Reviewerly/exHarmony huggingface dataset:
Multi-connected Tiny Object Detection for Color-Strip Recognition

Object detection is one of the most popular ap- plications in AI image recognition, and Multi-connected Tiny Object detection (MTOD) is an important but difficult branch. For example, we call the color bar carrying optical branching device port usage information and device ID information a color strip, where the color bar consists of multiple tiny color blocks with information bits connected to form a large information string, and color-strip recognition is a kind of MTOD. For MTOD, there are two solutions: bottom-up and top-down. We have previously proposed the bottom-up scheme. This paper proposes a top-down scheme—that is, first find a large object linked in a string, and then subdivide it into each unit. Next, the Optical Character Recognition (OCR) method was used to realize the multi-interference multi-connection dense tiny target detec- tion. Furthermore, to reduce the mutual interference between multi-connected objects, we introduce a "guard code" between multi-connected objects to improve the separability between the tiny object. Through experimental verification in real scenes, the proposed scheme achieves very competitive performance in recognizing multiple connected dense tiny objects.
A Tableau Calculus for Signed Maximum Satisfiability

We define a tableau calculus for solving the Maximum Satisfiability Problem for propositional signed formulas (Signed MaxSAT) and prove its soundness and completeness. We also describe how it can be naturally extended when we distinguish between hard and soft signed formulas.
Natural Deduction with Explosion and Excluded Middle

In this study, new natural deduction systems for intuitionistic, classical, and Gurevich logics are introduced using the new rules of explosion and excluded middle, which correspond to the principle of explosion and the law of excluded middle, respectively. Gurevich logic is an extended three-valued logic obtained from intuitionistic logic by adding strong negation. Theorems for equivalence between these natural deduction systems and the corresponding previously proposed sequent calculi for the logics are proved. Normalization theorems for the proposed natural deduction systems for intuitionistic and Gurevich logics are proved as well.
A Systematic Literature Review on the Effectiveness of Virtual Classrooms in Improving Teaching Techniques and Learning Processes

The concept of virtual classrooms has had an impact within the educational community ever since the COVID-19 outbreak and beyond. While many people would consider this an opportunity for the implementation of information and communications technologies that help improve the learning processes across multiple educational institutions, some may differ from this opinion, saying that it could have made a negative impact, due to an improper translation of the teaching techniques to a virtual outlet or due to accessibility-related problems (for students and teachers alike). This paper focuses in reviewing previous and current knowledge regarding the effectiveness of virtual classroom implementation to improve teaching techniques and learning processes. the databases included in this research were Scopus, IEEE Xplore and ScienceDirect, finding as many as 2,597 articles across all platforms, filtering 14 eligible articles due their quality and high relevance regarding the subject of this study. From this SLR, we can determine that, even though there were some cases in which the virtual classrooms presented a challenge to teaching techniques and learning processes already stablished, it also helped to improve their effectiveness, only if given the time and tools for teachers and students to adapt to these changes.
Properties of the Reed-Muller-Fourier Spectra of Maiorana-McFarland Bent Functions

The Reed-Muller-Fourier spectrum of p-valued (p prime, p > 2) Maiorana-McFarland bent functions is studied. It is shown that this spectrum exhibits a unique structure, which allows characterizing these functions and to distinguish them from other p-valued bent functions.
A Consideration on Ternary Adversarial Generative Networks

Generative adversarial networks (GANs), which can generate and transform data, have been attracting attention. However, the model must be lightweight and fast when applied in the field. As for the ternarization of GAN, (TernaryGAN,) which restricts the value of the weights to {−1, 0, +1} during forwarding propagation of the generator, has already been proposed. In this paper, we investigated by experiment how ternary generator and/or discriminator affects the training of GANs. To make not only generator ternary, but also discriminator, we propose the DGR (Decomposition with Gradient Retained) method, which can change discriminator’s input images to binary. We trained GANs for the cases where the generator and the discriminator are ternarized, and for the case where only one of them is ternarized, and measured the degree of image degradation using the FID (Fréchet inception distance) score. Only the ternarized generator is showed the lowest accuracy degradation, implying that GANs contain some parts that are not suitable for ternarization. We found the useful insight that when reducing the weight of GAN, the generator can be compressed relatively more, while the discriminator should not be so much.
ISC-FLAT: On the Conflict Between Control Flow Attestation and Real-Time Operations

The wide adoption of IoT gadgets and CyberPhysical Systems (CPS) makes embedded devices increasingly important. While some of these devices perform mission-critical tasks, they are usually implemented using Micro-Controller Units (MCUs) that lack security mechanisms on par with those available to general-purpose computers, making them more susceptible to remote exploits that could corrupt their software integrity. Motivated by this problem, prior work has proposed techniques to remotely assess the trustworthiness of embedded MCU software. Among them, Control Flow Attestation (CFA) enables remote detection of runtime abuses that illegally modify the program’s control flow during execution (e.g., control flow hijacking and code reuse attacks). Despite these advances, current CFA methods share a fundamental limitation: they preclude interrupts during the execution of the software operation being attested. Simply put, existing CFA techniques are insecure unless interrupts are disabled on the MCU. On the other hand, we argue that the lack of interruptability can obscure CFA usefulness, as most embedded applications depend on interrupts to process asynchronous events in real-time. To address this limitation, we propose Interrupt-Safe Control Flow Attestation (ISC-FLAT): a CFA technique that is compatible with existing MCUs (i.e., does not require hardware changes) and enables interrupt handling without compromising the authenticity of CFA reports. Similar to other CFA techniques that do not require customized hardware modifications, ISC-FLAT leverages a Trusted Execution Environment (TEE) (in particular, our prototype is built on ARM TrustZone-M) to securely generate unforgeable CFA reports without precluding applications from processing interrupts. We implement a fully functional ISC-FLAT prototype on the ARM Cortex-M33 MCU and demonstrate that it incurs minimal runtime overhead when compared to existing TEE-based CFA methods that do not support interrupts.
Degree-Aware Kernel Mapping for Graph Processing on GPUs

Social network graphs follow a power-law distribution, enabling us to exploit the GPU’s hierarchical execution model for efficient computation through a degree-aware kernel computation approach. In this approach, nodes are mapped to different levels of parallelism on the GPU, depending upon their in-degree. To take advantage of this execution model, the nodes must be arranged in decreasing order of in-degree. However, doing so distorts the community structure (a property responsible for providing memory locality during computation), impacting the degree-aware kernel’s performance gain. To balance ordering by in-degree and community structure, we propose DRBS, a graph reordering algorithm that sorts the graph nodes while preserving enough community structure to enhance cache efficiency.
LoopTree: Enabling Exploration of Fused-layer Dataflow Accelerators

Many accelerators today process deep neural networks layer by layer. As a consequence of this processing style, every intermediate feature map incurs expensive off-chip transfers. Layer fusion eliminates off-chip transfers of intermediate results, leading to better latency and energy efficiency. Prior works have explored only subsets of the fused-layer design space, looking only at a particular choice of tiling, scheduling, and buffering strategy. Their architectural models are also tailored for their proposed datatlow. The lack of a unified, systematic representation of designs and a versatile evaluation method has prevented thorough exploration of the design space. To enable systematic exploration of this design space, we present LoopTree, a framework for describing and evaluating any design in our expanded fused-layer datatlow design space. With a case study, we explore new designs to show that exploring our larger design space uncovers more efficient designs, especially for recent workloads with diverse layer types. Our design achieves 2.5× speedup and 2× lower energy compared to an optimized layer-by-layer design. Compared to a state-of-the-art fused-layer design, we match latency and energy while using 25% less onchip buffer space.
An Improved Multiple-Outlier Robust Filter Based on Maximum Correntropy Criterion for Integrated Navigation

In the integrated navigation of Global Navigation Satellite System (GNSS) and Inertial Navigation System (INS), the Kalman filter is frequently employed for state estimation. GNSS signals are subjected to interference from the outside environment, and it is difficult for the classical Kalman filter to handle observations with outliers. Numerous robust filters have been proposed based on the maximum correntropy criterion (MCC), which is insensitive to non-Gaussian noise, to address the issue of state estimation in the presence of outliers. However, a fixed prior covariance will restrict the use of a maximum correntropy criterion Kalman filter (MCKF) in integrated navigation systems because GNSS signals can be affected by both time-varying noise and outliers. Existing filters consider the prior measurement noise covariance matrix (MNCM) as a deterministic matrix and can only amplify signals unidirectionally during iteration cycles. The weight of effective observations is reduced despite these filters having strong robustness. To overcome this shortcoming, this article proposes a <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\tau $ </tex-math></inline-formula> -based MCKF ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\tau $ </tex-math></inline-formula> -MCKF). A strategy based on the chi-square test is designed to optimize the covariance of GNSS observations. By introducing <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\tau $ </tex-math></inline-formula> to bidirectionally adjust the initial MNCM, <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\tau $ </tex-math></inline-formula> -MCKF makes full use of effective observations. Then, the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\tau $ </tex-math></inline-formula> -corrected MNCM is used to weigh different elements of the innovation vector, and a robust estimation is calculated based on the multiple-outlier robust Kalman filter (MORKF) framework. The GNSS/INS integrated navigation system is taken as an example to verify the effectiveness of the filter through simulation and vehicular experiments.
Overview to Blockchain Scalability Challenges and Solutions

Blockchain-based decentralized cryptocurrencies have drawn much attention and been widely-deployed in recent years. Bitcoin, the first application of blockchain, achieves great success and promotes more development in this field. However, Bitcoin encounters performance problems of low throughput and high transaction latency. Other cryptocurrencies based on proof-of-work also inherit the flaws, leading to more concerns about the scalability of blockchain. This chapter attempts to cover the existing scaling solutions for blockchain and classify them by level. In addition, we make comparisons between different methods and list some potential directions for solving the scalability problem of blockchain.
Sharding-Based Scalable Consortium Blockchain

Blockchain performance cannot meet the requirement nowadays. One of the crucial ways to improve performance is sharding. However, most blockchain sharding research focuses on the public blockchain. As for consortium blockchain, previous studies cannot support high cross-shard efficiency, multiple-shard contract calling, strict transaction atomicity, and shard availability, which are essential requirements but also challenges in consortium blockchain systems. Facing these challenges, we propose Meepo, a systematic study on sharded consortium blockchain. Meepo enhances cross-shard efficiency via the cross-epoch and cross-call. Moreover, a partial cross-call merging strategy is designed to handle the multi-state dependency in contract calls, achieving flexible multiple-shard contract calling. Meepo employs a replay-epoch to ensure strict transaction atomicity, and it also uses a backup algorithm called shadow shard based recovery to improve the shard robustness. On a test-bed of 128 AliCloud servers, setting 32 shards and 4 consortium members, Meepo-OpenEtheruem can achieve more than 140,000 cross-shard TPS under the workload of 100,000,000 asset transactions. It also shows more than 50,000 TPS under the transactions of real-world shopping behaviors.
Mathematical Foundation for Classical and Modern Cryptography

The design of public-key cyphers prevents knowledge of the encryption key from revealing the identity of the decryption key. Making advantage of a mathematical problem that is “simple” in one direction but “difficult” in the other is one method for developing public-key cyphers. For instance, the RSA, relies on the idea that multiplying two large primes—one on the order of 1080 or larger for each—is relatively simple computationally sense, as opposed to attempting to factor a large number—say, one on the order of 10160 or larger—into its prime factors, which is ostensibly impossible to do in any reasonable amount of time. Hence, understanding of modern cryptography heavily rely on mathematics.
An Introduction to Classical and Modern Cryptography

The remote environment bounded us to share information, documents, money, etc. with the help of applications, devices, and tools. The information, document and money transfer are very sensitive for individuals. Therefore, securing information, documents and money is an essential for all of us. The cryptography is study and practice of secure communication in the presence of adversary or malicious entities. In this chapter we are going to explain theoretical aspects of cryptography and modern cryptography with help of various applications. In this chapter focus is given to ground principles of cryptography, terminologies, types of cryptography and techniques of cryptography that comprises concepts of transposition and substitution. The transposition is concepts where characters remain unchanged but rearranged. Whereas, in substitution a character is replaced by another character or symbol. The existing study confirms that we are having simple and easy as well as difficult and complex algorithms in cryptography. The use of algorithm depends on the individual tool, application, device, etc. But the existing study also claims that asymmetric key algorithms or techniques are more prominent. Hence, this chapter provides theorical foundation with examples and applications for cryptography which help readers to understand and apply cryptographic algorithm for various problems.
$$S^{2}$$-LOR: Supervised Stream Learning for Object Recognition

In a stream learning scenario, where new data arrives at a slow pace, it is crucial to leverage new knowledge at the same rate without losing prior knowledge, and without assuming data stationarity. This scenario presents a significant challenge for incremental learning, particularly for tasks such as object recognition in video streams. In this paper, a novel approach is proposed that uses a set of weak classifiers that evolves into ensembles to enhance the generalization power of the system, as new video subsequences of the same instances are presented. We evaluate the efficiency of our approach and compare with state-of-the-art methods using a benchmark dataset. The code is available at https://github.com/vilaB/object_recognition .
Proxemics-Net: Automatic Proxemics Recognition in Images

Proxemics is a branch of anthropology that studies how humans use personal space as a means of nonverbal communication; that is, it studies how people interact. Due to the presence of physical contact between people, in the problem of proxemics recognition in images, we have to deal with occlusions and ambiguities, which complicates the process of recognition. Several papers have proposed different methods and models to solve this problem in recent years. Over the last few years, the rapid advancement of powerful Deep Learning techniques has resulted in novel methods and approaches. So, we propose Proxemics-Net, a new model that allows us to study the performance of two state-of-the-art deep learning architectures, ConvNeXt and Visual Transformers (as backbones) on the problem of classifying different types of proxemics on still images. Experiments on the existing Proxemics dataset show that these deep learning models do help favorably in the problem of proxemics recognition since we considerably outperformed the existing state of the art, with the ConvNeXt architecture being the best-performing backbone.
Addressing Class Imbalance in Multilabel Prototype Generation for k-Nearest Neighbor Classification

Prototype Generation (PG) methods seek to improve the efficiency of the k-Nearest Neighbor (kNN) classifier by obtaining a reduced version of a given reference dataset following certain heuristics. Despite being largely addressed topic in multiclass scenarios, few works deal with PG in multilabel environments. Hence, the existing proposals exhibit a number of limitations, being label imbalance one of paramount relevance as it constitutes a typical challenge of multilabel datasets. This work proposes two novel merging policies for multilabel PG schemes specifically devised for label imbalance, as well as a mechanism to prevent inappropriate samples from undergoing a reduction process. These proposals are applied to three existing multilabel PG methods—Multilabel Reduction through Homogeneous Clustering, Multilabel Chen, and Multilabel Reduction through Space Partitioning—and evaluated on 12 different data assortments with different degrees of label imbalance. The results prove that the proposals overcome—in some cases in a significant manner—those obtained with the original methods, hence validating the presented approaches and enabling further research lines on this topic.
Real-Time Unsupervised Object Localization on the Edge for Airport Video Surveillance

Object localization is vital in computer vision to solve object detection or classification problems. Typically, this task is performed on expensive GPU devices, but edge computing is gaining importance in real-time applications. In this work, we propose a real-time implementation for unsupervised object localization using a low-power device for airport video surveillance. We automatically find regions of objects in video using a region proposal network (RPN) together with an optical flow region proposal (OFRP) based on optical flow maps between frames. In addition, we study the deployment of our solution on an embedded architecture, i.e. a Jetson AGX Xavier, using simultaneously CPU, GPU and specific hardware accelerators. Also, three different data representations (FP32, FP16 and INT8) are employed for the RPN. Obtained results show that optimizations can improve up to 4.1 $$\times $$ energy consumption and 2.2 $$\times $$ execution time while maintaining good accuracy with respect to the baseline model.
Evaluation of Regularization Techniques for Transformers-Based Models

In recent years the great success of transformers-based models initially employed in Natural Language (NLP) tasks has led to the development of several transformers variations to be employed in a wide range of domains, such as vision. With the correct amount of training data and proper training, transformers can perform excellently compared to the Convolution Neural Networks (CNN) counterpart in the vision tasks. However, the main drawback of transformers concerns the know memory requirements that often exceed the available training platform, growing in a quadratic form regarding the input image size, and a great tendency to overfit. Several works address the memory problem by relaxing the model architecture versions, but mainly with reduced prediction capabilities. In this work, we evaluate Random Patch erasing among the image patch level of the transformer model as a regularization technique to reduce overfitting while at the same time alleviating training time. The evaluated regularization technique achieves competitive results on several image classification medical datasets. The evaluated Visual Transformers (ViT) models allow to be trained in a single GPU, reaching similar results to CNN counterparts, obtaining an accuracy 91.2%, 79.2% in two competitive image datasets, and reducing the training time on average by 22% on the transformers models.
Learning to Search for and Detect Objects in Foveal Images Using Deep Learning

The human visual system processes images with varied degrees of resolution, with the fovea, a small portion of the retina, capturing the highest acuity region, which gradually declines toward the field of view’s periphery. However, the majority of existing object localization methods rely on images acquired by image sensors with space-invariant resolution, ignoring biological attention mechanisms. As a region of interest pooling, this study employs a fixation prediction model that emulates human objective-guided attention of searching for a given class in an image. The foveated pictures at each fixation point are then classified to determine whether the target is present or absent in the scene. Throughout this two-stage pipeline method, we investigate the varying results obtained by utilizing high-level or panoptic features and provide a ground-truth label function for fixation sequences that is smoother, considering in a better way the spatial structure of the problem. Additionally, we present a novel dual task model capable of performing fixation prediction and detection simultaneously, allowing knowledge transfer between the two tasks. We conclude that, due to the complementary nature of both tasks, the training process benefited from the sharing of knowledge, resulting in an improvement in performance when compared to the previous approach’s baseline scores.
Why Thoreau Spent a Night in Jail

"Why Thoreau Spent a Night in Jail." The Explicator, ahead-of-print(ahead-of-print), pp. 1–2Keywords: Henry David Thoreau19th century