Reviewerly/exHarmony huggingface dataset:
Cross-program taint analysis for IoT systems

Cross-program propagation of tainted data (such as sensitive information or user input) in an interactive IoT system is listed among the OWASP IoT top 10 most critical security risks. When programs run on distinct devices, as it occurs in IoT systems, they communicate through different channels in order to implement some functionality. Hence, in order to prove the overall system secure, an analysis must consider how these components interact. Standard taint analyses detect if a value coming from a source (such as methods that retrieve user input or sensitive data) flows into a sink (typically, methods that execute SQL queries or send data into the Internet), unsanitized (that is, not properly escaped). This work devises a cross-program taint analysis that leverages an existing intra-program taint analysis to detect security vulnerabilities in multiple communicating programs. The proposed framework has been implemented above the intra-program taint analysis of the Julia static analyzer. Preliminary experimental results on multi-program IoT systems, publicly available on GitHub, show that the technique is effective and detects inter-program flows of tainted data that could not be discovered by analyzing each program in isolation.
Unsupervised Deep Domain Adaptation Based on Weighted Adversarial Network

Recent studies indicate that adversarial learning can reduce distribution discrepancy between domains effectively, but when the samples belonged to different classes have similar characteristics in the domains, they may be incorrectly aligned to similar classes after domain adaption, which gives rise to negative transfer.To prevent such misalignment, we propose a weighted adversarial network based unsupervised domain adaptation method.Its contributions are mainly reflected in the following two aspects: 1) according to the similarity of features between classes, the different weights are given to the corresponding domain discriminators, which means that we will focus on the alignment of the classes with similar characteristics; 2) all domain discriminators will be given a certain weight again based on the entropy of true or pseudo label vectors, that is, the clearer the sample classification result, the greater its credibilty during domain discriminators learning.Experimental results on several cross-domain benchmark data sets show that our newly proposed approach outperforms state of the art methods.
Spectral Clustering Algorithm Based on Fast Search of Natural Neighbors

The spectral clustering is a typical and efficient clustering algorithm.However, the performance of spectral algorithm depends on the determination of the appropriate similarity matrix and the number of clusters.We propose a new spectral clustering algorithm based on fast search of natural neighbors (FSNN-SC) in this paper.In the algorithm, we design a fast search algorithm to obtain the natural characteristic value sup k of natural neighbor algorithm in order to improve the efficiency of searching neighbors and to construct a high-quality similarity matrix.At the same time, we design a deep traversal algorithm to adaptively determine the cluster number C. The experimental results verify that our methods are able to improve the search efficiency and find correct number of clusters.The compared experiments show that the accuracy and efficiency of the proposed algorithm are better than others.
Survey on Decentralized Fingerprinting Solutions: Copyright Protection through Piracy Tracing

Copyright protection is one of the most relevant challenges in the network society. This paper focuses on digital fingerprinting, a technology that facilitates the tracing of the source of an illegal redistribution, making it possible for the copyright holder to take legal action in case of copyright violation. The paper reviews recent digital fingerprinting solutions that are available for two particularly relevant scenarios: peer-to-peer distribution networks and broadcasting. After analyzing those solutions, a discussion is carried out to highlight the properties and the limitations of those techniques. Finally, some directions for further research on this topic are suggested.
Semantic diversity: Privacy considering distance between values of sensitive attribute

A database that contains personal information and is collected by crowdsensing can be used for various purposes. Therefore, database holders may want to share their databases with other organizations. However, since a database contains information about individuals, database recipients must take privacy concerns into consideration. One of the mainstream privacy protection indicators, l-diversity, guarantees that the probability of identifying a sensitive attribute value of an individual in a database is less than 1/l. However, when there are several semantically similar values in the sensitive attribute, there is a possibility that actual diversity is not satisfied, even if anonymization is performed to satisfy l-diversity. For example, an attacker may know that candidates of Alice's disease are a set of HIV-1(M), HIV-1(N), and HIV-2 if the anonymized database satisfies 3-diversity. In this case, the attacker can conclude that Alice has HIV, although the detailed type remains unknown. In this research, to solve how actual diversity cannot be taken into consideration with existing l-diversity, we proposed a novel privacy indicator, (l, d)-semantic diversity, and an algorithm that anonymizes a database to satisfy (l, d)-semantic diversity. We also proposed an analysis algorithm that is suitable for the proposed anonymizing algorithm because the output of the anonymizing algorithm is difficult to understand. Our proposed algorithms were experimentally evaluated using synthetic and real datasets.
Error-Corrected Margin-Based Deep Cross-Modal Hashing for Facial Image Retrieval

Cross-modal hashing facilitates mapping of heterogeneous multimedia data into a common Hamming space, which can be utilized for fast and flexible retrieval across different modalities. In this paper, we propose a novel cross-modal hashing architecture-deep neural decoder cross-modal hashing (DNDCMH), which uses a binary vector specifying the presence of certain facial attributes as an input query to retrieve relevant face images from a database. The DNDCMH network consists of two separate components: an attribute-based deep cross-modal hashing (ADCMH) module, which uses a margin (m)-based loss function to efficiently learn compact binary codes to preserve similarity between modalities in the Hamming space, and a neural error correcting decoder (NECD), which is an error correcting decoder implemented with a neural network. The goal of NECD network in DNDCMH is to error correct the hash codes generated by ADCMH to improve the retrieval efficiency. The NECD network is trained such that it has an error correcting capability greater than or equal to the margin (m) of the margin-based loss function. This results in NECD can correct the corrupted hash codes generated by ADCMH up to the Hamming distance of m. We have evaluated and compared DNDCMH with state-of-the-art cross-modal hashing methods on standard datasets to demonstrate the superiority of our method.
Refactoring Graphs: Assessing Refactoring over Time

Refactoring is an essential activity during software evolution. Frequently, practitioners rely on such transformations to improve source code maintainability and quality. As a consequence, this process may produce new source code entities or change the structure of existing ones. Sometimes, the transformations are atomic, i.e., performed in a single commit. In other cases, they generate sequences of modifications performed over time. To study and reason about refactorings over time, in this paper, we propose a novel concept called refactoring graphs and provide an algorithm to build such graphs. Then, we investigate the history of 10 popular open-source Java-based projects. After eliminating trivial graphs, we characterize a large sample of 1,150 refactoring graphs, providing quantitative data on their size, commits, age, refactoring composition, and developers. We conclude by discussing applications and implications of refactoring graphs, for example, to improve code comprehension, detect refactoring patterns, and support software evolution studies.
Structural architecture and geological relationships in the southern part of Chitradurga Schist Belt, Dharwar craton, South India

Detailed structural mapping in the southern part of Chitradurga Schist Belt (3.0–2.5 Ga) (CSB) distributed around the Chikkanayakanahalli–Kibbanahalli area was carried out. Sargur Group, Basement Gneiss, Bababudan Group, Chitradurga Group, and Hiriyur Group of rocks are well preserved in the investigated area. Unconformable relation between Basement Gneiss–Sargur Group and Bababudan Group is defined by oligomict conglomerate with quartzite clast and occasionally preserve granite clast. A polymictic conglomerate separates Bababudan and Chitradurga Groups; similarly, Chitradurga and Hiriyur Groups are also separated by a polymictic conglomerate. A new zone, Akkanahalli Zone, in the eastern margin of the study area is proposed which is belonging to Sargur Group. Zircon grains in the metatuff sample from this zone provide an age of 3313 ± 6 Ma. Six stages of deformation events are recognized in the study area. General trend and megascopic structures in the mapped area have resulted from the earlier two stages of deformations (D2 and D3). The D2 stage structure is distinctly characterized by a fold–and–thrust belt consisting of a NNW–SSE trending fold zone sandwiched between a pair of NNW–SSE trending thrust faults dipping east. Deformation during the D3 stage resulted in regional–scale sinistral shear zones, such as N–S striking Gadag–Mandya Shear Zone, and narrow N–S and NW trending sinistral 'echelon' shear zones. Based on our structural and field relationship it is proposed that CSB developed in an immature or failed rift setting where shallow marine sequence and shelf deposits are predominant. Sediments and volcanic rocks were unconformably deposited horizontally above Basement Gneiss and later got deformed together in a sinistral transpression setting.
A descriptive study of assumptions made in LINDDUN privacy threat elicitation

Threat modeling is widely adopted and increasingly recognized as an essential step in the secure software development life cycle (SDLC). Focused on privacy-specific threat categories, LINDDUN is a threat modeling framework that allows the identification of privacy-related design flaws at the stage of the initial architecture concept. LINDDUN advocates making explicit any assumptions during the identification and prioritization of privacy threats. These assumptions are in practice documented informally in a free-form, textual format, and the impact, nature and purpose of these assumptions within the context of LINDDUN is not well understood.
A Comparative Study of Handwriting Recognition Techniques

A computer's capacity is to obtain and understand the intelligible, handwritten information from sources, such as paper files, images, touch screens and other appliances is a handwritten word recognition (HWR) also recognized as handwritten text recognition (HTR). This initiative seeks to rank a single written term, to allow a digital translation of handwritten text. Handwriting recognition has been an active field of studies for over four centuries, but some of the key issues have not yet been resolved. This survey paper focuses on discussing the issues related to handwriting recognition and offers alternatives to the significant issues of handwriting recognition by means of machine learning methods. Also, this helps to list and explain the different approaches in the field of handwriting recognition, such as off-line and on-line handwriting. The paper is intended to explain various ways to resolve the issue of handwriting recognition.
“Alexa, Are You Spying on Me?”: Exploring the Effect of User Experience on the Security and Privacy of Smart Speaker Users

Smart speakers are useful and convenient, but they are associated with numerous security and privacy threats. We conducted thirteen interviews with users of smart speakers to explore the effect of user experience (UX) factors on security and privacy. We analyzed the data using Grounded Theory and validated our results with a qualitative meta-synthesis. We found that smart speaker users lack privacy concerns towards smart speakers, which prompts them to trade their privacy for convenience. However, various trigger points such as negative experiences evoke security and privacy needs. When such needs emerge, existing security and privacy features were not found to be user-friendly which resulted in compensatory behavior. We used our results to propose a conceptual model demonstrating UX's effect on risk, perceptions and balancing behavior. Finally, we concluded our study by recommending user-friendly security and privacy features for smart speakers.
Personality Features Identification from Handwriting Using Convolutional Neural Networks

Many evidence suggested that one's personality can be seen from his/her handwritten scratches, using Graphology analysis. Graphology consists of two techniques, structural and symbol analysis. The structural analysis which sees handwriting structurally as a unit. While symbol analysis based on how to write each letter. With a variety of handwriting features, make identifying personalities with Graphology is not easy to do. Computer-based Graphology is done by examining the handwriting image and then giving suggested personality results based on the pattern of each feature. This research conducted using both techniques of six features. The multi-structure analysis was done to feature of margin, a space between lines, a space between words, slope, and dominant zone, while four specific letters (a', `g', `s', `t') were analyzed using the Convolutional Neural Networks (CNN) classification approach. The results showed that the accuracy of the structured approach was up to 82.5-100%, while the accuracy of the symbol approach using CNN had an accuracy of up to 98.03% of new data with 7-10 minutes in the training process.
Analyzing Product Usage Based on Twitter Users Based on Datamining Process

This paper further enhances the techniques and chronological methods in order to perform the corresponding manipulation and further prediction analysis. We have acquired a real time dataset based on the twitter user's comments sections. The uniqueness of the dataset is that we have extracted only the particular comments which syntheses a particular word based on the product. Then further repetitive extraction is made in order to complete the dataset. Our dataset has three columns based on the ideology that the particular user or our focused subject has enhanced any detail about the product that we are observing. In this precise dataset we have taken the subject about the products usage by the users that have been manufactured by the companies Google and Apple. Both technology giants have well versed their technology reign in this era and their further focused in their upcoming cyber projects and their products will be more advanced in future. As they are in involved in further optimization in their devices and increasing their specifications. It would be complex task to accomplish their project without the feedback, pros and cons of their predecessor projects. They can extract such features from twitter api dataset and they could further enhance their product. They could analyze the drawback, whether the product has reached the market and came out with success all type of this information can be extracted from social media instead of conducting a survey. Such process would be more hectic classification and we cannot predict any accurate results. So we proceed with the social media Data mining Process.
When scenes speak louder than words: Verbal encoding does not mediate the relationship between scene meaning and visual attention

The complexity of the visual world requires that we constrain visual attention and prioritize some regions of the scene for attention over others. The current study investigated whether verbal encoding processes influence how attention is allocated in scenes. Specifically, we asked whether the advantage of scene meaning over image salience in attentional guidance is modulated by verbal encoding, given that we often use language to process information. In two experiments, 60 subjects studied scenes (N1 = 30 and N2 = 60) for 12 s each in preparation for a scene-recognition task. Half of the time, subjects engaged in a secondary articulatory suppression task concurrent with scene viewing. Meaning and saliency maps were quantified for each of the experimental scenes. In both experiments, we found that meaning explained more of the variance in visual attention than image salience did, particularly when we controlled for the overlap between meaning and salience, with and without the suppression task. Based on these results, verbal encoding processes do not appear to modulate the relationship between scene meaning and visual attention. Our findings suggest that semantic information in the scene steers the attentional ship, consistent with cognitive guidance theory.
Hate Speech Detection on Twitter Using Long Short-Term Memory (LSTM) Method

Along with the development of the times, the use of social media, especially Twitter, is increasingly being used. Of course this makes more people communicate on social media. Due to communication, it is possible that there will be utterances of hate speech delivered to certain parties,, especially before presidential election in 2019. The number of certain parties spread hatred in social media especially on Twitter. Therefore, as technology develops, we create a system that can detect a tweet based on the search for hashtag on Twitter whether it is classified as hate speech or not using the LSTM method as a classifier. The result of this system is to provide a label in the form of “hate speech” or “non-hate speech” on every tweet that becomes an input on this system.
Conditional Weighted Ensemble of Transferred Models for Camera Based Onboard Pedestrian Detection in Railway Driver Support Systems

Pedestrian Detection (PD) is one of the most studied issues of driver assistance systems. Although a tremendous effort is already given to create datasets and to develop classifiers for cars, studies about railway systems remain very limited. This article shows that direct application of neither existing advanced object detectors (such as AlexNet, VGG, YOLO etc.), nor specifically created systems for PD (such as Caltech/INRIA trained classifiers), can provide enough performance to overcome railway specific challenges. Fortunately, it is also shown that without waiting the collection of a mature dataset for railways as comprehensively diverse and annotated as the existing ones for cars, a Transfer Learning (TL) approach to fine-tune various successful deep models (pre-trained using both extensive image and pedestrian datasets) to railway PD tasks provides an effective solution. To achieve TL, a new RAilWay PEdestrian Dataset (RAWPED) is collected and annotated. Then, a novel three-stage system is designed. At its first stage, a feature-classifier fusion is created to overcome the localization and adaptation limitations of deep models. At the second stage, the complementarity of the transferred models and diversity of their results are exploited by conducted measurements and analyses. Based on the findings, at the third stage, a novel learning strategy is developed to create an ensemble, which conditionally weights the outputs of individual models and performs consistently better than its components. The proposed system is shown to achieve a log average miss rate of 0.34 and average precision of 0.93, which are significantly better than the performance of compared well-established models.
A face recognition algorithm based on feature fusion

Summary In the process of building a smart city, face recognition can be applied to the transformation of enterprises, communities, and parks. The combination of building security system and face recognition technology can improve the security experience of enterprises and citizens through the solution of hardware and software integration. Face recognition is still facing the challenges of illumination, occlusion, and attitude change in the actual application process. In addition, the end‐to‐end convolutional neural networks (CNN) seldom make use of the hierarchical feature of the network. So, we propose a hierarchy feature fusion method for face recognition, which uses supervisory information to learn shallow and deep facial features. The features are fused to enhance the recognition accuracy of face recognition against illumination and occlusion. The method is applied to the transformation of the visual geometry group network and Lightened CNN. The face recognition experiments are carried out using the hierarchy network. Our method has achieved good recognition results in the labeled faces in the wild (LFW) and AR face databases.
Interpreting Medical Image Classifiers by Optimization Based Counterfactual Impact Analysis

Clinical applicability of automated decision support systems depends on a robust, well-understood classification interpretation. Artificial neural networks while achieving class-leading scores fall short in this regard. Therefore, numerous approaches have been proposed that map a salient region of an image to a diagnostic classification. Utilizing heuristic methodology, like blurring and noise, they tend to produce diffuse, sometimes misleading results, hindering their general adoption. In this work we overcome these issues by presenting a model agnostic saliency mapping framework tailored to medical imaging. We replace heuristic techniques with a strong neighborhood conditioned inpainting approach, which avoids anatomically implausible artefacts. We formulate saliency attribution as a map-quality optimization task, enforcing constrained and focused attributions. Experiments on public mammography data show quantitatively and qualitatively more precise localization and clearer conveying results than existing state-of-the-art methods.
Bounded verification of message-passing concurrency in Go using Promela and Spin

This paper describes a static verification framework for the message-passing fragment of the Go programming language.Our framework extracts models that over-approximate the message-passing behaviour of a program.These models, or behavioural types, are encoded in Promela, hence can be efficiently verified with Spin.We improve on previous works by verifying programs that include communication-related parameters that are unknown at compile-time, i.e., programs that spawn a parameterised number of threads or that create channels with a parameterised capacity.These programs are checked via a bounded verification approach with bounds provided by the user.