Reviewerly/exHarmony huggingface dataset:
Few-shot multi-view object classification via dual augmentation network

Existing multi-view object classification algorithms usually rely on sufficient labeled multi-view objects, which substantially restricts their scalability to novel classes with few annotated training samples in real-world applications. Aiming to go beyond these limitations, we explore a novel yet challenging task, few-shot multi-view object classification (FS-MVOC), which expects the network to build its classification ability efficiently based on limited labeled multi-view objects. To this end, we design a dual augmentation network (DANet) to provide excellent performance for the under-explored FS-MVOC task. On the one hand, we employ an attention-guided multi-view representation augmentation (AMRA) strategy to help the model focus on salient features and suppress unnecessary ones on multiple views of multi-view objects, resulting in more discriminative multi-view representations. On the other hand, during the meta-training stage, we adopt the category prototype augmentation (CPA) strategy to improve the class-representativeness of each prototype and increase the inter-prototype difference by injecting Gaussian noise in the deep feature space. Extensive experiments on the benchmark datasets (Meta-ModelNet and Meta-ShapeNet) indicate the effectiveness and robustness of DANet.
FastTraffic: A lightweight method for encrypted traffic fast classification

Nowadays, most Internet communications have adopted encrypted network access technology for privacy protection, so encrypted traffic classification (ETC) has become a crucial research point to support network management and ensure cyberspace security. Meanwhile, off-the-shelf deep learning (DL)-based approaches suffer from long preprocessing time, large input size, and a trade-off between model complexity and accuracy. There is a tough challenge to deploy them on mainstream network devices and achieve fast and accurate traffic classification. In this paper, we design FastTraffic, a lightweight DL-based method for ETC on low-configuration network devices. To speed up processing, we set an IP packet as the granularity of FastTraffic, truncate the informative parts in packets as inputs, and utilize a text-like packet tokenization method. For a lightweight and effective model, we propose an N-gram feature embedding method to represent structured and sequential features of packets and design a three-layer MLP to complete fast classification. We compare FastTraffic with eight state-of-the-art ETC methods on three public benchmark datasets. The experimental results show that FastTraffic obtains better classification performance than the other seven methods with only 0.43M model parameters. Besides, it can also achieve high throughput on low-configuration devices and consume a small amount of computing and storage resources. Therefore, FastTraffic is a lightweight ETC method capable of large-scale deployment on Internet devices.
KDRank: Knowledge-driven user-aware POI recommendation

Accurate user modeling is crucial for point-of-interest (POI) recommendation as it can significantly improve user satisfaction with recommended POIs and enrich user experience. However, existing methods typically rely on simple time-series models for user check-in sequences, which ignore similar information of global users and fail to capture the user-preference knowledge hidden in complex social networks. To address this issue, we propose a novel knowledge-driven and user-aware POI recommendation method called KDRank. First, we construct a knowledge graph containing users' personal attributes for POI recommendation, which can reflect users' historical check-in preferences. Second, we derive users' knowledge representations using a cross-embedding method, which facilitates feature interaction by sharing information between segments of knowledge representations to achieve a more precise representation of low-dimensional embedding. Third, we propose a knowledge aggregation module to combine users' knowledge and historical check-in features to achieve knowledge enhancement of check-in data. Furthermore, to enhance global user awareness of our model, we introduce an attention mechanism that focuses on the most similar and significant users in the global context. It allows KDRank to capture more personalized user preferences and increases the precision of POI recommendations. The effectiveness of the proposed method was evaluated on two real datasets, and the results indicated its ability to increase the POI recommendation accuracy. The code associated with this study is available at https://github.com/itshardtocode/KDRank.
When CLIP meets cross-modal hashing retrieval: A new strong baseline

Recent days witness significant progress in various multi-modal tasks made by Contrastive Language-Image Pre-training (CLIP), a multi-modal large-scale model that learns visual representations from natural language supervision. However, the potential effects of CLIP on cross-modal hashing retrieval has not been investigated yet. In this paper, we for the first time explore the effects of CLIP on cross-modal hashing retrieval performance and propose a simple but strong baseline Unsupervised Contrastive Multi-modal Fusion Hashing network (UCMFH). We first extract the off-the-shelf visual and linguistic features from the CLIP model, as the input sources for cross-modal hashing functions. To further mitigate the semantic gap between the image and text features, we design an effective contrastive multi-modal learning module that leverages a multi-modal fusion transformer encoder supervising by a contrastive loss, to enhance modality interaction while improving the semantic representation of each modality. Furthermore, we design a contrastive hash learning module to produce high-quality modal-correlated hash codes. Experiments show that significant performance improvement can be made by our simple new unsupervised baseline UCMFH compared with state-of-the-art supervised and unsupervised cross-modal hashing methods. Also, our experiments demonstrate the remarkable performance of CLIP features on cross-modal hashing retrieval task compared to deep visual and linguistic features used in existing state-of-the-art methods. The source codes for our approach is publicly available at: https://github.com/XinyuXia97/UCMFH.
Caster: Cartoon style transfer via dynamic cartoon style casting

The cartoon style transfer has attracted widespread attention. Although many researchers have proposed various methods to develop this field, there are still two areas for improvement: (1) existing image-to-image cartoon style transfer methods can only perform domain-to-domain cartoon style transfer, neglecting the specific color and texture of the cartoon images, and (2) arbitrary style transfer methods only transfer the style of the style image onto the content image, neglecting the style information of the style domain. To address these issues, we observe that artists often refer to specific paintings to fine-tune the color of their artworks. This behavior inspires us to propose a method that can dynamically encode the style information of a specific cartoon image based on Variational Autoencoders, allowing the style feature to be cast onto the content feature dynamically. We also introduce a cartoon contrastive learning loss to push the cartoon stylized image closer to the same cartoon stylized image and otherwise pull away. Extensive experiments demonstrate that our proposed method, Caster, can generate a high-quality stylized image with specific and domain cartoon style information than state-of-the-art cartoon style transfer methods.
Finding Near-optimal Configurations in Colossal Spaces with Statistical Guarantees

A Software Product Line ( SPL ) is a family of similar programs. Each program is defined by a unique set of features, called a configuration , that satisfies all feature constraints. “What configuration achieves the best performance for a given workload?” is the SPL Optimization ( SPLO ) challenge. SPLO is daunting: just 80 unconstrained features yield 10 24 unique configurations, which equals the estimated number of stars in the universe. We explain (a) how uniform random sampling and random search algorithms solve SPLO more efficiently and accurately than current machine-learned performance models and (b) how to compute statistical guarantees on the quality of a returned configuration; i.e., it is within x% of optimal with y% confidence.
Similarity-based deduplication and secure auditing in IoT decentralized storage

Data storage is becoming more difficult due to the network technology's rapid development, especially with the significant advancement in Internet of Things (IoT). To solve this problem, various cloud storage-based deduplication schemes have been extensively studied to alleviate the storage burden of local IoT devices. However, recent evidence has revealed the deficiencies of these centralized data management schemes related to poor adaptability and lack of resistance to the single point of failure. Most existing deduplication approaches only perform deduplication on the same data, lacking consideration of the IoT data similarity characteristics. Also, these approaches keep only one copy of all data stored on the cloud server, making them valid only if the cloud storage provider is fully trustable, and the applicability of the solution is significantly reduced. What is important, outsourcing storage takes away control of data from their owners, security audits thus should also be considered in secure data storage research. To tackle these issues, we propose a similarity-based deduplication and secure auditing in the IoT decentralized storage scheme. Specifically, our scheme utilizes a novel similarity-based deduplication method based on the multi-server storage model to achieve efficient space saving while protecting data security. Furthermore, the proposed scheme ensures the integrity and availability of data by introducing similarity-based proofs of data ownership and similarity-based data integrity auditing. On this basis, our scheme further adopts a similarity-based secure auditing mechanism to achieve decentralized public auditing without the third-party auditor, in which the audit results of outsourced data are shared on blockchain to avoid repetitive audit tasks. We verify the correctness and effectiveness of our proposed scheme through rigorous theoretical analysis and extensive experimental results.
YOLO*C — Adding context improves YOLO performance

You Only Look Once (YOLO) algorithms deliver state-of-the-art performance in object detection. This research proposes a novel one-stage YOLO-based algorithm that explicitly models the spatial context inherent in traffic scenes. The new YOLO*C algorithm introduces the MCTX context module and integrates loss function changes, effectively leveraging rich global context information. The performance of YOLO*C models is tested on BDD100K traffic data with multiple context variables. The results show that including context improves YOLO detection results without losing efficiency. Smaller models report the most significant improvements. The smallest model accomplished more than a 10% increase in mAP .5 compared to the baseline YOLO model. Modified YOLOv7 outperformed all models on mAP .5, including two-stage and transformer-based detectors, available at the dataset zoo. The analysis shows that improvement mainly results from better detection of smaller traffic objects, which presents a significant detection challenge within the complex traffic environment.
A perspective from Turkey on construction of the new digital world: analysis of emotions and future expectations regarding Metaverse on Twitter

Abstract Metaverse is a new digital world where the real and virtual worlds merge on a technological ground. After Mark Zuckerberg, CEO of Facebook hyped the metaverse, attention was drawn to it all over the world. In Turkey, the Metaverse was hyped in the “Teke Tek Bilim” program and Twitter users shared their views for days afterwards. This study aims to analyse Twitter users’ feelings about the Metaverse and their expectations for the future. The 10,437 tweets shared in Turkey between 10 January 2022 and 17 January 2022 with the hashtag Metaverse were analyzed using sentiment analysis techniques. The findings of the study indicate the metaverse will increase technological investments, new services, products, and markets will emerge, it may lead to irregularities in ethical codes and rules, society may be unready for technological developments, and media addiction may occur.
Initial-condition effects on ReLU-type hyper-jerk system and its application in image encryption

Abstract The memristive hyper-jerk system described in this study has a non-smooth Rectified Linear Unit (ReLU) memductance function. A non-smooth memductance function of this kind might cause the system to have three line equilibria within it, bring about the arrival of extreme multistability with infinitely many coexisting attractors. Analyzing the three line equilibria’s stabilities and researching the intricate dynamical effects triggered by memristor and non-memristor initial conditions are related topic. Phase portraits, bifurcation diagrams, Lyapunov exponent spectra, and the basins of attraction all show the dynamical tendencies. According to the results, the novel system’s dynamical behaviors depend on both the memristor initial condition and the non-memristor initial conditions. Moreover, PSIM circuit simulations and MCU-based hardware measurements are run to confirm the coexisting infinitely many attractors. The ReLU-type hyper-jerk system is then utilized to encrypt images. Experimental findings support its exceptional robustness against a range of potential attacks.
Real-Time PageRank on Dynamic Graphs

Modern data generation has grown to enormous proportions, with events occurring at increasingly higher rates. Yet for graph analytics, this growth in scale and velocity has not been matched by improved algorithm or infrastructure techniques: most systems still focus on post-mortem or static analysis. This paper builds on an efficient graph processing abstraction that enables online analysis of dynamically evolving graphs at scale. Integral to this abstraction is that events tied to both graph topology changes as well as algorithmic maintenance occur and are processed asynchronously, concurrently, and autonomously (i.e., without shared state).
Expectations and beyond: The nexus of <scp>AI</scp> instrumentality and brand credibility in voice assistant retention using extended <scp>expectation‐confirmation</scp> model

Abstract AI‐based voice assistants (AIVA) are capable of interpreting human speech and responding with useful information, aiding with tasks, and controlling other devices. The usage of these AIVAs has grown significantly worldwide. Despite this growth, studies on user behavior related to continued usage intention of AIVAs and effects on the long‐term commercial sustainability of brands, remain low. What is less understood is the potential of AI instrumentality attributes and brand credibility components in provoking shifts in post‐use behavior of AIVAs. This study proposes a model which expands on the Expectation‐Confirmation Model for user continuance behavior of AIVAs, by integrating user's technology related traits, AI instrumentality attributes and brand credibility. To verify the research hypotheses, the study employed partial least square—structural equation modelling, based on 281 validated responses of a survey. This study highlights the significance of Optimism, Innovativeness, and Discomfort for post‐adoption confirmation. Higher post‐adoption confirmation is strongly associated with perceived intelligence, anthropomorphism, information quality, and system quality. Anthropomorphism and information quality are key factors for brand expertise, while anthropomorphism and system quality are significant for brand trustworthiness. The study confirms that brand expertise and trustworthiness lead to post‐use satisfaction and use continuance intention. Understanding the antecedents of satisfaction and continuance intention extends the existing literature on AIVAs and provides valuable insights for academics and practitioners alike. Some implications for researchers and managers are discussed.
MPNet: Multiscale predictions based on feature pyramid network for semantic segmentation

Semantic segmentation is a complex topic where they assign each pixel of an image with a corresponding class and demand accuracy at objective boundaries. The method plays a vital role in scene-understanding scenarios. For self-driving applications, the input source includes various types of objects such as trucks, people, or traffic signs. One receptive field is only effective in capturing a short range of sizes. Feature pyramid network (FPN) utilizes different fields of view to extract information from the input. The FPN approach obtains the spatial information from the high-resolution feature map and the semantic information from the lower scales. The final feature representation contains coarse and fine details, but it has some drawbacks. They burden the system with extensive computation and reduce the semantic information. In this paper, we devise an effective multiscale predictions network (MPNet) to address these issues. A multiscale pyramid of predictions effectively processes the prominent characteristics of each feature. A pair of adjacent features is combined together to predict the output separately. A lower-scale feature of each prediction is assigned as the contextual contributor, and the other provides coarser information. The contextual branch is passed through the atrous spatial pyramid pooling to improve performance. The segmentation scores are fused to obtain advantages from all predictions. The model is validated by a series of experiments on open data sets. We have achieved good results 76.5% mIoU at 50 FPS on Cityscapes and 43.9% mIoU on Mapillary Vistas.
Overcoming Environmental Challenges in CAVs through MEC-based Federated Learning

Connected autonomous vehicles (CAVs), through vehicle-to-everything communication and computing resources, enable the vital exchange of information. Although deep learning is crucial in this landscape, it requires extensive and intricate datasets covering all potential scenarios. Furthermore, this situation poses a hazard, as the likelihood of accidents associated with imbalanced datasets increases, particularly in scenarios where processing analysis is compromised due to fluctuating weather conditions. We propose a Federated Learning (FL) framework undergirded by Multi-Access Edge Computing (MEC) to counter these challenges. This local device-focused framework enhances task-specific models’ caching and continual updating across various conditions. In a more specific sense, edge nodes (ENs) operate as MEC, each caching multiple dedicated models and serving as the aggregator as part of the FL process. Additionally, we have engineered two innovative algorithms that categorize various states into multiple classes, thereby ensuring the efficient utilization of computing resources in ENs. Simulation results substantiate the effectiveness of our approach, showing that the proposed dedicated model consistently outperforms a general model designed for all situations.
Encrypted traffic classification: the QUIC case

The QUIC protocol is a new reliable and secure transport protocol that is an alternative to TLS over TCP. However, compared to TLS, QUIC obfuscates the connection hand-shake and the server name indication domain, making a simple inspection more challenging. The classification of QUIC traffic has also received less attention than that of TLS. In this work, we present a comprehensive study aiming to explore the challenges of QUIC traffic classification. We selected three models: 1) multi-modal CNN, 2) LighGBM, and 3) IP-based classifier, and evaluated their properties using a large one-month CESNET-QUIC22 dataset with 102 web service labels. The developed classifiers reached up to 88% accuracy and set the new baseline in fine-grained QUIC service classification. Moreover, the real nature of the dataset and its long time span allowed us to collect a number of insights and measure the classifiers' performance in the presence of data drift.
TinyFL: On-Device Training, Communication and Aggregation on a Microcontroller For Federated Learning

In federated learning (FL), in contrast to centralized ML learning processes, ML models are sent rather than the raw data. Therefore, FL is a decentralized and privacy-compliant process currently experiencing significant research interest. As a result, initial investigations were carried out with FL and microcontrollers (MCUs). However, each of these studies used a PC as a server. In this work, we introduce TinyFL, a method using only MCUs to build a low-cost, low-power, and low-storage system. TinyFL uses a hybrid master/slave protocol where the master MCU is responsible for communication and aggregation. Thereby, the communication is performed by inter-integrated circuit (I <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> C). TinyFL demonstrates that communication and aggregation for FL can be performed on only MCUs. Furthermore, we show that the training with TinyFL is 11.57 % faster than centralized training using a gesture recognition use case.
Approaches to Selective Imaging of Live Systems via Memory Forensics

Modern day forensic investigations rely on forensically sound digital evidence which is acceptable in a court of law. The increase cybersecurity attacks have enormously increased the need of forensic investigations leading to a huge corpus of data. Mostly, the memory image dump is so huge for individual cases out of which the critical evidence is present in a comparatively smaller amount of memory. Selective imaging provides a way to partially image the memory of target device without necessarily copying the rest of the image that may be of little or no use to the investigation. Selective imaging allows the investigator to forensically acquire memory in a strategic manner depending upon the nature of the case at hand. In this work, we explore the realm of selective imaging and present a consolidated literature review along with the various approaches available for considering selective memory imaging for live systems to conduct forensic investigations via live memory forensics. The work concludes by pointing the research directions around selective imaging for enhancing the effectiveness of live memory forensics.
Deep Learning-Based Object Detection Algorithms on Image and Video

The research led to a change in direction or approach toward automated object detection on images and video. Some challenges in the detection of video make them complex. The mobility of objects and the complexity of low-light conditions make object detection a highly important field of research. Many researchers have proposed several approaches for detecting the object in images and video. This paper approaches the deep-learning-based YOLO (You Only Look Once) algorithms for object detection on image and video sequences. We proposed YOLOv7 and YOLOv8 algorithms with an apple dataset for this experiment which has achieved Mean Average Precision is 94.2% and 91.2% respectively. Where the recall and precision are 98%, 93.8% for YOLOV7, and 98%, 93.9% for YOLOv8. In such cases, fine-tune the model's hyperparameters or train models on a more balanced dataset to improve its performance. All the positive predictions were made by the classifiers and identified the actual positive instances in the dataset. The algorithms process 120 frames per second with GPU, while YOLOv8 performed well and had more accurate results on images and video within 1.3ms speed per image size at 640x640. In addition to comparing the experimental models, this work focuses on object detection on images and videos taken in various complicated situations.