor implementation utilizes a long wave infrared (LWIR) micro sensor to capture sequences of thermal images and an on‐board computer to compute an optical flow estimate. We compared the performance of optical flow from the LWIR camera with the output of visible spectrum optical flow sensor. Flights were conducted spanning a 24 h window to explore how thermal flow performs relative to optical flow as the ground heats and cools. Agreement between optical and thermal flow was found during daylight when both sensors were functional. Additionally, thermal flow results were reliable in the middle of the day through to late evening, gradually degrading until shortly after sunrise.
A Systematic View of Model Leakage Risks in Deep Neural Network Systems

As deep neural networks (DNNs) continue to find applications in ever more domains, the exact nature of the neural network architecture becomes an increasingly sensitive subject, due to either intellectual property protection or risks of adversarial attacks. While prior work has explored aspects of the risk associated with model leakage, exactly which parts of the model are most sensitive and how one infers the full architecture of the DNN when nothing is known about the structure a priori are problems that have been left unexplored. In this paper we address this gap, first by presenting a schema for reasoning about model leakage holistically, and then by proposing and quantitatively evaluating DeepSniffer, a novel learning-based model extraction framework that uses no prior knowledge of the victim model. DeepSniffer is robust to architectural and system noises introduced by the complex memory hierarchy and diverse run-time system optimizations. Taking GPU platforms as a showcase, DeepSniffer performs model extraction by learning both the architecture-level execution features of kernels and the inter-layer temporal association information introduced by the common practice of DNN design. We demonstrate that DeepSniffer works experimentally in the context of an off-the-shelf Nvidia GPU platform running a variety of DNN models and that the extracted models significantly improve attempts at crafting adversarial inputs. The DeepSniffer project has been released in <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/xinghu7788/DeepSniffer</uri> .
Learning Patterns in Configuration

Large services depend on correct configuration to run efficiently and seamlessly. Checking such configuration for correctness is important because services use a large and continuously increasing number of configuration files and parameters. Yet, very few such tools exist because the permissible values for a configuration parameter are seldom specified or documented, existing at best as tribal knowledge among a few domain experts.In this paper, we address the problem of configuration pattern mining: learning configuration rules from examples. Using program synthesis and a novel string profiling algorithm, we show that we can use file contents and histories of commits to learn patterns in configuration. We have built a tool called ConfMiner that implements configuration pattern mining and have evaluated it on four large repositories containing configuration for a large-scale enterprise service. Our evaluation shows that ConfMiner learns a large variety of configuration rules with high precision and is very useful in flagging anomalous configuration.
An efficient task scheduling in cloud computing based on ACO algorithm

Resource allocation as a NP-hard problem is a very important part of cloud computing and is examined in the form of scheduling algorithms. An Ant Colony Optimization (ACO) algorithm was proposed in this study to improve the load balancing performance and makespan time parameters. Most of the tasks scheduling algorithms have been proposed to improve one of the service quality parameters for service providers or users and do not address the needs of both at the same time. Since an appropriate scheduling algorithm should be able to consider the quality requirements of users and service providers simultaneously, for this purpose in this paper we have proposed a new algorithm for scheduling tasks in cloud environment. The proposed algorithm is based on the ACO algorithm and studied in comparison to a Particle Swarm Optimization (PSO) algorithm, a Genetic Algorithm (GA) and also another research based on ACO. The proposed algorithm has showed the significant improvements concerning the makespan time, load balancing, execution time and resource utilization against the compared algorithms.
Augmenting Human Appearance Through Technological Design Layers

Augmenting human appearance with the means of technology can focus on different layers attached to or around the body. In this article, we present a categorization of human appearance and expression, with augmenting skin and its appendages, clothing and textile, accessories, body parts, and digital aura around the body. We report a non-systematic review of related works in each category and discuss their means in expressing functional, hedonic, and social aspects. In conclusion, our study contributes design perspectives on augmenting human appearances, as well as reveals challenges and opportunities.