Reviewerly/exHarmony huggingface dataset:
HEAPSTER: Analyzing the Security of Dynamic Allocators for Monolithic Firmware Images

Dynamic memory allocators are critical components of modern systems, and developers strive to find a balance between their performance and their security. Unfortunately, vulnerable allocators are routinely abused as building blocks in complex exploitation chains. Most of the research regarding memory allocators focuses on popular and standardized heap libraries, generally used by high-end devices such as desktop systems and servers. However, dynamic memory allocators are also extensively used in embedded systems but they have not received much scrutiny from the security community.In embedded systems, a raw firmware image is often the only available piece of information, and finding heap vulnerabilities is a manual and tedious process. First of all, recognizing a memory allocator library among thousands of stripped firmware functions can quickly become a daunting task. Moreover, emulating firmware functions to test for heap vulnerabilities comes with its own set of challenges, related, but not limited, to the re-hosting problem.To fill this gap, in this paper we present HEAPSTER, a system that automatically identifies the heap library used by a monolithic firmware image, and tests its security with symbolic execution and bounded model checking. We evaluate HEAPSTER on a dataset of 20 synthetic monolithic firmware images — used as ground truth for our analyses — and also on a dataset of 799 monolithic firmware images collected in the wild and used in real-world devices. Across these datasets, our tool identified 11 different heap management library (HML) families containing a total of 48 different variations. The security testing performed by HEAPSTER found that all the identified variants are vulnerable to at least one critical heap vulnerability. The results presented in this paper show a clear pattern of poor security standards, and raise some concerns over the security of dynamic memory allocators employed by IoT devices.
FuzzUSB: Hybrid Stateful Fuzzing of USB Gadget Stacks

Universal Serial Bus (USB) is the de facto protocol supported by peripherals and mobile devices, such as USB thumb drives and smart phones. For many devices, USB Type-C ports are the primary interface for charging, file transfer, audio, video, etc. Accordingly, attackers have exploited different vulnerabilities within USB stacks, compromising host machines via BadUSB attacks or jailbreaking iPhones from USB connections. While there exist fuzzing frameworks dedicated to USB vulnerability discovery, all of them focus on USB host stacks and ignore USB gadget stacks, which enable all the features within modern peripherals and smart devices. In this paper, we propose FuzzUSB, the first fuzzing framework for the USB gadget stack within commodity OS kernels, leveraging static analysis, symbolic execution, and stateful fuzzing. FuzzUSB combines static analysis and symbolic execution to extract internal state machines from USB gadget drivers, and uses them to achieve state-guided fuzzing through multi-channel inputs. We have implemented FuzzUSB upon the syzkaller kernel fuzzer and applied it to the most recent mainline Linux, Android, and FreeBSD kernels. As a result, we have found 34 previously unknown bugs within the Linux and Android kernels, and opened 8 CVEs. Furthermore, compared to the baseline, FuzzUSB has also demonstrated different improvements, including $ 3\times$ higher code coverage, $ 50\times$ improved bug-finding efficiency for Linux USB gadget stacks, $ 2\times$ higher code coverage for FreeBSD USB gadget stacks, and reproducing known bugs that could not be detected by the baseline fuzzers. We believe FuzzUSB provides developers a powerful tool to thwart USB-related vulnerabilities within modern devices and complete the current USB fuzzing scope.
R<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline" id="d1e1309" altimg="si3.svg"><mml:msup><mml:mrow /><mml:mrow><mml:mn>2</mml:mn></mml:mrow></mml:msup></mml:math>Net: Relight the restored low-light image based on complementarity of illumination and reflection

Low-light image enhancement has attracted great attention due to its capability in providing visible content and textures for video security and forensics. Unfortunately, conventional Retinex-based methods hardly pay attention to the complementarity of illumination and reflection. Inspired by the observation, we propose a new low-light image enhancement framework which relights the restored low-light image based on complementarity of illumination and reflection, named R 2 Net. The R 2 Net basically consists of three parts: image decomposition (Decom Net), reflection restoration (Restore Net), and illumination enhancement (Relight Net). In the Restore Net, we propose a mixed twofold attention (MTFA) module with linear complexity, introducing illumination information to model the mutual relationship between illumination and reflection. In MTFA, in order to capture richer information, we first map the inputs into different feature spaces, followed by combining them in different orders to obtain multiple sets of enhanced features. Accordingly, we design a differential enhanced fusion module (DEFM) to mix the multiple features. Ablation studies prove that the MTFA module significantly improves the performance of the restoration network. Finally, we propose a new illumination enhancement network (Relight Net), in which we introduce reflection to generate global information and define a luminance factor to tune the exposure level of output images, making our model more robust and flexible. Experiments show that our proposed method outperforms state-of-the-art methods in both quantitative comparison and visual perception. • A R 2 Net is proposed based on complementarity of illumination and reflection. • A mixed twofold attention builds interaction between illumination and reflection. • A flexible network that can control the exposure level of enhanced images.
NTM-Based Skill-Aware Knowledge Tracing for Conjunctive Skills

Knowledge tracing (KT) is the task of modelling students’ knowledge state based on their historical interactions on intelligent tutoring systems. Existing KT models ignore the relevance among the multiple knowledge concepts of a question and characteristics of online tutoring systems. This paper proposes a neural Turing machine-based skill-aware knowledge tracing (NSKT) for conjunctive skills, which can capture the relevance among the knowledge concepts of a question to model students’ knowledge state more accurately and to discover more latent relevance among knowledge concepts effectively. We analyze the characteristics of the three real-world KT datasets in depth. Experiments on real-world datasets show that NSKT outperforms the state-of-the-art deep KT models on the AUC of prediction. This paper explores details of the prediction process of NSKT in modelling students’ knowledge state, as well as the relevance of knowledge concepts and conditional influences between exercises.
Governing AI Applications To Monitoring and Managing Our Global Environmental Commons

Artificial Intelligence (AI) is playing a rapidly growing role in our response to global environmental change, from how we collect and process ecological and earth system data to how we make and enforce management decisions. Importantly, AI permits the use of increasingly high-resolution data, promising more accurate monitoring of environmental change and more targeted interventions. But, while AI promises several potential contributions to addressing global environmental change, how do we ensure these technologies stand to enhance equity rather than exasperate existing environmental injustices and power asymmetries?
Select Wisely and Explain: Active Learning and Probabilistic Local Post-hoc Explainability

Albeit the tremendous performance improvements in designing complex artificial intelligence (AI) systems in data-intensive domains, the black-box nature of these systems leads to the lack of trustworthiness. Post-hoc interpretability methods explain the prediction of a black-box ML model for a single instance, and such explanations are being leveraged by domain experts to diagnose the underlying biases of these models. Despite their efficacy in providing valuable insights, existing approaches fail to deliver consistent and reliable explanations. In this paper, we propose an active learning-based technique called UnRAvEL (Uncertainty driven Robust Active learning based locally faithful Explanations), which consists of a novel acquisition function that is locally faithful and uses uncertainty-driven sampling based on the posterior distribution on the probabilistic locality using Gaussian process regression (GPR). We present a theoretical analysis of UnRAvEL by treating it as a local optimizer and analyzing its regret in terms of instantaneous regrets over a global optimizer. We demonstrate the efficacy of the local samples generated by UnRAvEL by incorporating different kernels such as the Matern and linear kernels in GPR. Through a series of experiments, we show that UnRAvEL outperforms the baselines with respect to stability and local fidelity on several real-world models and datasets. We show that UnRAvEL is an efficient surrogate dataset generator by deriving importance scores on this surrogate dataset using sparse linear models. We also showcase the sample efficiency and flexibility of the developed framework on the Imagenet dataset using a pre-trained ResNet model.
Modeling Perspective Taking and Knowledge Use in Collaborative Explanation: Investigation by Laboratory Experiment and Computer Simulation Using ACT-R

This study focuses on modeling perspective taking behavior during collaborative explanation activities and the process of searching for relevant knowledge for constructive interaction. A laboratory experiment was conducted where dyads learned about concepts and engaged in a reasoning task using concept maps through discussions. We collected leaners conversational data and gaze using two eye trackers to examine perspective taking behavior. The results showed that leaners who considered others' diverse perspective expeditiously reflected upon their own knowledge and used the relevant knowledge for explaining the phenomenon to their partners. To further model the process of the perspective taking behavior and knowledge retrieval that occurred in the experiment, we developed a model using the cognitive architecture, Adaptive Control of Thought Rational (ACT-R). We further explained the mechanisms by implementing a model where perspective taking increases the opportunity to search and reconsidering the use common knowledge owing to the discrepancy triggered by diverse perspectives. This paper contributes to model-based research and provides knowledge on capturing the relationships of perspective taking and relevant knowledge retrieval in collaborative learning.
A Generic Interpreting Method for Knowledge Tracing Models

To interpret the deep learning based knowledge tracing models (DLKT), we introduce a generic method with four-step procedure. The proposed method and procedure are generally applicable to the DLKT models with diverse inner structures. The experiment results validate them on three existing knowledge tracing models, where the individual contributions of the input question-answer pairs to the models' decision are properly calculated. By leverage the calculated interpreting results, we explore the key information hidden in the DLKT models.
Multi-label Disengagement and Behavior Prediction in Online Learning

Student disengagement prediction in online learning environments is beneficial in various ways, especially to help provide timely cues to make some feedback or stimuli to the students. In this work, we propose a neural network-based model to predict students' disengagement, as well as other behavioral cues, which might be relevant to students' performance, using facial image sequences. For training and evaluating our model, we collected samples from multiple participants and annotated them with temporal segments of disengagement and other relevant behavioral cues with our multiple in-house annotators. We present prediction results of all behavior cues along with baseline comparison.
Self-attention in Knowledge Tracing: Why It Works

Knowledge tracing refers to the dynamic assessment of a learner's mastery of skills. There has been widespread adoption of the self-attention mechanism in knowledge-tracing models in recent years. These models consistently report performance gains over baseline knowledge tracing models in public datasets. However, why the self-attention mechanism works in knowledge tracing is unknown. This study argues that the ability to encode when a learner attempts to answer the same item multiple times in a row (henceforth referred to as repeated attempts) is a significant reason why self-attention models perform better than other deep knowledge tracing models. We present two experiments to support our argument. We use context-aware knowledge tracing (AKT) as our example self-attention model and dynamic key-value memory networks (DKVMN) and deep performance factors analysis (DPFA) as our baseline models. Firstly, we show that removing repeated attempts from datasets closes the performance gap between the AKT and the baseline models. Secondly, we present DPFA+, an extension of DPFA that is able to consume manually crafted repeated attempts features. We demonstrate that DPFA+ performs better than AKT across all datasets with manually crafted repeated attempts features.
Pedagogical Agent Support and Its Relationship to Learners’ Self-regulated Learning Strategy Use with an Intelligent Tutoring System

Oftentimes learners are unable to engage in effective self-regulated learning (SRL) strategies while learning about complex topics. To combat this, intelligent tutoring systems (ITSs) incorporate pedagogical agents to guide learners in understanding how to engage in several SRL strategies effectively and efficiently throughout learning. To identify how ITSs can best support learners' SRL strategy usage, data from 105 undergraduate students across several North American public universities were collected as they learned with MetaTutor, a hypermedia-based ITS about the human circulatory system. Participants were randomly assigned to two conditions – a prompt and feedback condition in which pedagogical agents prompted learners to engage in specific cognitive and metacognitive SRL strategies and provided feedback to performance in addition to learners' self-initiated SRL strategy usage, and the control condition in which learners were not prompted nor were provided feedback on their performance of self-initiated SRL strategies. Results found that learners receiving external support from pedagogical agents had greater learning gains and deployed a greater number of both cognitive and metacognitive SRL strategies than learners who only self-initiated strategies. While probabilities obtained from a Markov model did not find differences between conditions in learners' sequential transitions between SRL strategies, metrics from auto-recurrence quantification analysis found that learners receiving external support enacted less repetitive interactions of SRL strategies throughout their entire time interacting with MetaTutor. Implications of these results encourage the use of pedagogical agents in prompting more novel SRL strategies to increase learning within ITSs.
Fully Automated Short Answer Scoring of the Trial Tests for Common Entrance Examinations for Japanese University

Studies on automated short-answer scoring (SAS) have been conducted to apply natural language processing to education. Short-answer scoring is a task to grade the responses from linguistic information. Most answer sheets for short-answer questions are handwritten in an actual educational setting, which is a barrier to SAS. Therefore, we have developed a system that uses handwritten character recognition and natural language processing for fully automated scoring of handwritten responses to short-answer questions. This is the most extensive scoring data for responses to short-answer questions, and it may be the largest in the world. Applying the Cohen's kappa coefficient to the graded evaluations, the results show 0.86 in the worst case, and approximately 0.95 is recorded for the remaining five question answers. We observe that the fully automated scoring system proposed in our study can also score with a high degree of accuracy comparable to that of human scoring.
Knowledge Graph Entity Alignment Using Relation Structural Similarity

Embedding-based entity alignment, which represents knowledge graphs as low-dimensional embeddings and finds entities in different knowledge graphs that semantically represent the same real-world entity by measuring the similarities between entity embeddings, has achieved promising results. However, existing methods are still challenged by the error accumulation of embeddings along multi-step paths and the semantic information loss. This paper proposes a novel embedding-based entity alignment method that iteratively aligns both entities and relations with high similarities as training data. Newly-aligned entities and relations are used to calibrate the corresponding embeddings in the uniﬁed embedding space, which reduces the error accumulation. To reduce the negative impact of semantic information loss, the authors propose to use relation structural similarity instead of embedding similarity to align relations. Experimental results on five widely used real-world datasets show that the proposed method significantly outperforms several state-of-the-art methods for entity alignment.
Aligning Software Engineering Teaching Strategies and Practices with Industrial Needs

Several approaches have been proposed to reduce the gap between software engineering education and the needs and practices of the software industry. Many of them aim to promote a more active learning attitude in students and provide them with more realistic experiences, thus recreating industry software development environments and collaborative development and, in some cases, with the involvement of companies mainly acting as potential customers. Since many degree courses typically offer separate subjects to teach requirements engineering, analysis and design, coding, or validation, the integration of all these phases normally necessitates experience in a project context and is usually carried out in a final year project. The approach described in this article benefits from the close involvement of a software house company which goes beyond the common involvement of a potential customer. Students are integrated into distributed teams comprising students, teachers and IT professionals. Teams follow the agile Scrum methodology and use the OutSystems low-code development platform providing students with the experience of an almost real scenario. The results show that this approach complements the knowledge and practice acquired in course subjects, develops the students’ technical and non-technical skills, such as commitment, teamwork, and communication, and initiates them in the methodologies and development strategies used in these companies. The feedback from the teachers involved, software companies and students was very positive.
How Differing Audiences Were Associated with User Emotional Expression on a Well-Being App

In the last five years there has been an explosion of mobile apps that aim to impact emotional well-being, yet limited research has examined the ways that users interact, and specifically write to develop a therapeutic alliance within these apps. Writing is a developmental practice in which a narrator transforms amorphous thoughts and emotions into expressions, and according to narrative theory, the linguistic characteristics of writing can be understood as a physical manifestation of a narrator's affect. Informed by literacy theorists who have argued convincingly that narrators address different audiences in different ways, we used IBM Watson's Natural Language Processing software (IBM Watson NLP) to examine how users' expression of emotion on a well-being app differed depending on the audience. Our findings demonstrate that audience was strongly associated with the way users' expressed emotions in writing. When writing to an explicit audience users wrote longer narratives, with less sadness, less anger, less disgust, less fear and more joy. These findings have direct relevance for researchers and well-being app design.
U-Net rectal cancer image segmentation based on depth separable convolution

Aiming at the problem that manual diagnosis of rectal cancer has a large workload and is likely to cause subjective misdiagnosis, this paper proposes an improved U-Net rectal tumor segmentation method based on the U-Net network and combined with deep separable convolution: DS-UNet (Depthwise Separable Convolution U-Net). First, replace the convolution operation in the extraction part of the network with a deep separable convolution to deepen the depth of the network so that it can fully learn the image details, thereby improving the feature extraction ability of the model; finally, use the DiceLoss loss function to replace the cross entropy loss function, Optimize network parameters to solve the problem of sample imbalance, and then obtain higher sensitivity and generalization ability. The experimental results show that the accuracy of rectal cancer tumor segmentation of the proposed method reaches 91.20%, which is 3.94%, 24.31%, and 5.45% higher than that of U-Net, SegNet and DeepLab segmentation models, respectively, which verifies the effectiveness of the proposed method.
An Efficient Self-Healing Architecture for Improving the RAS Characteristics of RISC-V Server and Its Quantitative Evaluation Method

This letter proposes a self-healing called automatic monitoring architecture (AMA), which can help RISC-V server self-heal from hardware errors. AMA reduces redundant error classification, and only monitors hardware devices that have a relatively large proportion of errors in RISC-V server, thereby reducing the resource consumption of AMA on the basis of ensuring performance. In addition, AMA uses the Correctable-error Dynamic Threshold technology to further reduce the probability of serious uncorrectable hardware errors. Compared with an RISC-V server without a self-healing system, this architecture consumes very few hardware resources, reducing server downtime by about 80% each year. Compared with other self-healing architectures, such as Intel’s machine check architecture, AMA can reduce server downtime by an additional 5% per year. Therefore, AMA is highly efficient with little resource consumption.