Reviewerly/exHarmony huggingface dataset:
Mind the Gap: Studying the Insecurity of Provably Secure Embedded Trusted Execution Architectures

The security claims of a system can be supported or refuted by different kinds of evidence. On the one hand, attack research uses empirical, experimental, inductive methods to refute security claims. If motivated and competent attackers do not succeed in breaking a specific security property, this provides some support (but no definite proof) that the system is secure.On the other hand, formal methods use mathematical, deductive methods that can prove the security of a model of the system. The process of constructing a proof can uncover vulnerabilities that can then be fixed. The use of formal methods can be very powerful and is attractive because it seems to provide irrefutable evidence of security. However, that evidence applies only to the mathematical model, not to any actual system, and, hence, it is important to understand the gap between the model and the real-world system.In this paper, we present a case study that examines this gap for two embedded security architectures that use formal methods to prove their security properties. Despite strong formal evidence for security, we discover numerous attacks against the implementations, all of which falsify proven security properties. These attacks range from exploiting simple programming errors to a novel DMA-based side-channel attack. The simple attacks demonstrate that the construction of systems and proofs is error-prone, while some of the more sophisticated attacks serve as examples to show that formal methods alone can never guarantee the security of a real-world system.From our case study, we also distill actionable guidelines on how to provide stronger evidence for the security of a system.
SoK: Demystifying Binary Lifters Through the Lens of Downstream Applications

Binary lifters convert executables into an intermediate representation (IR) of a compiler framework. The recovered IR code is generally deemed “analysis friendly,” bridging low-level code analysis with well-established compiler infrastructures. With years of development, binary lifters are becoming increasingly popular for use in various security, systems, and software (re)-engineering applications. Recent studies have also reported highly promising results that suggest binary lifters can generate LLVM IR code with correct functionality, even for complex cases.This paper conducts an in-depth study of binary lifters from an orthogonal and highly demanding perspective. We demystify the “expressiveness” of binary lifters, and reveal how well the lifted LLVM IR code can support critical downstream applications in security analysis scenarios. To do so, we generate two pieces of LLVM IR code by compiling C/C++ programs or by lifting the corresponding executables. We then feed these two pieces of LLVM IR code to three keystone downstream applications (pointer analysis, discriminability analysis, and decompilation) and determine whether inconsistent analysis results are generated. We study four popular static and dynamic LLVM IR lifters that were developed by the industry or academia from a total of 252,063 executables generated by various compilers and optimizations and on different architectures. Our findings show that modern binary lifters afford IR code that is highly suitable for discriminability analysis and decompilation, and suggest that such binary lifters can be applied in common similarity- or code comprehension-based security analysis (e.g., binary diffing). However, the lifted IR code appears unsuited to rigorous static analysis (e.g., pointer analysis). To obtain a more comprehensive view of the utility of binary lifters, we also compare the performance of lifter-enabled approaches with that of binary-only tools in three security tasks, i.e., sanitization, binary diffing, and C decompilation. We summarize our findings and make suggestions for the correct use and further enhancement of binary lifters. We also explored practical ways to enhance the accuracy of pointer analysis using lifted IR code, by using and augmenting Debin, a tool for predicting debug information.
Exploring Students’ Increased Use of Tablets After Taking Online Courses During the COVID-19 Lockdown

The present study aimed to investigate the ownership and usage of Tablets among Chinese foreign language students. Adopted a quantitative approach, this research was considered a case study with exploratory nature. The data were collected from a self-reported questionnaire between May 31st and June 6th, and 276 valid responses were analyzed utilizing SPSS. Independent samples t-test and Fisher’s exact tests were carried out to perform inferential and descriptive analyses. Findings revealed that augmented ownership of tablets did not result from the implementation of online courses after the outbreak of COVID-19. However, some students demonstrated preferences for paperless learning, whose learning style might have changed during the intensive distance learning process for more than one semester. Still, students’ opinions towards the introduction of tablets into pedagogical practice weren’t overall positive; those who did not have or were not willing to purchase tablet devices were more likely to be unsupportive.
Leveraging Student Goal Setting for Real-Time Plan Recognition in Game-Based Learning

Goal setting and planning are integral components of self-regulated learning. Many students struggle to set meaningful goals and build relevant plans. Adaptive learning environments show significant potential for scaffolding students' goal setting and planning processes. An important requirement for such scaffolding is the ability to perform student plan recognition, which involves recognizing students' goals and plans based upon the observations of their problem-solving actions. We introduce a novel plan recognition framework that leverages trace log data from student interactions within a game-based learning environment called CRYSTAL ISLAND, in which students use a drag-and-drop planning support tool that enables them to externalize their science problem-solving goals and plans prior to enacting them in the learning environment. We formalize student plan recognition in terms of two complementary tasks: (1) classifying students' selected problem-solving goals, and (2) classifying the sequences of actions that students indicate will achieve their goals. Utilizing trace log data from 144 middle school students' interactions with CRYSTAL ISLAND, we evaluate a range of machine learning models for student goal and plan recognition. All machine learning-based techniques outperform the majority baseline, with LSTMs outperforming other models for goal recognition and naive Bayes performing best for plan recognition. Results show the potential for automatically recognizing students' problem-solving goals and plans in game-based learning environments, which has implications for providing adaptive support for student self-regulated learning.
“Teacher, Can You Say It Again?" Improving Automatic Speech Recognition Performance over Classroom Environments with Limited Data

Analyzing teachers' discourse plays a fundamental role in educational research and is a key component of Teaching Analytics. This usually involves transcribing lessons from audio recordings. As the number of recordings grows, Automatic Speech Recognition (ASR) systems gain popularity as a means for transcribing these recordings. However, most ASR systems are trained over very specific domains which usually involve read text and low environmental noise. This suggests common ASR systems available on the market may underperform over classroom recordings, as they present a unique type of environmental sound and spontaneous discourse, as opposed to the usual training domains. To address this challenge we present a system that automatically transcribes classroom discourse in a robust way with regard to classroom noise, which was trained over few annotated data. In particular, we used a state-of-the-art ASR model based on wav2vec 2.0 and fine-tuned it over a 6-h dataset of 4th to 8th grade Chilean lessons. We found that by leveraging its transformer-based architecture and changing the fine-tuning domain to classroom recordings, we can obtain a more accurate and robust transcriber for this source of audio which outperforms other popular cloud-based systems up to 35% and 59% in terms of Word and Character Error Rates, respectively. This work contributes by using state-of-the-art ASR techniques to develop a tool which is particularly adapted to classroom environments, making it robust and more reliable with regard to their environmental sound and the way teaching discourse is carried out.
Enhancing Auto-scoring of Student Open Responses in the Presence of Mathematical Terms and Expressions

Prior works have led to the development and application of automated assessment methods that leverage machine learning and natural language processing. The performance of these methods have often been reported as being positive, but other prior works have identified aspects on which they may be improved. Particularly in the context of mathematics, the presence of non-linguistic characters and expressions have been identified to contribute to observed model error. In this paper, we build upon this prior work by observing a developed automated assessment model for open-response questions in mathematics. We develop a new approach which we call the "Math Term Frequency" (MTF) model to address this issue caused by the presence of non-linguistic terms and ensemble it with the previously-developed assessment model. We observe that the inclusion of this approach notably improves model performance, and present an example of practice of how error analyses can be leveraged to address model limitations.
Fine-grained Main Ideas Extraction and Clustering of Online Course Reviews

Online course reviews have been an essential way in which course providers could get insights into students' perceptions about the course quality, especially in the context of massive open online courses (MOOCs), where it is hard for both parties to get further interaction. Analyzing online course reviews is thus an inevitable part for course providers towards the improvement of course quality and the structuring of future courses. However, reading through the often-time thousands of comments and extracting key ideas is not efficient and will potentially incur non-coverage of some important ideas. In this work, we propose a key idea extractor that is based on fine-grained aspect-level semantic units from comments, powered by different variations of state-of-the-art pre-trained language models (PLMs). Our approach differs from both previous topic modeling and keyword extraction methods, which lies in: First, we aim to not only eliminate the heavy reliance on human intervention and statistical characteristics that traditional topic models like LDA are based on, but also to overcome the coarse granularity of state-of-the-art topic models like top2vec. Second, different from previous keyword extraction methods, we do not extract keywords to summarize each comment, which we argue is not necessarily helpful for human readers to grasp key ideas at the course level. Instead, we cluster the ideas and concerns that have been most expressed throughout the whole course, without relying on the verbatimness of students' wording. We show that this method provides high and stable coverage of students' ideas.
Representing Scoring Rubrics as Graphs for Automatic Short Answer Grading

To score open ended responses, researchers often design a scoring rubric. Rubrics can help produce more consistent ratings and reduce bias. This project explores whether an automated short answer grading model can learn information from a scoring rubric to produce ratings closer to that of a human. We explore the impact of adding an additional transformer encoder layer to a BERT model and training the weights of this extra layer with only the scoring rubric text. Additionally, we experiment with using Node2Vec sampling to capture the graph-like ordinal structure in the rubric text to further pre-train the model. Results show superior model performance when further pre-training with the scoring rubric text. Specifically, questions that elicit a very simple rubric structure show the most improvement from incorporating rubric text. Using Node2Vec to capture the structure of the text had an inconclusive impact.
Evaluating AI-Generated Questions: A Mixed-Methods Analysis Using Question Data and Student Perceptions

Advances in artificial intelligence (AI) have made it possible to generate courseware and formative practice questions from textbooks. Courseware applies a learn by doing approach by integrating formative practice with text, a method proven to increase learning gains for students. By using AI for automatic question generation, the learn by doing method of courseware can be made available for nearly any textbook subject. As the generated questions are a primary learning feature in this environment, it is necessary to ensure they function as well for students as those written by humans. In this paper, we will use student data from an AI-generated Psychology courseware used in an online course at the University of Central Florida. The courseware has both generated questions and human-authored questions, allowing for a unique comparison of question engagement, difficulty, and persistence using student data from a natural learning context. The evaluation of quality metrics is critical in automatic question generation research, yet on its own is not comprehensive of students' experience. Student perception is a meaningful qualitative metric, as student perceptions can inform behavior and decisions. Therefore, student perceptions of the courseware and questions were also solicited via survey. Combining question data analysis with student perception feedback gives a more comprehensive evaluation of the quality of AI-generated questions used in a natural learning context.
The Power to Structure

Information systems are developed by people with intent—they are designed to help creators and users tell specific stories with data. Within information systems, the often invisible structures of metadata profoundly impact the meaning that can be derived from that data. The Linked Infrastructure for Networked Cultural Scholarship project (LINCS) helps humanities researchers tell stories by using linked open data to convert humanities datasets into organized, interconnected, machine-processable resources. LINCS provides context for online cultural materials, interlinks them, andgrounds them in sources to improve web resources for research. This article describes how the LINCS team is using the shared standards of linked data and especially ontologies—typically unseen yet powerful—to bring meaning mindfully to metadata through structure. The LINCS metadata—comprised of linked open data about cultural artifacts, people, and processes—and the structures that support it must represent multiple, diverse ways of knowing. It needs to enable various means of incorporating contextual data and of telling stories with nuance and context, situated and supported by data structures that reflect and make space for specificities and complexities. As it addresses specificity in each research dataset, LINCS is simultaneously working to balance interoperability, as achieved through a level of generalization, with contextual and domain-specific requirements. The LINCS team’s approach to ontology adoption and use centers on intersectionality, multiplicity, and difference. The question of what meaning the structures being used will bring to the data is as important as what meaning is introduced as a result of linking data together, and the project has built this premise into its decision-making and implementation processes. To convey an understanding of categories and classification as contextually embedded—culturally produced, intersecting, and discursive—the LINCS team frames them not as fixed but as grounds for investigation and starting points for understanding. Metadata structures are as important as vocabularies for producing such meaning.
Device-Independent Smartphone Eavesdropping Jointly Using Accelerometer and Gyroscope

Eavesdropping via inertial measurement units (IMUs) has brought growing concerns over smartphone users’ privacy. In such attacks, adversaries utilize IMUs, including accelerometers and gyroscopes, which require zero permissions for access to acquire speeches. A common countermeasure is to limit sampling rates (within 200 Hz) to reduce overlap of vocal fundamental bands (85 <inline-formula><tex-math notation="LaTeX">$\sim$</tex-math></inline-formula> 255 Hz) and inertial measurements (0 <inline-formula><tex-math notation="LaTeX">$\sim$</tex-math></inline-formula> 100 Hz). Nevertheless, we observe that IMUs sampling below 200 Hz still record adequate speech-related information because of aliasing distortions. Accordingly, we propose a practical side-channel attack, namely <i>InertiEAR</i> , to break the defense of sampling rate restriction on the zero-permission eavesdropping. It leverages accelerometers and gyroscopes jointly to eavesdrop on both top and bottom speakers in smartphones. We exploit coherence between responses of the built-in accelerometer and gyroscope using a mathematical model. The coherence allows precise segmentation without manual assistance. We also mitigate the impact of hardware diversity and achieve better device-independent performance than existing approaches that have to massively increase training data from different smartphones for a scalable network model. These two advantages re-enable zero-permission attacks but also extend the attacking surface and endangering degree to off-the-shelf smartphones. <i>InertiEAR</i> achieves the recognition accuracy of 78.8% with the cross-device accuracy of up to 60.9% among 12 smartphones.
Secure User Verification and Continuous Authentication Via Earphone IMU

Biometric plays an important role in user authentication. However, the most widely used biometrics, such as facial feature and fingerprint, are easy to capture or record, and thus vulnerable to spoofing attacks. On the contrary, intracorporal biometrics, such as electrocardiography and electroencephalography, are hard to collect, and hence more secure for authentication. Unfortunately, adopting them is not user-friendly due to their complicated collection methods or inconvenient constraints on users. In this paper, we propose a novel biometric-based authentication system, namely <i>MandiPass</i>. <i>MandiPass</i> leverages inertial measurement units, which have been widely deployed in portable devices, to collect intracorporal biometric from the vibration of user&#x0027;s mandible. It provides not only one-time verification function but also continuous authentication function. Both the two functions are secure and user-friendly. We theoretically validate the feasibility of <i>MandiPass</i> and develop a series of deep learning techniques for effective biometric extraction. We also utilize a Gaussian matrix to defend against replay attacks. Extensive experiment results with 34 volunteers show that <i>MandiPass</i> can achieve low equal error rate, even under various harsh environments.
Developing Smart Self Orienting Solar Tracker for Mobile PV Power Generation Systems

Photovoltaic (PV) devices are one of the most renewable energy sources in demand globally. To harvest the maximum possible energy output from PV panels, it is necessary to orient them in a position where the sunray can fall on them perpendicularly. In this paper, an autonomous dual-axis smart solar tracking system is designed and implemented for positioning PV panels in a way that would make them generate the highest achievable energy output automatically anywhere in the world. The designed tracking system is built based on a mathematical model which is integrated with a microcontroller (&#x03BC;C), a Global Positioning System (GPS), a digital compass, and a gyro orientation sensor. The designed system provides a smart solution to accurately track the sun at a minimum power budget to increase the overall efficiency of PV panels. The suggested model is implemented and tested using 50 W PV panels, and it is empirically tested in the Middle East region of Baghdad, IRAQ. For further evaluation, it is also tested using simulated tracking data collected from three different regions Berlin, Singapore, and Sydney. This was done by selecting a city above the Tropic of Cancer, a city below the Tropic of Capricorn, and a city within the tropical region near the Equator. The obtained results confirmed that the developed system can track the sun in any region around the world, optimizing power consumption by operating the tracker within specific intervals that enables mustering maximum possible power of PV panels while ensuring minimum power consumption by the tracking system. The developed tracking system expended a mere 0.62% to 0.68% of the energy gain made.
Universal Randomized Guessing Subject to Distortion

In this paper, we consider the problem of guessing a sequence subject to a distortion constraint. Specifically, we assume the following game between Alice and Bob: Alice has a sequence <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${x}$ </tex-math></inline-formula> of length <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$n$ </tex-math></inline-formula> . Bob wishes to guess <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${x}$ </tex-math></inline-formula> , yet he is satisfied with finding any sequence <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\hat {x}$ </tex-math></inline-formula> which is within a given distortion <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$D$ </tex-math></inline-formula> from <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$x$ </tex-math></inline-formula> . Thus, he successively submits queries to Alice, until receiving an affirmative answer, stating that his guess was within the required distortion. Finding guessing strategies which minimize the number of guesses (the guesswork), and analyzing its properties (e.g., its <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\rho $ </tex-math></inline-formula> –th moment) has several applications in information security, source and channel coding. Guessing subject to a distortion constraint is especially useful when considering contemporary biometrically–secured systems, where the “password” which protects the data is not a single, fixed vector but rather a ball of feature vectors centered at some <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${x}$ </tex-math></inline-formula> , and any feature vector within the ball results in acceptance. We formally define the guessing problem under distortion in four different setups: memoryless sources, guessing through a noisy channel, sources with memory and individual sequences. We suggest a randomized guessing strategy which is asymptotically optimal for all setups and is five–fold universal, as it is independent of the source statistics, the channel, the moment to be optimized, the distortion measure and the distortion level.