Reviewerly/exHarmony huggingface dataset:
ASCA vs. SASCA

We compare two key recovery methods for single trace attacks on the AES key schedule. The 2018 CHES capture-the-flag (CTF) challenge which includes an unprotected key schedule raises the question, which method performs best during key recovery: Soft Analytical Side-Channel Attacks (SASCAs) or Algebraic Side-Channel Attacks (ASCAs). SASCAs as well as ASCAs exploit knowledge about the attacked algorithm by leakage recombination and allow for a computationally efficient key recovery based on e.g. Hamming Weight (HW) leakage. We use Belief Propagation (BP), which is the most popular choice for SASCA and a SAT solver as an ASCA algorithm. In this work we attack real traces of the CTF challenge to demonstrate the limitations of SASCAs while handling the XOR operation. We exemplify that SASCAs may not always be the most favorable solution. The comparison is solidified by evaluating the success rate of SASCAs and ASCAs with simulated HW leakage on varying noise levels. During attacks on the AES key schedule the convergence of BP is not only graph dependent but data dependent. Further, we discuss possible graph clusters and adaptations of the input distributions to mitigate the influence of the XOR operations and increase the success rate of BP. All experiments are compared against equivalent SAT solver approaches. Based on our results we propose a combination of brute-force and BP to level the performance of the SAT solver and BP. Apart from this, we address unsolved questions regarding the benefit of an early break of BP and point out implementation details which lead to a better success rate.
Efficient Attack-Surface Exploration for Electromagnetic Fault Injection

Electromagnetic Fault Injection is a physical attack that aims to disrupt the operation of hardware circuits to bypass existing confidentiality and integrity protections. The success probability of the attack depends, among other things, on many different variables such as the probe used to inject the pulse, its position, the pulse intensity, and duration. The number of such parameter combinations and the stochastic nature of the induced faults make a comprehensive search of the parameter space impractical. However, it is of utmost importance for hardware circuit manufacturers to identify these vulnerability points efficiently and introduce countermeasures to mitigate them. This work presents a methodology to efficiently identify the subregion of the attack parameter space that maximizes the occurrence of a informative fault. The idea of this work consists in applying a multidimensional bisection method and exploiting the equilibrium between a pulse that is too strong and one that is too weak to produce a disruption on the circuit’s operation. We show that such a methodology can outperform existing methods on a concrete, state-of-the-art embedded multicore platform.
Machine Learning from User Interaction for Visualization and Analytics: A Workshop-Generated Research Agenda

At IEEE VIS 2018, we organized the Machine Learning from User Interaction for Visualization and Analytics workshop. The goal of this workshop was to bring together researchers from across the visualization community to discuss how visualization can benefit from machine learning, with a particular interest in learning from user interaction to improve visualization systems. Following the discussion at the workshop, we aggregated and categorized the ideas, questions, and issues raised by participants over the course of the morning. The result of this compilation is the research agenda presented in this work.
Tensor-Empowered Adaptive Learning for Few-Shot Streaming Tasks

Various stream learning methods are emerging in an endless stream to provide a wealth of solutions for artificial intelligence in streaming data scenarios. However, when each data stream is oriented to a different target space, it forces stream learning approaches oriented to the same task to be no longer applicable. Due to inconsistent target spaces for different tasks, the previous approaches fail on the new streaming tasks or it is impracticable to be trained from scratch with few labeled samples at the beginning. To this end, we have proposed an adaptive learning scheme for few-shot streaming tasks with the contributions of tensor and meta-learning. This adaptive scheme is conducive to mitigating the domain shift when a new task has few labeled samples. We elaborate a novel tensor-empowered attention mechanism derived from nonlocal neural networks, which enables to capture long-range dependency and preserve the high-dimensional structure to refine the global features of streaming tasks. Furthermore, we develop a fine-grained similarity computing approach, which is prone to better characterize the difference across few-shot streaming tasks. To show the superiority of our method, we have carried out extensive experiments on three popular few-shot datasets to simulate streaming tasks and evaluate the performance of adaptation. The results show that our proposed method has achieved competitive performance for few-shot streaming tasks compared with the state-of-the-art (SOTA).
Using Machine Learning and Visualization for Qualitative Inductive Analyses of Big Data

Many domains require analyst expertise to determine what patterns and data are interesting in a corpus. However, most analytics tools attempt to prequalify “interestingness” using algorithmic approaches to provide exploratory overviews. This overview-driven workflow precludes the use of qualitative analysis methodologies in large datasets. This paper discusses a preliminary visual analytics approach demonstrating how visual analytics tools can instead enable expert-driven qualitative analyses at scale by supporting computer-in-the-loop mixed initiative approaches. We argue that visual analytics tools can support rich qualitative inference by using machine learning methods to continually model and refine what features correlate to an analyst’s on-going qualitative observations and by providing transparency into these features in order to aid analysts in navigating large corpora during qualitative analyses. We illustrate these ideas through an example from social media analysis and discuss open opportunities for designing visualizations that support qualitative inference through computer-in-the-loop approaches.
“All Right, Mr. DeMille, I’m Ready for My Closeup:” Adding Meaning to User Actions from Video for Immersive Analytics

While the use of machine learning and computer vision to classify human behavior has grown into a large, well-established, interdisciplinary area of research, one area that is somewhat overlooked is the intersection of computer vision as a tool for evaluating user behavior in Virtual Reality, particularly in the context of immersive analytics and visualization. We draw on the literature from pattern recognition, computer vision, and machine learning to compose a simple, comparatively resource-cheap pipeline for camera-based extraction of features of professional analyst users and of their sessions in an existing VR visualization system, ImAxes. Our results show high accuracy in predicting self-reported features of the users, even as survey responses about user experience with the immersive interface are somewhat ambiguous in varying based on these features.
<i>DiffSeer</i>: Difference-Based Dynamic Weighted Graph Visualization

Existing dynamic weighted graph visualization approaches rely on users' mental comparison to perceive temporal evolution of dynamic weighted graphs, hindering users from effectively analyzing changes across multiple timeslices. We propose DiffSeer, a novel approach for dynamic weighted graph visualization by explicitly visualizing the differences of graph structures (e.g., edge weight differences) between adjacent timeslices. Specifically, we present a novel nested matrix design that overviews the graph structure differences over a time period as well as shows graph structure details in the timeslices of user interest. By collectively considering the overall temporal evolution and structure details in each timeslice, an optimization-based node reordering strategy is developed to group nodes with similar evolution patterns and highlight interesting graph structure details in each timeslice. We conducted two case studies on real-world graph datasets and in-depth interviews with 12 target users to evaluate DiffSeer. The results demonstrate its effectiveness in visualizing dynamic weighted graphs.
Device-Edge-Cloud Collaborative Acceleration Method Towards Occluded Face Recognition in High-Traffic Areas

Wearing masks can effectively inhibit the spread and damage of COVID-19. A device-edge-cloud collaborative recognition architecture is designed in this paper, and our proposed device-edge-cloud collaborative recognition acceleration method can make full use of the geographically widespread computing resources of devices, edge servers, and cloud clusters. First, we establish a hierarchical collaborative occluded face recognition model, including a lightweight occluded face detection module and a feature-enhanced elastic margin face recognition module, to achieve the accurate localization and precise recognition of occluded faces. Second, considering the responsiveness of occluded face detection services, a context-aware acceleration method is devised for collaborative occluded face recognition to minimize the service delay. Experimental results show that compared with state-of-the-art recognition models, the proposed acceleration method leveraging device-edge-cloud collaborations can effectively reduce the recognition delay by 16% while retaining the equivalent recognition accuracy.
Improving Side-channel Leakage Assessment Using Pre-silicon Leakage Models

Side-channel leakage assessment is an essential tool in the security evaluation of new chip designs. Pre-silicon side-channel analysis tools have made significant progress in delivering assessment results early in the chip design flow. However, a gap remains with actual implementations where measurements are affected by noise and distortions. These measurement imperfections degrade the assessment of the physical prototype and may lead to false negatives. In this contribution, we present a transfer learning technique to improve the assessment of physical prototypes using pre-silicon side-channel leakage simulation of the same implementation. The noiseless simulation traces are used for initial profiling to train a convolutional neural network (CNN). The trained CNN is then used in the assessment of measured traces. We apply this idea to Ascon and Xoodyak, two different sponge-based cryptographic primitives proposed in the NIST Lightweight Crypto competition. The target platform is a software implementation on a RISC-V (RV32IMC) microcontroller realized using 180 nm CMOS technology. Side-channel leakage is first captured using gate-level power simulation and then measured from a chip prototype of the same design. We investigate different side-channel analysis strategies under simulated and measured scenarios and demonstrate that, in each case, machine-learning-based side-channel leakage assessment outperforms other profiled and non-profiled analysis. However, using the proposed transfer learning technique, we can improve the side-channel leakage assessment even further. With the proposed transfer learning technique, we need approximately 2.87 less measured traces compared to the previous best profiled attack. We conclude that the proposed transfer learning using pre-silicon leakage models can improve the side channel leakage assessment of post-silicon implementations.
Fast First-Order Masked NTTRU

Even though Kyber is the lattice-based KEM selected for standardization by NIST, NTRU and its variants are still of great relevance to several practical applications. This is why we want to shed light on the side-channel resilience of NTTRU, which is a very fast variant of NTRU designed to use the Number-Theoretic Transform. It outperforms NTRU-HRSS significantly in an unprotected context, which raises the question of whether this performance advantage holds when side-channel attacks have to be considered. To answer that, we present the first masked implementation of NTTRU optimized for first-order. To achieve a fast performance, we present a table-based approach for the masked sampler and the modulus conversion, similar to the A2B conversion proposed by Debraize in 2012. The modulus conversion is also applicable to other NTRU variants. Due to its usage in NTTRU, we present a fully first-order masked SHA512 implementation based on A2B and B2A conversions. We come to the conclusion that performance is heavily impacted by the SHA2 family in masked implementations and strongly encourage the employment of SHA3 in these cases. This result is also of relevance for the 90s/AES variants of the NIST standardization candidates Kyber and Dilithium. We achieve a performance of the NTTRU-SHA3 of around 3.1 million cycles on the ARM Cortex M4. Finally, we show that our proposed methods provide side-channel security in practice by employing the well established TVLA methodology.
Whiteboxgrind – Automated Analysis of Whitebox Cryptography

Digital intellectual property is often protected by encrypting the data up to the point of use. Whitebox cryptography is an attempt to provide users with the ability to decrypt that data without actually revealing the key by embedding the key inside a cryptographic implementation. In this work, we design and implement Whiteboxgrind, a fast, fully automated toolchain that obtains execution traces from whitebox implementations and applies DCA to recover the hidden embedded keys. To evaluate Whiteboxgrind, we analysed whiteboxes of the CHES WhibOx 2019 competition, and found Whiteboxgrind to provide a significant performance improvement over the state-of-the-art tooling, enabling attacks that were previously infeasible due to memory constraints. Furthermore, we provide Whiteboxgrind’s source code.
Open-World Learning for Traffic Scenarios Categorisation

Categorisation of traffic scenarios is an important component of scenario-based development and validation of automated vehicles. This problem requires an open-world learning approach but most of the machine learning methods used for traffic scenario categorisation work under the closed-world assumption. A closed-world model will classify all the inputs to one of the classes from the training data. An open-world learning method can identify, collect and cluster unknown traffic scenarios and incrementally add new scenario categories to the already existing ones. In this work, a hierarchical architecture for open-world learning method is proposed. The open-world architecture consists of the following components: an open-set recognition model, storage buffer, outlier detection, class-conditioned generative replay model, and clustering method. The components in the architecture contain novel machine learning approaches to address the challenging open-world learning tasks, e.g., Extreme Value Theory (EVT) for open-set recognition, Random Forest Activation Patterns (RFAPs) for clustering, class-conditioned generative models for replay, and self-supervised pre-training for feature generation. The proposed architecture is tested using real-world and simulation-based datasets. The results show the performance advantages of the proposed method. Also, extensive analysis of each component of the hierarchical open-world architecture underlines their importance in the overall architecture.
A Super-Resolution-Based Feature Map Compression for Machine-Oriented Video Coding

Recently, video and image compression methods using neural networks have received much attention. In MPEG standardization, Video Coding for Machine (VCM) is a newly arising topic which attempts to compress features/images for the purpose of machine vision tasks. Especially, compressing features has advantages in terms of privacy protection and computation off-loading. In this paper, we propose an effective feature compression method equipped with a super-resolution (SR) module for features. Our main motivation comes from the observation that features are somewhat robust to spatial distortions (e.g., AWGN, blur, quantization distortions, coding artifacts), which leads us to integrating an SR module into the compression framework. We also further explore the best training strategy of the proposed method, i.e., finding the best combination of various losses and proper input feature shapes. Our comprehensive experiments show that the proposed method outperforms the baseline in the original VCM anchor scenario on various QP values with Versatile Video Coding (VVC). Specifically, the proposed framework achieved up to 50% BD-rate reduction compared to the conventional P-layer feature map compression method for the object detection task on the OpenImage dataset.
Privacy-Preserving Social Media With Unlinkability and Disclosure

Social media (SM) has become a primary communication tool in the modern world, with an ever-increasing volume of users. Many SM users use anonymous nicknames as their public usernames. However, Zhang et al. (2018) were able to demonstrate an attack that can identify users from the contents of their posts. This attack is caused by the fact that two different posts can be guessed to be the same user. Such linking of different posts is called a linkable feature. On the other hand, usually post under an anonymous nickname, but when a post is thrust into the limelight, we may want to claim the post as our own. Unfortunately, however, current SM offers only two options: using an anonymous nickname or publishing under our own name. In other words, the function of disclosure, which is to make some posts public even though they are usually anonymous, has not been realized in existing SM. In this paper, we propose a SM with unlinkability and disclosure simultaneously, which is achieved by applying a commitment scheme. A commitment scheme consists of commitment and decommitment phases. As for unlinkability, we newly introduce a one-time post name, which is a commitment value of nickname and post. As for disclosure, we use a decommitment phase to one-time post name. We also have demonstrated that our SM is practically feasible.
AN ANALYSIS OF INFLECTIONAL AFFIXES IN THE DISCOURSE ON THE METHOD TEXT BY RENE DESCARTES

The Objective of the study is to know about the process of inflectional affixes changing in the “Discourse on the Method Text by Rene Descartes”. This study applied qualitative method. This analysis used theory of George Yule, O’Grady and Guzman, and Mc-Carty. This study discussed about inflectional affixes changing based on Comparative, Superlative and Past tense form. The result of this studies is inflectional affixes changing occur in comparative, superlative and past tense form in the Discourse on the Method Text by Rene Descartes.
Lexical and Grammatical Cohesion in the Undergraduate Students’ Abstracts

This study aims to determine the lexical and grammatical cohesion of abstracts of STKIP PGRI Lubuklinggau students. The method used is a qualitative case study. The results showed that students made ungrammatical sentences, wrong prepositions, spelling errors, and improper cohesive devices. In conclusion, lexical and grammatical cohesion is used in the abstract. The repetition of words in lexical cohesion keeps the reader on track, while grammatical cohesion is used to describe available references, conjunctions, substitutions to make a good abstract.&#x0D; Keywords: Abstract, Grammatical Cohesion, Lexical
Developing of Listening Materials at the Tenth Grade Students

This study aims to develop listening material for tenth graders. The research method used is Research and Development (R&amp;D). The results of this study indicate that the development of listening material is valid, practical and effective in the teaching and learning process. The product developed was declared valid after being evaluated by three experts with an average score of 3.86 in the "high" category. This product is also practical by evaluating listening material for students with an average score of 4.10 with "high practicality" and effective with an evaluation with 83.33 students in the "high" category. In conclusion, the listening material developed for tenth-grade students of Islamic Senior High School 2 Lubuklinggau is valid, practical and effective categories.&#x0D; Keywords: Listening, Materials, Research and Development
The Lecturers’ English Ability Through the Toefl Test

This study aims to determine and describe lecturers' English language skills, which can be seen through the TOEFL test. This research method is descriptive qualitative. The researcher gave a TOEFL-like test to 109 lecturers as research subjects. TOEFL ability is mapped into the Common European Framework of Reference for Language (CEFR), measured at four different levels, namely A2, B1, B2, and C. The results show that no lecturer has C1 ability in the Listening section. Two lecturers are at level B2, 12 people are at level B1, and the remaining 95 people are at level A2. The average for Listening is in the A2-SD level category. For Structure and Written Expression, only four lecturers are at level B2; 25 at B1 level; and 80 at level A2. The average for SWE is 40 in the A2 elementary level category. As for the Reading section, only one lecturer is at level C1; 2 at level B2; 6 at level B1; 98 at level A2. The average score for Reading is in the A2-SD level category. In conclusion, most of the lecturers' English skills (Listening, Structure and Writing Expression, and Reading) are still very weak.&#x0D; Keywords: Lecturers’ English Ability, TOEFL
Penguasaan PUEBI terhadap Keterampilan Menulis Teks Laporan Hasil Observasi

This study aims to determine the relationship between the mastery of PUEBI with the skills of writing text reports on the results of observations. The research method uses correlation with quantitative types. Sampling was done using the cluster random sampling technique. The research instrument is an objective test and a performance test. The results showed that the average value of PUEBI and the value of the observation report were both 76 with good qualifications. The rcount obtained is 0.9030 and tcount 11.3193 with degrees of freedom n-2 (31-2=29), and a significant rate of 0.05. In conclusion, there is a significant relationship between PUEBI and the observation report.&#x0D; Keywords: Writing Observation Report Text, Mastery of PUEBI
Citra Perempuan dalam Antologi Puisi Rahi(i)m Karya Kedung Darma Romansha serta Implikasinya terhadap Pembelajaran Bahasa Indonesia di Sekolah

This study aims to describe the image of women in the poems of the writer Kedung Darma Romansha and describe the results of the description of the meaning of the image of women by learning the Indonesian language, namely the subject of poetry appreciation in high school class X. The research method used is a descriptive qualitative method in the form of paradigms in word descriptions. -words to describe and analyze facts naturally and then interpret them appropriately. The results showed that: (1) the image of women, consisting of self-image and social image; 2) the image of women in the anthology of poetry entitled Rahi(i)m by Kedung Rama Romansha can be used as implications for learning Indonesian at school. Conclusions, (1) there is an image of women in the poem in the anthology entitled Rahi(i)m by Kedung Darma Romansha; 2) the implications of Rahi(i)m's poetry anthology with learning Indonesian at school is as a reference in the subject of poetry appreciation in high school class X.&#x0D; Keywords: Image of Women, Rahi(i)m, Indonesian Language Learning