Reviewerly/exHarmony huggingface dataset:
MDRM Pattern: Relational heterogeneous metadata management patterns in a microservice environment

In microservice applications developed for specific tasks, multiple comprising units may have the same purpose, but the data generated from them may not be related or consistent. This leads to bottlenecks such as low data utilization, making it difficult to implement them in applications. Herein, we propose a microservice data relationship management (MDRM) pattern, which is a heterogeneous metadata management pattern with the ability to maintain relationships with data generated by multiple units via organic relationships. The MDRM pattern is basically a microservice architecture data management pattern that determines data keys shared with each other in metadata generated by a number of units comprising a microservice application. It performs data operations based on keys, including creation, read, update, and deletion, and manages relationships between heterogeneous metadata. This pattern aids in configuring microservice applications where data collected from service units with different metadata must be managed in a single report form, and aggregated or integrated for monitoring. In addition, this pattern can be easily used not only as relational metadata in microservice environments but also in distributed computing applications.
Analysis of the Effects of Positive and Negative VR Game Contents on Enhancing Environmental Awareness Based on Self-Reliant and Team-Based Play Styles

This paper presents a motion-capture-based projection VR system to explore the effectiveness of gamification in improving environmental awareness.We examine the key components of positive and negative VR game content and analyze the impact of individual and cooperative play methods on promoting sustainable behaviors.Our findings are as follows.Firstly, we discovered that the use of positive content in individual play mode was effective in improving awareness of the importance of recycling.Secondly, we confirmed that the use of positive content in cooperative play mode and the use of negative content in individual play mode were each effective in enhancing awareness of the seriousness of environmental pollution.Thirdly, we found that experiencing positive content first, followed by negative content, in individual play mode was effective in increasing interest in the environment.Based on these findings, we determined that adjusting the order of use of positive and negative content is more effective than simply using positive or negative content alone for improving environmental awareness.Moreover, considering the importance of recycling, the seriousness of environmental pollution, and the level of interest in the environment, we confirmed that individual play mode is effective and cooperative play mode can be more effective depending on the measure.
Deep Neural Network Analysis System by Visualizing Accumulated Weight Changes

Recently, interest in artificial intelligence has increased due to the development of artificial intelligence fields such as ChatGPT and self-driving cars.However, there are still many unknown elements in training process of artificial intelligence, so that optimizing the model requires more time and effort than it needs.Therefore, there is a need for a tool or methodology that can analyze the weight changes during the training process of artificial intelligence and help out understatnding those changes.In this research, I propose a visualization system which helps people to understand the accumulated weight changes.The system calculates the weights for each training period to accumulates weight changes and stores accumulated weight changes to plot them in 3D space.This research will allow us to explore different aspect of artificial intelligence learning process, such as understanding how the model get trained and providing us an indicator on which hyperparameters should be changed for better performance.These attempts are expected to explore better in artificial intelligence learning process that is still considered as unknown and contribute to the development and application of artificial intelligence models.
A Reservoir Computer-Based Modeling of Hunting Dynamics in Predator-Prey Scenarios

Several optimization algorithms such as particle swarm or evolutionary algorithms are inspired by biological processes. For instance, the hunting dynamics of predator-prey scenarios have been identified to model dynamical optimization problems. Inspired by this, we develop an ideal electrical circuit in combination with a delay-coupled reservoir computer as a model for each animal in the scenario. This reservoir computer accounts for the visual processing of the animal, while an RLC circuit and an RL circuit model the calculation of the animal’s velocity and its position, respectively. Here, the relative position of predator and prey is fed back into the reservoir computer. Simulation results for two environments with different terrain slopes show that this setup indeed mimics a predator chasing preys, where the choice of the hunted prey is strongly affected by the terrain.
Errare humanum est: What do RFC Errata say about Internet Standards?

Protocol standards, such as RFCs developed by the IETF, are crucial for the correct operation of the Internet, but many are published containing errors. The RFC Editor allows people to report errata, allowing anybody to flag such errors for subsequent correction. This represents an important part of the RFC publication process, and may reveal ways in which standards can be improved. This paper performs the first study of RFC errata reports. We characterize and perform a statistical analysis of the scale and nature of these errata and explore who submits them. Finally, we evaluate the impact, in terms of the number of errata filings, of three different strategies that are designed to improve the standards process. We find that specialist review teams and formal language checkers can reduce the volume of errata filed against standards documents.
Detecting IP-tracking proof interfaces by looking for NATs

In this poster, we propose an approach based on short-lived random identifiers to allow applications to detect when multiple users share the same IP address such as when they are behind NATs. Using NATed interfaces could provide a cheap way to evade IP-based tracking as the traffic of all users is merged into a single IP flow. As a result, it is harder for trackers to single out (and so re-identify by IP address) users behind a NAT. For many years, there has been a race between web trackers trying to find techniques to monitor user behaviour online, and privacy researchers looking for solutions to avoid such tracking. Despite progresses in browser privacy-preserving techniques, IP tracking is still highly effective because current solutions to hide an IP address such as VPNs, or the Tor network, rely on external services and often induce a high cost in terms of performance. Our proposal could lead to solutions that are cheaper to deploy and don't affect the performance as much. We developed an Android application detecting when an IP address was shared by multiple devices and reported the availability of such interfaces. We show that it is possible to identify networks where multiple users share the same IP address. We also discuss how our system can be protected from potential attackers.
Investigating Gaze Behavior in Phishing Email Identification

The pervasiveness of phishing signals the insufficiency of current measures. Through a multidisciplinary approach, we conducted an eye-tracking study on how and where users look when they have to classify an email as phishing or legitimate. Furthermore, we investigated whether there is a difference between expert and non-expert subjects. The study showed firstly, better performance in recognising phishing emails by experts. Secondly, eye movement data showed the use of different email inspection methods between experts and non-experts. This could open up scenarios in the area of the improvement of training courses and the development of more intuitive email client interfaces in the suggestion of important clues in the recognltion of phishing emails.
Streaming Convolutional Neural Network FPGA Architecture for RFSoC Data Converters

This paper presents a novel Convolutional Neural Network (CNN) FPGA architecture designed to perform processing of radio data in a streaming manner without interruption. The proposed architecture is evaluated for radio modulation classification tasks implemented on an AMD RFSoC 2x2 development board and operating in real-time. The proposed architecture leverages optimisation such as the General Matrix-to-Matrix (GEMM) transform, on-chip weights, fixed-point arithmetic, and efficient utilisation of FPGA resources to achieve constant processing of a stream of samples. The performance of the proposed architecture is demonstrated through accuracy results obtained during live modulation classification, while operating at a sampling frequency of 128 MHz before decimation. The proposed architecture demonstrates promising results for real-time, time-critical CNN applications.
Pose Aware RGBD-based Face Recognition System With Hierarchical Bilinear Pooling

The accuracy of face recognition is not satisfactory in large poses. It is because the existing face recognition methods rely only on RGB images, which lose information in the large head pose. Therefore, we utilize depth images containing 3D dimension information to complement the loss. In this work, we propose a pose-invariant RGBD face recognition method with the Hierarchical Bilinear Pooling module (HBP) and Pose-Aware Module (PAM). Most of the existing methods consider the final layer of the model. However, the final layer does not contain enough facial information, such as the eyes, nose, and mouth. Thus, we propose the HBP module to fuse high-level layers which can focus on important facial areas but with different perspectives than the final layer. To reduce the effect of head poses, we propose the PAM module via mapping the profile faces to frontal faces in feature space. We verify our method on the datasets proposed by [1] and KinectFaceDB [2]. Our method achieves 1.3% improvement and has 6.1 million fewer parameters than the method in [3].
Iterative pruning algorithm for efficient look-up table implementation of binary neural networks

The implementation of deep neural networks requires a significant amount of memory and computing power. As a result, they are often implemented in data centers or high-end computing devices such as GPUs. However, if the internal structure of an FPGA, particularly the LUTs, is considered, the FPGA implementation of these networks could prove to be more efficient. This paper presents an iterative pruning algorithm for removing unnecessary inputs from a layer's neurons so that they can be synthesized to a small number of LUTs in an FPGA. When the number of LUTs, throughput, and power consumption are considered, our design is $7.95 \times $ smaller, $1.66 \times $ faster, and $5.66 \times $ more power efficient than the baseline, with a 0.47% accuracy drop.
Sparq: A Custom RISC-V Vector Processor for Efficient Sub-Byte Quantized Inference

Convolutional Neural Networks (CNNs) are used in a wide range of applications, with full-precision CNNs achieving high accuracy at the expense of portability. Recent progress in quantization techniques has demonstrated that sub-byte Quantized Neural Networks (QNNs) achieve comparable or superior accuracy while significantly reducing the computational cost and memory footprint. However, sub-byte computation on commodity hardware is sub-optimal due to the lack of support for such precision. In this paper, we introduce Sparq, a Sub-byte vector Processor designed for the AcceleRation of QNN inference. This processor is based on a modified version of Ara, an open-source 64-bit RISC-V “V” compliant processor. Sparq is implemented in GLOBAL FOUNDRIES 22FDX FD-SOI technology and extends the Instruction Set Architecture (ISA) by adding a new multiply-shift-accumulate instruction to improve sub-byte computation effciency. The floating-point unit is also removed to minimize area and power usage. To demonstrate Sparq performance, we implement an ultra-low-precision (1-bit to 4-bit) vectorized conv2d operation taking advantage of the dedicated hardware. We show that Sparq can significantly accelerate sub-byte computations with respectively 3.2 times, and 1.7 times acceleration over an optimized 16-bit 2D convolution for 2-bit and 4-bit quantization.
Your Code is 0000: An Analysis of the Disposable Phone Numbers Ecosystem

Short Message Service (SMS) is a popular channel for online service providers to verify accounts and authenticate users registered to a particular service. Specialized applications, called Public SMS Gateways (PSGs), offer free Disposable Phone Numbers (DPNs) that can be used to receive SMS messages. DPNs allow users to protect their privacy when creating online accounts. However, they can also be abused for fraudulent activities and to bypass security mechanisms like Two-Factor Authentication (2FA). In this paper, we perform a large-scale and longitudinal study of the DPN ecosystem by monitoring 17,141 unique DPNs in 29 PSGs over the course of 12 months. Using a dataset of over 70M messages, we provide an overview of the ecosystem and study the different services that offer DPNs and their relationships. Next, we build a framework that (i) identifies and classifies the purpose of an SMS; and (ii) accurately attributes every message to more than 200 popular Internet services that require SMS for creating registered accounts. Our results suggest that the DPN ecosystem is globally abused for fraudulent account creation and access, affecting all major Internet platforms and online services.
Live Long and Prosper: Analyzing Long-Lived MOAS Prefixes in BGP

BGP exchanges reachability information in the form of prefixes, which are usually originated by a single Autonomous System (AS). If multiple ASes originate the same prefix, this is referred to as a Multiple Origin ASes (MOAS) prefix. One reason for MOAS prefixes are BGP prefix hijacks, which are mostly short-lived and have been studied extensively in the past years. In contrast to short-lived MOAS, long-lived MOAS have remained largely understudied. In this paper, we focus on long-lived MOAS prefixes and perform an in-depth study over six years. We identify around 24k long-lived MOAS prefixes in IPv4 and 1.4k in IPv6 being announced in January 2023. By analyzing the RPKI status we find that more than 40% of MOAS prefixes have all origins registered correctly, with only a minority of MOAS having invalid origins. Moreover, we find that the most prominent CIDR size of MOAS prefixes is /24 for IPv4 and /48 for IPv6, suggesting their use for fine-grained traffic steering. We attribute a considerable number of MOAS prefixes to mergers and acquisitions of companies. Additionally, more than 90% of MOAS prefixes are originated by two origin ASes, with the majority of detected origin AS relations being customer-provider. Finally, we identify that the majority of MOAS users are IT companies, and just 0.9% of IPv4 MOAS and 6.3% of IPv6 MOAS prefixes are used for anycast.
Towards Detecting and Geolocalizing Web Scrapers with Round Trip Time Measurements

Web scraping is the activity of continuously extracting data and/or processed output contained in web pages [16]. This process generates large amounts of traffic on the targeted websites, which suffer large losses of money from this phenomenon [3]. For this reason, they engage in a persistent fight against scrapers, trying to detect and mitigate their requests [7].
SqueezeNetVLAD: High-speed power and memory efficient GPS less accurate network model for visual place recognition on the edge

Visual place recognition (VPR) provides the ability for a machine to recognise any given place if it has been visited before by utilising visual graphics data. Classical handcrafted feature extraction doesn’t seem to perform well under varying environmental conditions, which is where the convolutional neural networks with their state-of-the-art performance act as a substitute however at the cost of an increased computational complexity making it difficult to port into resource-constrained edge devices like tiny robots, drones etc. This motivated us to propose an optimal NetVLAD network algorithm consisting of an optimised and efficient feature extraction base network performing feature extraction while satisfying both a given constraint of accuracy and the resource budget of a target implementation platform. Both the accuracy, which is marginally better, the achieved model size reduction is better by 17% and a nominal power reduction by 5% w.r.t the SOTA models. Also developed a customised CUDA kernel that dynamically compares the pair of query image feature vectors to the descriptor vectors of the already stored gallery image datasets in parallel utilising the maximum available tensor cores achieving hard-real time performance at 62.5 fps with input videos tested on the Nvidia Jetson Xavier NX platform.
Evaluation of Secure Circuit Styles Using Unipolar Logic Gates

A high level of resistance against power analysis attacks can be achieved with secure circuit styles. All existing secure circuit styles are based on CMOS logic, using both p-type and n-type transistors. However, emerging technologies, such as flexible electronics, achieve optimal price, performance and reliability with n-type-only unipolar logic gates. Therefore, in order to protect chips on flexible foil from power analysis attacks, secure circuit styles based on unipolar logic gates need to be considered. This paper presents a first step in this direction by applying the concepts of secure circuit styles, originally designed with CMOS logic in mind, to unipolar gates. The power consumption information leakage is simulated for two existing secure circuit styles, namely Wave Dynamic Differential Logic (WDDL) and Masked Dual-Rail Pre-charge Logic (MDPL). The paper shows that especially WDDL has the potential to improve the security of the design, after optimization of the transistor ratios.
Accelerating Relational Database Analytical Processing with Bulk-Bitwise Processing-in-Memory

Online Analytical Processing (OLAP) for relational databases is a business decision support application. The application receives queries about the business database, usually requesting to summarize many database records, and produces few results. Existing OLAP requires transferring a large amount of data between the memory and the CPU, having a few operations per datum, and producing a small output. Hence, OLAP is a good candidate for processing-in-memory (PIM), where computation is performed where the data is stored, thus accelerating applications by reducing data movement between the memory and CPU. In particular, bulk-bitwise PIM, where the memory array is a bitvector processing unit, seems a good match for OLAP. With the extensive inherent parallelism and minimal data movement of bulk-bitwise PIM, OLAP applications can process the entire database in parallel in memory, transferring only the results to the CPU. This paper shows a full stack adaptation of a bulk-bitwise PIM, from compiling SQL to hardware implementation, for supporting OLAP applications. Evaluating the Star Schema Benchmark (SSB), bulk-bitwise PIM achieves a 4.65× speedup over Monet-DB, a standard database system.
Multi-Level Image Thresholding Using Improvised Cuckoo Search Optimization Algorithm

With reference to image processing, segmentation is an essential step. It includes filtering the pixels into two or more categories based on their brightness levels relative to a predetermined threshold. The threshold-choice procedure directly impacts the segmentation's accuracy. Classical versions of Multilevel Thresholding are computationally costly since they explore all possible combinations of parameters to find one that maximizes the target function. In this context, evolutionary techniques to optimize have seen increased usage. In this research, an improvised version of cuckoo search algorithm has been proposed to find the thresholding values with Otsu as its objective function. Using the objective function as a guide, the cuckoo's brood parasitism algorithm evolves a collection of possible solutions to the problem. The method produces a multilevel segmentation algorithm that can accurately determine an image's threshold values with fewer iterations. The enhanced Cuckoo Search Optimization algorithm has been implemented, and the experimental results demonstrate its effectiveness in digital picture segmentation.
Fault Tolerant Processing Unit Using Gamma Distribution Sliding Window For Autonomous Landing Guidance System

To keep up with today’s dense metropolitan areas and their accompanying traffic problems, a growing number of towns are looking for more advanced and swift urban taxi drones. The safety parameters that must be taken into consideration may be the most important element in the widespread use of such technology. Most recent aviation mishaps have happened during the landing phase, making this a particularly important safety consideration for Vertical and/or Short Take-Off and Landing (V/STOL) drones.In this study, we focused on improving the fault tolerance of the processor architectures used by the predecessors of Autonomous Landing Guidance Assistance Systems (ALGAS), which in turn improves their decision-making capabilities. Furthermore, this is achieved by proposing a fault-tolerant processing architecture that depends on the Gamma Distribution Sliding Window Unit (GDSWU). This proposed GDSWU has been designed completely using VHDL, and the targeted FPFA was the Intel Cyclone V 5CGXFC9D6F27C7 chip. The GDSWU could operate at a maximum frequency of 369.96 MHz, as calculated by the synthesis results of the INTEL Quartus Prime program. The suggested GDSWU core only requires 20.36 mW for dynamic core and I/O power consumption.
Leakage Power Attack and Half Select Issue Resilient Split 8T SRAM Cell

There are various factors that harness the performance of an SRAM cell, out of which leakage current attack-based side-channel attack, as well as half select issue, are a major concern. Since there is a need for a circuit that could overcome both these problems at once, we propose a split 8T SRAM cell that uses split word lines unlike 6T and 8T SRAM cells. The two additional transistors like leakage power attack resilient (LPAR) 8T determine its compatibility for both cases. To demonstrate the capability of the proposed 8T cell, a comparison has been done among 6T, 8T, Split 6T and the Proposed Split 8T SRAM cell on the basis of noise margin, overlap percentage, flipping percentage, etc. Monte Carlo simulations depict the maximum overlap percentage of 96.6% for the proposed SRAM cell and a marginal time range i.e., 5.8ns for no flipping of data thus making it leakage power attack resilient as well as half-select issue free.
Phishing in Style: Characterizing Phishing Websites in the Wild

The prevalence of phishing domains is steadily rising as attackers exploit toolkits to create phishing websites. As web development expertise is no longer a prerequisite, phishing attacks have become more widespread, outpacing many existing detection methods. Developing novel techniques to identify malicious domains is crucial to safeguard potential victims online. While most current methods emphasize the visual aspects of phishing websites, in this paper, we investigate the underlying structure by collecting data on style sheets and certificates from both verified phishing domains and benign domains. Using a token-based similarity algorithm, we group the phishing domains into three categories and identify shared characteristics of these domains. Our work demonstrates the feasibility of using structural similarities to identify a website created using a phishing kit. By employing such detection, users would be able to browse the web with a reduced risk of falling victim to malicious activities.