Miracle-dz/UniFAR-FTUs huggingface dataset:
Congestion-aware dynamic routing for an overhead hoist transporter system using a graph convolutional gated recurrent unit

Abstract Overhead hoist transportors (OHT) that transport semiconductor wafers between tools/stockers, is a crucial component of an Automated Material Handling System (AMHS). As semiconductor fabrication plants (FABs) become larger, more OHT vehicles need to be operated. This necessitates the development of a scalable algorithm to effectively operate these OHTs and increase the productivity of the AMHS. This study proposes an algorithm that can predict the entire traveling times of the edges in an OHT rail network by utilizing past traffic information. The model first represents the OHT rail network and the dynamic traffic conditions using a graph. A sequence of graphs that represent the past traffic is then used as an input to produce a sequence of graphs that predicts the future traffic conditions as an output. Using the AutoMod simulator, we have shown that the proposed model scalably and effectively predicts the future edge-traveling time. We have also demonstrated that the predicted values can be used to reroute the OHTs optimally to avoid congestion.
Livestock Depredation by Jaguars Associated with Dry-Season Core-Use Areas in a Northeastern Mexico Agrolandscape

Abstract. Predation by jaguars (Panthera onca) on livestock can foster human intolerance and drive human–jaguar conflicts. Understanding distributional patterns that characterize depredation can help guide strategies to ameliorate these interactions, which is important because human conflicts are the main threat to the endangered jaguar in Mexico. We used clusters of satellite telemetry locations to evaluate spatial patterns of jaguars and jaguar predation/scavenging sites of livestock and wild ungulates in the Sierra del Abra-Tanchipa Biosphere Reserve (RBSAT) and surrounding agrolandscape of northeastern Mexico, where livestock composed 66% of the biomass of jaguar diets. Distribution of livestock sites was significantly associated with jaguar core areas (i.e., 50% autocorrelated KDEs) during the dry season, while wild ungulate sites were distributed similarly with respect to core areas of jaguars across both dry and wet seasons. It is unknown whether these results reflect increased chance encounters between jaguars and livestock during the dry season due to the presence of limited permanent water sources concentrating livestock (and natural prey), or due to jaguars actively seeking livestock or livestock carcasses during the dry season.
Incorporating a Global Perspective: Intercultural Learning through Work-Experience Projects

This paper elucidates the on-going efforts being made at a Japanese university to develop an intercultural-learning based curriculum through work-experience projects. The main goal of the current project is to provide students with the opportunities to be able to utilize knowledge in a practical sense, such as through work-experience projects at foreign affiliate companies in Japan. Following a brief overview of the current project design and development, this paper discusses the progress achieved thus far second year of its trial implementation. Feedback gained through questionnaires, reflection papers and follow-up interviews of the participating students and companies in the first year (2015) will be discussed in an effort to provide one reference for global-skills focused curriculum development.
FREE VIBRATION ANALYSIS OF LAMINATED STIFFENED CYLINDRICAL PANELS USING FINITE ELEMENT METHOD

A study on the free vibration analysis of stiffened laminated composite cylindrical shell is described in this paper. The eight-noded isoparamet ric degenerated shell element is developed to model both shell panel and stiffeners by using the degenerated solid concept based on ReissnerMindlin assumptions which taking into account the s hear deformation and rotatory effect. Numerical results are presented and comparison is m ade with the published results from the literature and the good agreement is found. Paramet ic studies considering different geometrical variables of shell and stiffeners have also been ca rried out.
CHARACTERISATION OF TIME-VARYING UNDERWATER ACOUSTIC COMMUNICATION CHANNEL WITH APPLICATION TO CHANNEL CAPACITY

The properties of the underwater acoustic communication channel at medium range and medium frequency are characterised by analysing a set of at sea data collected in coastal environment (depth 10-40 meters, range 1-3 km, frequency 11.2 kHz or 17.5 kHz). Based on an empirical mode decomposition, we infer that the channel behaves as trend stationary random process over a few minute duration. Moreover, according to statistical tests, the channel envelope is found to be in good agreement with a Rice process whose power ratio between the mean and the scattered part is time variant and delay dependent. As a conclusion, the results of attempts to apply some approximate bounds for the capacity of WSUSS Rice channel to the measured channels are given. It is found that the channel capacity is generally close to Shannon capacity when the channel response includes a few specular paths but significantly decreases with the number of paths and the diffuseness of the channel.
International Conference on Emerging Trends in Engineering , Science and Technology ( ICETEST-2015 ) An Intensity Modulation Transmission System for Under Water channel Modeling and Communication

Abstract—The importance of underwater optical communication has increased many fold due to its many inherent advantages such as faster propagation and high bandwidth. The proper design of the transmitter with a suitable modulation format, which can overcome many of the drawbacks of the traditional modulation schemes, is very critical for reliable underwater communication. In this paper, an intensity modulation scheme is proposed, which can be efficiently integrated to LED or nonLED based transmitter for optical underwater communication systems. Scattering model for the optical underwater wireless channel has also been determined in this work, using Monte Carlo simulation method. © 2016 The Authors.Published by Elsevier Ltd. Peer-review under responsibility of the organizing committee of ICETEST – 2015.
Local Correspondence Clustering, Global Pose Verification: Efficient and Robust 3D Object Recognition

Recognizing 3D objects from heavily cluttered and occluded 3D scenes is a very challenging task. This paper addresses this problem by developing a 3D object recognition method based on local and global geometric consistency constraints. Assuming an initial set of feature correspondences, we use the local constraint to group correspondences into several clusters; subsequently, we proposed local reference frames (LRFs) using the center feature point of a cluster and its neighboring feature point to generate candidate poses, and it is robust and computationally cheap. Finally, the global constraint is exploited to evaluate the correctness of candidate poses. Tests on three datasets (UWA, Queen's and self-made bin picking datasets) demonstrated that our method outperforms the existing 3D object recognition methods in terms of recognition accuracy and efficiency.
Chatbots to Support Young Adults’ Mental Health: An Exploratory Study of Acceptability

Despite the prevalence of mental health conditions, stigma, lack of awareness, and limited resources impede access to care, creating a need to improve mental health support. The recent surge in scientific and commercial interest in conversational agents and their potential to improve diagnosis and treatment seems a potentially fruitful area in this respect, particularly for young adults who widely use such systems in other contexts. Yet, there is little research that considers the acceptability of conversational agents in mental health. This study, therefore, presents three research activities that explore whether conversational agents and, in particular, chatbots can be an acceptable solution in mental healthcare for young adults. First, a survey of young adults (in a university setting) provides an understanding of the landscape of mental health in this age group and of their views around mental health technology, including chatbots. Second, a literature review synthesises current evidence relating to the acceptability of mental health conversational agents and points to future research priorities. Third, interviews with counsellors who work with young adults, supported by a chatbot prototype and user-centred design techniques, reveal the perceived benefits and potential roles of mental health chatbots from the perspective of mental health professionals, while suggesting preconditions for the acceptability of the technology. Taken together, these research activities: provide evidence that chatbots are an acceptable solution to offering mental health support for young adults; identify specific challenges relating to both the technology and environment; and argue for the application of user-centred approaches during development of mental health chatbots and more systematic and rigorous evaluations of the resulting solutions.
PeriScope: Comprehensive Vulnerability Analysis of Mobile App-defined Bluetooth Peripherals

Many IoT devices today talk to each other via Bluetooth Low Energy (BLE), a wireless communication technology often used to exchange data between a paired central and peripheral. These peripheral devices include not only firmware-defined bare-metal peripherals but also mobile application defined peripherals where a mobile app turns a smartphone into a peripheral instead of their usual central role. However, this role reversal increases the attack surface and brings vulnerabilities in bare-metal Bluetooth peripherals to mobile apps where relevant security and privacy have not been well studied. To fill this knowledge gap, this paper presents PeriScope, an automated tool to unveil the security and privacy vulnerabilities at the link layer of app-defined Bluetooth peripherals in the procedures of broadcasting, pairing, and communication by systematically analyzing their companion mobile apps. PeriScope has analyzed 1,160 Bluetooth peripheral apps from Google Play and identified 69.13% of them that broadcast device or personal identifiable information in cleartext, and, in addition, there are 95% pieces of data managed by these apps (e.g., personal health data and digital keys to unlock doors) to exchange with connected devices can be accessed without authentication. Finally, a set of guidelines for secure app-defined Bluetooth peripherals development is also provided.
Gatekeeper: A Gateway-based Broadcast Authentication Protocol for the In-Vehicle Ethernet

Automotive Ethernet is considered to be the next-generation in-vehicle network, because of its high bandwidth, high throughput, and low cost characteristics. However, no common standard has been established for the security protocol of Automotive Ethernet. While there are a few candidates, including MACsec, IPsec, and TLS, there is no widely favored candidate. Most importantly, existing candidates cannot fully satisfy the requirements of in-vehicle communication, specifically source authentication for broadcast/multicast communication. In this paper, we conduct a comprehensive analysis in both security and performance of existing security protocol candidates and identify source authentication and Denial-of-Service (DoS) prevention as two essential but missing properties in these candidates. We propose Gatekeeper, a gateway-based broadcast authentication protocol to ensure source authentication. In general, Gatekeeper introduces an on-path authenticator, which co-locates with the in-vehicle gateway or domain controllers and helps receivers to verify the sender's identity. To defend against DoS threats, we further integrate the time-lock puzzle with Gatekeeper to slow down malicious traffic. Our performance evaluation results show that Gatekeeper only results in 0.03 ms latency overhead for CAN data transmission and outperforms TESLA on both CAN and LiDAR transmission scenarios, highlighting the effectiveness and efficiency of Gatekeeper.
DynaGraph: dynamic graph neural networks at scale

In this paper, we present DynaGraph, a system that supports dynamic Graph Neural Networks (GNNs) efficiently. Based on the observation that existing proposals for dynamic GNN architectures combine techniques for structural and temporal information encoding independently, DynaGraph proposes novel techniques that enable cross optimizations across these tasks. It uses cached message passing and timestep fusion to significantly reduce the overhead associated with dynamic GNN processing. It further proposes a simple distributed data-parallel dynamic graph processing strategy that enables scalable dynamic GNN computation. Our evaluation of DynaGraph on a variety of dynamic GNN architectures and use cases shows a speedup of up to 2.7X compared to existing state-of-the-art frameworks.
File Packing from the Malware Perspective: Techniques, Analysis Approaches, and Directions for Enhancements

With the growing sophistication of malware, the need to devise improved malware detection schemes is crucial. The packing of executable files, which is one of the most common techniques for code protection, has been repurposed for code obfuscation by malware authors as a means of evading malware detectors (mainly static analysis-based detectors). This paper provides statistics on the use of packers based on an extensive analysis of 24,000 PE files (both malicious and benign files) for the past 10 years, which allowed us to observe trends in packing use during that time and showed that packing is still widely used in malware. This paper then surveys 23 methods proposed in academic research for the detection and classification of packed portable executable (PE) files and highlights various trends in malware packing. The paper highlights the differences between the methods and their abilities to detect and identify various aspects of packing. A taxonomy is presented, classifying the methods as static, dynamic, and hybrid analysis-based methods. The paper also sheds light on the increasing role of machine learning methods in the development of modern packing detection methods. We analyzed and mapped the different packing methods and identified which of them can be countered by the detection methods surveyed in this paper.
WearRF-CLA: Continuous Location Authentication with Wrist Wearables and UHF RFID

Continuous location authentication (CLA) seeks to continuously and automatically verify the physical presence of legitimate users in a protected indoor area. CLA can play an important role in contexts where access to electrical or physical resources must be limited to physically present legitimate users. In this paper, we present WearRF-CLA, a novel CLA scheme built upon increasingly popular wrist wearables and UHF RFID systems. WearRF-CLA explores the observation that human daily routines in a protected indoor area comprise a sequence of human-states (e.g., walking and sitting) that follow predictable state transitions. Each legitimate WearRF-CLA user registers his/her RFID tag and also wrist wearable during system enrollment. After the user enters a protected area, WearRF-CLA continuously collects and processes the gyroscope data of the wrist wearable and the phase data of the RFID tag signals to verify three factors to determine the user's physical presence/absence without explicit user involvement: (1) the tag ID as in a traditional RFID authentication system, (2) the validity of the human-state chain, and (3) the continuous coexistence of the paired wrist wearable and RFID tag with the user. The user passes CLA if and only if all three factors can be validated. Extensive user experiments on commodity smartwatches and UHF RFID devices confirm the very high security and low authentication latency of WearRF-CLA.
Server-Side Browsers: Exploring the Web's Hidden Attack Surface

As websites grow ever more dynamic and load more of their content on the fly, automatically interacting with them via simple tools like curl is getting less of an option. Instead, headless browsers with JavaScript support, such as PhantomJS and Puppeteer, have gained traction on the Web over the last few years. For various use cases like messengers and social networks that display link previews, these browsers visit arbitrary, user-controlled URLs. To avoid compromise through known vulnerabilities, these browsers need to be diligently kept up-to-date. In this paper, we investigate the phenomenon of what we coin server-side browsers at scale and find that many websites are running severely outdated browsers on the server-side. Remarkably, the majority of them had not been updated for more than 6 months and over 60% of the discovered implementations were found to be vulnerable to publicly available proof-of-concept exploits.
Provenance-based explanations: are they useful?

As machine-learning models and data-driven decisions continue to be on the rise, interpreting and explaining their results has recently gained increased interest as well. In this context, provenance has often been advertised as metadata that can be used to provide explanations about some data processing. Advantages of such explanations are possibly many: they may help data engineers get a better sense of their data processing pipelines, offer transparency to auditors or end users, and ultimately, improve the trust one has in the data processing. While today, provenance can be efficiently captured for a large variety of data processing pipelines, systematic research on how useful explanations based on provenance actually is scarce. This paper advocates that there is an urgent need to study in which settings what type of provenance-based explanation is useful, given the somewhat mitigated results that have become available for explanations of machine learning results. This is underlined by a first study that shows that provenance-based explanations of record linkage decisions may not have the desired effect.
Batch dynamic algorithm to find k-core hierarchies

Finding k-cores in graphs is a valuable and effective strategy for extracting dense regions of otherwise sparse graphs. We focus on the important problem of maintaining cores on rapidly changing dynamic graphs, where batches of edge changes need to be processed quickly. Many prior dynamic algorithms focus on the problem of maintaining a core decomposition. This finds vertices that are dense in some subgraph, but the subgraph itself is not returned. We develop a new dynamic batch algorithm to maintain cores, with their connected subgraphs, that improves efficiency over processing edge-by-edge. We implement our algorithm and experimentally show that with it core queries can be returned on rapidly changing graphs quickly enough for interactive applications. For 1 million edge batches, on many graphs we run over 100x faster than processing edge-by-edge while remaining under re-computing from scratch.
ShadowAuth: Backward-Compatible Automatic CAN Authentication for Legacy ECUs

Controller Area Network (CAN) is the de-facto standard in-vehicle network system. Despite its wide adoption by automobile manufacturers, the lack of security design makes it vulnerable to attacks. For instance, broadcasting packets without authentication allows the impersonation of electronic control units (ECUs). Prior mitigations, such as message authentication or intrusion detection systems, fail to address the compatibility requirement with legacy ECUs, stealthy and sporadic malicious messaging, or guaranteed attack detection. We propose a novel authentication system called ShadowAuth that overcomes the aforementioned challenges by offering backwardcompatible packet authentication to ECUs without requiring ECU firmware source code. Specifically, our authentication scheme provides transparent CAN packet authentication without modifying existing CAN packet definitions (e.g., J1939) via automatic ECU firmware instrumentation technique to locate CAN packet transmission code, and instrument authentication code based on the CAN packet behavioral transmission patterns. ShadowAuth enables vehicles to detect state-of-the-art CAN attacks, such as busoff and packet injection, responsively within 60ms without false positives. ShadowAuth provides a sound and deployable solution for real-world ECUs.
Towards a Utopia of Dataset Sharing: A Case Study on Machine Learning-based Malware Detection Algorithms

Working with a high-quality (complete and up-to-date) dataset is the key to building a good machine learning model, especially in security research areas. However, it is not easy to collect a good quality dataset for security research communities because of the sensitive property of most security datasets. We believe that having more contributors to share up-to-date samples would increase the quality of datasets. Therefore, this study aims to increase security dataset sharing for research communities by eliminating possible information leakage. We propose a dataset sharing model and the core algorithm, FeatureTransformer, which guarantees no sensitive information leakage from a shared dataset. FeatureTransformer transforms extracted raw features into intermediate features that conceal sensitive information. Meanwhile, models built from transformed features maintain similar performance compared to models built from the original raw features. We show the effectiveness of our model by evaluating FeatureTransformer with typical malware classification problems using (1) traditional machine learning classifiers and (2) neural network-based classifiers. The experiment results show that the models trained with transformed features merely suffer from 2.56% and 1.48% accuracy degradation on the investigated problems. It indicates that models validated by datasets processed by FeatureTransformer work well with the original raw (untransformed) datasets. We believe that our privacy-preserving model can stimulate dataset sharing and advance the development of machine learning approaches in solving security problems.
Position Estimation of Fault‐Tolerant Permanent Magnet Motor in Electric Power Propulsion Ship System

The electric power propulsion is becoming the important development direction of ship propulsion technology recent years, as it is conducive to the realization of automation, intelligence, reduced noise, enhanced propulsion efficiency, high power density, flexible arrangement, and energy saving. The good fault‐tolerant capacity and robustness of the fault‐tolerant permanent magnet motor (FTPMM) make it a potentially highly effective and economical propulsion motor in the electric power propulsion ship than other alternatives. In this paper, the structure and mathematical model of FTPMM are analyzed and the improved sliding mode observer (SMO) is constructed according to the deviation between the observed currents and actual currents. Combined with model reference adaptive control (MRAC) method and the phase‐lock loop (PLL), the switch function is replaced by the continuous Sigmoid function to reduce the chattering and finally the rotor position estimated accuracy is improved. When the motor winding open‐circuit and short‐circuit fault occur, the current vector fault‐tolerant control strategy is introduced into the improved SMO algorithm, and the rotor position is estimated accurately under faulty conditions. Finally, the proposed improved SMO position estimation algorithm under healthy and faulty conditions is verified in Matlab/Simulink. The FTPMM position sensorless technology would play a critical role in the electric power propulsion ship, and may have a profound impact on the high‐performance advanced ships and green shipping industry as well. © 2022 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC.