Reviewerly/exHarmony huggingface dataset:
A privacy-preserving trajectory data synthesis framework based on differential privacy

Trajectory data contains spatio-temporal information about a large number of moving objects, and analysis of this data can yield a lot of valuable information to support a variety of application requirements related to moving objects. However, publishing unprotected trajectory data carries the risk of revealing personal privacy. In the existing approaches, the utility of published trajectory data is sacrificed for privacy protection. In this paper, we propose a differential privacy mechanism DP-Std for data publication in synthetic trajectory databases. first, the DP-Std mechanism constructs a density- and distance-aware adaptive grid structure for discrete geographic regions, and adds noise in the process to satisfy differential privacy. Second, DP-Std extracts feature distributions to ensure the utility of synthetic trajectories. Third, DP-Std constructs a mobility model that samples intermediate location points. And then the trajectory dataset is synthesized by differential privacy distribution and mobility model. Finally, we conduct experiments on two datasets to evaluate our framework. The results show that DP-Std outperforms other feature-based trajectory synthesis methods in terms of data utility and achieves a trade-off between privacy and utility with strict privacy protection.
Combinatory categorial grammars as generators of weighted forests

Combinatory Categorial Grammar (CCG) is an extension of categorial grammar that is well-established in computational linguistics. It is mildly context-sensitive, so it is efficiently parsable and reaches an expressiveness that is suitable for describing natural languages. Weighted CCG (wCCG) are introduced as a natural extension of CCG with weights taken from an arbitrary commutative semiring. Their expressive power is compared to other weighted formalisms with special emphasis on the weighted forests generated by wCCG since the ability to express the underlying syntactic structure of an input sentence is a vital feature of CCG in the area of natural language processing. Building on recent results for the expressivity in the unweighted setting, the corresponding results are derived for the weighted setting for any commutative semiring. More precisely, the weighted forests generatable by wCCG are also generatable by weighted simple monadic context-free tree grammar (wsCFTG). If the rule system is restricted to application rules and composition rules of first degree, then the generatable weighted forests are exactly the regular weighted forests. Finally, when only application rules are allowed, then a proper subset of the regular weighted forests is generatable.
An Adaptive Plug-and-Play (PnP) Interactive Platform for an E-Voting Based Cybersecurity Curricula

An electronic voting (e-voting) based interactive cybersecurity education curriculum has been proposed recently. It is well-known that assignments and projects are coherent parts of and important for any curriculum. This paper proposes a set of course projects, assignment design, and a coherent online plug-and-play (PnP) platform implementation. The PnP platform and the proposed exemplary assignments and projects, are systematic (derived from the same system), adaptive (smoothly increasing difficulty), flexible (bound to protocols instead of implementations), and interactive (teacher-student and student-student interactions). They allow students to implement parts of the components of this e-voting system, which they can then plug into the PnP system, to run, test and modify their implementations, and to enhance their knowledge and skills on cryptography, cybersecurity, and software engineering.
The structural tuning of the convolutional neural network for speaker identification in mel frequency cepstrum coefficients space

As a result of the literature analysis, the main methods for speaker identification from speech signals were defined. These are statistical methods based on Gaussian mixture model and a universal background model, as well as neural network methods, in particular, using convolutional or Siamese neural networks. The main characteristics of these methods are the recognition performance, a number of parameters, and the training time. High recognition performance is achieved by using convolutional neural networks, but a number of parameters of these networks are much higher than for statistical methods, although lower than for Siamese neural networks. A large number of parameters require a large training set, which is not always available for the researcher. In addition, despite the effectiveness of convolutional neural networks, model size and inference efficiency remain important fordevices with a limited source of computing power, such as peripheral or mobile devices. Therefore, the aspects of tuning of the structure of existing convolutional neural networks are relevant for research. In this work, we have performed a structural tuning of an existing convolutional neural network based on the VGGNet architecture for speaker identification in the space of mel frequency cepstrum coefficients. The aim of the work was to reduce the number of neural network parameters and, as a result, to reduce the network training time, provided that the recognition performance is sufficient (the correct recognition is above 95 %). The neural network proposed as a result of structural tuning has fewer layers than the architecture of the basic neural network. Insteadof the ReLU activation function, the related Leaky ReLU function with a parameter of 0.1 was used. The number of filters and the size of kernels in convolutional layers are changed. The size of kernels for the max pooling layer has been increased. It is proposedto use the averaging of the results of each convolution to input a two-dimensional convolution results to a fully connected layer with the Softmax activation function. The performed experiment showed that the number of parameters of the proposed neural network is 29 % less than the number of parameters of the basic neural network, provided that the speaker recognition performance is almost the same. In addition, the training time of the proposed and basic neural networks was evaluated on five datasets of audio recordings corresponding to different numbers of speakers. The training time of the proposed network was reduced by 10-39 % compared to the basic neural network. The results of the research show the advisability of the structural tuning of the convolutional neural network for devices with a limited source of computing, namely, peripheral or mobile devices.
A Critical Commentary on 'Mapping Emoji Usage Among Youth'

This critical commentary provides an overview and critique of the article titled "Mapping emoji usage among youth" by Reema Shah and Ruchi Tewari. The study explores the psychological and socio-behavioral effects of emoji usage among youthful university students, drawing on the contagion theory and information-signal theory. The article emphasizes the potential of emojis to be developed as a separate language, influencing networks in a non-linear fashion and enabling targeted marketing campaigns. While the article offers a comprehensive literature review and employs a mixed research methodology, it lacks a new construct or philosophical underpinning. The limitations include a lack of diversity among participants and reliance on self-reported data from participants. The conclusion suggests future investigations into the multicultural context of emoticons and their impact on group activities. Overall, the article presents a well-structured exploration of visual communication through emojis.
Lazily-Verifiable SAT Proof Checker in SPARK 2014

Efficient and verifiable SAT proof checking is an important topic in logic-based AI as well as for trusted explanations from machine learning models. However, the development of efficient and verifiable methods often leads to advanced methods in interactive theorem provers or workarounds, such as untrusted proof-annotation phases, due to the difficulty of the implementation of efficient unit propagation. In this work-in-progress paper, we explore a way towards efficient and verifiable SAT proof checkers for the RUP proof format. In essence, we propose an architecture that is based on several assumptions corresponding to well-known theorems in propositional logic, that allows C-similar performance for the proof checker as well as a significant reduction in the verification effort. The architecture guarantees soundness of the resulting proof checker.
Augmented, Virtual and Mixed Realities and their potential in Teaching and Learning: A Systematic Literature Review

Immersive technologies such as Augmented Reality, Virtual Reality and Mixed Reality (AR/VR/MR) in education have been found to be effective both for the instructors and the students, in general education, in diverse levels and fields. The aim of this paper is to carry out a systematic literature review on the use of AR/VR/MR in education. The objective is to provide a comprehensive summary of the existing research on the use of AR/VR/MR, and to see whether its implementation in teaching has any impact on students’ learning outcomes. As such, this can also be used to inform future research, policy, or practice. In this study, during the data collection process, we selected 142 articles. After several screening process, a total of 58 papers were retained. Findings have revealed that motivation, self-study, satisfaction, retention of memory and overall performance amongst others, have been some of the benefits of AR/VR/MR in teaching and learning. Lack of resources, poorly designed user interface and lessons plans, high cost of apps development and maintenance, along with cybersickness and reticence from some teachers due to lack of training, are perceived as common barriers in the implementation of this emerging yet useful technology in education. Future investigation can be directed towards these challenges.
La prospettiva della lingua giuridica

The norms and uses of gender language are not mere practical rules, but imply a radical (even revolutionary) change in the philosophy of society and of language, and can constitute - if applied in a flexible and reasoned way - a fundamental stimulus for language juridical reaches what should be its essential character, according to the idea of ​​the greatest jurists: clarity.
Misuse of Data as a Teaching Tool

This work reports on the implementation of a self-contained data-literacy exercise designed for use in undergraduate classes to help students practice data literacy skills such as interpreting and evaluating evidence and assessing arguments based on data. The exercises use already developed data-visualizations to test and develop students’ ability to evaluate arguments based on data presented visually. Moreover, the exercises are designed to teach students positive lessons from evaluating flawed examples of data usage. We show that repeated use of these exercises has the potential to help students develop tools they can use across multiple contexts when evaluating data. Student feedback and panel survey data show that students like to learn using these tools and they report increased comfort levels in working with data. The paper argues that instructional tools like these data visualization exercises are a quick and effective tool for teaching data literacy skills and that they have significant utility in non-research-methods courses.
Data Poisoning Attacks to Session-Based Recommender Systems

Recommender systems are used to assist users in discovering their interests from websites. Companies deployed various types of recommender systems, including content-based, collaborative filtering-based, and session-based. Especially, session-based recommender systems have been deployed successfully in industry. In this work, we conduct the systematic study on data poisoning attacks to session-based recommender systems. An attacker's goal is to promote a target item such that it can be recommended to as many people as possible. Our attack injects fake users with carefully crafted interaction sessions (e.g., clicking sessions) into the recommender system to achieve this goal. The critical challenge is to choose and arrange the items in interaction sessions. We formulate our attack as an optimization problem to address this challenge, so that the injected sessions would maximize the number of users to whom the target items are recommended. We evaluate our experiments on several real-world datasets, which show that our attack methods outperform existing methods.
Improving transfer ability of adversarial sample based on adversarial transformation

In recent years, Deep neural networks (DNN) have caused a huge sensation, showing outstanding capabilities in the fields of computer vision tasks and natural language processing. It has been gradually discovered that DNN are easily disturbed by adversarial samples, which are formed by superimposing the original samples and tiny perturbations. Although these tiny perturbations are imperceptible to the naked eye, they can significantly interfere with the model output. In security-related fields, adversarial examples bring huge hidden dangers to the deployment of DNN systems. When testing and evaluating DNN systems, researchers usually study the robustness of deep neural networks using transfer-based attacks, which are black-box attacks using carefully crafted adversarial examples from the source model. Adversarial samples with strong transfer ability are more aggressive to black-box models, so how to improve the transfer ability of adversarial samples has attracted the attention of many scholars. Since the existing methods based on adversarial transformation to improve the transfer ability of adversarial samples are more complicated to train and cost to attack, this paper uses a cycle-consistent generative adversarial network to implement adversarial transformation, which reduces the attack cost and training cost. At the same time, our extensive experiments on the CIFAR series datasets verify the superiority of this generation method in improving the transfer ability.
A Modified Binary Arithmetic Optimization Algorithm for Feature Selection

Feature selection chooses the optimal subset from the feature set without scarifying the information carried by the dataset. It is considered a complex combinatorial problem, so classical optimization techniques fail to solve it when the feature set becomes larger. Meta-heuristic approaches are well known to solve complex optimization problems; hence these algorithms have been successfully applied to extract optimal feature subsets. The arithmetic Optimization Algorithm is a newly proposed mathematics-based meta-heuristic search algorithm successfully applied to solve optimization problems. However, it has been observed that AOA experiences a poor exploration phase. Hence in the present work, a Modified Binary Arithmetic Optimization Algorithm (MB-AOA) is proposed, which solves the poor exploration problem of standard AOA. In the MB-AOA, instead of utilizing a single best solution, an optimal solution set that gradually shrinks after each successive iteration is applied for better exploration during initial iterations. Also, instead of a fixed search parameter (μ), the MB-AOA utilizes a variable parameter suitable for binary optimization problems. The proposed method is evaluated over seven real-life datasets from the UCI repository as a feature selection wrapper method and compared with standard AOA over two performance metrics, Average Accuracy, F-score, and the generated feature subset size. MB-AOA has performed better in six datasets regarding F-score and average accuracy. The obtained results from the simulation process demonstrate that the MB-AOA can select the relevant features, thus improving the classification task’s overall accuracy levels.
A Method for Designing the Architecture of Intelligent Transportation Systems in the People's Republic of China

Relevance of the research topic. With the rapid development of the social economy and technology, the number of existing cars and drivers has increased rapidly, and the construction of the urban road information management system has been relatively lagging behind, leading to the incompatibility of the existing traffic management model with the rapidly growing demand for transportation. Intelligent transportation systems (ITS) have been developed very complete and mature in many developed countries in the world and are widely used. With the development of technology, intelligent transportation systems will be increasingly used in urban traffic. The development of intelligent transport will be a promising direction for the development of transport in cities of the second and third echelon.
Quantifying Attribution-based Explainable AI for Robustness Evaluations

On the one hand, Deep Neural Networks (DNNs) create new opportunities in many digitisation applications such as complex Object Detection (OD) in autonomous driving. On the other hand, the black-box properties of DNNs pose a serious challenge for their application in safety and security-critical domains, where liability and trustworthiness are key requirements. Because formal verification of DNNs is only feasible in very restricted settings, one generally has to rely on empirical robustness tests. Here we investigate the use of explainable artificial intelligence (XAI) to improve such robustness evaluations. Integrated gradients (IG) is an attribution-based XAI method and provides heatmaps that indicate the relevance of inputs for predictions. In contrast to many contributions on improving and comparing XAI techniques, this paper proposes two interpretability metrics to add XAI methods to empirical trustworthiness evaluations of DNNs. In this way XAI becomes usable in real-time applications. In another contribution, we introduce the integration of these metrics into a robustness testing framework where a model trained on the Audi Autonomous Driving Dataset (A2D2) is evaluated with perturbed images that mimic naturally occurring inputs.
Dictionary Fields: Learning a Neural Basis Decomposition

We present Dictionary Fields, a novel neural representation which decomposes a signal into a product of factors, each represented by a classical or neural field representation, operating on transformed input coordinates. More specifically, we factorize a signal into a coefficient field and a basis field, and exploit periodic coordinate transformations to apply the same basis functions across multiple locations and scales. Our experiments show that Dictionary Fields lead to improvements in approximation quality, compactness, and training time when compared to previous fast reconstruction methods. Experimentally, our representation achieves better image approximation quality on 2D image regression tasks, higher geometric quality when reconstructing 3D signed distance fields, and higher compactness for radiance field reconstruction tasks. Furthermore, Dictionary Fields enable generalization to unseen images/3D scenes by sharing bases across signals during training which greatly benefits use cases such as image regression from partial observations and few-shot radiance field reconstruction.
CommonsenseVIS: Visualizing and Understanding Commonsense Reasoning Capabilities of Natural Language Models

Recently, large pretrained language models have achieved compelling performance on commonsense benchmarks. Nevertheless, it is unclear what commonsense knowledge the models learn and whether they solely exploit spurious patterns. Feature attributions are popular explainability techniques that identify important input concepts for model outputs. However, commonsense knowledge tends to be implicit and rarely explicitly presented in inputs. These methods cannot infer models' implicit reasoning over mentioned concepts. We present CommonsenseVIS, a visual explanatory system that utilizes external commonsense knowledge bases to contextualize model behavior for commonsense question-answering. Specifically, we extract relevant commonsense knowledge in inputs as references to align model behavior with human knowledge. Our system features multi-level visualization and interactive model probing and editing for different concepts and their underlying relations. Through a user study, we show that CommonsenseVIS helps NLP experts conduct a systematic and scalable visual analysis of models' relational reasoning over concepts in different situations.
TSD-Depth: Using transformers and self-distilling for self-supervised indoor depth estimation

Supervised monocular depth estimation has always been one of the most important tasks in computer vision. With the convolution module as a basic operator, the U-shaped network architecture has become the de facto standard and has achieved tremendous success. However, due to the limited receptive field of the convolution operation, the CNNs are generally inferior in explicitly modeling the long-range dependencies. Originally proposed for natural language processing, the transformers are designed for performing sequence-to-sequence predictions based on global self-attention mechanism. Therefore, the transformers can capture long-range dependencies. However, they have limited localization abilities due to insufficient low-level details. In this work, we propose a TSD-Depth model, which merits both the transformers and the CNNs, as a strong alternative for self-supervised monocular depth estimation. The proposed model simultaneously extracts the global contextual information and local spatial detail features. Furthermore, by designing the hybrid encoder connection method and proper-sized transformer module, the global and local information can more effectively interact. In addition, a local multi-scale fusion block is first proposed to refine the fine-grained details. More importantly, the knowledge is learned by using self-distillation to skip the multi-scale fusion block concatenated with the encoder at the inference time, computed only during the training process for minimal overhead. The experimental results on NYU-v2 and ScanNet datasets show that the proposed TSD-Depth achieves the best performance as compared to the previous state-of-the-art methods.
Trajectory Data Collection with Local Differential Privacy

Trajectory data collection is a common task with many applications in our daily lives. Analyzing trajectory data enables service providers to enhance their services, which ultimately benefits users. However, directly collecting trajectory data may give rise to privacy-related issues that cannot be ignored. Local differential privacy (LDP), as the de facto privacy protection standard in a decentralized setting, enables users to perturb their trajectories locally and provides a provable privacy guarantee. Existing approaches to private trajectory data collection in a local setting typically use relaxed versions of LDP, which cannot provide a strict privacy guarantee, or require some external knowledge that is impractical to obtain and update in a timely manner. To tackle these problems, we propose a novel trajectory perturbation mechanism that relies solely on an underlying location set and satisfies pure $\epsilon$-LDP to provide a stringent privacy guarantee. In the proposed mechanism, each point's adjacent direction information in the trajectory is used in its perturbation process. Such information serves as an effective clue to connect neighboring points and can be used to restrict the possible region of a perturbed point in order to enhance utility. To the best of our knowledge, our study is the first to use direction information for trajectory perturbation under LDP. Furthermore, based on this mechanism, we present an anchor-based method that adaptively restricts the region of each perturbed trajectory, thereby significantly boosting performance without violating the privacy constraint. Extensive experiments on both real-world and synthetic datasets demonstrate the effectiveness of the proposed mechanisms.