Reviewerly/exHarmony huggingface dataset:
Image Captioning using Pretrained Language Models and Image Segmentation

Large-scale pre-trained language models, which have learned cross-modal representations on image-text pairs, are becoming popular for vision-language tasks because the fine-tuning to a specific task enables state-of-the-art results. Existing methods require features of image regions as input, but these regions are extracted with an object detection model that does not handle overlapping, noisy and ambiguous regions; this inevitably results in less meaningful features. In this paper we propose a new way to extract region features based on image segmentation, with the goal of reducing overlapping and noise. Our method is motivated by the observation that image segmentation can remove useless pixels using the binary mask to extract only the object of interest.
Simultaneous Security and EMC Evaluations Based on Measuring Electromagnetic Field Distribution on PCBs

This article introduces a method to simultaneously evaluate the security and electromagnetic compatibility (EMC) of information devices equipped with cryptographic modules by measuring the electromagnetic (EM) field distribution on a printed circuit board (PCB). The proposed method requires a single correlation analysis above the cryptographic integrated circuit (IC) to define the frequency band of the EM field containing the secret information. Then, focusing on the radiation intensity in the frequency domain, an efficient method capable of obtaining the EM field distribution that contributes to the leakage of confidential information is proposed. Simultaneous EMC and security evaluations can be conducted by analyzing the obtained EM field distribution. Using the proposed method, EM field radiation and information leakage of an actual cryptographic device are analyzed. Compared to conventional methods requiring a large computational resource and analysis time, the proposed method can efficiently obtain the distribution of electric and magnetic fields that cause leakage of confidential information. Lastly, discussion, design guide, and future research directions are briefly provided.
Integrating Problem Based Learning into Augmented Reality: The Effects on Learning Achievement in Chinese Writing Teaching

Writing is a complex mental activity, and many students have difficulties in learning to write. To address students' difficulties in writing, this research introduced augmented reality technology into the writing classroom, designed a Problem Based Learning (PBL) activity incorporating Augment Reality (AR), and compared the effects of Problem Based Learning and Teacher Based Instruction (TBI) on the learning achievement and learning engagement of Chinese writing. To assess the differences between the two instructional approaches in the AR environment, an experiment was conducted in an elementary school in Zhejiang Province, China, where 80 fourth-grade students were randomly assigned to the experimental and control groups. Students in the experimental group were taught PBL with AR support, while the control group was taught TBI with AR support. The results of the study showed that PBL was more effective in AR supported writing classrooms than TBI.
HandFruitNet: A Deep Learning Based Model for Fruits Classification from Hand

Detecting a hand and classifying fruits from an image is a significant research area in the field of computer vision. For many applications, detecting a hand and classifying fruits together from a single system is also significant. This type of system is also effective for shop owner to detect suspicious people, as well as for the disabled people to easily pick different fruits from fruits shop without any other help. In this regard, in this paper, a system is proposed where hand detection and fruit classification are performed together when the fruit is in a person’s hand. For that, initially, a hand is detected by using two methods. The first method is developed based on the hand features extraction, i.e., contour feature of the hand. Another method is developed based on deep learning, i.e., YOLOv3. After that, a convolutional neural network model based on deep learning is developed to classify the fruits. Finally, both models are combined to detect hands and classify the fruits from a single system. For training the model, 11 K and Fruits_360 datasets are used for hand detection and fruits classification, respectively. Beyond that, two different own datasets are created for hand detection and fruit classification. These datasets are utilized during testing the models. Among the models, the feature extraction and deep learning based hand detection model show 95.17% and 99.90% accuracy, whereas deep learning based fruits classification model shows 99.99% accuracy.
Resolution Transfer for Object Detection from Satellite Imagery

Smallsat constellations are an increasingly common source of global-scale overhead imagery that are refreshed with a higher frequency than traditional satellites. The smaller size and lower cost of smallsats enable frequent revisits, but result in images with lower resolution and quality than the high resolution (HR) images from traditional satellites. In order to benefit from the increased temporal frequency provided by smallsat constellations, new approaches are needed to automatically detect objects in their imagery. We present a super resolution (SR) approach that incorporates domain adaptation (DA) to enable object detection from low resolution (LR) images without the need for paired training data or annotations in the LR domain. Our Resolution Transfer approach addresses the resolution and quality loss for smallsats, as demonstrated via airplane detection.
Multi-Level Consistency Learning for Source-Free Model Adaptation

Source-free model adaptation (SFMA) plays an important role in robust robot automation, which aims to mitigate distributional inconsistency between source and target data while avoiding accessing source data. SFMA methods generally benefit from self-training, which can be prone to overfitting noisy pseudo labels. Previous methods used various techniques to filter out noisy predictions and improve self-training performance. Nonetheless, the filtering is heavily reliant on pre-defined thresholds and suffers from a class imbalance problem, resulting in a bias towards the majority classes and poor performance of the other classes. Aiming at this pitfall, this study proposes to combat the noisy pseudo labels via multi-level perturbation, which involves all the pseudo predictions in self-training. The proposed method introduces different perturbations at three different levels: input, feature, and model. These perturbations boost the diversity of training data and increase the difficulty of fitting pseudo labels, which avoids overfitting the noise without ignoring the minority classes. Besides, the proposed method helps mitigate the intra-domain gap by maximizing the consistency between the original predictions and the perturbed predictions. Experimental results on two domain adaptive segmentation benchmarks confirm the effectiveness of the proposed method, which outperforms state-of-the-art SFMA methods.
Research on Viterbi Decoding Algorithm in the Washback Effect of Language Testing

Language testing is a scientific method for detection, analysis and diagnosis of a language. In linguistic research, it belongs to the high-level language system. Viterbi algorithm is currently the most widely used and effective compilation method, which has the advantages of high efficiency and speed in the system. For this reason, this paper focuses on the application of Viterbi decoding algorithm in the washback effect of language testing system. The purpose of the research here is to analyze the washback effect of the algorithm on language testing and to propose new insights. The methods used in this article include text analysis method and questionnaire survey method. Through these methods, the Viterbi decoding algorithm and its application in the washback effect of language testing are studied accordingly. The survey results show that 20% of students and 11% of teachers believe that the washback effect of language testing has a positive effect, and the Viterbi decoding algorithm has a digital translation effect, so its application in the system is relatively meaningful.
Combinatorial optimization for low bit-width neural networks

Low-bit width neural networks have been extensively explored for deployment on edge devices to reduce computational resources. Existing approaches have focused on gradient-based optimization in a two-stage train-and-compress setting or as a combined optimization where gradients are quantized during training. Such schemes require high-performance hardware during the training phase and usually store an equivalent number of full-precision weights apart from the quantized weights. In this paper, we explore methods of direct combinatorial optimization in the problem of risk minimization with binary weights, which can be made equivalent to a non-monotone submodular maximization under certain conditions. We employ an approximation algorithm for the cases with single and multilayer neural networks. For linear models, it has $\mathcal{O}(nd)$ time complexity where n is the sample size and d is the data dimension. We show that a combination of greedy coordinate descent and this novel approach can attain competitive accuracy on binary classification tasks.
Adversarial Robust Classification by Conditional Generative Model Inversion

Most adversarial attack defense methods rely on obfuscating gradients. These methods are easily circumvented by attacks which either do not use the gradient or by attacks which approximate and use the corrected gradient Defenses that do not obfuscate gradients such as adversarial training exist, but these approaches generally make assumptions about the attack such as its magnitude. We propose a classification model that does not obfuscate gradients and is robust by construction against black-box attacks without assuming prior knowledge about the attack. Our method casts classification as an optimization problem where we "invert" a conditional generator trained on unperturbed, natural images to find the class that generates the closest sample to the query image. We hypothesize that a potential source of brittleness against adversarial attacks is the high-to-low-dimensional nature of feed-forward classifiers. On the other hand, a generative model is typically a low-to-high-dimensional mapping. Since the range of images that can be generated by the model for a given class is limited to its learned manifold, the "inversion" process cannot generate images that are arbitrarily close to adversarial examples leading to a robust model by construction. While the method is related to Defense-GAN, the use of a conditional generative model and inversion in our model instead of the feed-forward classifier is a critical difference. Unlike Defense-GAN, we show that our method does not obfuscate gradients. We demonstrate that our model is extremely robust against black-box attacks and does not depend on previous knowledge about the attack strength.
Analysis on the Development of Ethereum and Its Consensus Mechanisms

Ethereum is a decentralized application platform built on blockchain technology. The goal of Ethereum is to create an unstoppable, shield-resistant, and self-sustaining decentralized world computer. The emergence of Ethereum has led to the vigorous development of blockchain technology. The development of Ethereum is relatively rapid, and many innovative technologies are applied to Ethereum, such as smart contracts, ghost protocols, and state channels. These innovative technologies make Ethereum's mechanism more efficient and have a comprehensive range of applications. In order to provide a theoretical research basis for the stable development of Ethereum, the article introduces the critical technologies in Ethereum. The article analyzes the origin and advantages of the Ethereum platform, the composition of smart contracts and the two types of accounts in smart contracts. Ethereum's consensus mechanisms are also included in this article, such as Proof of Work and Proof of Stake. This article further discusses the benefits of the Ghost protocol and the principle of state channels. Moreover, the article discusses the development trend of Ethereum and the problems that may be encountered, which provides the researchers with a great reference.
A hybrid hyperchaotic based dynamic keystream generator using perturbance process for public key infrastructure application

A need for secured communication under hostile condition mandated to incorporate the cryptographic methods in the PKI system. Chaos is another promising paradigm which offshoots from nonlinear dynamics where its randomness has no stochastic origin. Hence a deterministic Pseudo Random Number Generator (PRNG) plays a vital role in chaotic-cryptography. Though many PRNG based chaotic systems are proposed, the low- key space chaotic systems and the finite precision of processor deteriorates the periodic function. The paper intent to achieve larger Lyapunov exponent by the proposed system in the post quantum ecosystem. The proposed system provides an improved key spacing and randomness with a novel approach of perturbing the sequence obtained from the hyperchaotic system with Non-Linear Feedback based Self-Shrinking Generators (NLFSR-SSG). As a result, an improved Lyapunov exponents (2.94, 0.1, 0, −15.341) are obtained and also a dynamic key stream using the proposed hyperchaotic system is generated with high resistance towards cryptanalytic attacks in the post quantum enabled PKI ecosystem.
The Comparison of Web History Forensic Tools with ISO and NIST Standards

Nowadays, the number of new websites in Thailand has been increasing every year. However, there is a lack of security on some of those websites which causes negative effects and damage. This has also resulted in numerous violations. As a result, these violations cause delays in the situation analysis. Additionally, the cost of effective and well-established digital forensics tools is still expensive. Therefore, this paper has presented the idea of using freeware digital forensics tools to test their performances and compare them with the standards of the digital forensics process. The results of the paper suggest that the tested tools have significant differences in functions and process. WEFA Web Forensics tool is the most effective tool as it supports 3 standards up to 8 out of 10 processes, followed by Browser History View which supports 7 processes, Browser History Spy and Browser Forensic Web Tool respectively, supports 5 processes. The Internet history Browser supports 4 processes as compared to the basic process of the standardization related to forensics.
A Solution to Adaptive Mobile Manipulator Throwing

Mobile manipulator throwing is a promising method to increase the flexibility and efficiency of dynamic manipulation in factories. Its major challenge is to efficiently plan a feasible throw under a wide set of task specifications. We show that the mobile manipulator throwing problem can be simplified to a planar problem, hence greatly reducing the computational costs. Using machine learning approaches, we build a model of the object's inverted flying dynamics and the robot's kinematic feasibility, which enables throwing motion generation within 1 ms for given query of target position. Thanks to the computational efficiency of our method, we show that the system is adaptive under disturbance, via replanning on the fly for alternative solutions, instead of sticking to the original throwing plan.
ROLE: Rotated Lorentzian Graph Embedding Model for Asymmetric Proximity

Graph embedding, which aims to learn low-dimensional node representations to preserve original graph structures, has attracted extensive research interests.However, most existing graph embedding models represent nodes in Euclidean spaces, which cannot effectively preserve complex patterns, e.g., hierarchical structures.Very recently, several hyperbolic embedding models have been proposed to preserve the hierarchical information in negative curvature spaces.Nevertheless, existing hyperbolic models fail to model the asymmetric proximity between nodes.To address this, we investigate a new asymmetric hyperbolic network representation problem, which targets at jointly preserving the hierarchical structures and asymmetric proximity for general directed graphs.We solve this problem by proposing a novel Rotated Lorentzian Embedding (ROLE) model, which yields two main benefits.First, our model can effectively capture both implicit and explicit hierarchical structures that come from the network topology and category information of nodes, respectively.Second, it can model the asymmetric proximity using rotation transformations.Specifically, we represent each node with a Lorentzian embedding vector, and learn two rotation matrices to reflect the direction of edges.We conduct extensive experiments on four real-world directed graph datasets.Empirical results demonstrate that the proposed approach consistently outperforms various state-of-the-art embedding models.In particular, ROLE achieves HR@1 scores up to 19.8% higher and NDCG@5 scores up to 11.3% higher than the best baselines on the task of node recommendation.
A Novel Framework For Malicious Encrypted Traffic Classification At Host Level And Flow Level

More and more data is transferred in encrypted ways, especially over HTTPS. As a result, network attackers often use encryption algorithms to transmit control commands to avoid detection. Malware or unidentified users may use unauthorized encrypted proxy to communicate and access malicious websites. Confirming the details of these behaviors facilitates the analysis of suspicious events, but intercepted encrypted traffic cannot be parsed. Since the payload of encrypted traffic is not observable, machine learning algorithm combined with domain knowledge is the mainstream method to detect malicious encrypted traffic. Considering the complexity of decrypting traffic, we start from host level and flow level, exploit packet length histogram, sequence features and statistical features to describe traffic. In particular, we use Word2Vec algorithm to represent packet length sequence. In experiment, we collect the encrypted traffic generated by malware communicating with multiple websites through encrypted proxy. Experiment result shows the effectiveness of our framework and achieve 96.2% Accuracy.
Human Activity Classification Using Deep Learning

This paper describes a method to classify human activities using accelerometer data by training a deep learning model and using it in an android app which gathers real time accelerometer data while the device is with user and classifies his activity by assigning a probabilistic value with highest probability being the activity predicated. The dataset used in this paper is freely available which is provided by WISDM Lab and Google cloud-based instances running tensor flow library for python to code and train the model.
Development and Verification of New Drum Filter Equipment for Nuclear Power Plants

Abstract Drum filter is a large-scale filtration equipment in nuclear power plants, providing clean cooling water for nuclear power plants. At present, there are problems of easy blockage, short response time of the unit after blockage, and shutdown of the unit in severe cases during the operation of the drum filter in the in-service nuclear power plants. The analysis shows that the drum filter adopts the structure of “internal water intake”, and the garbage such as marine organisms remain on “inside” of the drum filter during operation, which makes it difficult to clean up and operate the inner garbage. When the marine organisms suddenly break out, if the amount of garbage cleaned by the drum filter is not enough to offset the amount of garbage entering, the garbage will accumulate continuously inside, which may lead the pressure difference between the inside and outside of the drum filter to suddenly increase, and then cause the circulating pump to trip or even the reactor to shut down. The inherent safety of the drum filter of this water intake method is low. To solve this problem, this paper puts forward a new concept of water intake of drum filter, that is, the concept of “external water intake”, which changes the water intake method of the drum filter, intercepts the garbage on the “outside” of the drum filter. Under the action of water flow, the garbage outside is not easy to remain on the net and block the drum filter, thereby fundamentally improving the inherent safety of the drum filter. A new type of “external water intake” drum filter was developed by using parametric and modular design method. The reliability of the drum filter was analyzed by using the FMEA failure mode and impact analysis method, and the weak links of the drum filter were found out, and optimization and improvement measures have been taken for the weak links. The ANSYS finite element analysis software is used to conduct a seismic analysis of the drum filter, which proves that the drum filter meets the requirements of the RCC-M specification and the seismic requirements of the third-generation nuclear power plants, which demonstrates the safety of the drum filter. Finally, through the physical prototype test, the design concept of “external water intake” was verified, and garbage cleaning capacity of the new drum filter meets the requirements of nuclear power plants. This type of drum filter has been popularized and applied to many HPR1000 projects under construction and newly built ones.
Design Thinking for Human Interaction with Flexible Electronics

With increasing demand of better human-machine interaction with electronic devices, a flexible form factor in these devices is key for transparency in connection between human and machine. A variety of new approaches for human-machine interaction are available. These include voice, gesture, eye motion, brain wave and so on. Flexible electronics technologies could be key to enhance interaction with the human. To achieve a better intuitional connection, a design thinking approach is essential. This paper will review key factors to adopt this design thinking approach for flexible electronics.
Perfect Anonymous Authentication and Secure Communication in Internet-of-Things

The ever-expanding Internet-of-Things (IoT) does not only call for data security but also privacy. On the other hand, conventional secure communication protocols only consider confidentiality and integrity of data, where the anonymity of communication peers is not guaranteed. In this paper, we present concrete threats for the anonymity of things in IoT and provide new practical solutions with perfect anonymity during authentication and secure communication. We also present a formal model to evaluate the protection and prove our protocols are anonymous and secure. Moreover, we implement our solutions and show that they are efficient in the real world.