Reviewerly/exHarmony huggingface dataset:
Dynamic Policies Revisited

Information flow control and dynamic policies is a difficult relationship yet to be fully understood. While dynamic policies are a natural choice in many real-world applications that downgrade and upgrade the sensitivity of information, understanding the meaning of security in this setting is challenging. In this paper we revisit the knowledge-based security conditions to reinstate a simple and intuitive security condition for dynamic policies: A program is secure if at any point during the execution the attacker's knowledge is in accordance with the active security policy at that execution point. Our key observation is the new notion of policy consistency to prevent policy changes whenever an attacker is already in possession of the information that the new policy intends to protect. We use this notion to study a range of realistic attackers including the perfect recall attacker, bounded attackers, and forgetful attackers, and their relationship. Importantly, our new security condition provides a clean connection between the dynamic policy and the underlying attacker model independently of the specific use case. We illustrate this by considering the different facets of dynamic policies in our framework. On the verification side, we design and implement DynCoVer, a tool for checking dynamic information-flow policies for Java programs via symbolic execution and SMT solving. Our verification operates by first extracting a graph of program dependencies and then visiting the graph to check dynamic policies for a range of attackers. We evaluate the effectiveness and efficiency of DyncoVeron a benchmark of use cases from the literature and designed by ourselves, as well as the case study of a social network. The results show that DynCoVer can analyze small but intricate programs indicating that it can help verify security-critical parts of Java applications. We release Dyncover publicly to support open science and encourage researchers to explore the topic further.
A Mathematical Model for Latency Constrained Self-Organizing Application Placement in the Edge

The highly dynamic and heterogeneous environment that characterizes the edge of the Cloud/Edge Continuum calls for new intelligent methods for tackling the needs of such a complex scenario. In particular, adaptive and self-organizing decentralized solutions have been advanced for optimizing the placement of applications at the Edge. In this paper, we propose a probabilistic mathematical model that allows to describe one of such solutions. The goal of the model is twofold: i) to make it possible to demonstrate the convergence of the proposed solution; ii) to study the impact of the self-organizing solution without the need of an actual implementation or simulation of the system, allowing to evaluate the suitability of the solution in specific contexts. The paper presents the mathematical formulation of the proposed solution as well as the validation of the proposed model against a simulation of the system.
3D structural control and spatial distribution of Zn-Pb-Cu grades in the Palmeirópolis VMS deposit, Brazil

The Palmeirópolis Zn-Pb-Cu volcanogenic massive sulfide deposit consists of four areas, presenting distinct features regarding host rocks, the geometry of ore bodies, hydrothermal alteration, deformation intensity, and metal proportions. The seven modeled Zn-Pb-Cu ore bodies occur mainly along with the contact between the Mesoproterozoic acid and basic metavolcanics within amphibolite facies. Some characteristics such as the relation of higher Zn-Pb and Cu grades to a higher proportion of acid and basic metavolcanic host rocks respectively, the same metamorphic grade and the gradational contact between volcanic host rocks, and distribution of alteration halo suggest that the original stratigraphy is still preserved. A smooth folding superposing the initial tight folds that define the ore bodies indicates at least two deformation events after the Zn-Pb-Cu mineralisation in the Palmeirópolis deposit. Finally, the geometry of the 3D ore bodies in Palmeirópolis are discussed against other VMS deposits around the world with 3D data available in terms of age, metamorphic, and deformation grades.
The influence of social Q&amp;A systems on consumers' purchase intention: an empirical study based on Taobao's “Ask Everyone”

Purpose Social question and answer (Q&amp;A) systems have been rapidly developed on many e-commerce websites. The purpose of this paper is to explore how social Q&amp;A systems influence consumers' information processing and purchase intention. Design/methodology/approach The authors design this research based on the information adoption model (IAM). First, the auhors consider the impacts of the central route (information factor) and peripheral route (social factor) on consumers' perception of information usefulness in Q&amp;A systems. Then, the authors verify the influence of information and social aspects on purchase intention and empirically test the model with structural equation modelling (SEM) using 428 effective data samples. Findings On the whole, the authors prove that purchase intention is influenced by information and social aspects, which are two paths in Q&amp;A systems. Specifically, both answer quality and social presence positively influence information usefulness. Interestingly, respondent credibility and answer consistency do not significantly impact information usefulness. Moreover, information usefulness positively affects information adoption, which positively affects consumer purchase intention. Practical implications This paper provides insights on social Q&amp;A system mechanism design. Originality/value First, this paper is a useful complement to the research on social Q&amp;A systems on e-commerce websites. Second, the authors provide a new theoretical lens through which the impacts of social Q&amp;A systems on e-commerce websites are understood by extending the IAM. Third, the authors add answer consistency into original information process routes, which obtains a finding that is different from those of prior research.
Fuzzy Closed Filters in Bounded BE-Algebras

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Threat Modeling for Enterprise Cybersecurity Architecture

The traditional threat modeling methodologies work well on a small scale, when evaluating targets such as a data field, a software application, or a system component–but they do not allow for comprehensive evaluation of an entire enterprise architecture. They also do not enumerate and consider a comprehensive set of actual threat actions observed in the wild. Because of the lack of adequate threat modeling methodologies for determining cybersecurity protection needs on an enterprise scale, cybersecurity executives and decision makers have traditionally relied upon marketing pressure as the main input into decision making for investments in cybersecurity capabilities (tools). A new methodology, originally developed by the Department of Defense then further expanded by the Department of Homeland Security, for the first time allows for a threat-based, end-to-end evaluation of cybersecurity architectures and determination of gaps or areas in need of future investments. Although in the public domain, this methodology has not been used outside of the federal government. This paper examines the new threat modeling approach that allows organizations to look at their cybersecurity protections from the standpoint of an adversary. The methodology enumerates threat actions that have been observed in the wild using a cyber threat framework and scores cybersecurity architectural capabilities for their ability to protect, detect, and recover from each threat action. The results of the analysis form a matrix called capability coverage map that visually represents the coverage, gaps, and overlaps against threat actions. The threat actions can be further prioritized using a threat heat map – a visual representation of the prevalence and maneuverability of threat actions that can be overlaid on top of a coverage map. The paper discusses the new threat modeling methodology and proposes future research with a goal to establish a decision-making framework for selecting cybersecurity architectural capability portfolios that maximize protections against known cybersecurity threats.
Real Quadratic-Form-Based Graph Pooling for Graph Neural Networks

Graph neural networks (GNNs) have developed rapidly in recent years because they can work over non-Euclidean data and possess promising prediction power in many real-word applications. The graph classification problem is one of the central problems in graph neural networks, and aims to predict the label of a graph with the help of training graph neural networks over graph-structural datasets. The graph pooling scheme is an important part of graph neural networks for the graph classification objective. Previous works typically focus on using the graph pooling scheme in a linear manner. In this paper, we propose the real quadratic-form-based graph pooling framework for graph neural networks in graph classification. The quadratic form can capture a pairwise relationship, which brings a stronger expressive power than existing linear forms. Experiments on benchmarks verify the effectiveness of the proposed graph pooling scheme based on the quadratic form in graph classification tasks.
Hide and Seek: Revisiting DNS-based User Tracking

Domain name system (DNS) is the address book of the Internet and domain names are queried before almost every network activity. Since the entities like recursive resolvers can monitor users' DNS queries, privacy concerns such as user tracking arise. Though a number of prior works have looked into this issue, they all focus on the closed-world setting, which means that victim users must be known to the adversary. We argue that it does not reflect the adversary's true capabilities. Moreover, there lacks an effective approach to defend against DNS-based user tracking. In this work, we revisit these issues by investigating the attack surface in both open-world and closed-world settings and studying how to protect users. First, we introduce a new tracking mechanism DSCorr which incorporates domain-based word embedding to capture the fine-grained distance between domain names, and automatic threshold generation for fine-tuning the attack outcome. The evaluation result on a real-world DNS dataset shows DSCorr is able to outperform the existing works by a large margin especially in the open-world setting. On the defense side, we develop a system called LDPResolve, which incorporates a recently proposed differential privacy notion ULDP (Utility-optimized Local Differential Privacy) and a new technique named parallel domain resolving, to provide privacy guarantees without damaging the utility of legitimate applications. The evaluation result on the same dataset shows the DNS-based user tracking can be effectively curbed, e.g., tracking accuracy degraded from 93% to 10.1%.
Person-independent facial expression recognition based on local directional compact pattern

This paper proposes a novel descriptor for facial expression recognition called the Local Directional Compact Pattern (LDCP), which encodes the prominent information of local facial textures in a simple and compact way. We obtain edge responses for the local neighborhood of each pixel in four different directions by convolving the symmetric compass masks with the face image. LDCP uses direction, sign, and position information of the two top edge responses to generate a more distinctive code than existing methods that allows us to distinguish different textures with similar gradient directions. Unlike other methods in which the whole face is uniformly divided into several blocks to obtain a global feature vector by concatenating the histogram of each block, we select emotion-related blocks from the feature map to obtain the histogram-based feature vector of each block, which have a different contribution to exhibiting facial expressions. Again, we assign a weight to each block to classify facial expressions using a robust kernel representation algorithm. We conduct our experiments on the CK+ , FACES, MMI, and JAFFE datasets to compare our LDCP descriptor performance with 20 existing descriptors in terms of the number of bits, base length, feature extraction time, and recognition rate. In addition, we compare our proposed method with the recent state-of-the-art methods in different testing strategies.
Gait-based Authentication: Evaluation of Energy Consumption on Commercial Devices

Smartphones and smartwatches, thanks to the sensors they are equipped with, provide a suitable platform for implementing and deploying motion-based behavioral biometrics. In this paper, a gait-based authentication method is designed and implemented for commercially available devices, in order to fill the gap between prototypical and real-world solutions. Particular attention is given to the energy requirements of the system. To this purpose, the impact of the sampling frequency on the accuracy vs consumption trade-off is first analyzed. Then, several architectural configurations are compared to assess the benefits obtained by executing the whole method on the smartwatch, instead of offloading computation to a smartphone. Results show that a smartwatch-only solution is more energy-efficient, as the power consumption due to transferring the data to an external device outweighs the benefits deriving from the decreased computational load.
Colmade: Collaborative Masking in Auditable Decryption for BFV-based Homomorphic Encryption

This paper proposes a novel collaborative decryption protocol for the Brakerski-Fan-Vercauteren (BFV) homomorphic encryption scheme in a multiparty distributed setting, and puts it to use in designing a leakage-resilient biometric identification solution. Allowing the computation of standard homomorphic operations over encrypted data, our protocol reveals only one least significant bit (LSB) of a scalar/vectorized result resorting to a pool of N parties. By employing additively shared masking, our solution preserves the privacy of all the remaining bits in the result as long as one party remains honest. We formalize the protocol, prove it secure in several adversarial models, implement it on top of the open-source library Lattigo and showcase its applicability as part of a biometric access control scenario.
Benchmarking the Data Layer Across Serverless Platforms

The use of highly scalable serverless platforms for web microservices and IoT applications is well known. However, their use for data-intensive applications is restricted due to the stateless nature of serverless functions. Any data retrieval, storage, and the peer-to-peer communication requirement of an application in serverless deployment is satisfied using cloud storage services such as object storage, database, cache, etc. The heterogeneous cloud storage services offered by different cloud service providers have unique deliverable performance. One key challenge is to find the maximum achievable data transfer rate from serverless platforms to cloud storage services.
SECURE-D:Framework For Detecting and Preventing Attacks in SQL and NoSQL Databases

The advancements in internet technology have attracted more users for web applications over recent years. Users’ confidential data are stored in databases thus heightening the security needs. Lack of secure coding in web application causes data threats. Database security plays an important role in information security. Access to database grants attackers control over sensitive information. The SQL and NoSQL injection attacks are categorized as dangerous attacks by the Open Web Application Security Project (OWASP). This work presents a framework for improving website security by detecting and preventing attacks in SQL and NoSQL databases. The proposed system is tested on vulnerable web applications and its efficacy is compared to that of two other systems. Other systems can detect attacks in either SQL or NoSQL databases but not both together. However, the proposed and implemented system can detect and prevent attacks in both SQL and NoSQL databases.
Using Dynamic Pruned N-Gram Model for Identifying the Gender of the User

Organizations analyze customers’ personal data to understand and model their behavior. Identifying customers’ gender is a significant factor in analyzing markets that help plan the promotional campaigns, determine target customers and provide relevant offers. Several techniques were developed to analyze different types of data, including text, image, speech, and biometrics, to identify the gender of the user. The method of synthesis of the profile name differs from one customer to another. Using numerical substitutions of specific letters, known as Leet language, impedes the gender identification task. Moreover, using acronyms, misspellings, and adjacent names impose additional challenges. Towards this goal, this work uses the customers’ profile names associated with submitted reviews to recognize the customers’ gender. First, we create datasets of profile names extracted from the customers’ reviews. Secondly, we introduce a dynamic pruned n-gram model for identifying the gender of the user. It starts with data segmentation to handle adjacent parts, followed by data conversion and cleaning to fix the use of Leet language. Feature selection through a dynamic pruned n-gram model is the next step with the recurrent misspelling correction using fuzzy matching. We evaluate the proposed approach on the real data collected from active web resources. The obtained results demonstrate its validity and reliability.
TriReID: Towards Multi-Modal Person Re-Identification via Descriptive Fusion Model

The cross-modal person re-identification (ReID) aims to retrieve one person from one modality to the other single modality, such as text-based and sketch-based ReID tasks. However, for these different modalities of describing a person, combining multiple aspects can obviously make full use of complementary information and improve the identification performance. Therefore, to explore how to comprehensively consider multi-modal information, we advance a novel multi-modal person re-identification task, which utilizes both text and sketch as a descriptive query to retrieve desired images. In fact, the textual description and the visual description are understood together to retrieve the person in the database to be more aligned with real-world scenarios, which is promising but seldom considered. Besides, based on an existing sketch-based ReID dataset, we construct a new dataset, TriReID, to support this challenging task in a semi-automated way. Particularly, we implement an image captioning model under the active learning paradigm to generate sentences suitable for ReID, in which the quality scores of the three levels are customized. Moreover, we propose a novel framework named Descriptive Fusion Model (DFM) to solve the multi-modal ReID issue. Specifically, we first develop a flexible descriptive embedding function to fuse the text and sketch modalities. Further, the fused descriptive semantic feature is jointly optimized under the generative adversarial paradigm to mitigate the cross-modal semantic gap. Extensive experiments on the TriReID dataset demonstrate the effectiveness and rationality of our proposed solution.
An Efficient Compression of Gray Scale Images Using Wavelet Transform

In the field of digital image distribution and reproduction, the standardization of digital photography and the recent advancement in technology have led to explosive growth. This new impetus has increased the need for introducing scientifically, well designed image compression methods that would help to handle, store and transmit a huge volume of image data. Today, digital image compression is an important role in the fields of multimedia. DWT in corporation with threshold and quadtree decomposition of absorb new designed algorithm is the objective of the current study. In respect of quality execution of this design, it is observed in parity of the EZW image compression technique at the similar bit rating, however, any other habitual standard image does not encourage compression method.
Scaffolding Technique in Reading Comprehension in The Junior High School

The general objective of this research is to describe and analyze the steps in implementing scaffolding technique in reading comprehension and the students reading comprehension implemented through the scaffolding technique in seventh grade student in SMP Negeri 4 Busungbiu in the 2021/2022 academic year. This research is classroom action research. The research subjects were 31 seventh grade students. The researcher used two methods, namely quantitative (student ability in reading comprehension) and qualitative data (data on the implementation of reading comprehension sessions through scaffolding technique and the teachers’ difficulties in implementing reading comprehension sessions through scaffolding technique). The results show that: (1) Scaffolding teaching techniques can improve students' reading comprehension in descriptive and procedure texts, namely: ((a) finding general themes b) finding specific information contained in the text, 3) being able to know the appropriate pronouns in the text (d) students being able to understand the meaning of words in the text. Scaffolding teaching techniques can improve classroom situations, in terms of (a) increasing student participation; (b) creating a more interesting teaching atmosphere (c) increasing students' attention to the lesson; (d) increasing the class of cooperation. From the results of this study, it can be concluded that this technique of scaffolding teaching is effectively used to improve students' reading comprehension.
Intensity-Sensitive Similarity Indexes for Image Quality Assessment

The importance of Image quality assessment (IQA) is ever increasing due to the fast paced advances in imaging technology and computer vision. Among the numerous IQA methods, Structural SIMilarity (SSIM) index and its variants are better matched to the perceived quality of the human visual system. However, SSIM methods are insufficiently sensitive, when images contain low information, where the important information only occupies a low proportion of the image while most of the image is noise-like, which is common in scientific data. Therefore, we propose two new IQA methods, InTensity Weighted SSIM index and Low-Information Similarity Index, for such low information images. In addition, auxiliary indexes are proposed to assist with the assessment. The application of these new IQA methods to natural images and field-specific images, such as radio astronomical images, medical images, and remote sensing images, are also demonstrated. The results show that our IQA methods perform better than state-of-the-art SSIM methods for differences in high-intensity parts of the input images and have similar performance to that of the original and gradient-based SSIM for differences in low-intensity parts. Different similarity indexes are suitable for different applications, which we demonstrate in our results.