Reviewerly/exHarmony huggingface dataset:
RAVEN: A VR prototype for the training of aviation technicians

Every year, an ageing workforce of Aviation Maintenance Techni-cians (AMTs) is succeeded by a younger, tech-savvy generation with completely different learning habits. In this work, we propose a virtual reality system for undergraduate and entry-level AMTs, to reliably absorb knowledge contained within technical maintenance manuals in an experiential and cost-effective manner. Our system simulates the procedures involved in the replacement of an airliner battery, by creating virtual interactive spaces both inside and outside of the aircraft. With such experiences, we believe that high-fidelity VR systems like the one presented could be a welcome innovation in future AMT training programs.
Person Re-identification Based on CNN with Multi-scale Contour Embedding

Person Re-identification (Re-ID) plays an important role in the search for missing people and the tracking of suspects. Person Re-identification based on deep learning has made great progress in recent years, and the application of pedestrian contour map has also been concerned. In the study, we found that the pedestrian contour feature is not enough in the shallow expression of CNN. On this basis, in order to improve the recognition performance of Re-ID network, we propose a multi-scale pedestrian contour embedding method. The proposed method uses ResNet50 as the backbone to extract the global features of people. We use the pedestrian contour map as a feature map and reduce its dimension. The pedestrian contour is added to the previous layers of CNN network as a supplement to feature map extraction. In addition, we also apply our method to the local convolution baseline (AlignedReID++). The experimental results show that our method improves the mAP and Rank-1 of the baseline model by 4.5% and 3.6% respectively on Market1501. The mAP and Rank-1 of the local baseline model AlignedReID++ are increased by 0.8% and 1.4%. The final result of our experiment achieves 88.3% mAP and 92.3% Rank-1 accuracy for Market1501, 81.7% mAP and 86.5% Rank-1 accuracy for DukeMTMC-reID.
Almost-Orthogonal Layers for Efficient General-Purpose Lipschitz Networks

It is a highly desirable property for deep networks to be robust against small input changes. One popular way to achieve this property is by designing networks with a small Lipschitz constant. In this work, we propose a new technique for constructing such Lipschitz networks that has a number of desirable properties: it can be applied to any linear network layer (fully-connected or convolutional), it provides formal guarantees on the Lipschitz constant, it is easy to implement and efficient to run, and it can be combined with any training objective and optimization method. In fact, our technique is the first one in the literature that achieves all of these properties simultaneously. Our main contribution is a rescaling-based weight matrix parametrization that guarantees each network layer to have a Lipschitz constant of at most 1 and results in the learned weight matrices to be close to orthogonal. Hence we call such layers almost-orthogonal Lipschitz (AOL). Experiments and ablation studies in the context of image classification with certified robust accuracy confirm that AOL layers achieve results that are on par with most existing methods. Yet, they are simpler to implement and more broadly applicable, because they do not require computationally expensive matrix orthogonalization or inversion steps as part of the network architecture. We provide code at https://github.com/berndprach/AOL .
How to Obfuscate MPC Inputs

We introduce the idea of input obfuscation for secure two-party computation (io2PC). Suppose Alice holds a private value x and wants to allow clients to learn $$f(x,y_i)$$ , for their choice of $$y_i$$ , via a secure computation protocol. The goal of io2PC is for Alice to encode x so that an adversary who compromises her storage gets only oracle access to the function $$f(x,\cdot )$$ . At the same time, there must be a 2PC protocol for computing f(x, y) that takes only this encoding (and not the plaintext x) as input. We show how to achieve io2PC for functions that have virtual black-box (VBB) obfuscation in either the random oracle model or generic group model. For functions that can be VBB-obfuscated in the random oracle model, we provide an io2PC protocol by replacing the random oracle with an oblivious PRF. For functions that can be VBB-obfuscated in the generic group model, we show how Alice can instantiate a “personalized” generic group. A personalized generic group is one where only Alice can perform the algebraic operations of the group, but where she can let others perform operations in that group via an oblivious interactive protocol.
DeChain: A Blockchain Framework Enhancing Decentralization via Sharding

In a blockchain system, full nodes store all the history data generated by the whole network. As time goes by, the increasing data will place a heavy burden on the full nodes. Rational nodes may discard history data, which results in the decrease of the number of full nodes. Moreover, huge storage requirement prevents storage-constrained users from participating in the network. These factors weaken decentralization and are harmful to the whole blockchain network. In this paper, we propose a new shard-based blockchain framework called DeChain which distributes the blockchain database into different nodes in protocol level. Specifically, we design a new mechanism to shard the UTXOs into specific nodes and set special rules for transaction generation. Each node in DeChain is in charge of the verification of some specified transactions. We propose an RSA accumulator-based method to support inter-shards verification of transactions. With this framework, users can participate in the consensus of the whole network by only maintaining a small portion of blockchain database. This greatly reduces the storage burden and enhances the decentralization of blockchain network.
ISTIC’s Thai-to-Chinese Neural Machine Translation System for CCMT’ 2022

This paper introduces technical details of Thai-to-Chinese neural machine translation system of Institute of Scientific and Technical Information of China (ISTIC) for the 18th China Conference on Machine Translation (CCMT’ 2022). ISTIC participated in a low resource evaluation task: Thai-to-Chinese MT task. The paper mainly illuminates its system framework based on Transformer, data preprocessing methods and some strategies adopted in this system. In addition, the paper evaluates the system performance under different methods.
Securing hard drives with the Security Protocol and Data Model (SPDM)

In modern computing systems, it is usually hard to defend against attacks made against the low level communication between hardware components. In this work, we address this issue by adapting a hard disk controller and a corresponding device driver to communicate securely using the recently proposed Secure Protocol and Data Model (SPDM). Essentially, SPDM standardizes authentication of hardware components, besides enabling the establishment of secure communication channels among them. To assess the overhead introduced by SPDM in this scenario, we tested our solution against an unmodified, unsecured hard disk on an emulated environment. Our experiments show that, while SPDM can make the task of copying a large up to <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$7\times$</tex> slower, the transference of small files is virtually unaffected.
Explaining Predictive Scheduling in Cloud

The importance of cloud computing has been rapidly growing due to the increasing number of users’ requests for diverse sets of resources. Although clouds have rich resources to handle these incoming requests, under or over-provisioning of resources can lead to failure. Therefore, it is important to provision cloud resources appropriately. Machine-learning based techniques have been proven to be effective in the management of resources along with maintaining a Service Level Agreement (SLA). These techniques require complete data to produce better prediction results. In practice, it may happen that the data is incomplete and data with more missing attribute values can negatively affect the outcome of the predictions. Therefore, interpolation of missing attribute values is crucial for better predictions. However, the existing methods for interpolation of missing attribute values are heavy in terms of computation. This paper first predicts resource usage in terms of CPU by applying the lightGBM model to a real dataset. Furthermore, using the explanations of SHapley Additive exPlanations (SHAP) in combination with the K-Nearest Neighbor (KNN) to interpolate missing values in the dataset for CPU usage prediction. The experimental results show that SHAP explanations can be helpful for cloud providers in the selection of important features for interpolation of missing values. This SHAP-based interpolation results in lower computational time along with acceptable accuracy in comparison with KNN-based interpolation.
Exploring Timing Covert Channel Performance over the IEEE 802.15.4

As IoT technologies mature, they are increasingly finding their way into more sensitive domains, such as Medical and Industrial IoT, in which safety and cyber-security are paramount. While the number of deployed IoT devices continues to increase annually, they still present severe cyber-security vulnerabilities, turning them into potential targets and entry points to support further attacks. Naturally, as these nodes are compromised, attackers aim at setting up stealthy communication behaviours, to exfiltrate data or to orchestrate nodes of a botnet in a cloaked fashion. Network covert channels are increasingly being used with such malicious intents. The IEEE 802.15.4 is one of the most pervasive protocols in IoT, and a fundamental part of many communication infrastructures. Despite this fact, the possibility of setting up such covert communication techniques on this medium has received very little attention. We aim at analysing the performance and feasibility of such covert-channel implementations upon the IEEE 802.15.4 protocol. This will enable a better understanding of the involved risk and help supporting the development of further cyber-security mechanisms to mitigate this threat.
Quantum Technologies – A New Field That Needs Assessment

Quantum technology (QT) is an emergent area with the potential to be disruptive and affect many human activities. QT also is a typical technology with significant dual-use applications. Specifically, the potential QT military applications were recently mapped. Although QTs do not introduce any new standalone weapon systems, they can significantly sharpen present weapon systems as well as command and control systems, including in the cyber domain. On the other hand, they also can provide non-eavesdropping secure communication or enhanced medical imaging. This contribution aims to introduce QTs and their possible applications, current status and activities related to the defence sector. Then, the military-related applications will be discussed more in detail, and potential problems for international security will be mentioned. In the end, we provide arguments why QTs should be assessed, and we ask selected questions that should be answered.
Multi-domain Multi-definition Landmark Localization for Small Datasets

We present a novel method for multi image domain and multi-landmark definition learning for small dataset facial localization. Training a small dataset alongside a large(r) dataset helps with robust learning for the former, and provides a universal mechanism for facial landmark localization for new and/or smaller standard datasets. To this end, we propose a Vision Transformer encoder with a novel decoder with a definition agnostic shared landmark semantic group structured prior, that is learnt, as we train on more than one dataset concurrently. Due to our novel definition agnostic group prior the datasets may vary in landmark definitions and domains. During the decoder stage we use cross- and self-attention, whose output is later fed into domain/definition specific heads that minimize a Laplacian-log-likelihood loss. We achieve state-of-the-art performance on standard landmark localization datasets such as $$\texttt{COFW}$$ and $$\texttt{WFLW}$$ , when trained with a bigger dataset. We also show state-of-the-art performance on several varied image domain small datasets for animals, caricatures, and facial portrait paintings. Further, we contribute a small dataset (150 images) of pareidolias to show efficacy of our method. Finally, we provide several analysis and ablation studies to justify our claims.
TransFusion: Transcribing Speech with Multinomial Diffusion

Diffusion models have shown exceptional scaling properties in the image synthesis domain, and initial attempts have shown similar benefits for applying diffusion to unconditional text synthesis. Denoising diffusion models attempt to iteratively refine a sampled noise signal until it resembles a coherent signal (such as an image or written sentence). In this work we aim to see whether the benefits of diffusion models can also be realized for speech recognition. To this end, we propose a new way to perform speech recognition using a diffusion model conditioned on pretrained speech features. Specifically, we propose TransFusion: a transcribing diffusion model which iteratively denoises a random character sequence into coherent text corresponding to the transcript of a conditioning utterance. We demonstrate comparable performance to existing high-performing contrastive models on the LibriSpeech speech recognition benchmark. To the best of our knowledge, we are the first to apply denoising diffusion to speech recognition. We also propose new techniques for effectively sampling and decoding multinomial diffusion models. These are required because traditional methods of sampling from acoustic models are not possible with our new discrete diffusion approach.
Attention-Based Multi-View Feature Collaboration for Decoupled Few-Shot Learning

Decoupled Few-shot learning (FSL) is an effective methodology that deals with the problem of data-scarce. Its standard paradigm includes two phases: (1) Pre-train. Generating a CNN-based feature extraction model (FEM) via base data. (2) Meta-test. Employing the frozen FEM to obtain the novel data features, then classifying them. Obviously, one crucial factor, the category gap, prevents the development of FSL, i.e., it is challenging for the pre-trained FEM to adapt to the novel class flawlessly. Inspired by a common-sense theory: the FEMs based on different strategies focus on different priorities, we attempt to address this problem from the multi-view feature collaboration (MVFC) perspective. Specifically, we first denoise the multi-view features by subspace learning method, then design three attention blocks (loss-attention block, self-attention block and graph-attention block) to balance the representation between different views. The proposed method is evaluated on four benchmark datasets and achieves significant improvements of 0.9%-5.6% compared with SOTAs.
Bitcoin Flash Crash on May 19, 2021: What did really happen on Binance?

Bitcoin plunged by 30% on May 19, 2021. We examine the outage the largest crypto exchange Binance experienced during the crash, when it halted trading for retail clients and stopped providing transaction data. We find evidence that Binance back-filled these missing transactions with data that does not conform to Benford's Law. The Bitcoin futures price difference between Binance and other exchanges was seven times larger during the crash period compared to a prior reference period. Data manipulation is a plausible explanation for our findings. These actions are in line with Binance aiming to limit losses for its futures-related insurance fund.
Attribute Based Tracing for Securing Group Signatures Against Centralized Authorities

This paper proposes a group signature scheme with a tracing mechanism that limits the tracing ability of tracers based on their attributes and decentralizes the tracing key generation method. Thus, no other party than the attributes satisfying tracer can identify the signer. The proposing scheme answers the single point of failure of the tracing mechanism in the existing group signature schemes. On the other hand, the multiple tracers setting of the proposing scheme reduces the tracing workload that the single tracer had, and provides selection flexibility for users to choose a tracer for their signatures based on tracers’ attributes. This paper discussed the related security definitions against outsiders and honest but curious authorities.
The Final Round: Benchmarking NIST LWC Ciphers on Microcontrollers

In this work, we present our benchmarking results for the ten finalist ciphers of the Lightweight Cryptography (LWC) project initiated by National Institute of Standards and Technology (NIST). We evaluate the speed and code size of various software implementations on five different platforms featuring four different architectures. Moreover, we benchmark the dynamic memory utilization of the remaining NIST LWC algorithms on one 32-bit ARM controller. We describe our test cases and methodology and provide some information regarding the design and properties of the finalists before showing and discussing our results. Altogether, we evaluated almost 300 implementations of the 3rd round candidates and pick the most appropriate and best (primary) implementation of each cipher for our comparisons. We include a variant of AES-GCM in our benchmarking in order to be able to compare the state-of-the-art to the novel LWC ciphers. Our research gives an overview over the performance of the latest software implementations of the NIST LWC finalists and shows under which circumstances which candidate is performing the best in our individual test cases. Additionally, we make all benchmarking results, the code for our test framework and every tested implementation available to the public to ensure a transparent testing process.
AntiPhiMBS-TRN: A New Anti-phishing Model to Mitigate Phishing Attacks in Mobile Banking System at Transaction Level

With the continuous improvement and growth at a rapid pace in the utility of mobile banking payment technologies, fraudulent mobile banking transactions are being multiplied using bleeding-edge technologies sharply and a significant economic loss is made every year around the world. Phishers seek new vulnerabilities with every advance in fraud prevention and have become an even more pressing issue of security challenges for banks and financial institutions. However, researchers have focused mainly on the prevention of fraudulent transactions on the online banking system. This paper proposes a new anti-phishing model for mobile banking systems at the transaction level (AntiPhiMBS-TRN) that mitigates fraudulent transactions in the mobile banking payment system. This model applies a unique id for the transactions and an application id for the bank application known to the bank, bank application, users, and the mobile banking system. In addition, AntiPhiMBS-TRN also utilizes the international mobile equipment identity (IMEI) number of the registered mobile device to prevent fraudulent transactions. Phishers cannot execute fraudulent transactions without knowing the unique id for the transaction, application id, and IMEI number of the mobile device. This paper employs a process meta language (PROMELA) to specify system descriptions and security properties and builds a verification model of AntiPhiMBS-TRN. Finally, AntiPhiMBS-TRN is successfully verified using a simple PROMELA interpreter (SPIN). The SPIN verification results prove that the proposed AntiPhiMBS-TRN is error-free, and banks can implement the verified model for mitigating fraudulent transactions in the mobile banking system globally.
Activity Classification of an Unmanned Aerial Vehicle Using Tsetlin Machine

The activity classification for aerial vehicles plays a vital role in privacy monitoring and security surveillance applications, which is crucial and valuable in modern times. This paper presents the Tsetlin Machine model for aerial vehicle’s activity classification using the mm-Wave frequency modulated continuous wave (FMCW) Radar data. The proposed Tsetlin Machine (TM) model is based on propositional logic, which is much more transparent and lighter than the existing models. It can also be easily transferred to hardware, making it more useful even in practical circumstances. Furthermore, the model has a 92.5% accuracy in activity classification, which is close to other lightweight classification models like logistic regression, light gradient boosting machine (GBM) and support vector machine (SVM). Furthermore, the proposed model’s accuracy is much better than the pre-trained models such as VGG16, ResNet50, and InceptionResNet with at least $98 \times$ reduction in memory size.
PENGARUH LITERASI DIGITAL TERHADAP HASIL BELAJAR SISWA SEKOLAH DASAR KOTA MAKASSAR

This study aims to determine the effect of digital literacy on student learning outcomes at SD Inpres Bangkala III Makassar City. This research is classified into the type of Ex Post Facto research using a quantitative approach. This research was conducted at SD Inpres Bangkala III Makassar City. The sample of this study were all students of class VA SD Inpres Bangkala III Makassar City. Which consisted of 30 students. The total number of samples used is 30 students, using non-random sampling technique. The results of this study using descriptive analysis, the average value of digital literacy is 70.10 which is in the good category and the value of learning outcomes is 82.63 which is in the good category. Inferential analysis, the results of the digital literacy normality test are 0.327 and the learning outcomes are 0.248 &gt; 0.05 (normality distributed), the homogenity test results are 0.114 &gt; 0.05 (homogenity), the linearity test results are 0.080 &gt; 0.05 based on the data results, there is a relationship which is linear digital literacy on learning outcomes. The results of the hypothesis test showed a sig value of 0.003, with the criteria of sig (0.003) &lt; Î± (0.05). The conclusion in this study is that there is an influence of digital literacy on student learning outcomes of SD Inpres Bangkala III Makassar City.
Time Constraint Finite-Horizon Path Planning Solution for Micromouse Extreme Problem

The original Micromouse competition was designed to educate future engineers, in robotic problem solving through active learning methods. The Micromouse Extreme (ME) Competition is proposed, to augment the pedagogical power of the original competition with a modern task set requiring skills in deep learning and creative solving methods. It also preserves the goal of original Micromouse Problem will to educate future engineers in robotic problem solving through active learning methods. The ME is a natural evolution to the simple programming nature of the original competition through the incorporation of advanced robotic tools and ideas. The platform described is fully virtual and inexpensive, providing an excellent platform for students to further their robotics education. As such, several methods of solving this setup are required to guide students along their path to proficiency in path planning. This paper discusses solutions to the ME problem such as the Traveling Salesman Problem and similar solvers to suit the proposed need. Once the problem is established we solve it using Time Constraint Finite Horizon Path Planning solution. The approach can provide a guaranteed global optimal path solution for the ME problem and can introduce students to solving methods for complex problems.