Reviewerly/exHarmony huggingface dataset:
Adaptive Call-Site Sensitive Control Flow Integrity

Low-level languages like C/C++ are widely used in various applications for their performance and flexibility.Unfortunately, these languages are prone to memory corruption vulnerabilities, leading to control-flow hijacking attacks.Control flow integrity (CFI) is a general principle to enforce run-time control flow of a program to a pre-computed control-flow graph (CFG).While the traditional context-insensitive CFI falls short in protecting critical control transfers, recent context-sensitive CFI research shows promising improvements but has various limitations.We present Control Flow Integrity with Look Back (CFI-LB), a call-site sensitive CFI in which a conventional sourcetarget control transfer is strengthened by a look back into its call-sites (return addresses).CFI-LB features the adaptive callsite sensitivity in which each indirect call has its own level of sensitivity and the multi-scope CFG to improve the security even if a precise context-sensitive static CFG is not available, especially for large programs such as GCC and NGINX.One of the CFGs is constructed by our localized concolic execution, which significantly extends the dynamic CFG with very low false positives.In addition, CFI-LB is the first CFI system explicitly designed to protect its reference monitors from race conditions.We have built a prototype of CFI-LB.The evaluation with SPEC CPU2006 benchmarks and NGINX indicates that CFI-LB has a low-performance overhead (less than 5% on average for the full protection) while increasing the security.
Insights into Learning Competence Through Probabilistic Graphical Models

One-digit multiplication problems is one of the major fields in learning mathematics at the level of primary school that has been studied over and over. However, the majority of related work is focusing on descriptive statistics on data from multiple surveys. The goal of our research is to gain insights into multiplication misconceptions by applying machine learning techniques. To reach this goal, we trained a probabilistic graphical model of the students' misconceptions from data of an application for learning multiplication. The use of this model facilitates the exploration of insights into human learning competence and the personalization of tutoring according to individual learner's knowledge states. The detection of all relevant causal factors of the erroneous students answers as well as their corresponding relative weight is a valuable insight for teachers. Furthermore, the similarity between different multiplication problems - according to the students behavior - is quantified and used for their grouping into clusters. Overall, the proposed model facilitates real-time learning insights that lead to more informed decisions.
TEE-Perf: A Profiler for Trusted Execution Environments

We introduce TEE-PERF, an architecture-and platform-independent performance measurement tool for trusted execution environments (TEEs). More specifically, TEE-PERF supports method-level profiling for unmodified multithreaded applications, without relying on any architecture-specific hardware features (e.g. Intel VTune Amplifier), or without requiring platform-dependent kernel features (e.g. Linux perf). Moreover, TEE-PERF provides accurate profiling measurements since it traces the entire process execution without employing instruction pointer sampling. Thus, TEE-PERF does not suffer from sampling frequency bias, which can occur with threads scheduled to align to the sampling frequency. We have implemented TEE-P ERF with an easy to use interface, and integrated it with Flame Graphs to visualize the performance bottlenecks. We have evaluated TEE-PERF based on the Phoenix multithreaded benchmark suite and real-world applications (RocksDB, SPDK, etc.), and compared it with Linux perf. Our experimental evaluation shows that TEE-PERF incurs low profiling overheads, while providing accurate profile measurements to identify and optimize the application bottlenecks in the context of TEEs. TEE-PERF is publicly available.
Bayesian Neural Network Based Encrypted Traffic Classification using Initial Handshake Packets

Traffic classification has garnered significant attention from researchers owing to its applicability in a wide range of network management systems. The identification and categorization of network traffic are usually based on various parameters such as the port numbers, payload signatures, and statistical features. These methods face difficulty in classifying encrypted traffic flows for secure communication. We propose a novel payload-based classification that exploits unencrypted handshake packets, which are exchanged between the end hosts for transport layer security establishment. We use Bayesian neural network as the classifier, which takes cipher suite, compression method, and TLS extension information of the handshake packets as the inputs. We conducted comparative experiments to show that the proposed method outperforms other traditional payload-based classifiers.
BenchIoT: A Security Benchmark for the Internet of Things

Attacks against IoT systems are increasing at an alarming pace. Many IoT systems are and will be built using low-cost micro-controllers (IoT-uCs). Different security mechanisms have been proposed for IoT-uCs with different trade-offs. To guarantee a realistic and practical evaluation, the constrained resources of IoT-uCs require that defenses must be evaluated with respect to not only security, but performance, memory, and energy as well. Evaluating security mechanisms for IoT-uCs is limited by the lack of realistic benchmarks and evaluation frameworks. This burdens researchers with the task of developing not only the proposed defenses but applications on which to evaluate them. As a result, security evaluation for IoT-uCs is limited and ad-hoc. A sound benchmarking suite is essential to enable robust and comparable evaluations of security techniques on IoT-uCs. This paper introduces BenchIoT, a benchmark suite and evaluation framework to address pressing challenges and limitations for evaluating IoT-uCs security. The evaluation framework enables automatic evaluation of 14 metrics covering security, performance, memory usage, and energy consumption. The BenchIoT benchmarks provide a curated set of five real-world IoT applications that cover both IoT-uCs with and without an OS. We demonstrate BenchIoT's ability by evaluating three defense mechanisms. All benchmarks and the evaluation framework is open sourced and available to the research community.
A Fourier Domain Training Framework for Convolutional Neural Networks Based on the Fourier Domain Pyramid Pooling Method and Fourier Domain Exponential Linear Unit

Training convolutional neural networks (CNNs) in the frequency domain is of great significance for extending the deep learning principle to the frequency domain.However, the frequency domain representation of the convnet architecture is highly demanding due to their complicated Fourier domain training features.Therefore, high accuracy and unambiguous representation strategies are needed for training convolutional neural networks entirely in the Fourier domain.Being founded on the bin decomposition mechanism and the non-saturated activation theory, this paper proposes an accurate, stable and efficient Fourier domain training framework for convolutional neural networks.The framework contains two important Fourier domain representations: one is the Fourier domain exponential linear unit, and the other is the pyramid pooling layer.The former alleviates the vanishing phenomenon and makes CNNs easier to converge in the Fourier domain; the latter avoids the original cropping or warping steps and improves the classification accuracy.With the framework, the Fourier domain training accuracy is improved without sacrificing the throughput of the graphic processing unit (GPU).With the Re-50 as the backbone, the top-1 and top-5 classification errors are reduced from 28.85 and 9.55 to 18.63 and 4.05, respectively, while the speedup ratios of the framework can reach up to 4.9877 and 1.8997, respectively, at a batch size of 128 on an NVIDIA GEFORCE RTX 2080 GPU (8.92 TFLOPS).The average difference between the classification value and the ground truth value is only 0.21 on the MetaGram-1 set, which indicates great goodness-offit and robustness of the framework.This investigation illustrates that the proposed Fourier domain CNN framework using the sophisticated Fourier domain representation strategy is highly efficient and accurate.Therefore, it may serve as a baseline framework to establish the training pipelines for Fourier domain CNNs, which can improve the deep learning accuracy of CNNs and extend the Fourier domain representation strategy to other deep learning networks.
Making Archaeological Collections Available for Research: Recommendations for Repositories

Abstract Archaeological collections repositories have two principal aims: preserving collections while also making them accessible. This accessibility is critical for the growing number of researchers turning to collections to study the past. This article describes steps that repositories can take to enhance access to collections in their custody, based on the experience of the Maryland Archaeological Conservation Laboratory, the state's public archaeological curation facility. These steps include the identification of stakeholders—archaeologists, Native American tribes, and stakeholder communities; creation of a detailed and prioritized collection inventory including artifacts and records; development of finding aids; reconstruction of provenience systems; and exploration of the digital delivery of collection information. For repositories ill equipped to hold archaeological collections, consideration should be given to transferring the collection to one with the appropriate resources and expertise.
Master and Rookie Networks for Person Re-identification

Recognizing different visual signatures of people across non-overlapping cameras is still an open problem of great interest for the computer vision community, especially due to its importance in automatic video surveillance on large-scale environments. A main aspect of this application field, known as person re-identification (re-id), is the feature extraction step used to define a robust appearance of a person. In this paper, a novel two-branch Convolutional Neural Network (CNN) architecture for person re-id in video sequences is proposed. A pre-trained branch, called Master, leads the learning phase of the other un-trained branch, called Rookie. Using this strategy, the Rookie network is able to learn complementary features with respect to those computed by the Master network, thus obtaining a more discriminative model. Extensive experiments on two popular challenging re-id datasets have shown increasing performance in terms of convergence speed as well as accuracy in comparison to standard models, thus providing an alternative and concrete contribution to the current re-id state-of-the-art.
EOVNet: Earth-Observation Image-Based Vehicle Detection Network

Vehicle detection from earth-observation (EO) image has been attracting remarkable attention for its critical value in a variety of applications. Encouraged by the stunning success of deep learning techniques based on convolutional neural networks (CNNs), which have revolutionized the visual data processing community and obtained the state-of-the-art performance in a variety of classification and recognition tasks on benchmark datasets, we propose a network, called EOVNet (EO image-based vehicle detection network), to bridge the gap between the advanced deep learning research of object detection and the specific task of vehicle detection in EO images. Our network has integrated nearly all advanced techniques including very deep residual networks for feature extraction, feature pyramid to fuse multiscale features, network for proposal generation with feature sharing, and hard example mining. Moreover, our novel designs of probability-based localization and homography-based data augmentation have been investigated, resulting in further improvement of the detection performance. For performance evaluation, we have collected nearly all the representative EO datasets associated with vehicle detection. Extensive experiments on the representative datasets demonstrate that our method outperforms the state-of-the-art object detection approach Faster R-CNN++ (which is based on the Faster R-CNN framework, but with significant improvement) with 5% average precision improvement. The source code will be made available after the review process.
Trusted information project platform based on blockchain for sharing strategy

Abstract South Korea invests a budget of trillions in national R&amp;D projects every year, and has achieved excellent performance doing so each year. However, since the projects are planned, evaluated, and managed by different departments and institutions, duplicate planning and submission leads to insufficient sharing of research results. Currently, the National Technology Information Service (NTIS) inspects project duplication based on keywords, which leads to duplicate planning among departments and closed management of research results. Since the NTIS builds in centralized systems, the inspection systems supports one-way management for duplication checking and information sharing. Therefore, we propose a new platform, called the Trusted Information Project Platform (TIP-Platform), for easily checking for project duplication, sharing research results, and updating research results. TIP-Platform adopts a new concept for user authority setting, the distributed ledger structure, transaction structure, and service. For the adaption, the TIP-Platform uses blockchain technology that performs recording and management via blocks by distributing the right to record and managing transactions. This platform makes it easy for anyone to view and use project-related information such as research results and duplication review. In this paper, we describe how the TIP-Platform can achieve excellent research results through information sharing of a project. This platform needs to be based on trust, because it shares information and continually updates information.
Denouncing Sexual Violence: A Cross-Language and Cross-Cultural Analysis of #MeToo and #BalanceTonPorc

#MeToo, a social media movement that denounced sexual violence against women was lauded as a global phenomenon. In this paper, we present a cross-language and cross-cultural quantitative examination of the English #MeToo and French #BalanceTonPorc. The goal of our study was to examine the global to local adoption and personalization of this social media movement. In part one of our study, we sought to understand linguistic differences by comparing #MeToo tweets in English and #BalanceTonPorc tweets in French. In the second part, we sought to understand cultural differences in the way #MeToo was adopted in the US and India. We found that the movement did not share a unified perspective, instead it was shaped by the culture and social reality of the posters; tweets in French were more aggressive and accusing than those in English, while English tweets from India involved more religion and society than those from the US.
Alphapwd: A Password Generation Strategy Based on Mnemonic Shape

Password-based authentication is the first line of defense of most information systems.Password security concerns the security of the whole information system.Therefore, administrators will formulate corresponding password strategies to help users improve the security and usability of passwords.Several common password composition strategies are analyzed.Aiming at the problem that traditional password strategies cannot take security and usability into account, a new password generation strategy based on mnemonic shape, Alphapwd, is proposed.Alphapwd combines the order of writing strokes of letters with password generation to help users create safe and memorable passwords.The designed experiment compares the security of Alphapwd-based passwords with three leaked password sets.The result shows that Alphapwd-based password is generally stronger than real password sets in resisting unknown attacks.In addition, by analyzing the passwords generated by Alphapwd and KbCg (Keyboard Change), SpIns (Special Character Insertion), it can be found that the security of Alphapwd password is better than that of KbCg mnemonic password, and Alphapwd password is easier to remember than SpIns mnemonic password.
A Novel Algorithm for Skeleton Extraction From Images Using Topological Graph Analysis

Skeletonization, also called thinning, is an important pre-processing step in computer vision and image processing tasks such as shape analysis and vectorization. It is a morphological process that generates a skeleton from an input image. Many thinning algorithms have been proposed, but accurate and fast algorithms are still in demand. In this paper, we propose a novel algorithm using embedded topological graphs and computational geometry that can extract skeletons from input binary images. We compare three well-known thinning algorithms with our method, with the experimental results showing effectiveness of the proposed method and algorithms.
Exploring the database of a soil environmental survey using a geo-self-organizing map: A pilot study

A model integrating geo-information and self-organizing map (SOM) for exploring the database of soil environmental surveys was established. The dataset of 5 heavy metals (As, Cd, Cr, Hg, and Pb) was built by the regular grid sampling in Hechi, Guangxi Zhuang Autonomous Region in southern China. Auxiliary datasets were collected throughout the study area to help interpret the potential causes of pollution. The main findings are as follows: (1) Soil samples of 5 elements exhibited strong variation and high skewness. High pollution risk existed in the case study area, especially Hg and Cd. (2) As and Pb had a similar topo-logical distribution pattern, meaning they behaved similarly in the soil environment. Cr had behaviours in soil different from those of the other 4 elements. (3) From the U-matrix of SOM networks, 3 levels of SEQ were identified, and 11 high risk areas of soil heavy metal-contaminated were found throughout the study area, which were basically near rivers, factories, and ore zones. (4) The variations of contamination index (CI) followed the trend of construction land (1.353) > forestland (1.267) > cropland (1.175) > grassland (1.056), which suggest that decision makers should focus more on the problem of soil pollution surrounding industrial and mining enterprises and farmland.
Timing Attacks on Machine Learning: State of the Art

Machine learning plays a significant role in today's business sectors and governments, in which it is becoming more utilized as tools to help in decision making and automation process. However, these tools are not inherently robust and secure, and could be vulnerable to adversarial modification and cause false classification or risk in the system security. As such, the field of adversarial machine learning has emerged to study vulnerabilities of machine learning models and algorithms, and make them secure against adversarial manipulation. In this paper, we present the recently proposed taxonomy for attacks on machine learning and draw distinctions between other taxonomies. Moreover, this paper brings together the state of the art in theory and practice needed for decision timing attacks on machine learning and defense strategies against them. Considering the increasing research interest in this field, we hope this study provides readers with the essential knowledge to successfully engage in research and practice of machine learning in adversarial environment.
Learning Overlapping Community-Based Networks

Learning graph Laplacian matrices plays a crucial role in network analytics when a meaningful graph is not readily available from the datasets. However, graph Laplacian inference is an ill-posed problem, since multiple solutions may exist to associate a graph with the data. Recent papers have exploited signal smoothness or graph sparsity to handle this problem, without considering specific graph topological property such as community structure. Community structure is prevalent in many real-world networks, which can be exploited to learn the data better. In this paper, we propose a framework that learns the graph Laplacians with overlapping community structure, named LOCN (Learning Overlapping Community-based Networks). Our framework encompasses and leverages the community structure information, along with attributes such as sparsity and signal smoothness to capture the intrinsic relationships between data entities, such that the estimated graph can optimally fit the data. Furthermore, the refined graph Laplacian can be leveraged to further improve the detection of network communities. As a result, LOCN can not only learn the graph Laplacian with a good data fit, but also detect the underlying network communities with a high quality. We show that LOCN can achieve good results for both synthetic and real data.
A multi-level neural network for implicit causality detection in web texts

Mining causality from text is a complex and crucial natural language understanding task corresponding to human cognition. Existing studies on this subject can be divided into two categories: feature engineering-based and neural model-based methods. In this paper, we find that the former has incomplete coverage and intrinsic errors but provides prior knowledge, whereas the latter leverages context information but has insufficient causal inference. To address the limitations, we propose a novel causality detection model named MCDN, which explicitly models the causal reasoning process, and exploits the advantages of both methods. Specifically, we adopt multi-head self-attention to acquire semantic features at the word level and develop the SCRN to infer causality at the segment level. To the best of our knowledge, this is the first time the Relation Network is applied with regard to the causality tasks. The experimental results demonstrate that: i) the proposed method outperforms the strong baselines on causality detection; ii) further analysis manifests the effectiveness and robustness of MCDN.