Reviewerly/exHarmony huggingface dataset:
Rank-metric Encryption on Arm-Cortex M0

Since its invention by McEliece in 1978, cryptography based on Error Correcting Codes (ECC) has suffered from the reputation of not being suitable for constrained devices. Indeed, McEliece's scheme and its variants have large public keys and relatively long ciphertexts. Recent works on these downsides explored the possible use of ECC based on rank metric instead of Hamming metric. These codes were introduced in the late 80's to eliminate errors with repeating patterns, regardless of their Hamming weight. Numerous proposals for the NIST Post-Quantum Cryptography (PQC) competition rely on these codes. It has been proven that lattice-based cryptography and even hash-based signatures can run on lightweight devices, but the question remains for code-based cryptography. In this work, we demonstrate that this is actually possible for rank metric: we have implemented the encryption operation of 5 schemes based on ECC in rank metric and made them run on an Arm Cortex-M0 processor, the smallest Arm processor available. We describe the technical difficulties of porting rank-based cryptography to a resource-constrained device while maintaining decent performance and a suitable level of security against side-channel attacks, especially timing attacks.
UAV multiple dynamic objects path planning in air-ground coordination using receding horizon strategy

In this paper, a UAV path planning problem in air-ground coordination is addressed for the scenario where a UAV visits multiple dynamic objects and the UAV shortest route is optimized. Considering the curvature constraints of the drone, Dubins model is used to describe the UAV. Because the UAV can access the neighborhood of the objects, the UAV path planning problem under such circumstances can be formulated as a Dynamic Dubins Traveling Salesman Problem with Neighborhood (DDTSPN). In the receding horizon strategy, the visiting sequence and the access locations are solved simultaneously by improved genetic algorithm. An example in a simulated environment is provided to illustrate the effectiveness of the proposed methods.
Does "www." Mean Better Transport Layer Security?

Experience shows that most researchers and developers tend to treat plain-domains (those that are not prefixed with "www" subdomains, e.g. "example.com") as synonyms for their equivalent www-domains (those that are prefixed with "www" sub-domains, e.g. "www.example.com"). In this paper, we analyse datasets of nearly two million plain-domains against their equivalent www-domains to answer the following question: Do plain-domains and their equivalent www-domains differ in TLS security configurations and certificates? If so, to what extent? Our results provide evidence of an interesting phenomenon: plain-domains and their equivalent www-domains differ in TLS security configurations and certificates in a non-trivial number of cases. Furthermore, www-domains tend to have stronger security configurations than their equivalent plain-domains. Interestingly, this phenomenon is more prevalent in the most-visited domains than in randomly-chosen domains. Further analysis of the top domains dataset shows that 53.35% of the plain-domains that show one or more weakness indicators (e.g. expired certificate) that are not shown in their equivalent www-domains perform HTTPS redirection from HTTPS plain-domains to their equivalent HTTPS www-domains. Additionally, 24.71% of these redirections contains plain-text HTTP intermediate URLs. In these cases, users see the final www-domains with strong TLS configurations and certificates, but in fact, the HTTPS request has passed through plain-domains that have less secure TLS configurations and certificates. Clearly, such a set-up introduces a weak link in the security of the overall interaction.
Dual Graphical Models for Relational Modeling of Indoor Object Categories

There are three levels for indoor scene understanding, pixel level labeling, object level recognition and scene level holistic understanding. The three levels provide complementary bottom-up scene representation. Traditional research often addresses these three tasks separately where the three levels of semantic data are seldom jointly considered. We propose a new method to bridge the three semantic levels by using dual graphical models for relational modeling of object categories in indoor scenes. The vertical placement model captures top-down object configuration by which the visible pixels of some accessory objects could be used to infer the presence of a supportive object underneath. The horizontal placement model reveals how multiple object categories are related to each other on the ground in different indoor scenes. The experimental results show improvements on the bounding box accuracy using both vertical and horizontal placement models from pixel level labeling.
UX Aspects of Kinect-Based Movement Schemes Inside Virtual Environments for Museum Installations

Museum installations, especially those related to the display of virtual archaeology, often make use of natural user interaction (NUI). Those sets require methods of interaction that are intuitive and easy to all users, independent of their previous skills and experience with similar or related technologies. The use of depth cameras such as the Kinect system is a common way to allow visitors to move and interact within the digital replicas of buildings and spaces. This paper presents a study of User Experience (UX) applied to four movement schemes implemented on one such installation. For this research, a mixed method approach is used, using a sample of users segmented into three groups based on their previous skills and experience with video games. The four movement schemes studied combine a user gesture to move forward with another gesture for turning. The quantitative and qualitative data obtained for each movement scheme and user group were analyzed, and several usability metrics were combined to obtain a single UX score, which were then used to compare their performance and suitability for their use in the context of a museum.
Osteoarthritis Stages Classification to Human Joint Imagery Using Texture Analysis: A Comparative Study on Ten Texture Descriptors

Osteoarthritis (OA) is a disease that commonly affects the joints in humans. It is the major diseases in aging and obesity society. OA has affected millions of people around the world. OA has affected 10% of Thai population in 2014. According to National Statistical Office (NSO) estimates, 31.8% of Thai population will be affected in OA by 2050. It is difficult to bring back to normal when people suffer from OA. X-ray imaging is the basic method to detect OA. There are 5 grades to differentiate the level of OA which starts from grade 0 to grade 4. This proposed work is to classify the stage of OA by applying image analysis, and classification techniques. The first step of the proposed technique is to identify the region of interest (ROI). Which regions can specify of each texture image. There are four ROIs: (i) Lateral Femur (LF), (ii) Medial Femur (MF), (iii) Lateral Tibia (LT), (iv) Medial Tibia (MT). When ROI was identified, next each ROI (sub-image) was applied with texture descriptor for extractin the feature vectors. To reduce the dimension of feature space, Correlation-based Feature Selection (CFS) approach was implemented. Finally, these selected feature vectors were used to generate the desired classifiers. The research challenge is to find which ROI can produce the better classification result. With respect to a collection of 130 images data set, the obtained result found that Medial Tibia (MT) produced the best performance with Area Under the ROC Curve (AUC) value of 0.871.
Segmenting Images Using Hybridization of K-Means and Fuzzy C-Means Algorithms

Image segmentation is an essential technique of image processing for analyzing an image by partitioning it into non-overlapped regions each region referring to a set of pixels. Image segmentation approaches can be divided into four categories. They are thresholding, edge detection, region extraction and clustering. Clustering techniques can be used for partitioning datasets into groups according to the homogeneity of data points. The present research work proposes two algorithms involving hybridization of K-Means (KM) and Fuzzy C-Means (FCM) techniques as an attempt to achieve better clustering results. Along with the proposed hybrid algorithms, the present work also experiments with the standard K-Means and FCM algorithms. All the algorithms are experimented on four images. CPU Time, clustering fitness and sum of squared errors (SSE) are computed for measuring clustering performance of the algorithms. In all the experiments it is observed that the proposed hybrid algorithm KMandFCM is consistently producing better clustering results.
A Lightweight Residual-Inception Convolutional Neural Network

Abstract Deep convolutional neural networks have become a powerful tool to solve practical problems, especially in the field of image recognition and machine learning. This paper introduces a lightweight convolutional neural network model which named RINet, based on a combination of the Inception structure and blocks inspired by ResNet. This model achieves high accuracy while reducing the number of parameters and increases the training speed. It has reached the correct rate of 93.7% on the dataset of ISIC and the recognition accuracy improves 6.3% and 1.5% compared to deeper networks called InceptionV1 and transfer learning of InceptionV3.
Assessing Software Understandability in Systems by Leveraging Fuzzy Method and Linguistic Analysis

Many if not most software-intensive systems have their mission-critical software modified by people other than its developers. The resulting misunderstandings often seriously compromise the missions the system is supporting (over 80% of the functionality in most current ground, sea, air, and space vehicles depends on software). There is a major need for models, methods, processes, and tools for identifying sources and effects of software misunderstandings, both in preparation for the system’s software use and evolution, and in evaluating modified software to avoid further examples of misunderstanding. Emphasizing high software understandability enables system maintainers to avoid these misunderstandings as they modify existing software systems. However, while many metrics for understandability have been developed, with the majority of them being source code based, little to no correlation has been found for these metrics with actual software understandability. In this paper, we instead focus on issue summaries as a non-source code alternative for measuring understandability. We generate fuzzy rules and linguistic patterns using a sample of issue summaries from the Mozilla community and evaluate 1416 issue summaries from two other software systems to measure the performance of our model. Our results suggest that this is a viable method to measure understandability and has the potential to be extended to other software maintainability qualities.
An Experiment of the Impacts of Workplace Configuration on Virtual Team Creativity

Enabled by advances in information and communication technologies, virtual team has been serving as an effective form to unite knowledge workers beyond various physical and social constraints for more than one decade. Nevertheless, some organizations recently started to question the effectiveness of virtual team. Scholars also argued that virtual team members remain embedded in their situated surroundings and configuration of virtual team members situating in different workplaces would be even more complicated. This study intends to solve this emerging puzzle that is not yet considered in prior work. We draw on literatures of physical workplaces and virtual team creativity and propose a research model to investigate the influence of workplace configuration on virtual team creativity and the influential mechanism of interaction balance among virtual team members. A laboratory experiment is designed to test the proposed research model and hypotheses. We discuss the potential theoretical contributions and practical implications in the end.
Prone to Supine Surface Based Registration Workflow for Breast Tumor Localization in Surgical Planning

Breast cancer is the most frequent cancer in women worldwide. Screening programs and imaging improvements have increased the detection of clinically occult non-palpable lesions requiring preoperative localization. Image-guided wire localization (WGL) is the current standard of care for the excision of non-palpable carcinomas during breast conserving surgery (BCS). Due to the current limitations of intraoperative tumor localization approaches, the integration of the information from multimodal imaging may be especially relevant in surgical planning. This work presents a workflow to perform a prone image-to-surgical physical data alignment in order to determine the correspondence between the tumor identified in the preoperative image and the final position of the tumor in the surgical position. The evaluation of the methodology has been carried out in 18 cases achieving an average localization error of 10.40 mm and 9.84 mm in 11 small lesion cases (less than 1 cm in diameter).
W-net: the Convolutional Network for Multi-temporal High-resolution Remote Sensing Image Arable Land Semantic Segmentation

Abstract Aiming at the problem that manual arable land semantic segmentation is time-consuming, inefficient, segmentation results rely heavily on the staff’s experience and traditional methods cannot meet the requirements of land segmentation, this paper first proposes a new neural network W-net to realize multi-temporal high-resolution remote sensing image arable land semantic segmentation. We use some data augmentation methods, build the W-net neural network, train with the training set, and test the performance of the neural network with the test set. In this paper, the results of manual labeling are used as the reference. In the case of maintaining the same training set, verification set and test set, FCN, SegNet and W-net are used for control-experiments. The experimental results show that the segmentation accuracy of W-net is higher than that of FCN and SegNet, and the edge segmentation is flatter. W-net has higher accuracy of arable land semantic segmentation for multi-temporal high-resolution remote sensing images than single-temporal high-resolution remote sensing images. W-net neural network can effectively fulfil the task of arable land semantic segmentation.
Robust Auto-Calibration for Practical Scanning Setups from Epipolar and Trifocal Relations

The most important part of auto-calibration is the estimation of the fundamental matrices and the correction of the distortions caused by optical systems. From the fundamental matrices the state-of-the-art method of Lourakis [17] can determine the intrinsic calibration parameters. The prerequisite is that the fundamental matrices are very accurate, so that subsequent methods can converge. State-of-the-art methods minimize the epipolar error to approximate fundamental matrices. If more than two views are given, the trifocal error can theoretically also be used, but it is very noise sensitive and therefore less practical. In this paper, we propose a combination of both error types that leads to consistently improved fundamental matrices compared to the state of the art. The proposed method has been thoroughly evaluated on both synthetic and real data sets. Besides the increased probability that Lourakis' method converges, the resulting intrinsic and extrinsic parameters are of superior quality. The method is quasi parameter-free, easy to implement, and requires only a slightly increased computational effort.
Mitigating Threats to Validity in Empirical Software Engineering: A Traceability Case Study

The issue of validity threats in empirical software engineering research is important. However, some authors overlook this, focusing on validating their work through application of fundamental testing techniques, instead. However, testing is different to empirical validation, with the latter being more concerned about how experimental conclusions are justified. An important factor that can render an experimental conclusion incorrect is researcher's bias, which can be especially relevant when setting the experimental parameters. Therefore, consideration of validity threats is essential to enable confidence in research results and assure the research quality. This paper provides a practical approach for mitigating threats to validity in empirical software engineering using a sequence of software activities. The paper is based on a real-world traceability case study for illustration purposes.
An Internal Threat Detection Model Based on Denoising Autoencoders

Internal user threat detection is an important research problem in the field of system security. Recently, the analysis of abnormal behaviors of users is divided into supervised learning method (SID) and unsupervised learning method (AD). However, supervised learning method relies on domain knowledge and user background, which means it cannot detect previously unknown attacks and is not suitable for multi-detection domain scenarios. Most existing AD methods use the clustering algorithm directly. But for threat detection on internal users’ behavior, mostly for high-dimensional cross-domain log files, as far as we know, there are few methods of multi-domain audit log data with effective feature extraction. An effective feature extraction method which can not only reduce testing cost greatly, but also detect the abnormal behavior of users more accurately. We propose a new unsupervised log abnormal behavior detection method which is based on the denoising autoencoders to encode the user log file, and adopts the integrated method to detect the abnormal data after encoding. Compared with the traditional detection method, it can analyze the abnormal information in the user behavior more effectively, thus playing a preventive role against internal threats. In addition, the method is completely data driven and does not rely on relevant domain knowledge and user’s background attributes. Experimental results verify the effectiveness of the integrated anomaly detection method under the multi-domain detection scenario of user log files.
Cognitive Maps for Risk Estimation in Software Development Projects

This article is the result of research in terms of risk management in the implementation of software development projects. In the conditions of active digitalization of the economy and the growth of both the number and budgets of projects in the software industry, this study is relevant. The article provides a detailed comparative analysis of existing methods and tools for project risk management, the authors define the comparison criteria and scale. To take into account the industry features of it projects, especially in the early stages of implementation, the authors propose a comprehensive approach, which includes an assessment of the cost and complexity of projects. There was also built a cognitive map of risks of software development projects. Twenty projects of different scale and type, including projects on development of technical documentation, on completion of already ready solutions were evaluated. The most accurate and significant results were achieved in the framework of projects for the development of reporting documentation. At the same time, the complexity of the project Manager involved in the calculation of risks and cost and complexity, decreased by 3 times. Author acknowledges support from the MEPhI Academic Excellence Project (Contract No. 02.a03.21.0005).
High Efficiency, Low-noise Meltdown Attack by using a Return Stack Buffer

Meltdown attack exploits out-of-order execution in modern micro-architectures to extract sensitive data in kernel space of operating systems. Out-of-order execution opens a window of transient execution in which unauthorized access to kernel space is allowed. The original Meltdown attack utilizes an OS signal handler and hardware transactional memory support (e.g., Intel TSX) to create transient executions. Both methods, however, restrict the effectiveness of the attack due to a large amount of system noise from signal handlers and a limited number of processors that support TSX. To overcome this limitation, we propose a new variant of Meltdown attack by using a return stack buffer (RSB). Without the aid of TSX, the proposed attack introduces lower level of noise than the signal handler-based method, which broadens the impact of Meltdown attacks to a wide range of processors. We conclude this paper by presenting several countermeasures against the proposed attack.
A Meta-Reinforcement Learning Approach to Optimize Parameters and Hyper-parameters Simultaneously

In the last few years, we have witnessed a resurgence of interest in neural networks. The state-of-the-art deep neural network architectures are however challenging to design from scratch and requiring computationally costly empirical evaluations. Hence, there has been a lot of research effort dedicated to effective utilisation and adaptation of previously proposed architectures either by using transfer learning or by modifying the original architecture. The ultimate goal of designing a network architecture is to achieve the best possible accuracy for a given task or group of related tasks. Although there have been some efforts to automate network architecture design process, most of the existing solutions are still very computationally intensive. This work presents a framework to automatically find a good set of hyper-parameters resulting in reasonably good accuracy, which at the same time is less computationally expensive than the existing approaches. The idea presented here is to frame the hyper-parameter selection and tuning within the reinforcement learning regime. Thus, the parameters of a meta-learner, RNN, and hyper-parameters of the target network are tuned simultaneously. Our meta-learner is being updated using policy network and simultaneously generates a tuple of hyper-parameters which are utilized by another network. The network is trained on a given task for a number of steps and produces validation accuracy whose delta is used as reward. The reward along with the state of the network, comprising statistics of network’s final layer outcome and training loss, are fed back to the meta-learner which in turn generates a tuned tuple of hyper-parameters for the next time-step. Therefore, the effectiveness of a recommended tuple can be tested very quickly rather than waiting for the network to converge. This approach produces accuracy close to the state-of-the-art approach and is found to be comparatively less computationally intensive.
Remarks on Similarities Among Ternary Bent Functions

Bent functions have low autocorrelation and it is interesting to consider if there are some relationships that may be found among values a bent function takes, i.e., to find some possible patterns expressing similarity among certain bent functions in terms of the structure of their value-vectors. A possible approach towards exploring that problem proposed in this paper is based on partial Vilenkin-Chrestenson spectra, which are conveniently interpreted as matrix-valued Vilenkin-Chrestenson spectra of matrix-valued equivalents of ternary bent functions.