Reviewerly/exHarmony huggingface dataset:
A fault-tolerant sensor fusion in mobile robots using multiple model Kalman filters

Accurate localization is crucial in the navigation of mobile robots. However, in other circumstances, single-sensor localization faces different challenges, including software and hardware problems or data outages. Sensor fusion is used in most autonomous vehicles (including aerial and ground vehicles) to overcome such challenges. In this paper, the localization of a mobile robot is studied in the presence of sensor faults. The mobile robot has two sensors: two Inertial Measurement Units (IMU) and wheel encoders. Regarding the fault-tolerant scheme, measurements of both sets of sensors are fused using an Interacting Multiple Model (IMM) Kalman filter based on both unscented and extended Kalman filters (UKF and EKF). UKF and EKF-based IMM are chosen for this study since the dynamic model of the localization is highly nonlinear. Regarding contributions, it should be noted that this scheme eliminates the need to model every single fault scenario and use an additional sensor to oversee the performance of the sensing system. Also, comparing this method with similar approaches adopted by other studies shows better performance regarding the cost of computations and RMSE. To evaluate performance, the outputs of the proposed filters are simulated and compared for different trajectories where the data of each sensor is intentionally corrupted to observe the fault detection capability. Simulations are performed for different trajectories and noises to demonstrate this method’s efficiency in different situations. In addition, the results of unscented and extended Kalman filter-based IMM are compared in terms of error and computational costs to evaluate their performance. Overall, simulation and experiments indicate accurate 3D estimations in all cases. Moreover, designated weights vividly show that sensor fault detection is achieved by both unscented and extended IMM Kalman filters, which enable complete fault isolation consequently. This approach provides mobile robots with a reliable and straightforward sensor fault detection and localization solution.
Planting Undetectable Backdoors in Machine Learning Models : [Extended Abstract]

Given the computational cost and technical expertise required to train machine learning models, users may delegate the task of learning to a service provider. Delegation of learning has clear benefits, and at the same time raises serious concerns of trust. This work studies possible abuses of power by untrusted learners.We show how a malicious learner can plant an undetectable backdoor into a classifier. On the surface, such a backdoored classifier behaves normally, but in reality, the learner maintains a mechanism for changing the classification of any input, with only a slight perturbation. Importantly, without the appropriate “backdoor key,” the mechanism is hidden and cannot be detected by any computationally-bounded observer. We demonstrate two frameworks for planting undetectable backdoors, with incomparable guarantees.•First, we show how to plant a backdoor in any model, using digital signature schemes. The construction guarantees that given query access to the original model and the backdoored version, it is computationally infeasible to find even a single input where they differ. This property implies that the backdoored model has generalization error comparable with the original model. Moreover, even if the distinguisher can request backdoored inputs of its choice, they cannot backdoor a new input—a property we call non-replicability.•Second, we demonstrate how to insert undetectable backdoors in models trained using the Random Fourier Features (RFF) learning paradigm (Rahimi, Recht; NeurIPS 2007). In this construction, undetectability holds against powerful white-box distinguishers: given a complete description of the network and the training data, no efficient distinguisher can guess whether the model is “clean” or contains a backdoor. The backdooring algorithm executes the RFF algorithm faithfully on the given training data, tampering only with its random coins. We prove this strong guarantee under the hardness of the Continuous Learning With Errors problem (Bruna, Regev, Song, Tang; STOC 2021). We show a similar white-box undetectable backdoor for random ReLU networks based on the hardness of Sparse PCA (Berthet, Rigollet; COLT 2013).Our construction of undetectable backdoors also sheds light on the related issue of robustness to adversarial examples. In particular, by constructing undetectable backdoor for an “adversarially-robust” learning algorithm, we can produce a classifier that is indistinguishable from a robust classifier, but where every input has an adversarial example! In this way, the existence of undetectable backdoors represent a significant theoretical roadblock to certifying adversarial robustness.
Image Watermarking between Conventional and Learning-Based Techniques: A Literature Review

Currently, most transactions and exchanges are conducted through the Internet thanks to technological tools, running the risk of the falsification and distortion of information. This is due to the massive demand for the virtual world and its easy access to anyone. Image watermarking has recently emerged as one of the most important areas for protecting content and enhancing durability and resistance to these kinds of attacks. However, there is currently no integrated technology able to repel all possible kinds of attacks; the main objective of each technology remains limited to specific types of applications, meaning there are multiple opportunities to contribute to the development of this field. Recently, the image watermarking field has gained significant benefits from the sudden popularity of deep learning and its outstanding success in the field of information security. Thus, in this article, we will describe the bridge by which the watermarking field has evolved from traditional technology to intelligent technologies based on deep learning.
Parallel random swap: An efficient and reliable clustering algorithm in Java

Solving large-scale clustering problems requires an efficient algorithm that can also be implemented in parallel. K-means would be suitable, but it can lead to an inaccurate clustering result. To overcome this problem, we present a parallel version of the random swap clustering algorithm. It combines the scalability of k-means with the high clustering accuracy of random swap. The algorithm is implemented in Java in two ways. The first implementation uses Java parallel streams and lambda expressions. The solution exploits a built-in multi-threaded organization capable of offering competitive speedup. The second implementation is achieved on top of the Theatre actor system which ensures better scalability and high-performance computing through fine-grain resource control. The two implementations are then applied to standard benchmark datasets, with a varying population size and distribution of managed records, dimensionality of data points and the number of clusters. The experimental results confirm that high-quality clustering can be obtained together with a very good execution efficiency. Our Java code is publicly available at: https://github.com/uef-machine-learning.
Understanding AI-based customer service resistance: A perspective of defective AI features and tri-dimensional distrusting beliefs

Communicating with customers through AI-based chatbots in customer service (AISC) has become increasingly popular for many companies. However, in actual service encounters, AISC seems defective and is not always accepted by customers. Occasionally it is even resisted. This study aims to investigate such customer resistance. In addition to two cognition-centered AI features (i.e., irrelevant and biased information) discussed in prior studies, this study proposes that lack of empathy is another key feature of defective AI (i.e., in its emotional dimension) and investigates the underlying mechanism of empathy. Specifically, this study proposes three pathways in which empathy functions are lacking. A survey was conducted to test our hypotheses, and the results suggest that lack of empathy has three effects on customer resistance: direct, indirect, and moderating. Finally, theoretical contributions and practical implications are discussed.
A Strategy for Detecting Malicious Spam Emails using various Classifiers

Today's society relies heavily on electronic mail as the primary method of exchanging essential information. Emails are a simple and quick way to share and receive electronic communication. Gmail, Hotmail, Yahoo! Mail, and Outlook are just a few software platforms and programs that may transmit and receive data. Spam emails have risen in tandem with the enormous increase in email usage. This paper proposes a strategy for detecting malicious spam emails using various classifiers, and compares typical Machine Learning algorithms based on accuracy, recall, and precision.
Semantic Communications With Discrete-Time Analog Transmission: A PAPR Perspective

Recent progress in deep learning (DL)-based joint source-channel coding (DeepJSCC) has led to a new paradigm of semantic communications. Two salient features of DeepJSCC-based semantic communications are the exploitation of semantic-aware features directly from the source signal, and the discrete-time analog transmission (DTAT) of these features. Compared with traditional digital communications, semantic communications with DeepJSCC provide superior reconstruction performance at the receiver and graceful degradation with diminishing channel quality, but also exhibit a large peak-to-average power ratio (PAPR) in the transmitted signal. An open question has been whether the gains of DeepJSCC come at the expense of high-PAPR continuous-amplitude signal, which can limit its adoption in practice. In this letter, we first show that conventional DeepJSCC does suffer from high PAPR. Then, we explore three PAPR reduction techniques and confirm that the superior image reconstruction performance of DeepJSCC can be retained while the PAPR is suppressed to an acceptable level. This is an important step towards the implementation of DeepJSCC in practical semantic communication systems.
Multiple Graph Affinity Interactive Network and a Variable Illumination Dataset for RGBT Image Salient Object Detection

Salient object detection (SOD) of images refers to simulating the attention mechanism of human vision to capture the most attractive objects in an image. Current SOD mainly relies on RGB images captured by optical cameras. However, existing optical cameras are not comparable to the human visual system, especially in poorly illumination scenes. Our human visual system is able to resolve scenes well in low light conditions, while optical cameras can barely image without enough illumination. To make machine vision closer to the imaging of the human eye, we propose to use thermal infrared (T) images to compensate RGB images and build a variable illumination RGBT dataset named VI-RGBT1500 for SOD. This dataset is collected under three different illumination conditions including sufficient illumination, uneven illumination and insufficient illumination to fully demonstrate the superiority of the RGBT image combination. Furthermore, we propose a multiple graph affinity interactive (MGAI) network to validate the proposed dataset. Our network structure is simple using only the MGAI to fuse the features of different modalities. Meanwhile, the MGAI model highlights valuable information during the interaction, which facilitates feature representation under variable illumination. The proposed VI-RGBT1500 dataset and three publicly available RGBT SOD datasets are used for the comparison experiments, and the results with the state-of-the-art methods prove that our VI-RGBT1500 dataset is valuable and the performance of the MGAI network is competitive. The VI-RGBT1500 dataset and the MGAI network are available at: <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/huanglm-me/VI-RGBT1500</uri> .
Coding energy knowledge in constructed responses with explainable<scp>NLP</scp>models

Abstract Background Formative assessments are needed to enable monitoring how student knowledge develops throughout a unit. Constructed response items which require learners to formulate their own free‐text responses are well suited for testing their active knowledge. However, assessing such constructed responses in an automated fashion is a complex task and requires the application of natural language processing methodology. In this article, we implement and evaluate multiple machine learning models for coding energy knowledge in free‐text responses of German K‐12 students to items in formative science assessments which were conducted during synchronous online learning sessions. Dataset The dataset we collected for this purpose consists of German constructed responses from 38 different items dealing with aspects of energy such as manifestation and transformation. The units and items were implemented with the help of project‐based pedagogy and evidence‐centered design, and the responses were coded for seven core ideas concerning the manifestation and transformation of energy. The data was collected from students in seventh, eighth and ninth grade. Methodology We train various transformer‐ and feature‐based models and compare their ability to recognize the respective ideas in students' writing. Moreover, as domain knowledge and its development can be formally modeled through knowledge networks, we evaluate how well the detection of the ideas within responses translated into accurate co‐occurrence‐based knowledge networks. Finally, in terms of the descriptive accuracy of our models, we inspect what features played a role for which prediction outcome and if the models pick up on undesired shortcuts. In addition to this, we analyze how much the models match human coders in what evidence within responses they consider important for their coding decisions. Results A model based on a modified GBERT‐large can achieve the overall most promising results, although descriptive accuracy varies much more than predictive accuracy for the different ideas assessed. For reasons of comparability, we also evaluate the same machine learning architecture using the SciEntsBank 3‐Way benchmark with an English RoBERTa‐large model, where it achieves state‐of‐the‐art results in two out of three evaluation categories.
Object Counting in Remote Sensing via Triple Attention and Scale-Aware Network

Object counting is a fundamental task in remote sensing analysis. Nevertheless, it has been barely studied compared with object counting in natural images due to the challenging factors, e.g., background clutter and scale variation. This paper proposes a triple attention and scale-aware network (TASNet). Specifically, a triple view attention (TVA) module is adopted to remedy the background clutter, which executes three-dimension attention operations on the input tensor. In this case, it can capture the interaction dependencies between three dimensions to distinguish the object region. Meanwhile, a pyramid feature aggregation (PFA) module is employed to relieve the scale variation. The PFA module is built in a four-branch architecture, and each branch has a similar structure composed of dilated convolution layers to enlarge the receptive field. Furthermore, a scale transmit connection is introduced to enable the lower branch to acquire the upper branch’s scale, increasing the output’s scale diversity. Experimental results on remote sensing datasets prove that the proposed model can address the issues of background clutter and scale variation. Moreover, it outperforms the state-of-the-art (SOTA) competitors subjectively and objectively.
A Secure Certificateless Signcryption Scheme Without Pairing for Internet of Medical Things

The Internet of Medical Things (IoMT), which integrates medical sensors with the Internet of Things, is helpful for providing remote diagnosis and real-time decision making. Massive data collected by medical and healthcare monitoring sensors in the IoMT involves sensitive patient information. It brings some security challenges to validate the legitimacy of participating entities and protect patient data privacy. A certificateless signcryption (CLSC) scheme combines encryption and signature that can offer authenticity, confidentiality, and unforgeability, providing a viable solution to the data privacy issue of the IoMT. However, existing CLSC schemes fail to meet confidentiality or unforgeability, or require expensive computation overhead to perform pairing operations. This article first presents a new CLSC scheme for secure data transmission and better smart services in IoMT, which replaces the signature part with the Schnorr signature. We then give a thorough security proof under the random oracle model. Besides, we elaborately evaluate the performance and security of some existing solutions with our solution. Finally, the experiment results indicate that our solution can achieve a better balance between security and performance than some existing schemes. Therefore, in terms of feasibility, our scheme is more suitable for the IoMT scenario.
Blockchain and Sustainability Disclosure: A Scenario-Based Application for Supply Chains

This paper presents the implications of blockchain technologies on sustainability reporting and disclosure, and specifically proposes blockchain use-cases as a possible solution for problems experienced in the field of supply chain carbon information. This study addresses how the reliability of supply chains’ carbon-related information can become more transparent and reliable through a decentralized approach based on blockchain thinking (BT), issues that have been identified as a gap in the literature and in the practice. Scenario analysis and design science research (DSR) are used as a methodological driver to conceptualize over the nature of practical solutions using unified modeling language (UML) diagrams. The resulting use-case focuses on data retrieval in the supply chain. The paper also presents implications for the audit industry and their role in the assurance of such technological architecture implementations. The study is visionary as it offers a conceptualization based on scenario analysis. Developing a scenario enables researchers to depict a prospective situation, develop ability to solve future problems, and to back cast them in current policies, technologies, and actions.
Multilayer vectorization to develop a deeper image feature learning model

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Visual extensions and anomaly detection in the pNEUMA experiment with a swarm of drones

The usage of Unmanned Aerial Systems (UAS) in traffic monitoring has advantages such as broader vision, flexibility, privacy, and cost-efficiency compared to other traffic monitoring sensors like loop detectors or fixed surveillance cameras. UAS has been instrumental in the recent pNEUMA experiment, where a swarm of drones collected a large-scale urban traffic dataset containing vehicle trajectories. These trajectory data are subject to challenges such as noise due to visual restrictions, perspective distortions, and human-induced errors. While the pNEUMA dataset is missing its imagery part, we present an extended version of it named pNEUMA Vision, which incorporates imagery data and annotations of vehicles in the form of image coordinates along with newly added vehicle trajectory features like azimuth. Moreover, we demonstrate that visually restricted trajectories are also highly prone to become anomalies, and vice-versa, through novel anomaly detection proposed in this literature. Specifically, we distinguish between stationary and non-stationary errors and argue that the latter account for the largest part of the noise. Furthermore, we analyze the new visual dataset with two different computer vision methods for estimating the number of vehicles on the roads from the input images. Particularly, we show that using a density map to count vehicles from drone images achieves comparable results to conventional vehicle counting methods via detection. Results show that the time–space diagrams plotted from density map predictions could identify the congested urban roads’ queues better than a widely used vehicle detection method.
Research on multi-label user classification of social media based on ML-KNN algorithm

Several research studies have been conducted on multi-label classification algorithms for text and images, but few have been conducted on multi-label classification for users. Moreover, the existing multi-label user classification algorithm does not provide an effective representation of users, and it is difficult to use directly in social media scenarios. By analyzing complex social networks, this paper aims to achieve multi-label classification of users based on research in single-label classification. Considering the limitations of existing research, this paper proposes a user topic classification method based on heterogeneous networks as well as a user multi-label classification method based on community detection. The model is trained using the ML-KNN multi-label classification algorithm. In actual scenarios, the algorithm is more effective than existing multi-label classification methods when applied to multi-label classification tasks for social media users. According to the results of the analysis, the algorithm has a high level of accuracy in classifying different theme users into a variety of different scenarios using different theme users. Furthermore, this study contributes to the advancement of classification research by expanding its perspective.
Multitasking of sentiment detection and emotion recognition in code-mixed Hinglish data

As the number of non-native English speakers on social media has skyrocketed in recent years, sentiment and emotion analysis on regional languages and code-mixed data has gained traction. Despite extensive research on English, the area of Hindi–English code-mixed texts is still relatively new and understudied. We create an emotion annotated Hindi–English (Hinglish) code-mixed dataset by performing emotion annotation on the benchmark SentiMix dataset to solve this problem and enable future researchers to contribute to this domain. We propose an end-to-end transformer-based multitask framework for sentiment detection and emotion recognition from the SentiMix code-mixed dataset. We fine-tune the pre-trained cross-lingual embedding model, XLMR, using task-specific data to further exploit the efficacy of transfer learning to improve the overall efficiency of our methods. Our proposed multi-task solution outperforms the state-of-the-art single-task and multitask baselines by a considerable margin, implying that the auxiliary task (i.e. emotion recognition) increases the efficiency of the primary task (i.e. sentiment detection) in a multi-task environment. It should be noted that the reported findings were obtained without the use of any ensemble techniques, thereby adhering to a model of effective and production-ready NLP.