Reviewerly/exHarmony huggingface dataset:
ABCFI: Fast and Lightweight Fine-Grained Hardware-Assisted Control-Flow Integrity

Code-reuse attack is a severe threat to computer systems as it can circumvent many existing security defenses and perform arbitrary behavior. Control-flow integrity (CFI) is a security technique that restricts control-flow transfers to prevent the attack. Although CFI has been implemented via various methods, including hardware-assisted extensions, the current designs of hardware-assisted fine-grained CFI fail to meet practical needs. The main hurdles include: 1) the lack of cost-effective design and 2) insufficient security as they cannot enforce a complete control-flow graph (CFG) with only one label for each target. This article presents a novel hardware-assisted fine-grained CFI design that reformulates labels as the lower bits of addresses (called address-based CFI). Thus, it costs negligible runtime overhead (≤ 0.55%) and hardware overhead (only ten LUTs and no flip flop based on coarse-grained extension). Among all the current hardware-assisted fine-grained CFI designs, ABCFI has the lowest hardware overhead and runtime performance overhead. With our novel design, the commercial coarse-grained CFI extensions can be advanced to fine-grained CFI extensions through few efforts.
Indoor Navigation for Mobile Agents: A Multimodal Vision Fusion Model

Indoor navigation is a challenging task for mobile agents. The latest vision-based indoor navigation methods make remarkable progress in this field but do not fully leverage visual information for policy learning and struggle to perform well in unseen scenes. To address the existing limitations, we present a multimodal vision fusion model (MVFM). We implement a joint modality of different image recognition networks for navigation policy learning. The proposed model incorporates object detection for target searching, depth estimation for distance prediction, and semantic segmentation to depict the walkable region. In design, our model provides holistic vision knowledge for navigation. Evaluation on AI2-THOR indicates that MVFM improves on the results of a strong baseline model by 3.49% for Success weighted by Path Length (SPL) and 4% for success rate respectively. In comparison with other state-of-the-art systems, MVFM performs in the lead in terms of SPL and success rate. Extensive experiments show the effectiveness of the proposed model.
XSS Attack Detection With Machine Learning and n-Gram Methods

Cross-Site Scripting (XSS) is an attack most often carried out by attackers to attack a website by inserting malicious scripts into a website. This attack will take the user to a webpage that has been specifically designed to retrieve user sessions and cookies. Nearly 68% of websites are vulnerable to XSS attacks. In this study, the authors conducted a study by evaluating several machine learning methods, namely Support Vector Machine (SVM), K-Nearest Neighbour (KNN), and Naïve Bayes (NB). The machine learning algorithm is then equipped with the n-gram method to each script feature to improve the detection performance of XSS attacks. The simulation results show that the SVM and n-gram method achieves the highest accuracy with 98%.
Fast and Safe Path-Following Control using a State-Dependent Directional Metric

This paper considers the problem of fast and safe autonomous navigation in partially known environments. Our main contribution is a control policy design based on ellipsoidal trajectory bounds obtained from a quadratic state-dependent distance metric. The ellipsoidal bounds are used to embed directional preference in the control design, leading to system behavior that is adapted to local environment geometry, carefully considering medial obstacles while paying less attention to lateral ones. We use a virtual reference governor system to adaptively follow a desired navigation path, slowing down when system safety may be violated and speeding up otherwise. The resulting controller is able to navigate complex environments faster than common Euclidean-norm and Lyapunov-function-based designs, while retaining stability and collision avoidance guarantees.
The communicative power of knowledge visualizations in mobilizing information and communication technology research

This editorial explores knowledge visualization, a field of study that investigates the power of visual formats to represent knowledge, as a strategy to enhance knowledge mobilization of results from ICT4D research. We highlight the fact that there are evidence-based guidelines for creating and crafting visualizations in academic writing. We also provide some visualization examples that highlight general knowledge visualization criteria such as anchor and extend, familiarity, clarity and consistency, include text, prudent simplicity and aesthetics. Although visualization is not the central theme of any of the papers in this issue, the papers offer a variety of visualization techniques as appropriate to the knowledge domain.
Blockchain Based Data Integrity Verification for Cloud Storage with T-Merkle Tree

Blockchain based verification has recently been studied as a new emerging approach for cloud data integrity protection without a centralized third party auditor. Existing schemes are mostly limited to scalability barrier and search efficiency of blockchain, which restricts their capability to support large-scale application. To address the problem above, we propose a blockchain based integrity verification scheme for large-scale cloud data using T-Merkle Tree. In our design, data tags are generated by ZSS short signature and stored on blockchain, and a new verification method based on ZSS short signature is proposed. The integrity of cloud data can be efficiently and undeniably verified with the property of bilinear pairing by offloading the computation from verifier to prover. Furthermore, a new blockchain storage structure called T-Merkle tree and its search algorithm is designed to improves the storage utilization and supports binary search in a block. Finally, a prototype system based on Hyperledger Fabric is implemented to evaluate our scheme. Theoretic analysis and experimental results demonstrate the security and efficiency of our proposed scheme.
A sociotechnical approach to causes of urban blight using fuzzy cognitive mapping and system dynamics

Urban blight often is prominent in urban environments. It consists of real estate degradation to the extent that blighted residential properties, unfortunately, may provide housing only for the poorest social classes. Urban blight reduces real property values in urban neighborhoods and urban centers, reduces business investment and increases unemployment. Urban blight is not static, but continuously changing, either getting worse or improving. Thus, it is an issue of growing concern for urban decision makers who may attempt to determine its causes. Determining causes of urban blight facilitates the development of strategic measures for the prevention and potential eradication of this phenomenon. This study seeks to bolster the identification of factors that cause urban blight and makes important contributions to both the academic communities and society at large. By combining fuzzy cognitive mapping and the system dynamics approach, our proposal facilitates the identification of urban blight determinants and the development of alternatives to control and eradicate them. Data used in our study were provided by a panel of urban blight experts, and allowed for the identification of the following major causes of urban blight: deficiencies in sociability and/or culture; unfavorable economic and financial factors; policy and/or administrative shortages; lack of urban regeneration; insecurity for people and assets; and building and/or surroundings defects. The results of our decision-support system were analyzed and validated by an independent expert who works for the Lisbon City Council in Portugal. Recommendations for future research are also provided.
Multi-objective Optimization of Block Size Based on CPU Power and Network Bandwidth for Blockchain Applications

Distributed ledger technology (DLT) is fast growing as a solution that could address pertinacious challenges in the financial sector offering a more efficient and resilient approach to transaction record keeping. Its popularity is principally determined by its potential to transmit data in bulk securely over a peer-to-peer network. Block size optimization is an important issue in blockchain network as scalability bottlenecks prevent higher throughput and minimized latencies. Increasing adoption has raised concerns about its ability to scale and serve as a real-world usable network. An increased block size may cause a higher transmission time as the rate of transactions made will increase and may also cause the system to reach its maximum capacity to clear transactions. As compared to larger block size, small block size is more efficient, but creating too small a block might cause higher block building/creation time. An efficient blockchain-based application requires an optimal block size so that a justified performance is achieved. In the proposed approach, multi-objective optimization is performed, and 40 different solutions are obtained based on transaction selection time and block building time. The selection of a particular block size as an optimal solution is based purely on processing power of CPU, throughput and the available network bandwidth and latencies, where the application is to be deployed. The results establish that for various systems having 1.1 GHz CPU–3.0 GHz CPU, a 3.8 MB of block size selection optimizes the transaction selection and block building time.
An evaluation framework for privacy‐preserving solutions applicable for blockchain‐based internet‐of‐things platforms

Abstract Blockchain‐based applications provide many promising opportunities to overcome the challenges associated with the Internet of Things (IoT) ecosystems (eg, centralized architecture, data integrity, and reliability). In particular, blockchain technology offers many desirable features for IoT infrastructures, such as decentralization, trustworthiness, trackability, and immutability. However, while logging all transactions in a distributed blockchain ledger provides transparency, it also makes it possible to compromise user's privacy, thus posing a grand challenge to IoT architects and implementers. Over the past years, a set of solutions have been proposed for various scenarios, to address these privacy issues. In this paper, we survey these solutions, classify, and analyze their advantages and disadvantages. We also introduce an evaluation framework to evaluate the quality of the privacy‐preserving based on an adjustable weighting scheme. Finally, we rate the analyzed solutions based on their privacy ranks, and hope our evaluation can shed light on the future design of privacy‐preserving solutions applicable for blockchain‐based IoT platforms.
Open Student Modeling Research and its Connections to Educational Assessment

Research in the area of Open Student Models (OSMs) has shown that external representations of the student model can be used to facilitate educational processes such as student reflection, knowledge awareness, learning, collaboration, negotiation, and student model diagnosis. OSMs can be integrated into existing learning systems or become a framework for the creation of learning systems. This paper discusses how early work with Jim Greer in the area of open student modeling has inspired and continues to inspire a line of research on innovative assessments and the design and evaluation of score report systems that are used to share assessment/student modeling information with various educational stakeholders to support learning. Several projects are discussed as well as their connections to my work with Jim while working at the ARIES laboratory.
CNN Model for American Sign Language Recognition

This paper proposes a model based on convolutional neural network for hand gesture recognition and classification. The dataset uses 26 different hand gestures, which map to English alphabets A–Z. Standard dataset called Hand Gesture Recognition available in Kaggle website has been considered in this paper. The dataset contains 27,455 images (size 28 * 28) of hand gestures made by different people. Deep learning technique is used based on CNN which automatically learns and extracts features for classifying each gesture. The paper does comparative study with four recent works. The proposed model reports 99% test accuracy.
Causal cognitive architecture 1: Integration of connectionist elements into a navigation-based framework

The brain-inspired Causal Cognitive Architecture 1 (CCA1) tightly integrates the sensory processing capabilities found in neural networks with many of the causal abilities found in human cognition. Causality emerges not from a central controlling stored program but directly from the architecture. Sensory input vectors are processed by robust association circuitry and then propagated to a navigational temporary map. Instinctive and learned objects and procedures are applied to the same temporary map, with a resultant navigation signal obtained. Navigation can similarly be for the physical world as well as for a landscape of higher cognitive concepts. There is good explainability for causal decisions. A simulation of the CCA1 controlling a search and rescue robot is presented with the goal of finding and rescuing a lost hiker within a grid world. A simulation of the CCA1 controlling a repair robot is presented that can predict the movement of a series of gears.
Scientific Discovery by Generating Counterfactuals Using Image Translation

Model explanation techniques play a critical role in understanding the source of a model's performance and making its decisions transparent. Here we investigate if explanation techniques can also be used as a mechanism for scientific discovery. We make three contributions: first, we propose a framework to convert predictions from explanation techniques to a mechanism of discovery. Second, we show how generative models in combination with black-box predictors can be used to generate hypotheses (without human priors) that can be critically examined. Third, with these techniques we study classification models for retinal images predicting Diabetic Macular Edema (DME), where recent work [] showed that a CNN trained on these images is likely learning novel features in the image. We demonstrate that the proposed framework is able to explain the underlying scientific mechanism, thus bridging the gap between the model's performance and human understanding.
Deep Volumetric Universal Lesion Detection Using Light-Weight Pseudo 3D Convolution and Surface Point Regression

Identifying, measuring and reporting lesions accurately and comprehensively from patient CT scans are important yet time-consuming procedures for physicians. Computer-aided lesion/significant-findings detection techniques are at the core of medical imaging, which remain very challenging due to the tremendously large variability of lesion appearance, location and size distributions in 3D imaging. In this work, we propose a novel deep anchor-free one-stage volumetric lesion detector (VLD) framework that incorporates (1) pseudo 3D convolution operators to recycle the architectural configurations and pre-trained weights from the off-the-shelf 2D networks, especially ones with large capacities to cope with data variance, and (2) a new surface point regression method to effectively regress the 3D lesion spatial extents by pinpointing their representative key points on lesion surfaces. Experimental validations are first conducted on the public large-scale NIH DeepLesion dataset where our proposed method delivers new state-of-the-art quantitative performance. We also test VLD on our in-house dataset for liver tumor detection. VLD generalizes well in both large-scale and small-sized tumor datasets in CT imaging.
Deep Doubly Supervised Transfer Network for Diagnosis of Breast Cancer with Imbalanced Ultrasound Imaging Modalities

Elastography ultrasound (EUS) provides additional bio-mechanical information about lesion for B-mode ultrasound (BUS) in the diagnosis of breast cancers. However, joint utilization of both BUS and EUS is not popular due to the lack of EUS devices in rural hospitals, which arouses a novel modality imbalance problem in computer-aided diagnosis (CAD) for breast cancers. Current transfer learning (TL) pay little attention to this special issue of clinical modality imbalance, that is, the source domain (EUS modality) has fewer labeled samples than those in the target domain (BUS modality). Moreover, these TL methods cannot fully use the label information to explore the intrinsic relation between two modalities and then guide the promoted knowledge transfer. To this end, we propose a novel doubly supervised TL network (DDSTN) that integrates the Learning Using Privileged Information (LUPI) paradigm and the Maximum Mean Discrepancy (MMD) criterion into a unified deep TL framework. The proposed algorithm can not only make full use of the shared labels to effectively guide knowledge transfer by LUPI paradigm, but also perform additional supervised transfer between unpaired data. We further introduce the MMD criterion to enhance the knowledge transfer. The experimental results on the breast ultrasound dataset indicate that the proposed DDSTN outperforms all the compared state-of-the-art algorithms for the BUS-based CAD.
PCNN: Pattern-based Fine-Grained Regular Pruning Towards Optimizing CNN Accelerators

Weight pruning is a powerful technique to realize model compression. We propose PCNN, a fine-grained regular 1D pruning method. A novel index format called Sparsity Pattern Mask (SPM) is presented to encode the sparsity in PCNN. Leveraging SPM with limited pruning patterns and non-zero sequences with equal length, PCNN can be efficiently employed in hardware. Evaluated on VGG-16 and ResNet-18, our PCNN achieves the compression rate up to 8.4× with only 0.2% accuracy loss. We also implement a pattern-aware architecture in 55nm process, achieving up to 9.0× speedup and 28.39 TOPS/W efficiency with only 3.1% on-chip memory overhead of indices.
Causal Learning From Predictive Modeling for Observational Data

We consider the problem of learning structured causal models from observational data. In this work, we use causal Bayesian networks to represent causal relationships among model variables. To this effect, we explore the use of two types of independencies -- context-specific independence (CSI) and mutual independence (MI). We use CSI to identify the candidate set of causal relationships and then use MI to quantify their strengths and construct a causal model. We validate the learned models on benchmark networks and demonstrate the effectiveness when compared to some of the state-of-the-art Causal Bayesian Network Learning algorithms from observational Data.
QLens: Visual Analytics of MUlti-step Problem-solving Behaviors for Improving Question Design

With the rapid development of online education in recent years, there has been an increasing number of learning platforms that provide students with multi-step questions to cultivate their problem-solving skills. To guarantee the high quality of such learning materials, question designers need to inspect how students' problem-solving processes unfold step by step to infer whether students' problem-solving logic matches their design intent. They also need to compare the behaviors of different groups (e.g., students from different grades) to distribute questions to students with the right level of knowledge. The availability of fine-grained interaction data, such as mouse movement trajectories from the online platforms, provides the opportunity to analyze problem-solving behaviors. However, it is still challenging to interpret, summarize, and compare the high dimensional problem-solving sequence data. In this paper, we present a visual analytics system, QLens, to help question designers inspect detailed problem-solving trajectories, compare different student groups, distill insights for design improvements. In particular, QLens models problem-solving behavior as a hybrid state transition graph and visualizes it through a novel glyph-embedded Sankey diagram, which reflects students' problem-solving logic, engagement, and encountered difficulties. We conduct three case studies and three expert interviews to demonstrate the usefulness of QLens on real-world datasets that consist of thousands of problem-solving traces.
Data-Driven Space-Filling Curves

We propose a data-driven space-filling curve method for 2D and 3D visualization. Our flexible curve traverses the data elements in the spatial domain in a way that the resulting linearization better preserves features in space compared to existing methods. We achieve such data coherency by calculating a Hamiltonian path that approximately minimizes an objective function that describes the similarity of data values and location coherency in a neighborhood. Our extended variant even supports multiscale data via quadtrees and octrees. Our method is useful in many areas of visualization including multivariate or comparative visualization ensemble visualization of 2D and 3D data on regular grids or multiscale visual analysis of particle simulations. The effectiveness of our method is evaluated with numerical comparisons to existing techniques and through examples of ensemble and multivariate datasets.
Interpreting Image Classifiers by Generating Discrete Masks

Deep models are commonly treated as black-boxes and lack interpretability. Here, we propose a novel approach to interpret deep image classifiers by generating discrete masks. Our method follows the generative adversarial network formalism. The deep model to be interpreted is the discriminator while we train a generator to explain it. The generator is trained to capture discriminative image regions that should convey the same or similar meaning as the original image from the model's perspective. It produces a probability map from which a discrete mask can be sampled. Then the discriminator is used to measure the quality of the sampled mask and provide feedbacks for updating. Due to the sampling operations, the generator cannot be trained directly by back-propagation. We propose to update it using policy gradient. Furthermore, we propose to incorporate gradients as auxiliary information to reduce the search space and facilitate training. We conduct both quantitative and qualitative experiments on the ILSVRC dataset. Experimental results indicate that our method can provide reasonable explanations for predictions and outperform existing approaches. In addition, our method can pass the model randomization test, indicating that it is reasoning the attribution of network predictions.
Knowledge Graphs for Efficient Integration and Access of Manufacturing Data

Achieving interoperability is a crucial factor for realizing the Industry 4.0 (I4.0) vision. In I4.0 different systems exist, and the demand for the creation of an integrated view using the existing data increases. However, interoperability between data sources is hampered due to different representation of similar processes or production parts. In this paper, we present a knowledge graph based approach for semantically integrating data sources in I4.0 scenarios. To tackle the problem of interoperability, we developed the Bosch Industry 4.0 Knowledge Graph (BI40KG). The BI40KG comprises a set of ontologies for describing common concepts and relations that can be reused in the domain. We present a general approach for using the BI40KG to semantically integrate data from different data sources in I4.0. The benefits of such an approach are manifold. Besides achieving interoperability, the semantics encoded in BI40KG allow for creating a common understating across I4.0 applications that are developed. In addition, KG based data integration enables effortless tracebility of product parts for production lines which in general is a tedious task. Two use cases are presented to demonstrate the applicability of the described approach.