Reviewerly/exHarmony huggingface dataset:
Threat Analysis in Dynamic Environments: The Case of the Smart Home

The rapid advancement of information and communication technologies has fostered the development and deployment of complex interrelated systems, many of which also present highly dynamic operational characteristics. These are further integrated within highly connected environments such as smart cities, smart homes, and smart cars, continuously adopting new technological developments. In this article, we focus on the smart home environment, as a case study for such ecosystems, where the integration of IoT devices increases the attack surface, evaluating whether existing risk assessment methods can be utilized for the identification and monitoring of risks, while also capturing the dynamic operational aspects. Accordingly, we review existing dynamic risk assessment methodologies and we leverage a smart home reference architecture to identify the security threats of a smart home's physical and communication viewpoints by leveraging the STRIDE methodology and Microsoft's threat modelling tool.
Two-Level Attention Model Based Video Action Recognition Network

The complex environment background, lighting conditions, and other action-irrelevant visual information in the video frame bring a lot of redundancy and noise to the action spatial features, which seriously affects the accuracy of action recognition.Aiming at this point, we propose a recurrent region attention cell to capture the action-relevant regional visual information in the spatial feature, and according to the temporal sequential natures of the video, on the basis of the recurrent region attention cell, a Recurrent Region Attention model (RRA) is proposed.The recurrent region attention cell in the RRA iterates according to the temporal sequence of the video, so that the attention performance of the RRA is gradually improved.Secondly, we propose a Video Frame Attention model (VFA) that can highlight the more important frames in the whole action video sequence, so as to reduce the interference caused by the similarity between the heterogeneous action video sequences.Finally, we propose an end-to-end trainable network: Two-level Attention Model based video action recognition network (TAMNet).We experimented on two video action recognition benchmark datasets: UCF101 and HMDB51.Experiments show that our end-to-end TAMNet network can reliably focus on the more important video frames in the video sequence, and effectively capture the action-relevant regional visual information in the spatial features of each frame of the video sequence.Inspired by the two-stream structure, we construct a two-modalities TAMNet network.In the same training conditions, the two-modalities TAMNet network achieved optimal performance on both datasets.
Information-Flow Control for Database-Backed Applications

Securing database-backed applications requires tracking information across the application program and the database together, since securing each component in isolation may still result in an overall insecure system. Current research extends language-based techniques with models capturing the database's behavior. This research, however, relies on simplistic database models, which ignore security-relevant features that may leak sensitive information. We propose a novel security monitor for database-backed applications. Our monitor tracks fine-grained dependencies between variables and database tuples by leveraging database theory concepts like disclosure lattices and query determinacy. It also accounts for a realistic database model that supports security-critical constructs like triggers and dynamic policies. The monitor automatically synthesizes program-level code that replicates the behavior of database features like triggers, thereby tracking information flows inside the database. We also introduce symbolic tuples, an efficient approximation of dependency-tracking over disclosure lattices. We implement our monitor for Scala programs and demonstrate its effectiveness on four case studies.
Meaning-preserving contraposition of natural language conditionals

It is argued that contraposition is valid for a class of natural language conditionals, if some modifications are allowed to preserve the meaning of the original conditional. In many cases, implicit temporal indices must be considered, making a change in verb tense necessary. A suitable contrapositive for implicative counterfactual conditionals can also usually be found. In some cases, the addition of certain words is necessary to preserve meaning that is present in the original sentence and would be lost or changed in the contrapositive without them. A distinction is made between adding new meaning and adding new words to preserve existing meaning. For concessive conditionals and relevance conditionals, however, no valid contrapositive can be found. They do not belong to the class of contraposable conditionals, which can be independently defined. Difficult cases are also discussed in which the contradictory of the consequent semantically entails the truth of the antecedent. In such cases the content of the antecedent is implicit in the meaning of the consequent. Contraposition becomes possible if what is implicit in the original consequent is made explicit in the contrapositive antecedent.
UChecker: Automatically Detecting PHP-Based Unrestricted File Upload Vulnerabilities

Unrestricted file upload vulnerabilities enable attackers to upload and execute malicious scripts in web servers. We have built a system, namely UChecker, to effectively and automatically detect such vulnerabilities in PHP server-side web applications. Towards this end, UChecker first interprets abstract syntax trees (AST) of program source code to perform symbolic execution. It then models vulnerabilities using SMT constraints and further leverages an SMT solver to verify the satisfiability of these constraints. UChecker features a novel vulnerability-oriented locality analysis algorithm to reduce the workload of symbolic execution, an AST-driven symbolic execution engine with compact data structures, and rules to translate PHP-based constraints into SMT-based constraints by mitigating their semantic gaps. Experiments based on real-world examples have demonstrated that UChecker has accomplished a high detection accuracy. In addition, it detected three vulnerable PHP scripts that are previously unknown.
Adaptive Image Binarization Based on Multi-layered Stack of Regions

The main purpose of conducted research is the development of a new image thresholding method, which is faster than typical adaptive methods and more accurate than global binarization. Since natural images captured by cameras are usually unevenly illuminated, due to unknown and various lighting conditions, an appropriate binarization influences the results of further image analysis significantly. In this paper, the analysis of multi-layered stack of regions, being the enhancement of the single-layer version, is proposed to calculate the local image properties. Since the balance between the global and local adaptive thresholding requires the choice of an appropriate number of shifted layers and block size, its verification has been made using a database of test images. The proposed local threshold value is chosen as the mean local intensity corrected using two additional parameters subjected to optimization. The developed procedure allows for more accurate and faster binarization, which can be applied in many technical systems. It has been verified by the example of text recognition accuracy for the non-uniformly illuminated document images in comparison to alternative global and local methods of similar of lower computational complexity.
Fusion of interpolated frames superresolution in the presence of atmospheric optical turbulence

An extension of the fusion of interpolated frames superresolution (FIF SR) method to perform SR in the presence of atmospheric optical turbulence is presented. The goal of such processing is to improve the performance of imaging systems impacted by turbulence. We provide an optical transfer function analysis that illustrates regimes where significant degradation from both aliasing and turbulence may be present in imaging systems. This analysis demonstrates the potential need for simultaneous SR and turbulence mitigation (TM). While the FIF SR method was not originally proposed to address this joint restoration problem, we believe it is well suited for this task. We propose a variation of the FIF SR method that has a fusion parameter that allows it to transition from traditional diffraction-limited SR to pure TM with no SR as well as a continuum in between. This fusion parameter balances subpixel resolution, needed for SR, with the amount of temporal averaging, needed for TM and noise reduction. In addition, we develop a model of the interpolation blurring that results from the fusion process, as a function of this tuning parameter. The blurring model is then incorporated into the overall degradation model that is addressed in the restoration step of the FIF SR method. This innovation benefits the FIF SR method in all applications. We present a number of experimental results to demonstrate the efficacy of the FIF SR method in different levels of turbulence. Simulated imagery with known ground truth is used for a detailed quantitative analysis. Three real infrared image sequences are also used. Two of these include bar targets that allow for a quantitative resolution enhancement assessment.
Trust-Aware Process Design

Longitudinal studies point to the global erosion of trust in institutions and their business processes. As a result, the provision of trusted processes has become a new design criterion that exceeds the traditional Business Process Management (BPM) goals of time, cost, and quality, and also goes beyond security and privacy concerns. The notion of trust, however, has rarely been studied in the context of BPM. This paper initiates the conceptualization of trust in BPM by providing two new artefacts, i.e. a four-stage model for the design of trusted processes and a related meta model. Both have been derived from relevant theories and existing, general trust conceptualizations. Two exploratory case studies and secondary data have facilitated the identification of an initial set of application scenarios and trust requirements.
Quantum Topological Error Correction Codes are Capable of Improving the Performance of Clifford Gates

The employment of quantum error correction codes (QECCs) within quantum computers potentially offers a reliability improvement for both quantum computation and communications tasks. However, incorporating quantum gates for performing error correction potentially introduces more sources of quantum decoherence into the quantum computers. In this scenario, the primary challenge is to find the sufficient condition required by each of the quantum gates for beneficially employing QECCs in order to yield reliability improvements given that the quantum gates utilized by the QECCs also introduce quantum decoherence. In this treatise, we approach this problem by firstly presenting the general framework of protecting quantum gates by the amalgamation of the transversal configuration of quantum gates and quantum stabilizer codes (QSCs), which can be viewed as syndrome-based QECCs. Secondly, we provide examples of the advocated framework by invoking quantum topological error correction codes (QTECCs) for protecting both transversal Hadamard gates and CNOT gates. The simulation and analytical results explicitly show that by utilizing QTECCs, the fidelity of the quantum gates can be beneficially improved, provided that quantum gates satisfying a certain minimum depolarization fidelity threshold (F <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">th</sub> ) are available. For instance, for protecting transversal Hadamard gates, the minimum fidelity values required for each of the gates in order to attain fidelity improvements are 99.74%, 99.73%, 99.87%, and 99.86%, when they are protected by colour, rotated-surface, surface, and toric codes, respectively. These specific F <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">th</sub> values are obtained for a very large number of physical qubits (n → ∞), when the quantum coding rate of the QTECCs approaches zero (r <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Q</sub> → 0). Ultimately, the framework advocated can be beneficially exploited for employing QSCs to protect large-scale quantum computers.
What is optimal in optimal inference?

Inferring hidden structure from noisy observations is a problem addressed by Bayesian statistical learning, which aims to identify optimal models of the process that generated the observations given assumptions that constrain the space of potential solutions. Animals and machines face similar “model-selection” problems to infer latent properties and predict future states of the world. Here we review recent attempts to explain how intelligent agents address these challenges and how their solutions relate to Bayesian principles. We focus on how constraints on available information and resources affect inference and propose a general framework that uses benefit versus accuracy and accuracy versus cost curves to assess optimality under these constraints.
A novel unsupervised Globality-Locality Preserving Projections in transfer learning

In this paper, a novel unsupervised dimensionality reduction algorithm, unsupervised Globality-Locality Preserving Projections in Transfer Learning (UGLPTL) is proposed, based on the conventional Globality-Locality Preserving dimensionality reduction algorithm (GLPP) that does not work well in real-world Transfer Learning (TL) applications. In TL applications, one application (source domain) contains sufficient labeled data, but the related application contains only unlabeled data (target domain). Compared to the existing TL methods, our proposed method incorporates all the objectives, such as minimizing the marginal and conditional distributions between both the domains, maximizing the variance of the target domain, and performing Geometrical Diffusion on Manifolds, all of which are essential for transfer learning applications. UGLPTL seeks a projection vector that projects the source and the target domains data into a common subspace where both the labeled source data and the unlabeled target data can be utilized to perform dimensionality reduction. Comprehensive experiments have verified that the proposed method outperforms many state-of-the-art non-transfer learning and transfer learning methods on two popular real-world cross-domain visual transfer learning data sets. Our proposed UGLPTL approach achieved 82.18% and 87.14% mean accuracies over all the tasks of PIE Face and Office-Caltech data sets, respectively.
Robust mobile robot navigation using fuzzy type 2 with wheel slip dynamic modeling and parameters uncertainties

In this paper, a full dynamic model for mobile robot with wheel slip is developed for robust navigation. The type 2 fuzzy logic controller algorithm is implemented and compared to the type 1 fuzzy controller using a differential drive wheels mobile robot (DDWMR) in the presence of wheel slipping from the perspective of control design. We present a dynamic model that explicitly relates perturbations to the vehicle slipping and parameters uncertainties. Both controllers are validated on different behaviors of navigation to evaluate their performances. Furthermore, many advanced robustness tests (modeling errors, localization uncertainties, loss of efficiency of motors, sliding ground) are established to compare the efficiency of each controller. Type 1 and interval type 2 fuzzy logic controllers are validated using V-REP robotic simulator on MATLAB® environment and real scenario are considered. The obtained results show clearly the performances of the IT2FLC comparing to the T1FLC controller especially when significant wheel slip and parameters uncertainty are considered.
Is cross‐lingual readability assessment possible?

Most research efforts related to automatic readability assessment focus on the design of strategies that apply to a specific language. These state‐of‐the‐art strategies are highly dependent on linguistic features that best suit the language for which they were intended, constraining their adaptability and making it difficult to determine whether they would remain effective if they were applied to estimate the level of difficulty of texts in other languages. In this article, we present the results of a study designed to determine the feasibility of a cross‐lingual readability assessment strategy. For doing so, we first analyzed the most common features used for readability assessment and determined their influence on the readability prediction process of 6 different languages: English, Spanish, Basque, Italian, French, and Catalan. In addition, we developed a cross‐lingual readability assessment strategy that serves as a means to empirically explore the potential advantages of employing a single strategy (and set of features) for readability assessment in different languages, including interlanguage prediction agreement and prediction accuracy improvement for low‐resource languages.
Vulnerability prediction capability: A comparison between vulnerability discovery models and neural network models

In this paper, we introduce an approach for predicting the cumulative number of software vulnerabilities that is in most cases more accurate than vulnerability discovery models (VDMs). Our approach uses a neural network model (NNM) to model the nonlinearities associated with vulnerability disclosure. Nine common VDMs were used to compare their prediction capability with our approach. The different models were applied to vulnerabilities associated with eight well-known software (four operating systems and four web browsers). The models were assessed in terms of prediction accuracy and prediction bias. Out of eight software we analyzed, the NNM outperformed the VDMs in all the cases in terms of prediction accuracy, and provided smaller values of absolute average bias in seven cases. This study shows that NNMs are promising for accurate predictions of software vulnerabilities disclosures.
Do financial reforms promote entrepreneurship?

This paper investigates whether financial reforms promote entrepreneurship. Using a panel of 41 developed and developing countries from around the world, we find that financial sector reforms are positively associated with early-stage entrepreneurial activity. In a variety of robustness checks, including a falsification test, we fail to find the evidence that this relationship is driven due to the omission of unobserved, country-specific factors. Investigating the relationship between reforms in different dimensions of the financial sector and entrepreneurship, we find reforms in directed credit, credit controls, banking supervision, and international capital flows dimensions to be significantly associated with early-stage entrepreneurial activity.
A QoE-Driven Multicast Strategy With Segment Routing—A Novel Multimedia Traffic Engineering Paradigm

Quality of Experience (QoE) reflects end users' overall experience and feeling with network services, but needs support in terms of end-to-end Quality of Service (QoS). Segment routing (SR) as a new routing paradigm can provide good end-to-end QoS guarantee, making traditional multimedia traffic routing more efficient and scalable. In this paper, we address two problems related to the new SR mechanism: enabling fine-grained end-to-end QoS routing under a complex network environment and constructing the multicast routing tree with branch node load balancing. To solve these problems, an Inaccurate information-based QoE-driven Routing algorithm (IQdR) and a Branch-aware Multicast Tree (BaMT) algorithm were proposed. Simulation test results that have compared the performance of our proposed solution against that of other algorithms show that the previous works were outperformed. Additionally, the results also show that our multicast architecture improves the scalability of the network in terms of the number of flows.
Detection of fake high definition for HEVC videos based on prediction mode feature

A high definition (HD) video usually means a good visual quality, and the video with low quantization parameter (QP) or high bit rate has a good video definition. However, forgers prefer to directly re-encode lower definition videos using lower QP or higher bit rate without video quality improvement to pretend to be HD videos. Therefore, fake HD video detection is necessary in video forensics. In this paper, a novel method is proposed to detect the fake HD for high efficient video coding (HEVC) videos based on prediction mode feature (PMF). According to our analysis, the HEVC prediction mode scheme of the subsequent encoding could be influenced by the previous lower quality encoding, hence the frequencies of prediction unit with all types of prediction modes can be used to detect the fake HD videos. Firstly, a 4-D features are extracted from Planar, DC, H0 direction and V0 direction intra prediction modes. Secondly, a 6-D features are extracted from Skip, Merge and AMVP inter prediction modes. Finally, these two feature sets are combined into the PMF to detect fake HD videos and further estimate their original QPs and bit rates. Experimental results show that the performance of the proposed method outperforms state-of-the-art works.
High-resolution angular measurement arithmetic based on pixel interpolations

Image angular displacement measurement devices are equipped with image sensors which identify calibration grating to digitalize angular displacement outputs. It is easier to realize high-resolution angular displacement measurement than the traditional optical moiré fringe measurement method in a miniaturized device. This paper proposes a high-resolution angular displacement measurement algorithm based on pixel interpolation which was designed to resolve the measurement output error caused by low pixel resolution in the image sensor. An optical path for angular displacement measurement using a linear image sensor close to the calibration grating is proposed. We find that the effects of insufficient pixel quantity in the range of the grating marking line can be ameliorated using a high-resolution measurement algorithm based on pixel interpolation. The proposed algorithm is applied to simulated and experimental image angular displacement measurements to verify its performance. The results show that the proposed algorithm markedly improves the measurement resolution when the number of pixels in the range of marking lines detected by the image sensor is insufficient. We achieve a resolution of 0.15″ and accuracy of 12.93″ with circular grating diameter of 38 mm. The results presented here may enhance the theoretical basis of high-resolution angular displacement measurement.