Reviewerly/exHarmony huggingface dataset:
Efficient Block Propagation in Wireless Blockchain Networks and Its Application in Bitcoin

As the supporting architecture of cryptocurrencies, blockchain is also showing its promising potential to be applied to mobile devices. Being WiFi supported, mobile devices nowadays are able to perform ad-hoc local communications in multi-hop manners, thus can potentially extend cryptocurrency service into areas without stable Internet connection. However, traditional blockchain architecture suffers from the intolerably high block propagation latency and communication cost which will be even trickier if adopted in wireless multi-hop networks (WMNs). This paper aims to reduce this latency and cost by leveraging node weights to distinguish the feedback speed of different nodes and constructing a low-length multicast tree to select a subset of nodes with higher feedback speed to participate in the block verification. Thus, block propagation is depicted by the minimum length multicast tree, intrinsically the Steiner Tree Problem. The primary challenge lies in that the WMNs only allow local communication and the distributed consensus protocol of the blockchain makes a predetermination of receiver nodes impossible. We design our algorithm via a ``toward source'' Steiner tree approach in favor of the distributed environment. Tree construction proceeds by progressively enlarging the searching areas until the accumulated weight of receiver nodes reaches a threshold. Our algorithm provably returns a tree length fairly close to that of the optimal Steiner tree with latency <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$O(\sqrt{n\log n})$</tex-math></inline-formula> (where <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$n$</tex-math></inline-formula> represents the number of nodes in the network), the best so far. Furthermore, our algorithm is empirically validated to provide instructive insights for efficient block propagation with applications in Bitcoin network.
Camera-Tracklet-Aware Contrastive Learning for Unsupervised Vehicle Re-Identification

Recently, vehicle re-identification methods based on deep learning constitute remarkable achievement. However, this achievement requires large-scale and well-annotated datasets. In constructing the dataset, assigning globally available identities (Ids) to vehicles captured from a great number of cameras is labour-intensive, because it needs to consider their subtle appearance differences or viewpoint variations. In this paper, we propose camera-tracklet-aware contrastive learning (CTACL) using the multi-camera tracklet information without vehicle identity labels. The proposed CTACL divides an unlabelled domain, i.e., entire vehicle images, into multiple camera-level subdomains and conducts contrastive learning within and beyond the subdomains. The positive and negative samples for contrastive learning are defined using tracklet Ids of each camera. Additionally, the domain adaptation across camera networks is introduced to improve the generalisation performance of learnt representations and alleviate the performance degradation resulted from the domain gap between the subdomains. We demonstrate the effectiveness of our approach on video-based and image-based vehicle Re-ID datasets. Experimental results show that the proposed method outperforms the recent state-of-the-art unsupervised vehicle Re-ID methods. The source code for this paper is publicly available on https://github.com/andreYoo/CTAM-CTACL-VVReID.git.
Efficient access control with traceability and user revocation in IoT

Abstract With the universality and availability of Internet of Things (IoT), data privacy protection in IoT has become a hot issue. As a branch of attribute-based encryption (ABE), ciphertext policy attribute-based encryption (CP-ABE) is widely used in IoT to offer flexible one-to-many encryption. However, in IoT, different mobile devices share messages collected, transmission of large amounts of data brings huge burdens to mobile devices. Efficiency is a bottleneck which restricts the wide application and adoption of CP-ABE in Internet of things. Besides, the decryption key in CP-ABE is shared by multiple users with the same attribute, once the key disclosure occurs, it is non-trivial for the system to tell who maliciously leaked the key. Moreover, if the malicious mobile device is not revoked in time, more security threats will be brought to the system. These problems hinder the application of CP-ABE in IoT. Motivated by the actual need, a scheme called traceable and revocable ciphertext policy attribute-based encryption scheme with constant-size ciphertext and key is proposed in this paper. Compared with the existing schemes, our proposed scheme has the following advantages: (1) Malicious users can be traced; (2) Users exiting the system and misbehaving users are revoked in time, so that they no longer have access to the encrypted data stored in the cloud server; (3) Constant-size ciphertext and key not only improve the efficiency of transmission, but also greatly reduce the time spent on decryption operation; (4) The storage overhead for traceability is constant. Finally, the formal security proof and experiment has been conducted to demonstrate the feasibility of our scheme.
GIMD-Net: An effective General-purpose Image Manipulation Detection Network, even under anti-forensic attacks

The digital image information can be easily tampered to harm the integrity of someone. Thus, recognizing the truthfulness and processing history of an image is one of the essential concerns in multimedia forensics. Numerous forensic methods have been developed by researchers with the ability to detect targeted editing operations. But, creating a unified forensic approach capable of detecting multiple image manipulations is still a challenging problem. In this paper, a new GIMD network is designed that exploits local dense connections and global residual learning for better classification by using robust residual dense blocks (RDBs). The network input and high-level hierarchical features produced by proposed residual dense blocks are fused globally for better information flow across the network. The extensive experiment results show that the proposed scheme outperforms the existing state-of-the-art general-purpose forensic schemes even under anti-forensic attacks, when tested on large scale publicly available datasets. Our model offers overall detection accuracies of 95.09% and 97.31 % for BOSSBase and Dresden datasets, respectively for multiple image manipulation detection.
RF Signal Classification using Boolean Reservoir Computing on an FPGA

Networks of Boolean logic gates exhibiting complex dynamical behavior are promising reservoirs for hardware-accelerated reservoir computing. Using an FPGA, we explore the parameter space of both clocked and unclocked Boolean networks, and identify configurations that are suitable for information processing. We use an FPGA-based reservoir to process a subset of the DeepSig 2016 dataset, showing classification accuracy using logistic regression competitive with a state-of-the-art convolutional neural network, achieved with a fraction of the trainable parameters.
Implementation of Online Teaching Behavior Analysis System

Online teaching is gradually spreading widely, but there are problems in online teaching evaluation, such as lack of reference index, students' learning state feedback being hard to get in real-time. To improve online teaching quality and evaluation, a classroom behavior analysis and evaluation system based on deep learning face recognition technology is proposed. This system conducts the model training and tests on the deep learning development framework (TensorFlow). It implements the frame image processing through the Mask R-CNN network, extracts the skeleton and the angle direction information to establish the vector, thus carries out the classroom behavior recognition and analysis, and serves as the appraisal important index. The behavior analysis is made and the results show that the recognition rate is high and the learning situation feedback is given in real time. The experiment shows that the system has certain robustness and high accuracy in the real scene. It is convenient for classroom teaching management and implementation, and is helpful to improve the teaching quality.
Intrinsic Motivation to Learn Action-State Representation with Hierarchical Temporal Memory

In this paper, we propose a biologically plausible model for learning the decision-making sequence in an external environment with internal motivation. As a computational model, we propose a hierarchical architecture of an intelligent agent acquiring experience based on reinforcement learning. We use the basal ganglia model to aggregate a reward, and sparse distributed representation of states and actions in hierarchical temporal memory elements. The proposed architecture allows the agent to build a compact model of the environment and to form an effective strategy, which is experimentally demonstrated to search for resources in grid environments.
Onboard visual drone detection for drone chasing and collision avoidance

We proposed a method for real-time detection of flying drones and UAVs that could be run with a limited computing device like an onboard computer for a drone. This method is approached from a machine learning perspective resulting in an object detection model to detect and localize multiple drones in a given image. The constraint is that the model will have to be lightweight and performant while still maintaining good accuracy. This leads us to choose SSD Mobilenet V2 model architecture to train and test how effective it is for detecting drones. Our method gained an average precision and recall of 0.586 and 0.622 (IoU 0.50 - 0.95, all area) respectively.
Comparative Analysis and Enhancement of CFG-based Hardware-Assisted CFI Schemes

Subverting the flow of instructions (e.g., by use of code-reuse attacks) still poses a serious threat to the security of today’s systems. Various control flow integrity (CFI) schemes have been proposed as a powerful technique to detect and mitigate such attacks. In recent years, many hardware-assisted implementations of CFI enforcement based on control flow graphs (CFGs) have been presented by academia. Such approaches check whether control flow transfers follow the intended CFG by limiting the valid target addresses. However, these papers all target different platforms and were evaluated with different sets of benchmark applications, which makes quantitative comparisons hardly possible. For this paper, we have implemented multiple promising CFG-based CFI schemes on a common platform comprising a RISC-V within FPGA. By porting almost 40 benchmark applications to this system we can present a meaningful comparison of the various techniques in terms of run-time performance, hardware utilization, and binary size. In addition, we present an enhanced CFI approach that is inspired by what we consider the best concepts and ideas of previously proposed mechanisms. We have made this approach more practical and feature-complete by tackling some problems largely ignored previously. We show with this fine-grained scheme that CFI can be achieved with even less overheads than previously demonstrated.
Gaussian Process Encoders: VAEs with Reliable Latent-Space Uncertainty

Variational autoencoders are a versatile class of deep latent variable models. They learn expressive latent representations of high dimensional data. However, the latent variance is not a reliable estimate of how uncertain the model is about a given input point. We address this issue by introducing a sparse Gaussian process encoder. The Gaussian process leads to more reliable uncertainty estimates in the latent space. We investigate the implications of replacing the neural network encoder with a Gaussian process in light of recent research. We then demonstrate how the Gaussian Process encoder generates reliable uncertainty estimates while maintaining good likelihood estimates on a range of anomaly detection problems. Finally, we investigate the sensitivity to noise in the training data and show how an appropriate choice of Gaussian process kernel can lead to automatic relevance determination.
Conflict-Driven Satisfiability for Theory Combination: Lemmas, Modules, and Proofs

Abstract Search-based satisfiability procedures try to build a model of the input formula by simultaneously proposing candidate models and deriving new formulae implied by the input. Conflict-driven procedures perform non-trivial inferences only when resolving conflicts between formulæ and assignments representing the candidate model. CDSAT ( Conflict-Driven SATisfiability ) is a method for conflict-driven reasoning in unions of theories . It combines inference systems for individual theories as theory modules within a solver for the union of the theories. This article augments CDSAT with a more general lemma learning capability and with proof generation . Furthermore, theory modules for several theories of practical interest are shown to fulfill the requirements for completeness and termination of CDSAT. Proof generation is accomplished by a proof-carrying version of the CDSAT transition system that produces proof objects in memory accommodating multiple proof formats. Alternatively, one can apply to CDSAT the LCF approach to proofs from interactive theorem proving, by defining a kernel of reasoning primitives that guarantees the correctness by construction of CDSAT proofs.
Learning in Immersive Virtual Reality: How Does the 4E Cognition Approach Fit in Virtual Didactic Settings?

Immersive virtual reality (iVR) is becoming increasingly popular in the field of learning. However, due to the large number of scientific publications, it is questionable whether a substantial increase in knowledge in the use of this technology faces a manageable number of challenges. The question of the theoretical-conceptual embedding in the state of the art of psychological learning research (4E Cognition) must also be examined in order to enable the optimization of future iVR learning applications.
Combining and Relating Aristotelian Diagrams

Combining and relating logical diagrams is a relatively new area of study in the community of people who work on Aristotelian diagrams. Most attempts now have been relatively ad hoc. In this paper I outline a more systematic research program inspired by a category-theoretic perspective. As concerns the logical diagrams, I will mainly focus on the Demey–Smessaert tradition.
Using Negative Detectors for Identifying Adversarial Data Manipulation in Machine Learning

With the increased popularity of Machine Learning (ML) in real-world applications, adversarial attacks are emerging to subvert the ML-based decision support systems. It appears that the existing adversarial defenses are ineffective against adaptive attacks since these are highly depend on knowledge of prior attacks and the ML model architecture. To alleviate the challenges, We propose a negative filtering strategy that does not require any adversarial knowledge and can work independent of ML models. This filtering strategy relies on salient features of clean (training) data and employs a complementary approach to cover possible attack surface in an application. Our empirical experiments with different data sets demonstrate that the negative filters could effectively detect wide-range of adversarial inputs and update itself to protect against adaptive attacks.
Palmira: A Deep Deformable Network for Instance Segmentation of Dense and Uneven Layouts in Handwritten Manuscripts

Handwritten documents are often characterized by dense and uneven layout. Despite advances, standard deep network based approaches for semantic layout segmentation are not robust to complex deformations seen across semantic regions. This phenomenon is especially pronounced for the low-resource Indic palm-leaf manuscript domain. To address the issue, we first introduce Indiscapes2, a new large-scale diverse dataset of Indic manuscripts with semantic layout annotations. Indiscapes2 contains documents from four different historical collections and is \(150\%\) larger than its predecessor, Indiscapes. We also propose a novel deep network Palmira for robust, deformation-aware instance segmentation of regions in handwritten manuscripts. We also report Hausdorff distance and its variants as a boundary-aware performance measure. Our experiments demonstrate that Palmira provides robust layouts, outperforms strong baseline approaches and ablative variants. We also include qualitative results on Arabic, South-East Asian and Hebrew historical manuscripts to showcase the generalization capability of Palmira.
FOOD: Fast Out-Of-Distribution Detector

Deep neural networks (DNNs) perform well at classifying inputs associated with the classes they have been trained on, which are known as in-distribution inputs. However, out-of-distribution (OOD) inputs pose a great challenge to DNNs and consequently represent a major risk when DNNs are implemented in safety-critical systems. Extensive research has been performed in the domain of OOD detection. However, current state-of-the-art methods for OOD detection suffer from at least one of the following limitations: (1) increased inference time - this limits existing methods' applicability to many real-world applications, and (2) the need for OOD training data - such data can be difficult to acquire and may not be representative enough, thus limiting the ability of the OOD detector to generalize. In this paper, we propose FOOD - Fast Out-Of-Distribution detector - an extended DNN classifier capable of efficiently detecting OOD samples with minimal inference time overhead. Our architecture features a DNN with a final Gaussian layer combined with the log likelihood ratio statistical test and an additional output neuron for OOD detection. Instead of using real OOD data, we use a novel method to craft artificial OOD samples from in-distribution data, which are used to train our OOD detector neuron. We evaluate FOOD's detection performance on the SVHN, CIFAR-10, and CIFAR-100 datasets. Our results demonstrate that in addition to achieving state-of-the-art performance, FOOD is fast and applicable to real-world applications.
Introduction to Mask Malangan with Augmented Reality Technology

Indonesia has a diverse culture, but nowadays the demand for it is decreasing. This is because the method for introducing culture is less attractive. By using Augmented Reality, the introduction of culture becomes more interesting. So that it can increase interest in learning about existing cultures. The concept of augmented reality is inspired by Malang mask tourism. We use Malang masks because we see the potential of users so that it makes it easier to introduce them. The process of making augmented reality is to create 3d assets on 3d paint, we try make 2 assets as a first step. followed by designing a prototype design using Figma. the last step is to use unity to combine all aspects and start making AR based on face recognition. we use the AR kit face tracking package to help detect faces. We also provide history and music background to introduce more Malang masks. Our application uses the base android. After exporting to android, we do tests based on distance and angle, and the result is our AR mask appears under certain conditions.
Saliency Guided Experience Packing for Replay in Continual Learning

Artificial learning systems aspire to mimic human intelligence by continually learning from a stream of tasks without forgetting past knowledge. One way to enable such learning is to store past experiences in the form of input examples in episodic memory and replay them when learning new tasks. However, performance of such method suffers as the size of the memory becomes smaller. In this paper, we propose a new approach for experience replay, where we select the past experiences by looking at the saliency maps which provide visual explanations for the model’s decision. Guided by these saliency maps, we pack the memory with only the parts or patches of the input images important for the model’s prediction. While learning a new task, we replay these memory patches with appropriate zero-padding to remind the model about its past decisions. We evaluate our algorithm on CIFAR-100, miniImageNet and CUB datasets and report better performance than the state-of-the-art approaches. With qualitative and quantitative analyses we show that our method captures richer summaries of past experiences without any memory increase, and hence performs well with small episodic memory.
Redefined Fuzzy Min-Max Neural Network

The classical fuzzy min-max (FMM) neural network easy to cause the overlap of hyperboxes from different classes, which affect the pattern classification performance. In this paper, we propose a redefined fuzzy min-max (RFMM) neural network to solve this problem. The main contribution is to modify the basic architecture of FMM by adding a redefined hyperbox layer. The proposed RFMM is a four-layer feedforward neural network. The generated hyperbox layer and the redefined hyperbox layer are connected through the proposed hyperbox filter, hyperbox optimization and hyperbox combination. The RFMM learning algorithm is an expansion/contraction/redefinition process. The effectiveness of RFMM is evaluated based on ten benchmarks. Experimental results indicate that RFMM leads to better classification performance than various FMM-based, support vector machine-based models and lower sensitivity to the maximum size of expansion coefficient.
Supporting Requirements to Code Traceability Creation by Code Comments

Requirements-to-code tracing is an important and costly task that creates trace links from requirements to source code. These trace links help engineers reduce the time and complexity of software maintenance. Code comments play an important role in software maintenance tasks. However, few studies have focused intensively on the impact of code comments on requirements-to-code trace links creation. Different types of comments have different purposes, so how different types of code comments provide different improvements for requirements-to-code trace links creation? We focus on learning whether code comments and different types of comments can improve the quality of trace links creation. This paper presents a study to evaluate the contribution of code comments and different types of code comments to the creation of trace links. More specifically, this paper first experimentally evaluates the impact of code comments on requirements-to-code trace links creation, and then divides code comments into six categories to evaluate its impact on trace links creation. The results show that the precision increases by an average of 15% (based on the same recall) after adding code comments (even for different trace links creation techniques), and the type of Purpose comments contributes more to the tracing task than the other five. This empirical study provides evidence that code comments are effective in tracing links creation, and different types of code comments contribute differently. Purpose comments can be used to improve the accuracy of requirements-to-code trace links creation.