Reviewerly/exHarmony huggingface dataset:
Deep robust multi-channel learning subspace clustering networks

Subspace clustering methods are now widely used for unsupervised high-dimensional data processing in computer vision and other domains. Deep subspace clustering methods based on auto-encoder networks have made a significant improvement in nonlinear subspace clustering problems in comparison to previous works. However, these methods ignore the valid information lost during feature extraction, resulting in incomplete information and imprecise feature representations for subspace clustering. In addition, the clustering performance of the existing clustering methods is excessively dependent on hyper-parameters, making training difficult and unstable. In this paper, we propose Deep Robust Multi-Channel Learning Subspace Clustering Networks (DRMCLSC), a novel deep subspace clustering network for learning more comprehensive feature representations with good robustness for subspace clustering. The multi-channel learning strategy allows the model to extract, retain and fuse features simultaneously, enabling all valid information from the sample data to be obtained. Moreover, the multi-channel learning structure of the proposed method produces a more stable integration network that is less dependent on hyper-parameters and more resistant to training errors than previous works. Extensive experimental results on four benchmark datasets demonstrate the proposed method is superior and more effective than the state-of-the-art subspace clustering methods.
A stable variant of linex loss SVM for handling noise with reduced hyperparameters

Support Vector Machine (SVM) primarily uses the hinge loss function with maximum margin. The boundary instances determine the separating hyperplanes. However, in real-world scenarios, noisy instances around the decision boundary can degrade the performance of hinge SVM. To address this issue, researchers introduced the pinball SVM, which uses all the instances to determine the separating hyperplane and has a discontinuous piecewise linear loss function. A more recent variant, the linex loss SVM with NAG, employs a combination of linear and exponential loss functions, resulting in an asymmetric and continuous loss function. Due to the employment of Nesterov accelerated gradient (NAG), the older linex SVM formulation was unstable. This study presents a novel noise-robust variant of linex SVM, termed linexSVM, which is stable in performance compared to the linex SVM with NAG. Additionally, the proposed linexSVM has fewer hyperparameters in the dual formulation than other noise-robust SVM variants, such as pinball SVM, ϵ-insensitive pinball SVM, and ϵ1,ϵ2 pinball SVM. We evaluated the performance of the proposed linexSVM, hinge SVM, and pinball SVM using the UCI dataset repository. The experimental results indicate that the proposed linexSVM outperforms hinge and pinball loss SVMs on datasets with and without noise.
Unsupervised dimensionality reduction of medical hyperspectral imagery in tensor space

Compared with traditional RGB images, medical hyperspectral imagery (HSI) has numerous continuous narrow spectral bands, which can provide rich information for cancer diagnosis. However, the abundant spectral bands also contain a large amount of redundancy information and increase computational complexity. Thus, dimensionality reduction (DR) is essential in HSI analysis. All vector-based DR methods ignore the cubic nature of HSI resulting from vectorization. To overcome the disadvantage of vector-based DR methods, tensor-based techniques have been developed by employing multi-linear algebra.To fully exploit the structure features of medical HSI and enhance computational efficiency, a novel method called unsupervised dimensionality reduction via tensor-based low-rank collaborative graph embedding (TLCGE) is proposed. TLCGE introduces entropy rate superpixel (ERS) segmentation algorithm to generate superpixels. Then, a low-rank collaborative graph weight matrix is constructed on each superpixel, greatly improving the efficiency and robustness of the proposed method. After that, TLCGE reduces dimensions in tensor space to well preserve intrinsic structure of HSI.The proposed TLCGE is tested on cholangiocarcinoma microscopic hyperspectral data sets. To further demonstrate the effectiveness of the proposed algorithm, other machine learning DR methods are used for comparison. Experimental results on cholangiocarcinoma microscopic hyperspectral data sets validate the effectiveness of the proposed TLCGE.The proposed TLCGE is a tensor-based DR method, which can maintain the intrinsic 3-D data structure of medical HSI. By imposing the low-rank and sparse constraints on the objective function, the proposed TLCGE can fully explore the local and global structures within each superpixel. The computational efficiency of the proposed TLCGE is better than other tensor-based DR methods, which can be used as a preprocessing step in real medical HSI classification or segmentation.
Fuzzy granular anomaly detection using Markov random walk

Fuzzy information granulation is an important mathematical model in the theory of granular computing that can effectively handle fuzzy or uncertain information. To address the deficiencies of existing anomaly detection techniques that are mainly suitable for certainty data, this study proposes an anomaly detection method based on fuzzy granules. This method uses the fuzzy information granulation model as a unified framework to gradually develop a fuzzy granular anomaly detection method for calculating the anomaly score of each sample. First, the fuzzy granular distance is defined by fusing single-attribute fuzzy granules to represent the dissimilarity between two samples. Second, a matrix is constructed using the fuzzy granular distance between the samples as the state transition matrix of the Markov random walk. Anomalies in the dataset are then detected using the stationary distribution generated by iterative calculations. Finally, the fuzzy granular anomaly score for each object is obtained by normalizing the stationary distribution. Experiments are conducted on public datasets to compare the proposed method with some state-of-the-art anomaly detection methods. The results indicate that the proposed method is effective. The code is publicly available online at https://github.com/BElloney/FGAS.
3DCascade-GAN: Shape completion from single-view depth images

Depth images can be easily acquired using depth cameras. However, these images only contain partial information about the shape due to unavoidable self-occlusion. Thanks to the availability of large datasets of shapes, it is possible to use a learning-based approach to produce complete shapes from single depth images. State-of-the-art generative adversarial network (GAN) architectures can produce reasonable results. However, the use of relatively local convolutions restricts GAN architectures from producing globally plausible shapes. In this study, we develop a novel dynamic latent code selection mechanism in which the model learns to select only important codes from the latent space. Furthermore, a novel 3D self-attention (3DSA) layer is introduced that is able to capture non-local relationships across the 3D space. We further design a GAN architecture that uses a multistage encoder–decoder to recover the shape, where our 3DSA layer is introduced to the discriminator to help attend to global features, which stabilizes the model learning and encourages shape refinement, making our reconstruction more structurally plausible. Through extensive experiments, we demonstrate that our method outperforms other state-of-the-art methods for single depth image 3D reconstruction.
Adaptive multi-teacher softened relational knowledge distillation framework for payload mismatch in image steganalysis

In this paper, we focus on improving the detection accuracy when payload mismatch occurs in steganalysis, by proposing an adaptive multi-teacher softened relational knowledge distillation framework, which combines multiple balanced payload difference networks (BPDNets), adaptive weight allocation (AWA) and softened relational knowledge distillation (SRKD). BPDNets aims to equivalently concern about the payload difference signal, so that the student can still achieve satisfactory detection accuracy even under unseen payloads due to that a stego signal can be considered as the sum of multiple payload difference signals. AWA assigns appropriate weights to each BPDNet based on the confidence evaluated using the cross entropy and batch entropy loss, thus effectively integrating the knowledge of BPDNets. SRKD combining the softmax with temperature τ other than standard logits to guide the student to inherit more knowledge of relationships between images. Extensive experiments verify that the student achieves better detection performance than state-of-the-art networks.
Blockchain Technology Adoption in Financial Services

FinTech has been widely coined to distinguish the substantial use of technology in the field of finance because technology has transformed the industrial age into the silicon age. One such potent technology that has accompanied Industry 4.0 is blockchain. Blockchain is a decentralized database made up of blocks that store data and enable the network's nodes to track any data transfer. Blockchain is a network of decentralized and distributed blocks that store information with digital signatures. Blockchain properties such as decentralization, consistency, accountability, and transparency make transactions more reliable and safer. Blockchain technology has been implemented in various areas other than bitcoin, such as financial services, risk management, and healthcare facilities. The purpose of this research is to describe the various advantages and disadvantages of implementing blockchain technology in the financial services business. This study also contributes to the development of a comprehensive framework that have highlighted the opportunities and challenges of blockchain in the financial services sector. There are three primary divisions within the chapter: 1) Industry 4.0 and blockchain, 2) influence of technology in the financial sector along with financial services, and 3) upcoming technological problems and obstacles.
Barriers and Potential of Blockchain Technology in FinTech

This chapter explores the barriers and potential of blockchain technology in the FinTech sector. It begins by highlighting the fundamental characteristics of blockchains, emphasizing their ability to securely store and transfer data without the involvement of intermediaries. The transparency provided by blockchain's transaction visibility is contrasted with traditional banking systems. The decentralized nature of blockchain is discussed, along with its applicability in various industries such as cryptocurrencies, financial services, risk management, IoT, and public and social services. The chapter acknowledges the extensive research conducted to analyze blockchain technology and its applications, leading to a need for a comprehensive examination. It addresses blockchain taxonomy, consensus techniques, applications, technological challenges, and recent developments, offering insights into the future possibilities of this technology. By identifying both the barriers and potential, this chapter aims to contribute to the understanding of blockchain technology's role in the FinTech industry.
Classifying Customer Complaints of a Large Fixed Broadband Service Provider using Machine Learning

With the advancement in technology, many organizations use Trouble Ticket Systems (TTS) to record and manage problems, facilitating the process of assigning it to the right technical team. However, in large organizations, which receive a huge number of complaints, the task of allocating or classifying a problem becomes a challenge. In this paper, we propose a solution for automatically classifying customer complaints related to fixed broadband service for TIM, large Brazilian telecommunications company. We consider the fixed broadband customer complaints reported for a period of 31 days starting from December 28, 2020. We propose a custom textual preprocessing technique and use several machine learning classifiers to accurately classify a complaint in one of the six problem classes. Our results demonstrate that the proposed technique generated an increase in accuracy of 8.4% when compared to techniques commonly used in textual preprocessing. Our results also demonstrate that the Extra Tree Classifier achieves the best performance among all models with an accuracy of around 89%. Our work can assist TIM to improve their complaint resolution process.
Overview of single-stage object detection models: from Yolov1 to Yolov7

Recently, with the rise of Artificial Intelligence (AI) and computer vision techniques, object detection using visual automated methods has attracted much attention from computer vision researchers. It is leveraged and integrated in a wide range of real-time applications in view of the reliability of the delivered information. The visual detection relies on processing the appearance and motion features received from Electro-optical (EO) and thermal sensors. Moreover, the detection methods have known a significant evolution in terms of methods used, they moved from using handcrafted feature-based methods to deep learning algorithms with two-shot and single shot object detectors. Real time applications use Single Shot object Detectors (SSD) thanks to the optimal compromise that they present between speed and performance in comparison to their counterpart. You Only Look Once (YOLO) algorithms have demonstrated high performance and fast inference speed in several applications, thus they are extensively exploited and improved properly from the first version. This paper present an exhaustive overview and comparison of Yolo algorithms including the major contributions of each version as well as their evolution with respect parameters used and the followed architecture.
Discop: Provably Secure Steganography in Practice Based on "Distribution Copies"

Steganography is the act of disguising the transmission of secret information as seemingly innocent. Although provably secure steganography has been proposed for decades, it has not been mainstream in this field because its strict requirements (such as a perfect sampler and an explicit data distribution) are challenging to satisfy in traditional data environments. The popularity of deep generative models is gradually increasing and can provide an excellent opportunity to solve this problem. Several methods attempting to achieve provably secure steganography based on deep generative models have been proposed in recent years. However, they cannot achieve the expected security in practice due to unrealistic conditions, such as the balanced grouping of discrete elements and a perfect match between the message and channel distributions. In this paper, we propose a new provably secure steganography method in practice named Discop, which constructs several "distribution copies" during the generation process. At each time step of generation, the message determines from which "distribution copy" to sample. As long as the receiver agrees on some shared information with the sender, he can extract the message without error. To further improve the embedding rate, we recursively construct more "distribution copies" by creating Huffman trees. We prove that Discop can strictly maintain the original distribution so that the adversary cannot perform better than random guessing. Moreover, we conduct experiments on multiple generation tasks for diverse digital media, and the results show that Discop’s security and efficiency outperform those of previous methods.
Low-Cost Privilege Separation with Compile Time Compartmentalization for Embedded Systems

Embedded systems are pervasive and find various applications all around us. These systems run on low-power microcontrollers with real-time constraints. Developers often sacrifice security to meet these constraints by running the entire software stack with the same privilege. Existing work has utilized compartmentalization to mitigate the situation but suffers from a high overhead due to extensive runtime checking to achieve isolation between different compartments in the system, resulting in a rare adoption. In this paper, we present Compartmentalized Real-Time C (CRT-C), a low-cost compile-time compartmentalization mechanism for embedded systems to achieve privilege separation in a linear address space using specialized programming language dialects. Each programming dialect restricts the programming capabilities of a part of a program, formalizing different compartments within the program. CRT-C uses static analysis to identify various compartments in firmware and realizes the least privilege in the system by enforcing compartment-specific policies. We design and implement a new compiler to compile CRT-C to generate compartmentalized firmware that is ready to run on commodity embedded systems. We evaluate CRT-C with two Real-Time Operating Systems (RTOSs): FreeRTOS and Zephyr. Our evaluation shows that CRT-C can provide compartmentalization to embedded systems to thwart various attacks while incurring an average runtime overhead of 2.63% and memory overhead of 1.75%. CRT-C provides a practical solution to both retrofit legacy and secure new applications for embedded systems.
A Security RISC: Microarchitectural Attacks on Hardware RISC-V CPUs

Microarchitectural attacks threaten the security of computer systems even in the absence of software vulnerabilities. Such attacks are well explored on x86 and ARM CPUs, with a wide range of proposed but not-yet deployed hardware countermeasures. With the standardization of the RISC-V instruction set architecture and the announcement of support for the architecture by major processor vendors, RISC-V CPUs are on the verge of becoming ubiquitous. However, the microarchitectural attack surface of the first commercially-available RISC-V hardware CPUs still needs to be explored.This paper analyzes the two commercially-available off-the-shelf 64-bit RISC-V (hardware) CPUs used in most RISC-V systems running a full-fledged commodity Linux system. We evaluate the microarchitectural attack surface and introduce 3 new microarchitectural attack techniques: Cache+Time, a novel cache-line-granular cache attack without shared memory, Flush+Fault exploiting the Harvard cache architecture for Flush+Reload, and CycleDrift exploiting unprivileged access to instruction-retirement information. We also show that many known attacks apply to these RISC-V CPUs, mainly due to non-existing hardware countermeasures and instruction-set subtleties that do not consider the microarchitectural attack surface. We demonstrate our attacks in 6 case studies, including the first RISC-V-specific microarchitectural KASLR break and a CycleDrift-based method for detecting kernel activity. Based on our analysis, we stress the need to consider the microarchitectural attack surface during every step of a CPU design, including custom ISA extensions.
IPvSeeYou: Exploiting Leaked Identifiers in IPv6 for Street-Level Geolocation

We present IPvSeeYou, a privacy attack that permits a remote and unprivileged adversary to physically geolocate many residential IPv6 hosts and networks with street-level precision. The crux of our method involves: 1) remotely discovering wide area (WAN) hardware MAC addresses from home routers; 2) correlating these MAC addresses with their WiFi BSSID counterparts of known location; and 3) extending coverage by associating devices connected to a common penultimate provider router.We first obtain a large corpus of MACs embedded in IPv6 addresses via high-speed network probing. These MAC addresses are effectively leaked up the protocol stack and largely represent WAN interfaces of residential routers, many of which are all-in-one devices that also provide WiFi. We develop a technique to statistically infer the mapping between a router’s WAN and WiFi MAC addresses across manufacturers and devices, and mount a large-scale data fusion attack that correlates WAN MACs with WiFi BSSIDs available in wardriving (geolocation) databases. Using these correlations, we geolocate the IPv6 prefixes of >12M routers in the wild across 146 countries and territories. Selected validation confirms a median geolocation error of 39 meters. We then exploit technology and deployment constraints to extend the attack to a larger set of IPv6 residential routers by clustering and associating devices with a common penultimate provider router. While we responsibly disclosed our results to several manufacturers and providers, the ossified ecosystem of deployed residential cable and DSL routers suggests that our attack will remain a privacy threat into the foreseeable future.
Bitcoin-Enhanced Proof-of-Stake Security: Possibilities and Impossibilities

Bitcoin is the most secure blockchain in the world, supported by the immense hash power of its Proof-of-Work miners. Proof-of-Stake chains are energy-efficient, have fast finality but face several security issues: susceptibility to non-slashable long-range safety attacks, low liveness resilience and difficulty to bootstrap from low token valuation. We show that these security issues are inherent in any PoS chain without an external trusted source, and propose a new protocol, Babylon, where an off-the-shelf PoS protocol checkpoints onto Bitcoin to resolve these issues. An impossibility result justifies the optimality of Babylon. A use case of Babylon is to reduce the stake withdrawal delay: our experimental results show that this delay can be reduced from weeks in existing PoS chains to less than 5 hours using Babylon, at a transaction cost of less than 10K USD per annum for posting the checkpoints onto Bitcoin.
The Leaky Web: Automated Discovery of Cross-Site Information Leaks in Browsers and the Web

When browsing the web, none of us want sites to infer which other sites we may have visited before or are logged in to. However, attacker-controlled sites may infer this state through browser side-channels dubbed Cross-Site Leaks (XS-Leaks). Although these issues have been known since the 2000s, prior reports mostly found individual instances of issues rather than systematically studying the problem space. Further, actual impact in the wild often remained opaque.To address these open problems, we develop the first automated framework to systematically discover observation channels in browsers. In doing so, we detect and characterize 280 observation channels that leak information cross-site in the engines of Chromium, Firefox, and Safari, which include many variations of supposedly fixed leaks. Atop this framework, we create an automatic pipeline to find XS-Leaks in real-world websites. With this pipeline, we conduct the largest to-date study on XS-Leak prevalence in the wild by performing visit inference and a newly proposed variant cookie acceptance inference attack on the Tranco Top10K. In addition, we test 100 websites for the classic XS-Leak attack vector of login detection.Our results show that XS-Leaks pose a significant threat to the web ecosystem as at least 15%, 34%, and 77% of all tested sites are vulnerable to the three attacks. Also, we present substantial implementation differences between the browsers resulting in differing attack surfaces that matter in the wild. To ensure browser vendors and web developers alike can check their applications for XS-Leaks, we open-source our framework and include an extensive discussion on countermeasures to get rid of XS-Leaks in the near future and ensure new features in browsers do not introduce new XS-Leaks.