Reviewerly/exHarmony huggingface dataset:
An Identity Privacy-Preserving Scheme against Insider Logistics Data Leakage Based on One-Time-Use Accounts

Digital transformation of the logistics industry triggered by the widespread use of Internet of Things (IoT) technology has prompted a significant revolution in logistics companies, further bringing huge dividends to society. However, the concurrent accelerated growth of logistics companies also significantly hinders the safeguarding of individual privacy. Digital identity has ascended to having the status of a prevalent privacy-protection solution, principally due to its efficacy in mitigating privacy compromises. However, the extant schemes fall short of addressing the issue of privacy breaches engendered by insider maleficence. This paper proposes an innovative identity privacy-preserving scheme aimed at addressing the quandary of internal data breaches. In this scheme, the identity provider furnishes one-time-use accounts for logistics users, thereby obviating the protracted retention of logistics data within the internal database. The scheme also employs ciphertext policy attribute-based encryption (CP-ABE) to encrypt address nodes, wherein the access privileges accorded to logistics companies are circumscribed. Therefore, internal logistics staff have to secure unequivocal authorization from users prior to accessing identity-specific data and privacy protection of user information is also concomitantly strengthened. Crucially, this scheme ameliorates internal privacy concerns, rendering it infeasible for internal interlopers to correlate the users’ authentic identities with their digital wallets. Finally, the effectiveness and reliability of the scheme are demonstrated through simulation experiments and discussions of security.
Interpreting Black-box Machine Learning Models for High Dimensional Datasets

Many datasets are of increasingly high dimension- ality, where a large number of features could be irrelevant to the learning task. The inclusion of such features would not only introduce unwanted noise but also increase computational complexity. Deep neural networks (DNNs) outperform machine learning (ML) algorithms in a variety of applications due to their effectiveness in modelling complex problems and handling high-dimensional datasets. However, due to non-linearity and higher-order feature interactions, DNN models are unavoidably opaque, making them black-box methods. In contrast, an interpretable model can identify statistically significant features and explain the way they affect the model’s outcome. In this paper, we propose a novel method to improve the interpretability of blackbox models in the case of high-dimensional datasets. First, a black-box model is trained on full feature space that learns useful embeddings on which the classification is performed. To decompose the inner principles of the black-box and to identify top-k important features (global explainability), probing and perturbing techniques are applied. An interpretable surrogate model is then trained on top-k feature space to approximate the black-box. Finally, decision rules and counterfactuals are derived from the surrogate to provide local decisions. Our approach outperforms tabular learners, e.g., TabNet and XGboost, and SHAP-based interpretability techniques, when tested on a number of datasets having dimensionality between 54 and 20,531 <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> GitHub: https://github.com/rezacsedu/DeepExplainHidim
Audio-Driven 3D Talking Face for Realistic Holographic Mixed-Reality Telepresence

Machines' ability to effectively understand human speech based on visual input is crucial for efficient communication. However, distinguishing between the semantics of speech and the facial appearance poses a challenge. This article presents a taxonomy of 3D talking human face methods, categorizing them into GAN-based, NeRF-based, and DLNN-based approaches. The evolution of mixed-reality telepresence now focuses on developing talking 3D faces that synthesize natural human faces in response to text or audio inputs. Audio-video datasets aid in training algorithms across different languages and enabling speech recognition. Addressing audio data noise is vital for robust performance, utilizing techniques like integrating DeepSpeech and adding noise. Latency optimization enhances the user experience, and careful technique selection reduces latency levels. Quantitative and qualitative evaluation methods measure synchronization, face quality, and performance comparison. Talking 3D faces hold potential for advancing mixed-reality communication, necessitating considerations of audio-video datasets, noise reduction, latency, and evaluation techniques.
Visualizing Maps of Visitors' Interest for Museum Exhibits with Single-Board Computers

Understanding the degree of satisfaction for visitors has been a key factor in selecting attractive collections and designing appealing layouts in art galleries and museums. Although monitoring the actual spatiotemporal behaviors of visitors is essential for this purpose, introducing an expensive monitoring system would impose a heavy burden on the financial management and leads to unwanted restrictions on the layout design in the exhibition rooms. This paper presents an approach to visualizing the spatiotemporal changes in the maps of visitors' interest with a system of installed single-board computers such as Raspberry Pi devices. Employing single-board computers as IoT sensors facilitates monitoring systems to maximally covers the entire exhibition space while keeping the associated installation cost and power consumption sufficiently low. Our approach for this novel system organization begins by first detecting individuals from camera images using machine learning techniques and reconstructing their spatial positions from perspective views. Kernel density estimation was employed to represent the distribution of interest across the entire exhibition room as a continuous function by respecting the reconstructed positions of visitors. This allowed the use of heatmaps to visualize the changes in the map of interest reflecting the travel history of individual visitors and the accumulated distribution of interest over a specific period. Experimental results from eight months of measurement data demonstrate the capability of the proposed approach, including meaningful trends that reveal how the layout of collections attracted visitors to the exhibitions.
Geez Part of Speech Tagging Using Deep Learning Approaches

Geez is an ancient language used in Ethiopia and Eritrea as a liturgical language and it has also gained attention in academic areas in Ethiopia and abroad. Despite its historical significance, there is a lack of computational resources for natural language processing tasks in Geez. To address this issue, this study develops a deep learning-based Geez Part-of-Speech (POS) tagger model. POS tagging is the process of labeling words in a text according to their grammatical category. A manually annotated dataset of 4981 sentences containing 30K words and 11K unique words is collected and used for training and evaluation. The dataset undergoes preprocessing techniques such as tokenization, sequencing, and sequence padding. Two experiments are conducted using LSTM, BiLSTM, GRU, and BiGRU deep learning models. The results show that the BiLSTM model achieves higher performance, with an accuracy of 94.5% in the 70-15-15 splitting and 95.01% in the 80-10-10 splitting. These experimental findings suggest that deep learning models have the potential to identify the part of speech of the Geez language. Consequently, they can be used in the development of natural language processing tools and resources for low-resource languages. Future studies may explore more sophisticated architectures and techniques to further enhance the model's accuracy on complex and diverse datasets.
A complete SHA-3 hardware library based on a high efficiency Keccak design

Hash functions are a crucial part of the cryptographic primitives. So much so that in 2007 a new competition was launched to select new standards for the SHA-3 function, which was won by Keccak. Since then, many software and hardware implementations have been submitted, claiming to reduce the number of operation cycles or increase design efficiency. Thus, this work aims to present a new hardware solution for the Keccak function, which forms the core of SHA-3, that achieves a high degree of tunability and is competitive with the state of the art. In addition, this work presents the integration of these designs into a hardware IP module together with the relevant drivers and functions that allow their use in software environments. Preliminary tests have shown an acceleration of up to 10 times compared to pure software code.
Variational quantum state discriminator for supervised machine learning

Abstract Quantum state discrimination (QSD) is a fundamental task in quantum information processing with numerous applications. We present a variational quantum algorithm that performs the minimum-error QSD, called the variational quantum state discriminator (VQSD). The VQSD uses a parameterized quantum circuit that is trained by minimizing a cost function derived from the QSD, and finds the optimal positive-operator valued measure (POVM) for distinguishing target quantum states. The VQSD is capable of discriminating even unknown states, eliminating the need for expensive quantum state tomography. Our numerical simulations and comparisons with semidefinite programming demonstrate the effectiveness of the VQSD in finding optimal POVMs for minimum-error QSD of both pure and mixed states. In addition, the VQSD can be utilized as a supervised machine learning algorithm for multi-class classification. The area under the receiver operating characteristic curve obtained in numerical simulations with the Iris flower dataset ranges from 0.97 to 1 with an average of 0.985, demonstrating excellent performance of the VQSD classifier.
Diversified Cover Selection for Image Steganography

This paper proposes a new cover selection method for steganography. We focus on the scenario that the available images for selection contain diversified sources, i.e., nature images and metaverse images. For the scenario, we design a targeted strategy to evaluate the suitability for steganography of a candidate image, which selects images according to the undetectability against steganalytic tools symmetrically. Firstly, steganalytic features of the candidate images are extracted. Then, the features are fed on a steganalytic classifier, and the possibility of carrying secret data is calculated for cover selection. As a result, the selected images are the best candidates to resist steganalysis. Experimental results show that our method performs better than existing cover selection schemes when checked by steganalytic tools.
Finite-Sample Bounds for Two-Distribution Hypothesis Tests

With the rapid growth of large language models, big data, and malicious online attacks, it has become increasingly important to have tools for anomaly detection that can distinguish machine from human, fair from unfair, and dangerous from safe. Prior work has shown that two-distribution (specified complexity) hypothesis tests are useful tools for such tasks, aiding in detecting bias in datasets and providing artificial agents with the ability to recognize artifacts that are likely to have been designed by humans and pose a threat. However, existing work on two-distribution hypothesis tests requires exact values for the specification function, which can often be costly or impossible to compute. In this work, we prove novel finite-sample bounds that allow for two-distribution hypothesis tests with only estimates of required quantities, such as specification function values. Significantly, the resulting bounds do not require knowledge of the true distribution, distinguishing them from traditional p-values. We apply our bounds to detect student cheating on multiple-choice tests, as an example where the exact specification function is unknown. We additionally apply our results to detect representational bias in machine-learning datasets and provide artificial agents with intention perception, showing that our results are consistent with prior work despite only requiring a finite sample of the space. Finally, we discuss additional applications and provide guidance for those applying these bounds to their own work.
An Intelligent Fashion Object Classification Using CNN

Every year the count of visually impaired people is increasing drastically around the world. At present time, approximately 2.2 billion people are suffering from visual impairment. One of the major areas where our model will affect public life is the area of house assistance for specially-abled persons. Because of visual improvement, these people face lots of issues. Hence for this group of people, there is a high need for an assistance system in terms of object recognition. For specially-abled people sometimes it becomes really difficult to identify clothing-related items from one another because of high similarity. For better object classification we use a model which includes computer vision and CNN. Computer vision is the area of AI that helps to identify visual objects. Here a CNN-based model is used for better classification of clothing and fashion items. Another model known as Lenet is used which has a stronger architectural structure. Lenet is a multi-layer convolution neural network that is mainly used for image classification tasks. For model building and validation MNIST fashion dataset is used.
On low-depth algorithms for quantum phase estimation

Quantum phase estimation is one of the critical building blocks of quantum computing. For early fault-tolerant quantum devices, it is desirable for a quantum phase estimation algorithm to (1) use a minimal number of ancilla qubits, (2) allow for inexact initial states with a significant mismatch, (3) achieve the Heisenberg limit for the total resource used, and (4) have a diminishing prefactor for the maximum circuit length when the overlap between the initial state and the target state approaches one. In this paper, we prove that an existing algorithm from quantum metrology can achieve the first three requirements. As a second contribution, we propose a modified version of the algorithm that also meets the fourth requirement, which makes it particularly attractive for early fault-tolerant quantum devices.
Fault tolerant trust based task scheduler using Harris Hawks optimization and deep reinforcement learning in multi cloud environment

Abstract Cloud Computing model provides on demand delivery of seamless services to customers around the world yet single point of failures occurs in cloud model due to improper assignment of tasks to precise virtual machines which leads to increase in rate of failures which effects SLA based trust parameters (Availability, success rate, turnaround efficiency) upon which impacts trust on cloud provider. In this paper, we proposed a task scheduling algorithm which captures priorities of all tasks, virtual resources from task manager which comes onto cloud application console are fed to task scheduler which takes scheduling decisions based on hybridization of both Harris hawk optimization and ML based reinforcement algorithms to enhance the scheduling process. Task scheduling in this research performed in two phases i.e. Task selection and task mapping phases. In task selection phase, all incoming priorities of tasks, VMs are captured and generates schedules using Harris hawks optimization. In task mapping phase, generated schedules are optimized using a DQN model which is based on deep reinforcement learning. In this research, we used multi cloud environment to tackle availability of VMs if there is an increase in upcoming tasks dynamically and migrate tasks to one cloud to another to mitigate migration time. Extensive simulations are conducted in Cloudsim and workload generated by fabricated datasets and realtime synthetic workloads from NASA, HPC2N are used to check efficacy of our proposed scheduler (FTTHDRL). It compared against existing task schedulers i.e. MOABCQ, RATS-HM, AINN-BPSO approaches and our proposed FTTHDRL outperforms existing mechanisms by minimizing rate of failures, resource cost, improved SLA based trust parameters.
Deep is Better? An Empirical Comparison of Information Retrieval and Deep Learning Approaches to Code Summarization

Code summarization aims to generate short functional descriptions for source code to facilitate code comprehension. While Information Retrieval (IR) approaches that leverage similar code snippets and corresponding summaries have led the early research, Deep Learning (DL) approaches that use neural models to capture statistical properties between code and summaries are now mainstream. Although some preliminary studies suggest that IR approaches are more effective in some cases, it is currently unclear how effective the existing approaches can be in general, where and why IR/DL approaches perform better, and whether the integration of IR and DL can achieve better performance. Consequently, there is an urgent need for a comprehensive study of the IR and DL code summarization approaches to provide guidance for future development in this area. This paper presents the first large-scale empirical study of 18 IR, DL, and hybrid code summarization approaches on five benchmark datasets. We extensively compare different types of approaches using automatic metrics, we conduct quantitative and qualitative analyses of where and why IR and DL approaches perform better, respectively, and we also study hybrid approaches for assessing the effectiveness of integrating IR and DL. The study shows that the performance of IR approaches should not be underestimated, that while DL models perform better in predicting tokens from method signatures and capturing structural similarities in code, simple IR approaches tend to perform better in the presence of code with high similarity or long reference summaries, and that existing hybrid approaches do not perform as well as individual approaches in their respective areas of strength. Based on our findings, we discuss future research directions for better code summarization.
“Highly nuanced policy is very difficult to apply at scale”: Examining researcher account and content takedowns online

Abstract Since 2019, researchers examining, archiving, and collecting extremist and terrorist materials online have increasingly been taken offline. In part a consequence of the automation of content moderation by different technology companies and national governments calling for ever quicker takedowns. Based on an online survey of peers in the field, this research highlights that up to 60% of researchers surveyed have had either their accounts or content they have posted or stored online taken down from varying platforms. Beyond the quantitative data, this research also garnered qualitative answers about concerns individuals in the field had related to this problem set, namely, the lack of transparency on the part of the technology companies, hindering actual research and understanding of complicated and evolving issues related to different extremist and terrorist phenomena, undermining potential collaboration within the research field, and the potential of self‐censorship online. An easy solution to this would be a whitelist, though there are inherent downsides related to this as well, especially between researchers at different levels in their careers, institutional affiliation or lack thereof, and inequalities between researchers from the West versus Global South. Either way, securitizing research in however form it evolves in the future will fundamentally hurt research.
Ecological Thinking about the Metaverse from a Posthumanist Perspective

The metaverse is a burgeoning technological domain, ranging from data-driven algorithms to advanced artificial intelligence. Immersive virtual reality does not reflect the ecological crisis in fictional worlds through interactivity. In the footsteps of Félix Guattari, this article elaborates on ecological thinking in three categories, each of which reflects the ecological risk and potential positive improvement of the metaverse. In the first category, environmental ecology, the article argues that the technological infrastructure of the metaverse is intensely power-consuming and materialises data and information fluxes. Moreover, the structure of digitised societies promotes intangible production. In the second category, social ecology, it argues that cognitive capitalism further stimulates consumerism because of big data classification and user-targeted algorithms and further amplifies climate change due to the empowered logistics industry. Additionally, capitalism causes continuous competence in the culture industry, leading to a high elimination rate of device production and low efficiency of intellectual labour. In the last category, mental ecology, the article argues that by precisely targeting users, the attention economy of the metaverse empowers individuals who are eager to pursue valorisation. These digital dwellers overlook ecological issues and the foundations of their virtual lives. In the posthumanist approach, humans are flattened by capitalism and become surplus value as natural forces. Humans’ minds are alienated from the idea that nature and humans coexist. Instead, the metaverse is directed toward individual power that immerses itself in a capital-driven network of information technology. This article proposes the merit of forming a nature-culture continuum to promote an ecological ideology in the metaverse. On the one hand, the nature-culture continuum of the metaverse spreads ecological knowledge and praxis via interactivity. On the other hand, the key mediation of nature-human metabolism through human labour contributes to environmentally friendly products, which cultivate an ecological culture of consumerism. Thus, the metaverse functions in a posthumanist context that consumes nature and humans for profit; however, the metaverse can also arouse thoughts of sustainability.
GNSS Carrier Tracking via a Variational Bayesian Adaptive Kalman Filter for High Dynamic Conditions

Under high dynamic conditions, a robust carrier tracking approach is essential for global navigation satellite system (GNSS) receivers. In this paper, the powerful Kalman filter (KF) technique is adopted in GNSS carrier tracking. The correlation signals are used as system measurements to discard the discriminator restricted by linear region. Then, a linear measurement equation is established based on the error-state, so that the system model is linear. Hence the KF can be used instead of the nonlinear KF which requires more computational costs. Furthermore, to exploit the potential of the KF under different conditions, an adaptive KF (AKF) based on the variational Bayesian approach is proposed. The proposed filter has accurate and robust estimation performance, especially under high dynamic conditions. Simulation results verify the applicability of the proposed system model and the superiority of the proposed filter compared with the traditional KF and existing AKFs.