Reviewerly/exHarmony huggingface dataset:
An Anonymous Network Website Fingerprinting Attack Method Based on Wavelet Transformation

Abstract The anonymous network in protecting user privacy information, but also for the use of criminals, for its illegal acts, and increase the cover monitoring, tracking and obstacle of the government organs forensics, Aiming at the problem of anonymous network, this paper proposes a website fingerprinting attack method for TOR anonymous network. Wavelet transform is introduced to deal with the traffic generated by visiting the website. SVM algorithm is used to learn and generate classifiers, establish fingerprint feature library, and then compare the data generated by the target visiting website with the feature library to confirm its visiting website. In this way, the experimental results show that the average recognition accuracy of target visited websites is 96.7% under all known features of websites, which is superior to literature [17].
From Verb to Action: A Mechanics-based Approach for Trajectory-Pattern Interpretation of Manipulation Verbs

Interpreting manipulation verbs in natural-language instructions into manipulation actions is an indispensable ability for future service robots. As a typical example of manipulation verbs, cutting and breaking (C&B) verbs has gained increasing attention, and various interpretation methods have been proposed in both fields of robotics and natural language processing. However, the existing methods are either relying on the manual meaning matching with tools, objects, and demonstration videos, or based on qualitative and empirical knowledge. Consequently, it is unable to quantitatively interpret the C&B verbs into the robots' trajectory patterns, which are the necessary information for performing manipulation actions. To address this problem, in this paper, we propose a mechanics-based approach, using the contact, twist, and wrench formulations, for interpreting the C&B verbs into quantitative, computable, and verifiable forms. The proposed method leads to the quantitative constraints and unified validation criteria for robot trajectory generation of manipulation tasks, as well as the semantic analysis in natural language processing.
Security of Enterprise Information Resources in Cyberspace

The aim of the article is to present the issues of security of enterprise information resources in cyberspace as key intangible assets conditioning its development and competitiveness. The implementation of this aim is based on a literature analysis and conducted surveys among companies operating in both the private and public sectors in Poland. In this survey have participated thirty one enterprises classified according to the type of the industry in which they operate and the size of enterprises measured by the number of employees employed in them. The research covered enterprises belonging to the IT sector and companies that do not provide typical IT services, but are active in many industries like investment, banking, logistics, car, bicycle, clothing, cosmetics, aviation and catering as well as in real estate.
Image Memorability Using Diverse Visual Features and Soft Attention

In this paper we present a method for still image memorability estimation. The proposed solution exploits feature maps extracted from two Convolutional Neural Networks pre-trained for object recognition and memorability estimation respectively. The feature maps are then enhanced using a soft attention mechanism in order to let the model focus on highly informative image regions for memorability estimation. Results achieved on a benchmark dataset demonstrate the effectiveness of the proposed method.
Text Watermarking for OOXML Format Documents Based on Color Transformation

A robust text watermarking approach has been proposed in this paper, in which the watermarks are embedded into the text by the transformation of characters' RGB-style color in the OOXML format document. To get high watermark embedding capacity, two bits of the watermarking information are embedded between every two characters. The initial text content of the document is not changed after the embedding because of the good characteristics of OOXML format and the subtle adjustment of characters' color, which ensure the good imperceptibility of the watermarking method. Most attacks such as “copy”, “save as”, “insert” and “delete” operations can be resisted by the proposed approach, and the location of the “insert” and “delete” operations can be detected by redundant embedding of the watermarking information. The proposed approach can be applied to files in “docx” and “doc” format.
The Summarization of Commodity Short Comments Based on Topic Clustering

In recent years, the world economy is being transformed into a digital one. E-commerce has brought a lot of convenience people's life, which is followed by the exponential growth of online reviews of e-commerce. These comments are very valuable for e-commerce and users. This paper proposes an effective method of summarizing short comments information based on Chinese. This method uses a series of phrases composed by noun and adjective as the final presentation. Firstly, the noun and adjective phrases in each comment were extracted, then the LDA probability model was used for content clustering, and finally the content summary of each category was extracted according the frequency of occurrence. In this paper, the real comment data of Taobao is used for experiments, and the results show that the proposed method can effectively summarize valuable information of commodities.
Period length optimization linear feedback shift register by adopting bistable multivibrator

Abstract Linear Feedback Shift Register (LFSR) is one of the algorithms for getting keys in the form the random number that keeps changing unpredictably in cryptographic systems. But the random number generated by the algorithm has a repetitive period of numbers. Repetition of the number will reappear when the LFSR reaches the limit of maximum length and to be new period based on the previous composition of the number that generated. It will make a weakness of the cryptographic system because it will make it easier to break the key of the cryptographic system that using pseudo-random generator based the number at the previous period. Therefore, this research tries to increase the maximum length of the LFSR algorithm by adopting the bistable multivibrator system steps that applied to electronics. The bistable multivibrator has the ability to change high state to low state and vice versa when it triggered. The adoption process of the bistable multivibrator system is by interpreting high state as a value of “1” and low state as a value of “0”. The changes output of the bistable multivibrator will then be used to influence the LFSR algorithm works with XOR gate. Experiments carried out by producing pseudo-random numbers using 30-bit registers 30 times and 4-bit registers 60 times successfully increasing the maximum length with the result twice from the previous maximum period length. Thus the LFSR optimization results with bistable multivibrator have a better impact than the normal LFSR for use in cryptographic systems.
Optimized Lightweight Image Steganography on Embedded Device via LUT Approach

Lightweight property has been an indispensable requisite for embedded devices that insists security on its data. Less complex operations, minimal memory footprint and execution in fewer clock cycles are the prime features that guarantee the lightweight property of security algorithms. This paper presents the formation of exclusive Look-Up Tables (LUTs) that brings in the lightweight property to employ the steganography algorithm that embeds the encrypted secret bits based on an encoder circuit. The suggested LUT method has been realized on LPC 2148, an embedded device with ARM 7 core. The above said lightweight features are analyzed through code size and timing analysis; while the security analysis from visual and statistical tests ensure the algorithm's security level. When compared against procedural technique, LUT method provides 25% to 30% improvement in execution speed with no additional demand in code space and compromise in security level.
The impact of experiments in physics lessons – “Why, when, how often?”

Practical activities are an essential component of science teaching and learning. Nowadays the majority of school laboratories is well equipped with experimental devices and computer based experiment systems (Vernier, Pasco). The key question of our research is, whether using all this equipment make teaching physics more effective. The aim of the paper is to discuss the efficiency of laboratory work and experiments provided by teachers so as by learners at various types of schools. What strategies within the use of experiments we need to develop or enrich students’ scientific knowledge? Examples how to design learning outcomes of hands on experiments so as computer based experiments in the content of school curricula will be presented.
Disentangling Latent Factors of Variational Auto-encoder with Whitening

After deep generative models were successfully applied to image generation tasks, learning disentangled latent variables of data has become a crucial part of deep generative model research. Many models have been proposed to learn an interpretable and factorized representation of latent variable by modifying their objective function or model architecture. To disentangle the latent variable, some models show lower quality of reconstructed images and others increase the model complexity which is hard to train. In this paper, we propose a simple disentangling method based on a traditional whitening process. The proposed method is applied to the latent variables of variational auto-encoder (VAE), although it can be applied to any generative models with latent variables. In experiment, we apply the proposed method to simple VAE models and experiment results confirm that our method finds more interpretable factors from the latent space while keeping the reconstruction error the same as the conventional VAE’s error.
The Graphs for Elliptic Curve Cryptography

The scalar multiplication on elliptic curves defined over finite fields is a core operation in elliptic curve cryptography (ECC). Several different methods are used for computing this operation. One of them, the binary method, is applied depending on the binary representation of the scalar v in a scalar multiplication vP, where P is a point that lies on elliptic curve E defined over a prime field Fp. On the binary method, two methodologies are performed based on the implementation of the binary string bits from the right to the left (RLB) [or from the left to the right (LRB)]. Another method is a nonadjacent form (NAF) which depended on the signed digit representation of a positive integer v. In this chapter, the graphs and subgraphs are employed for the serial computations of elliptic scalar multiplications defined over prime fields. This work proposed using the subgraphs H of the graphs G or the (simple, undirected, directed, connected, bipartite, and other) graphs to represent a scalar v directly. This usage speeds up the computations on the elliptic scalar multiplication algorithms. The computational complexities of the proposed algorithms and previous ones are determined. The comparison results of the computational complexities on all these algorithms are discussed. The experimental results show that the proposed algorithms which are used the sub-graphs H and graphs G need to the less costs for computing vP in compare to previous algorithms which are employed the binary representations or NAF expansion. Thus, the proposed algorithms that use the subgraphs or the graphs to represent the scalars v are more efficient than the original ones.
Hierarchical Feature-Pair Relation Networks for Face Recognition

We propose a novel face recognition method using a Hierarchical Feature Relational Network (HFRN) which extracts facial part representations around facial landmark points, and predicts hierarchical latent relations between facial part representations. These hierarchical latent relations should be unique relations within the same identity and discriminative relations among different identities for face recognition task. To do this, the HFRN extracts appearance features as facial parts representations around facial landmark points on the feature maps, globally pool these extracted appearance features onto single feature vectors, and captures the relations for the pairs of appearance features. The HFRN captures the locally detailed relations in the low-level layers and the locally abstracted global relations in the high-level layers for the pairs of appearance features extracted around facial landmark points projected on each layer, respectively. These relations from low-level layers to high-level layers are concatenated into a single hierarchical relation feature. To further improve the accuracy of face recognition, we combine the global appearance feature with the hierarchical relation feature. In experiments, the proposed method achieves the comparable performance in the 1:1 face verification and 1:N face identification tasks compared to existing state-of-the-art methods on the challenging IARPA Janus Benchmark A (IJB-A) and IARPA Janus Benchmark B (IJB-B) datasets.
A flowchart to reduce mutual reinforcement effect on web page ranking based on web strucuture mining

In this dynamic web it is very difficult task to find related and productive information. Bunch of pages with lots of information newly added and deleted from the web as it is dynamic in nature. Basically, Web mining divided into three categories web content mining, web structure mining and web usage mining. One of the important factor of web mining is Link analysis. Web structure mining plays a major role in link analysis. The main objective of web structure mining is to find significant search results. The google search engine uses PageRank algorithm which is based on web structure mining. Everyone knows that PageRank algorithm works on links, so web users creates links between web pages' weather that are related or not. The reason behind this link creation is to get higher page rank then other web pages. This problem is known as mutual reinforcement. In this paper author proposed flowchart which reduce the effect of mutual reinforcement. To reduce effect of mutual reinforcement author used different parameters like number of different domain inlinks, outlinks and reachability. We also compare the result of proposed flowchart with PageRank and WPR algorithm.
Frame Interpolation Using Phase Information and Guided Image Filtering

Videos with low frame rate lacks the visual quality element and unable to meet the standards of new multimedia systems. In this paper, a technique for frame interpolation utilizing phase information is proposed. Phase information gives the intuition that the motion of signals can be depicted as a phase shift. The two consecutive input frames of video are passed through a guided filter to preserve edges of objects in frames. These frames then decompose into multi scale pyramid and the difference in each pixel is calculated to compute the phase difference which then used to interpolate the in-between frame. The proposed technique can be used to increase the frame rate of videos. Subjective and objective comparison is performed with the state of art existing technique to prove the significance of proposed technique.
Formalizing Requirement Specifications for Problem Solving in a Research Domain

The paper presents the research of a methodology of conceptual scheme development to solve problems in subject domains. A semantic approach to domain model specifications building is principal for it. The development process involves formulating a model of requirements to the domain from verbal specifications of domain requirements, developing a domain ontology, transforming it into a conceptual scheme, and reusing domain knowledge specifications in the domain. Relevant data sources are mapped to conceptual schemes of domains in data infrastructures. Requirement specifications are implemented over conceptual schemes for entity resolution and problem-solving in domains using accessible data sources.
Combinatorial Testing of Web Services

Complete coverage of all possible test instances in the testing of integrated systems could achieve by using combinatorial testing (CT) strategies. Combinatorial testing has widely been studied in Web service testing research. Few studies related to testing basic web services while others focus on the composite service behavior of web services to generate minimum test cases or test data. This paper is a part of another larger paper that systematically map the available research work on combinatorial testing of web services. The paper presents a Classification scheme to structure the area and to report results based on the answers of three selected research questions. A classification scheme comprises combinatorial techniques algorithms and tools for testing and standard based SUT. This study exposes that even web service testing is getting noteworthy attention still very few approaches of CT applied to test web services.
Real-Time Laser Obstacle Detection System for Autonomous Mobile Robot Navigation

This paper presents a laser obstacle detection system for mobile robot navigation. Algorithms for segmentation, where the set of measurements from 2D laser range finder is separated into segments representing each obstacle, line fitting and corner extraction for polygonal obstacles are developed. Simulation and experimental results are presented to illustrate the effectiveness of the developed real time laser obstacle detection system for autonomous mobile robot navigation.
A framework for managing uncertainty in software architecture

Software architecture records key decisions about software systems. Changes to architecture decisions can, therefore, have significant costs. One of the main causes of architecture changes is uncertainty, which is an inherent feature in open, real-world software systems. We hypothesise that capturing uncertainties, as far as they can be predicted, pertaining to structure, behaviour and resources of systems in software architecture and treating them as first-class concerns, would make architecture decisions more resilient to change. Explicitly considering and representing uncertainty in architecture can help developers design for change and minimise architecture erosion as changes invariably occur. In this PhD project, we aim to develop a conceptual framework for the management of uncertainty in software architecture. The conceptual framework will be realised in the form of a workbench of tools for managing uncertainty at the architectural level. The effectiveness of the framework and the workbench will be evaluated using case studies. An ideal case study for evaluation will demonstrate different aspects of uncertainty in software architecture. Potential domains under consideration for case studies include wireless sensor networks and health care systems.
A fast classification method based on sequential Bayesian analysis for figures in irradiation images

Radiography is often employed to analyze whether a closed container contains the targeted object in customs inspection, medical diagnosis and arms control verification. Based on the analysis of whether the irradiation image of the closed container contains the specific figure of the targeted object, one can determine the existence of the targeted object effectively. In the figure determination of the irradiation image, the classification and identification of the figures is a key technique. In many cases, considering the time cost of radiography, the existence determination of the targeted object should be done as fast as possible, and fast classification and identification of the figures in the irradiation image is required. In this paper, a fast figure classification method based on the sequential Bayesian analysis with the ability of fast classifying the figures in irradiation images is proposed. First, the theoretical framework of this method was established. Second, the feasibility of this method in analyzing positive irradiation images (the data point density of the image region where the inspected object locates is higher than those of other image regions) was proved by establishing its mathematical models. Third, through the Monte Carlo numerical simulation, the feasibility of this method in analyzing negative irradiation images (the data point density of the image region where the inspected object locates is lower than those of other image regions) was also proved. At last, the feasibility of this method in classifying the complicated figures in irradiation images by establishing and identifying figure templates was proved through the numerical simulation calculation.
Using Neuroevolution for Predicting Mobile Marketing Conversion

This paper addresses user Conversion Rate (CVR) prediction within the context of Mobile Performance Marketing. Specifically, we adapt two main neuroevolution methods: Neuroevolution of Augmenting Topologies (NEAT) and Hypercube-based NEAT (HyperNEAT). First, we discuss two mechanisms for increasing execution speed (parallelism and data sampling); a strategy for preventing excessive network complexity with NEAT; and a rolling window scheme for performing an online learning. Then, we present experimental results, using distinct datasets and testing both offline and online learning environments.
Towards Automated Visualisation of Scientific Literature

Nowadays, an exponential growth in biological data has been recorded, including both structured and unstructured data. One of the main computational and scientific challenges in the modern age is to extract useful information from unstructured textual corpora to effectively support the decision making process. Since the emergence of topic modelling, new and interesting approaches to compactly represent the content of a document collection have been proposed. However, the effective exploitation of the proposed strategies requires a lot of expertise. This paper presents a new scalable and exploratory data visualisation engine, named ACE-HEALTH (AutomatiC Exploration of textual collections for HEALTH-care), whose target is to easily analyse medical document collections through the Latent Dirichlet Allocation. To streamline the analytics process and enhance the effectiveness of data and knowledge exploration, a variety of data visualisation techniques have been integrated in the engine to provide navigable informative dashboards without requiring any a-priori knowledge on the analytics techniques. Preliminary results obtained on a real PubMed collection show the effectiveness of ACE-HEALTH in correctly capturing the high-level overview of textual medical collections through innovative visualisation techniques.