Reviewerly/exHarmony huggingface dataset:
A survey on the (in)security of trusted execution environments

As the number of security and privacy attacks continue to grow around the world, there is an ever increasing need to protect our personal devices. As a matter of fact, more and more manufactures are relying on Trusted Execution Environments (TEEs) to shield their devices. In particular, ARM TrustZone (TZ) is being widely used in numerous embedded devices, especially smartphones, and this technology is the basis for secure solutions both in industry and academia. However, as shown in this paper, TEE is not bullet-proof and it has been successfully attacked numerous times and in very different ways. To raise awareness among potential stakeholders interested in this technology, this paper provides an extensive analysis and categorization of existing vulnerabilities in TEEs and highlights the design flaws that led to them. The presented vulnerabilities, which are not only extracted from existing literature but also from publicly available exploits and databases, are accompanied by some effective countermeasures to reduce the likelihood of new attacks. The paper ends with some appealing challenges and open issues.
A Privacy-preserving mean–variance optimal portfolio

Following strong regulations such as the European General Data Protection Regulation (GDPR), privacy protection in the financial sector has recently emerged as an urgent issue. To manage the privacy risk in robo-advisor, a representative fintech service, we propose a novel framework that allows robo-advisors to offer the optimal portfolio while complying with the privacy of their customers by encrypting individual risk aversion with homomorphic encryption (HE). By introducing an HE-friendly method for constrained optimization, our model can find a mean–variance quadratic programming solution even with inequality constraints. This study makes two main findings through empirical evaluation (i) our model can approximate optimal solution at an acceptable level of accuracy loss and the cost of preserving privacy, and (ii) the number of assets and the degree of correlation between assets affect the accuracy loss.
Measuring the Concept of PID Literacy: User Perceptions and Understanding of PIDs in Support of Open Scholarly Infrastructure

Abstract The increasing centrality of persistent identifiers (PIDs) to scholarly ecosystems and the contribution they can make to the burgeoning “PID graph” has the potential to transform scholarship. Despite their importance as originators of PID data, little is known about researchers’ awareness and understanding of PIDs, or their efficacy in using them. In this article, we report on the results of an online interactive test designed to elicit exploratory data about researcher awareness and understanding of PIDs. This instrument was designed to explore recognition of PIDs (e.g. Digital Object Identifiers [DOIs], Open Researcher and Contributor IDs [ORCIDs], etc.) and the extent to which researchers correctly apply PIDs within digital scholarly ecosystems, as well as measure researchers’ perceptions of PIDs. Our results reveal irregular patterns of PID understanding and certainty across all participants, though statistically significant disciplinary and academic job role differences were observed in some instances. Uncertainty and confusion were found to exist in relation to dominant schemes such as ORCID and DOIs, even when contextualized within real-world examples. We also show researchers’ perceptions of PIDs to be generally positive but that disciplinary differences can be noted, as well as higher levels of aversion to PIDs in specific use cases and negative perceptions where PIDs are measured on an “activity” semantic dimension. This work therefore contributes to our understanding of scholars’ “PID literacy” and should inform those designing PID-centric scholarly infrastructures that a significant need for training and outreach to active researchers remains necessary.
All WARC and no playback: The materialities of data-centered web archives research

This paper examines the Web ARChive (WARC) file format, revealing how the format has come to play a central role in the development and standardization of interoperable tools and methods for the international web archiving community. In the context of emerging big data approaches, I consider the sociotechnical relationships between material construction of data and information infrastructures for collecting and research. Analysis is inspired by Star and Griesemer's historical case of the Museum of Vertebrate Zoology which reveals how boundary objects and methods standardization are used to enroll actors in the work of collecting for natural history. I extend these concepts by pairing them with frameworks for studying digital materiality and the representational qualities of data artifacts. Through examples drawn from fieldwork observations studying two data-centered research projects, I consider how the materiality of the WARC format influences research methods and approaches to data extraction, selection, and transformation. Findings identify three modalities researchers use to configure WARC data for researcher needs: using indexes to support search queries, constructing derivative formats designed for certain types of analysis, and generating custom-designed datasets tailored for specific research purposes. Findings additionally reveal similarities in how these distinct methods approach automated data extraction by relying upon the WARC's standardized metadata elements. By interrogating whose information needs are being met and taken into account in the design of the WARC's underlying information representation, I reveal effects on the emerging field of web history, and consider alternative approaches to knowledge production with archived web data.
Gujarati Language Automatic Speech Recognition Using Integrated Feature Extraction and Hybrid Acoustic Model

In the case of low resource language, there is still the requirement for developing more efficient Automatic Speech Recognition (ASR) systems. In the proposed work, the ASR system is developed for the Gujarati language publicly available dataset. The approach in this paper applies the combination of Mel-frequency Cepstral Coefficients (MFCC) with Constant Q Cepstral Coefficients (CQCC)-based integrated front-end feature extraction techniques. To implement the backend part of the system, hybrid acoustic model is applied. Two-dimensional Convolutional Neural Network (Conv2D) with Bi-directional Gated Recurrent Units-based (BiGRU) backend model is used as the model. To build the ASR system, Connectionist Temporal Classification (CTC) loss function, CTC and prefix-based greedy decoder are also used with the acoustic model. The proposed work shows that the joint MFCC and CQCC feature extraction techniques show the 10–19% improvement in Word Error Rate (WER) as compared to isolated delta-delta features with the available integrated model.
A Novel Day-to-Night Obstacle Detection Method for Excavators Based on Image Enhancement and Multisensor Fusion

Traditional excavator driving relies only on manual observation, resulting in increased hazards in unstructured environments. When the excavator works in a relatively dark environment, there will be potential risks for both the driver and the surrounding pedestrians. In order to address this issue, this study takes the advantage of three different sensors, including infrared cameras, RGB cameras, and Light detection and ranging (LiDAR) sensors, and proposes a novel day-to-night obstacle detection approach by fusing data from multiple sensors. For the dark environment at night, the infrared camera is adopted for the detection task. However, compared with RGB cameras, the infrared camera usually has lower resolutions, making it difficult to be directly applied for obstacle detection. Therefore, an image enhancement processing method for low-resolution infrared images is developed based on the Difference of Gaussian (DoG). Then, an image recognition method based on YOLO-v5 is proposed to detect images after image enhancement. Finally, a multisensor fusion method is suggested to identify the semantic information and 3-D coordinates of objects. Experimental studies are carried out to assess image quality and the effectiveness of various object recognition tasks. The results of the experiments demonstrate that our method is capable of not only accurately extracting pedestrian position information from a complicated background environment and realizing timely pedestrian alarms but also maintaining detection performance in an excavator working environment at night.
LLL-CAdViSE: Live Low-Latency Cloud-Based Adaptive Video Streaming Evaluation Framework

Live media streaming is a challenging task by itself, and when it comes to use cases that define low-latency as a must, the complexity will rise multiple times.In a typical media streaming session, the main goal can be declared as providing the highest possible Quality of Experience (QoE), which has proved to be measurable using quality models and various metrics.In a low-latency media streaming session, the requirements are to provide the lowest possible delay between the moment a frame of video is captured and the moment that the captured frame is rendered on the client screen, also known as end-to-end (E2E) latency and maintain the QoE.This paper proposes a sophisticated cloud-based and open-source testbed that facilitates evaluating a low-latency live streaming session as the primary contribution.Live Low-Latency Cloud-based Adaptive Video Streaming Evaluation (LLL-CAdViSE) framework is enabled to asses the live streaming systems running on two major HTTP Adaptive Streaming (HAS) formats, Dynamic Adaptive Streaming over HTTP (MPEG-DASH) and HTTP Live Streaming (HLS).We use Chunked Transfer Encoding (CTE) to deliver Common Media Application Format (CMAF) chunks to the media players.Our testbed generates the test content (audiovisual streams).Therefore, no test sequence is required, and the encoding parameters (e.g., encoder, bitrate, resolution, latency) are defined separately for each experiment.We have integrated the ITU-T P.1203 quality model inside our testbed.To demonstrate the flexibility and power of LLL-CAdViSE, we have presented a secondary contribution in this paper; we have conducted a set of experiments with different network traces, media players, ABR algorithms, and with various requirements (e.g., E2E latency (typical/reduced/low/ultra-low), diverse bitrate ladders, and catch-up logic) and presented the essential findings and the experimental results.
The orchestration of <scp>Machine Learning</scp> frameworks with data streams and <scp>GPU</scp> acceleration in <scp>Kafka‐ML</scp>: A deep‐learning performance comparative

Abstract Machine Learning (ML) applications need large volumes of data to train their models so that they can make high‐quality predictions. Given digital revolution enablers such as the Internet of Things (IoT) and the Industry 4.0, this information is generated in large quantities in terms of continuous data streams and not in terms of static datasets as it is the case with most AI (Artificial Intelligence) frameworks. Kafka‐ML is a novel open‐source framework that allows the complete management of ML/AI pipelines through data streams. In this article, we present new features for the Kafka‐ML framework, such as the support for the well‐known ML/AI framework PyTorch, as well as for GPU acceleration at different points along the pipeline. This pipeline will be described by taking a real Industry 4.0 use case in the Petrochemical Industry. Finally, a comprehensive evaluation with state‐of‐the‐art deep learning models will be carried out to demonstrate the feasibility of the platform.
LAFED: A lightweight authentication mechanism for blockchain-enabled federated learning system

Federated learning, as an emerging distributed machine learning technology, can use cross-device data to train a usable and secure shared model under the premise of protecting data privacy. However, the existing federated learning usually uploads the intermediate parameters to the central server to achieve model aggregation, which will cause significant privacy leakage. Recently, blockchain technology has become a research hotspot due to its advantages of decentralized and non-tampering features, providing new ideas for the realization of security certification for federated learning. However, blockchain-enabled federated learning also faces the following two challenges: (1) the identity authentication relies on the central server being fully trusted and the computation cost is heavy; (2) center-less authentication faces the challenges of efficiency and privacy leakage. To solve the above challenges, we propose a lightweight authentication mechanism for blockchain-enabled federated learning system, named LAFED. The innovations of LAFED are three-fold: (1) a lightweight authentication framework for blockchain-enabled federated learning; (2) a flexible consensus algorithm with zero-knowledge proof to verify the identity of each participant; (3) an adaptive model aggregation algorithm based on the model quality and node contribution to improve the performance. Extensive experimental results demonstrate that the proposed LAFED can achieve lightweight authentication while ensuring a high model accuracy.
Chaotification of 1D Maps by Multiple Remainder Operator Additions—Application to B-Spline Curve Encryption

In this work, a chaotification technique is proposed for increasing the complexity of chaotic maps. The technique consists of adding the remainder of multiple scalings of the map’s value for the next iteration, so that the most- and least-significant digits are combined. By appropriate parameter tuning, the resulting map can achieve a higher Lyapunov exponent value, a result that was first proven theoretically and then showcased through numerical simulations for a collection of chaotic maps. As a proposed application of the transformed maps, the encryption of B-spline curves and patches was considered. The symmetric encryption consisted of two steps: a shuffling of the control point coordinates and an additive modulation. A transformed chaotic map was utilised to perform both steps. The resulting ciphertext curves and patches were visually unrecognisable compared to the plaintext ones and performed well on several statistical tests. The proposed work gives an insight into the potential of the remainder operator for chaotification, as well as the chaos-based encryption of curves and computer graphics.
CNN Attention Enhanced ViT Network for Occluded Person Re-Identification

Person re-identification (ReID) is often affected by occlusion, which makes most of the features extracted by ReID models contain a lot of identity-independent noise. Recently, the use of Vision Transformer (ViT) has enabled significant progress in various visual artificial intelligence tasks. However, ViT suffers from insufficient local information extraction capability, which should be of concern to researchers in the field of occluded ReID. This paper conducts a study to exploit the potential of attention mechanisms to enhance ViT in ReID tasks. In this study, an Attention Enhanced ViT Network (AET-Net) is proposed for occluded ReID. We use ViT as the backbone network to extract image features. Even so, occlusion and outlier problems still exist in ReID. Then, we combine the spatial attention mechanism into the ViT architecture, by which we enhance the attention of ViT patch embedding vectors to important regions. In addition, we design a MultiFeature Training Module to optimize the network by the construction of multiple classification features and calculation of the multi-feature loss to enhance the performance of the model. Finally, the effectiveness and superiority of the proposed method are demonstrated by broad experiments on both occluded and non-occluded datasets.
Augmented Reality: An Emergent Technology for Students’ Learning Motivation for Chemical Engineering Laboratories during the COVID-19 Pandemic

In higher education, the learning of Unit Operations in Chemical Engineering and the development of practical activities became a real challenge. Therefore, the use of emerging technologies became necessary to develop practical laboratory activities of the Unit Operations due to the inaccessibility to the equipment infrastructure. In this study, Project-Based Learning methodology was assisted with the Augmented Reality (AR) technology for the development of subjects. The development of a real educational experiment for the application of a basic topic of the course as a project for each subject was proposed. The results were presented using the Zappar application, and a unique rubric was used for the evaluation of project. The evaluation of students’ motivation for learning was measured using Keller’s Attention, Relevance, Confidence and Satisfaction (ARCS) model of motivation by Instructional Materials Motivation Survey (IMMS). The attention, confidence and satisfaction demonstrate an acceptable reliability in comparison to relevance, which was considered as moderate reliability. Above 96% of students considered that the activities, materials, and organization of information used for the AR project caught their attention and encouraged their interest towards the fundamentals applied in the project. Around 80% of students expressed concern about the ease of AR technology use, and understood the learning aim of the project. Above 85% of students recognized the relevance of activities and their usefulness, and considered AR as a meaningful educational tool. 90% of students considered that AR technology helped them to develop the subject competencies. Cronbach’s Alpha was used to indicate an acceptable reliability of IMMS instrument. Regarding IMMS, values were superior to 0.7, which could be considered acceptable. For the individual ARCS dimensions, values of Cronbach’s alpha reached values of 0.94.
MultiFed: A fast converging federated learning framework for services QoS prediction via cloud–edge collaboration mechanism

Federated learning (FL) has become a common approach for distributed training of quality of service (QoS) prediction tasks in smart city solutions. However, FL is vulnerable to heterogeneity. Existing FL-based QoS prediction methods usually update the global model by directly aggregating the divergent local gradients computed on heterogeneous datasets, which does not effectively capture the heterogeneity of users’ QoS data distribution and requires many rounds of training and much communication time to achieve satisfactory accuracy. In addition, the high communication latency between the central server and the client further slows down the convergence process. To address the above issues, we propose a multi-centre federated learning framework for services QoS prediction via a cloud–edge collaboration mechanism, namely MultiFed. In this framework, we sink the central server in traditional FL from cloud to edge and train a global model in each edge region. The edge server performs two gradient aggregation strategies to update the global model in each training round. One is an internal aggregation strategy for regional users; the other is an external aggregation among edge servers with the assistance of a cloud server. Extensive experiments have shown that 9.52%∼40.00% reduces the communication delay, and 1.91%∼38.89% reduces the communication rounds required to reach the target prediction in this method compared to existing methods, validating the effectiveness of MultiFed.
On the gathering of Tor onion addresses

Exploring the Tor network requires acquiring onion addresses, which are crucial for accessing anonymous websites. However, the Tor protocol presents a challenge, as it lacks a standard method for finding these complex links composed of either 16 or 56 base32-coded characters and featuring the unique ".onion" top-level domain. This study delves into the existing literature analyzing onion services and categorizes the various strategies employed to gather their addresses. The success of each approach is measured by the number of addresses obtained, while the relevancy of the work is evaluated by comparing the number of services uncovered to Tor's official count. The results indicate that the most used techniques are Tor crawling and repositories, whereas the most effective methods are relay injection, repositories, and Tor crawling. This paper also estimates the representativeness of literature collections, revealing that most past works explored a small portion of the Tor network. The study also uncovers the limitations of onion gathering and sheds light on the challenges for future research to provide more representative datasets for dark web exploration.
DiTingMotion: A deep-learning first-motion-polarity classifier and its application to focal mechanism inversion

Accurate P-wave first-motion-polarity (FMP) information can contribute to solving earthquake focal mechanisms, especially for small earthquakes, to which waveform-based methods are generally inapplicable due to the computationally expensive high-frequency waveform simulations and inaccurate velocity models. In this paper, we propose a deep-learning-based method for the automatic determination of the FMPs, named “DiTingMotion”. DiTingMotion was trained with the P-wave FMP labels from the “DiTing” and SCSN-FMP datasets, and it achieved ∼97.8% accuracy on both datasets. The model maintains ∼83% accuracy on data labeled as “Emergent”, of which the FMP labels are challenging to identify for seismic analysts. Integrated with HASH, we developed a workflow for automated focal mechanism inversion using the FMPs identified by DiTingMotion and applied it to the 2019 M 6.4 Ridgecrest earthquake sequence for performance evaluation. In this case, DiTingMotion yields comparable focal mechanism results to that using manually determined FMPs by SCSN on the same data. The results proved that the DiTingMotion has a good generalization ability and broad application prospect in rapid earthquake focal mechanism inversion .
A Lightweight and High-Accuracy Deep Learning Method for Grassland Grazing Livestock Detection Using UAV Imagery

Unregulated livestock breeding and grazing can degrade grasslands and damage the ecological environment. The combination of remote sensing and artificial intelligence techniques is a more convenient and powerful means to acquire livestock information in a large area than traditional manual ground investigation. As a mainstream remote sensing platform, unmanned aerial vehicles (UAVs) can obtain high-resolution optical images to detect grazing livestock in grassland. However, grazing livestock objects in UAV images usually occupy very few pixels and tend to gather together, which makes them difficult to detect and count automatically. This paper proposes the GLDM (grazing livestock detection model), a lightweight and high-accuracy deep-learning model, for detecting grazing livestock in UAV images. The enhanced CSPDarknet (ECSP) and weighted aggregate feature re-extraction pyramid modules (WAFR) are constructed to improve the performance based on the YOLOX-nano network scheme. The dataset of different grazing livestock (12,901 instances) for deep learning was made from UAV images in the Hadatu Pasture of Hulunbuir, Inner Mongolia, China. The results show that the proposed method achieves a higher comprehensive detection precision than mainstream object detection models and has an advantage in model size. The mAP of the proposed method is 86.47%, with the model parameter 5.7 M. The average recall and average precision can be above 85% at the same time. The counting accuracy of grazing livestock in the testing dataset, when converted to a unified sheep unit, reached 99%. The scale applicability of the model is also discussed, and the GLDM could perform well with the image resolution varying from 2.5 to 10 cm. The proposed method, the GLDM, was better for detecting grassland grazing livestock in UAV images, combining remote sensing, AI, and grassland ecological applications with broad application prospects.