Reviewerly/exHarmony huggingface dataset:
Parameterized Formal Graph Systems and Their Polynomial-Time PAC Learnability

A formal graph system (FGS for short) is a logic program consisting of definite clauses whose arguments are graph patterns instead of first-order terms. The definite clauses are referred to as graph rewriting rules. An FGS is shown to be a useful unifying framework for learning graph languages. In this paper, we show the polynomial-time PAC learnability of a subclass of FGS languages defined by parameterized hereditary FGSs with bounded degree, from the viewpoint of computational learning theory. That is, we consider VH-FGSLk,Δ(m, s, t, r, w, d) as the class of FGS languages consisting of graphs of treewidth at most k and of maximum degree at most Δ which is defined by variable-hereditary FGSs consisting of m graph rewriting rules having TGP patterns as arguments. The parameters s, t, and r denote the maximum numbers of variables, atoms in the body, and arguments of each predicate symbol of each graph rewriting rule in an FGS, respectively. The parameters w and d denote the maximum number of vertices of each hyperedge and the maximum degree of each vertex of TGP patterns in each graph rewriting rule in an FGS, respectively. VH-FGSLk,Δ(m, s, t, r, w, d) has infinitely many languages even if all the parameters are bounded by constants. Then we prove that the class VH-FGSLk,Δ(m, s, t, r, w, d) is polynomial-time PAC learnable if all m, s, t, r, w, d, Δ are constants except for k.
Two for the price of one: communication efficient and privacy-preserving distributed average consensus using quantization

Both communication overhead and privacy are main concerns in designing distributed computing algorithms. It is very challenging to address them simultaneously as encryption methods required for privacy-preservation often incur high communication costs. In this paper, we argue that there is a fundamental link between communication efficiency and privacy-preservation through quantization. Based on the observation that quantization, which can save communication bandwidth, will introduce error into the system, we propose a novel privacy-preserving distributed average consensus algorithm which uses the error introduced by quantization as noise to obfuscate the private data for protecting it from being revealed to others. Similar to existing differential privacy based approaches, the proposed approach is robust and has low computational complexity in dealing with two widely considered adversary models: the passive and eavesdropping adversaries. In addition, the method is generally applicable to many distributed optimizers, like PDMM and (generalized) ADMM. We conduct numerical simulations to validate that the proposed approach has superior performance compared to existing algorithms in terms of accuracy, communication bandwidth and privacy.
“Hey Alexa, Where Are You?” A Drawing Study to Understand Users’ Mental Models of the Environments Surrounding Conversational Agents

Although conversational agents (CA) have increased in the control of smart devices, recent research has revealed that the frequency of interaction with agents decreases over time due to a gap between the user’s expectations and the actual experience. To reduce the gap, previous studies explored the mental model related to the user’s expectation for designing a CA through a verbal approach such as an interview, but this was insufficient because the mental model can contain abstract images that are difficult to express in words. Therefore, in this paper, we aim to understand user perceptions through a drawing approach. We asked 34 smart speaker users to draw what the CA looks like. We found that the participants drew not only the CA but also the environment surrounding the CA, and the perception of the environment influences the expectation and intimacy with the CA. Based on these findings, we suggest that environmental factors be considered significant in designing CA persona.
Comparison of the Resilience of Convolutional and Cellular Neural Networks Against Adversarial Attacks

Convolutional architectures are commonly used in practice and the popularity of Cellular Neural Networks has also increased significantly in the past years, since these architectures can provide an efficient, low-energy, analogue implementation of deep neural networks.Both architectures were applied with success in various tasks, but in safety critical applications, like medical imaging or self-driving cars the high accuracy of these approaches is not enough, adversarial attacks still pose a significant threat in these areas.In this paper I will examine and compare how resilient these structures are towards various adversarial attacks and I will also investigate possible causes of these differences.
Copyright and AI: Are Extant Laws Adequate?

On September 22, 2022, the UK government released "Examining patent applications relating to artificial intelligence (AI) inventions: The Guidance", which clarified nine implementation guidelines. It specifically states that in the UK, AI inventions in all technical fields are patentable, and AI inventions are not excluded when the tasks or processes performed by AI inventions reveal a technical contribution to known technology and are patentable qualifications. However, does the existing copyright system provide sufficient protection for AI's creative innovations? Starting from the existing legal provisions, case law and doctrines, this paper analyzes the different attitudes of the EU, the US, Australia, the UK and China towards AI output works, and examines the inadequacy of the current copyright system in protecting AI's creative innovations by combining cases and survey reports. Based on the above analysis, at the end of the article, suggestions are made for the improvement of copyright law for AI protection, and conclusions are finally drawn.
Energy-efficient and Accurate Object Detection Design on an FPGA Platform

With the innovation of hardware equipment, the development of artificial intelligence has broken through the limitations of the past. Neural networks have been continuously deepened to improve the accuracy of detection, so that the parameters have increased with a direct proportional rate. In this way, however, high energy consumption has been induced which obstacles the deployment of AI algorithms on portable devices. Therefore, the design of neural network must consider not only detection accuracy but also energy efficiency. In this paper, we analyzed energy consumption, detection accuracy and execution speed of our neural network model as well as the state-of-the-art models based on an FPGA platform called ZCU-102. We adopt the performance index from Low Power Computer Vision (LPCV) challenge which considers power dissipation, mean Average Precision (mAP) and Frames Per Second (FPS) at the same time to evaluate these models in an overall point of view. Agilev4 can achieve 59.9% of mAP@50 on MS COCO test-dev2017 datasets. If the input frame resolution is turned into $416\times 416$, the processing frame rate can reach 20.7 FPS on ZCU-102. Compared with the state-of-the-art models, the LPCV score of Agilev4-416 is 1475. S which is 1.56 times of that of YOLOv4-416.
BlockRAT: An Enhanced Remote Access Trojan Framework via Blockchain

Remote Access Trojan (RAT) is a type of malicious software, aiming to infect victims’ computers through targeted attacks. Most existing RATs require a hacker to purchase a server, a domain name and many network resources to construct the infrastructure with a Command and Control (C2) channel. However, hackers’ information may be leaked or become traceable during the purchase of C2 channels and network resources. In this work, we propose BlockRAT, a blockchain-based RAT framework that can hide the hacker’s personal information with untraceability and low cost. We also introduce a method to help assess the suitability of blockchain types. In the evaluation, we take Network Infrastructure for Decentralized Internet (NKN) as a case study, and compare our BlockRAT with existing studies. The results indicate that BlockRAT can achieve upstream and downstream anonymity, low cost, and good extensibility.
Natural Language Processing

Abstract This chapter discusses natural language processing (NLP) which deals with regression modeling of non-tabular or unstructured text data. We explain how words can be embedded into low-dimension spaces that serve as numerical word encodings. These can then be used for text recognition, either using RN networks or attention layers. We give an example where we aim at predicting claim perils from claim descriptions.
Image segmentation and pore network analysis of the Barra Velha Formation carbonate reservoirs, Santos Basin, Brazil

Disclaimer Text and Data Mining. Any automated analytical technique aimed at analysing text and data in digital form in order to generate information including - but not limited to - patterns, trends and correlations (so-called Text and Data Mining) may only be applied if it is done by a research organisation or cultural heritage institution solely for the purpose of (non-commercial) scientific research as mentioned in Clause 3 of Directive (EU) 2019/790 (the Digital Single Market Directive) and Clause 15n of the Dutch Copyright Act [Auteurswet]. Text and Data Mining for commercial purposes is not allowed; all rights in this respect are reserved pursuant to Clause 4 paragraph 3 of the Digital Single Market Directive and Clause 15o paragraph 1 of the Dutch Copyright Act.
Extraction of Person Entities Affiliated with Telkom University using Long Short-Term Memory (LSTM) on Related News Articles

News text is text that contains information about an incident that is submitted in writing or not. In the news text there is a lot of unstructured information and makes it difficult for people to know what entities are contained in the news text. To overcome these problems, information extraction is carried out which is useful for adapting information entities in the form of structured text. In this study, information extraction was carried out using the Long Short-Term Memory (LSTM) method in a news data related to Telkom University. Therefore, in this study, the development of an information extraction system was carried out by focusing on the Person entity using the LSTM method. The test results obtained an F1 Score of 81% with a tuning parameter 256 word embedding size, 32 batch, 150 epochs, 0.1 dropout, 150 units, and 1 layer on LSTM.
Track Fusion Fractional Kalman Filter

Compared with the integer order system model, the fractional order system model can describe the actual system more accurately, so it can achieve higher accuracy of state estimation. In this paper, a track fusion fractional order Kalman filter is proposed for multi-sensor fractional order system with uncorrelated noise, which can effectively improve the state estimation accuracy of multi-sensor fractional order system. The algorithm is simple, the amount of calculation is small, and it is convenient for engineering application. Simulation results show the effectiveness and feasibility of the proposed algorithm.
Leaf Vocabulary: Fine-Grained Leaf Image Retrieval Using Bag-of-Visual-Words Representation

This paper addresses the issue of fine-grained leaf image retrieval (FGLIR) which focuses on differentiating between different leaf cultivars within the same species. We investigate a novel bag-of-visual-words approaches (BoVW) to FGLIR. Firstly, we treat each leaf boundary point as the key-point from which to spread a chord pair for measuring the local characteristics including shape, gray-level and gradient co-occurrence texture features of the leaf image. By varying the length of the chord, we obtain multiscale local features which are then used to form two local shape and texture feature vectors. Secondly, we separately collect all the local shape and texture vectors from the database images to learn a leaf shape vocabulary and a leaf texture vocabulary by k-means clustering algorithm. By mapping the two kinds of local feature vectors to visual words in their corresponding leaf vocabularies, we can represent each leaf image as two bags of visual words (one for shape, another for texture). Finally, we convert them into two visual-word vectors by counting the occurrence of each leaf visual word in the image and concatenate them as the final image representation. The proposed leaf vocabulary representation is applied to two challenging FGLIR tasks, soybean cultivar identification and peanut cultivar identification. The experimental results indicate its superior performance over the state-of-the-art leaf descriptors and show its potential to address the issue of FGLIR.
A Hierarchical Database of One Million Websites

As part of a broader cyber-policy experiment on the Use and Abuse of Personal Information, we are seeking efficient methods to generate a hierarchical and malleable database of over one million websites for use in a future large-scale, semi-automated establishment of fake online accounts. Available directories of reputable Internet sites are often incomplete, outdated, or not well categorized. This paper describes the design and challenges associated with a custom web scraper to refine Curlie [1], an online repository of websites, into a concise, readable format. The scraper recursively and distributively crawls Curlie for unique URLs and plain-text names and parses them into our database. We will use the hierarchy functionality of this new database to answer future research questions focused on website stewardship of personal information (PI). This data normalization challenge is one of many we have encountered in the larger open-source intelligence (OSINT) Use and Abuse (U&A) collection framework.
Kelas Praktis English Public Speaking

English is a universal language because it is used by most of the countries in the world as the main language. In addition, English is one of the important international languages ​​to be mastered or learned. In terms of education, teaching foreign languages ​​is also a challenge. How to make students understand easily and quickly not to feel bored with the material given. Practical classes are one way to easily teach public speaking. Practical English Public Speaking Class is a method of improving speaking skills in English which is packaged in practical classes in order to create a more pleasant atmosphere. The main objective of this practical class is to increase students' confidence to be able to speak in public with more confidence. In addition, this activity also aims to break the stigma of participants that English is a difficult language to learn. With a fun method and delivery, students will believe that English is an easy and fun subject to learn.
An Asynchronous-Logic Masked Advanced Encryption Standard (AES) Accelerator and its Side-Channel Attack Evaluations

We present a side-channel-attack (SCA) resistant asynchronous-logic (async-logic) Advanced Encryption Standard (AES) accelerator embodying both the masking and hiding SCA countermeasures. Our async-logic masked AES accelerator adopts a dual-rail data encoding to perform the masked 128-bit AES operations, and to enable dual-hiding to moderate both the amplitude (vertical dimension) and the time (horizontal dimension) of the side-channel signals. We implement our async-logic masked AES accelerator in FPGA and comprehensively perform the SCA evaluations based on the electromagnetic (EM) emanation. The SCA evaluations are performed based on bus-wise Hamming Distance model, bus-wise & bit-wise Hamming Weight models, and Zero-Value (ZV) model. Based on our experiment results, we show that our async-logic masked AES is secured against SCA with 1 million EM emanations. This is at least $8.3 \times$ more resistant than synchronous-logic masked AES and $200 \times$ more resistant than the synchronous-logic unmasked AES.
HPAKE: Honey Password-Authenticated Key Exchange for Fast and Safer Online Authentication

Password-only authentication is one of the most popular secure mechanisms for real-world online applications. But it easily suffers from a practical threat - password leakage, incurred by external and internal attackers. The external attacker may compromise the password file stored on the authentication server, and the insider may deliberately steal the passwords or inadvertently leak the passwords. So far, there are two main techniques to address the leakage: Augmented password-authentication key exchange (aPAKE) against insiders and honeyword technique for external attackers. But none of them can resist both attacks. To fill the gap, we propose the notion of honey PAKE (HPAKE) that allows the authentication server to detect the password leakage and achieve the security beyond the traditional bound of aPAKE. Further, we build an HPAKE construction on the top of the honeyword mechanism, honey encryption, and OPAQUE which is a standardized aPAKE. We formally analyze the security of our design, achieving the insider resistance and the password breach detection. We implement our design and deploy it in the real environment. The experimental results show that our protocol only costs 71.27 ms for one complete run, within 20.67 ms on computation and 50.6 ms on communication. This means our design is secure and practical for real-world applications.
Extending the Universal Approximation Theorem for a Broad Class of Hypercomplex-Valued Neural Networks

The universal approximation theorem asserts that a single hidden layer neural network approximates continuous functions with any desired precision on compact sets. As an existential result, the universal approximation theorem supports the use of neural networks for various applications, including regression and classification tasks. The universal approximation theorem is not limited to real-valued neural networks but also holds for complex, quaternion, tessarines, and Clifford-valued neural networks. This paper extends the universal approximation theorem for a broad class of hypercomplex-valued neural networks. Precisely, we first introduce the concept of non-degenerate hypercomplex algebra. Complex numbers, quaternions, and tessarines are examples of non-degenerate hypercomplex algebras. Then, we state the universal approximation theorem for hypercomplex-valued neural networks defined on a non-degenerate algebra.
An Ultra Energy Efficient Streaming-based FPGA Accelerator for Lightweight Neural Network

Convolutional Neural Networks (CNN) is widely applied in modern machine learning and pattern recognition area. Streaming hardware accelerator is an efficient design that reduces power dissipation by lowering off-chip memory access. Streaming architecture requires putting the entire network on-chip, which is a challenging task for resource-constrained embedded systems. This paper presents a streaming hardware accelerator to implement SkyNet, a lightweight neural network to address real-time object detection. The net is retrained and quantized to a very low bit in order to put all the weights into the on-chip BRAMs. We designed a highly balanced pipeline based on each layer’s calculation to improve resource utilization. We demonstrate the streaming-based accelerator on the Ultra96v2 board and achieve a throughput of 536 frames per second (FPS) at 333MHz and intersection over Union (IoU) of 73.1% on the DAC-SDC dataset. The overall computational efficiency of our accelerator is 98%. Compared to the state-of-the-art, our work achieves 2.5× throughput, 2.7× energy efficiency.
Adaptation of MobileNetV2 for Face Detection on Ultra-Low Power Platform

Designing Deep Neural Networks (DNNs) running on edge hardware remains a challenge. Standard designs have been adopted by the community to facilitate the deployment of Neural Network models. However, not much emphasis is put on adapting the network topology to fit hardware constraints. In this paper, we adapt one of the most widely used architectures for mobile hardware platforms, MobileNetV2, and study the impact of changing its topology and applying post-training quantization. We discuss the impact of the adaptations and the deployment of the model on an embedded hardware platform for face detection.
Incorporating Self Attention Mechanism into Semantic Segmentation for Lane Detection

Lane detection is a challenging task in the field of vision detection. The annotation information of lane is very sparse, and it is faced with the interference of occlusion, illumination and other factors, which seriously affects the capture of lane features by neural network. In this paper, we propose the Self-Attention Lane Segmentation Network (SALSN) which allows attention-driven, long-range dependency modeling for lane detection task. Although traditional convolutional neural networks have demonstrated their powerful performance, their ability to capture global relationships in images has not been fully explored. We introduce a self-attentive module to model the long-range dependencies between lane features. Lanes have strong shape constraints but weak coherence. In SALSN, we utilize a dense feature fusion framework to better capture lane context information and use all element information to generate lane segmentation images. Experimental results show that SALSN is not only effective in learning the remote dependencies of lane features, but also significantly improves the lane detection performance. We have validated our approach on two large-scale lane detection datasets, and our method can achieve more competitive results.
Comparison of Texture Classifiers with Deep Learning Methods for Flooded Region Identification in UAV Aerial Images

With the increase in natural disasters, there is a need for better management and planning of post-disaster relief and rescue efforts to minimize loss of lives and property. An Unmanned Aerial Vehicle (UAV)-based system offers the advantage of mobility and a customized flight path that could be utilized to survey areas affected by a disaster. However, the images acquired by UAV must be analysed rapidly with minimum user intervention. In this context, the present work compares the performance of traditional handcrafted feature-based classifiers with that of deep learning methods for classifying images as flooded/non-flooded. The pixels corresponding to water in the UAV aerial image exhibit a characteristic texture as compared to roads, greenery etc. This motivated the use of handcrafted texture features (gray-level co-occurrence matrix (GLCM), local binary patterns (LBP)), which were then used to train a Support Vector Machine (SVM) classifier. Besides, Supervised (ResNet18) and Self-Supervised (Sim-CLR) deep learning methods are also studied for classifying UAV aerial images as flooded/non-flooded. The traditional and deep learning methods are compared on FloodNet dataset containing images acquired after hurricane Harvey. An F1 score of 0.84 for flooded class was obtained with the LBP texture classifier, compared to 0.87 using the self-supervised deep learning method. This result demonstrates that a hand-crafted texture-based classifier performs competitively with deep learning methods. Therefore, a traditional texture classifier could be preferred over deep learning methods for a rapid post- flood scene understanding in UAV aerial images.