Reviewerly/exHarmony huggingface dataset:
Biometric data transformation for cryptographic domains and its application

A large class of biometric template protection algorithms assume that feature vectors are integer valued. However, biometric data is generally represented through real-valued feature vectors. Therefore, secure template constructions are not immediately applicable when feature vectors are composed of real numbers. We propose a generic transformation and extend the domain of biometric template protection algorithms from integer-valued feature vectors to real valued feature vectors. We show that our transformation is accuracy-preserving and verify our theoretical findings by reporting the implementation results using a public keystroke dynamics dataset.
Defending against adversarial attacks in deep neural networks

We focus on defending against adversarial attacks in deep neural networks using signal analysis technology. The method employs a novel signal processing theory as a defense to adversarial perturbations. The method neither modifies the protected network nor requires knowledge of the process for generating adversarial examples. Extensive evaluation experiments demonstrate the efficiency and effectiveness of the proposed adversarial defending method.
Advancing remote attestation via computer-aided formal verification of designs and synthesis of executables

Remote Attestation (RA) of embedded/smart/IoT devices is a very important issue on today's security landscape. RA enables a verifier to measures the current internal memory state of an untrusted remote device (prover). RA helps the verifier establish a static or dynamic root of trust in prover. Despite much prior work, state-of-the-art RA techniques unfortunately still lack any solid foundation and offer no ironclad security, safety or robustness guarantees. This paper argues that <u>computer-aided formal verification</u>, and synthesis of executables, of RA protocols and hybrid (software-hardware) architectures is required and currently unaddressed. We believe that this is achievable with current (computer-aided) formal methods frameworks and tools, and that this can help advance and mature RA research if used to establish more rigorous and clear security arguments. To support our opinion, we highlight several examples where subtle issues were missed in the design and security analysis of RA techniques. Despite deceptive simplicity of such protocols, manual analyses and ad hoc implementations often lead to over-simplification of (and subsequent glossing over) important details in the underlying processor and system architectures. Computer-aided formal verification forces a more scrupulous and disciplined consideration of such details, since, otherwise, verification simply fails. The key objective of the research direction we propose is to increase confidence in correctness and security guarantees of current and future RA techniques and their implementations.
On the Average Case of MergeInsertion

MergeInsertion, also known as the Ford-Johnson algorithm, is a sorting algorithm which, up to today, for many input sizes achieves the best known upper bound on the number of comparisons. Indeed, it gets extremely close to the information-theoretic lower bound. While the worst-case behavior is well understood, only little is known about the average case. This work takes a closer look at the average case behavior. In particular, we establish an upper bound of $$n \log n - 1.4005n + o(n)$$ comparisons. We also give an exact description of the probability distribution of the length of the chain a given element is inserted into and use it to approximate the average number of comparisons numerically. Moreover, we compute the exact average number of comparisons for n up to 148. Furthermore, we experimentally explore the impact of different decision trees for binary insertion. To conclude, we conduct experiments showing that a slightly different insertion order leads to a better average case and we compare the algorithm to the recent combination with (1,2)-Insertionsort by Iwama and Teruyama.
(In)Security of the AAβ Cryptosystem for Transmitting Large Data

The AAβ cryptosystem is a well-designed encryption scheme for securing a message (or plaintext) which could transmit large dataset than its key size. Nevertheless, the idea to transmit data larger than the specified condition is not a good idea. This work will explain why it is the case. As a result, we show that some most significant bit of the data can be recovered. In spite the very fact that the complete parameter cannot be recovered fully, anyway by leaks of its most significant bits, even of a very little amount could lead any cryptosystem insecure.
Panel 3 Position Paper: Blockchain can be the Backbone of India’s Economy

There has been an increase in the interest in blockchain technologies. While the most popular application of this technology is in the cryptocurrency sector in the developed world, recent studies have shown that they hold promise toward deployment in many sectors such as digital contracts and healthcare in the developing world. This article discusses the various use cases of the blockchain technology, particularly from the point of view of developing countries in general and for India in particular.
A weak-form method for strain field calculation with digital image correlation

In digital image correlation (DIC), iterative spatial-domain cross-correlation algorithms have been routinely used to extract displacement fields from the recorded images. Then, strain fields are computed from the noisy displacement fields by a proper numerical different approach, like pointwise least squares fitting, which is tricky in choosing optimal calculation parameters. This work proposes an alternative weak-form framework to calculate strain fields from noisy displacement measurements by DIC. While direct numerical differentiation may amplify the noise in the displacement fields, the proposed method transfers differentiation into integration by employing the idea of the weak-form, which is the foundation of modern computational mechanics. By selecting cosine series as trial/test functions and employing their orthogonality, a semi-analytical formula for calculating the strain field is given. Moreover, the Filon's method is used in this study to improve the accuracy of numerical integration as sinusoidal terms are involved. The effectiveness of the proposed method is verified by numerical examples. It is found that the proposed method is comparable to the famous PLS algorithm when it is for simple strain fields, while it is better in accuracy and its insensitivity to noise when large-gradient or highly-oscillating strain fields are to be measured.
An Approach for Domain-Specific Design Pattern Identification Based on Domain Ontology

In this work, we present an approach for supporting the identification of domain-specific design patterns based on domain’s ontology, since the latter encapsulates the knowledge about the problem domain. More specifically, the proposed approach automatically analyzes the designs of a collection of domain-specific websites in terms of all the recurrent patterns occurring among them, both in the organization of their content and the front-end interface of their pages, resulting in a set of reusable design solutions which are commonly used in them by designers as building blocks for addressing typical domain problems. Then, evaluation is performed according to a number of inspection steps. At a first level, the recurrent patterns occurring at content organization, i.e., the common configurations of domain concepts occurring among website pages are evaluated by matching them against the domain’s ontology and selecting the ones which are in alignment with the domain’s context. At a second level, the recurrent patterns occurring at front-end organization (i.e., the common configurations of front-end design elements) are evaluated towards their consistent and effective use in designs of the collected websites. Finally, the approach categorizes the various reusable design solutions and recommends the ones with the best evaluation results as candidate domain-specific design patterns.
Multicategory Classification Via Forward–Backward Support Vector Machine

In this paper, we propose a new algorithm to extend support vector machine (SVM) for binary classification to multicategory classification. The proposed method is based on a sequential binary classification algorithm. We first classify a target class by excluding the possibility of labeling as any other classes using a forward step of sequential SVM; we then exclude the already classified classes and repeat the same procedure for the remaining classes in a backward step. The proposed algorithm relies on SVM for each binary classification and utilizes only feasible data in each step; therefore, the method guarantees convergence and entails light computational burden. We prove Fisher consistency of the proposed forward–backward SVM (FB-SVM) and obtain a stochastic bound for the predicted misclassification rate. We conduct extensive simulations and analyze real-world data to demonstrate the superior performance of FB-SVM, for example, FB-SVM achieves a classification accuracy much higher than the current standard for predicting conversion from mild cognitive impairment to Alzheimer’s disease.
Optimal VM Coalition for Multi-Tier Applications Over Multi-Cloud Broker Environments

In the current cloud market tradition, multi-cloud broker environments have an important role to support multi-tier cloud applications where a set of interconnected virtual machines (VMs) are required for the deployment and execution of the application. There are several cloud service providers (SP) who provide services in the form of different VM instances. For different SPs, pricing of different configurations is different based on various technical, service level as well as economic factors. Also, these SPs have multiple data centers located at different geographical locations with diverse resources and pricing schemes due to differences in electricity costs, maintenance costs etc. in different regions. Considering these facts, in this paper, we propose a multi-cloud broker environment to select an optimal VM coalition for multi-tier applications from an SP with minimum coalition pricing and better quality of service. We model the problem as a bi-objective optimization problem and solve it using ant-colony optimization based technique. The entire interaction of SPs is modeled using game theory and results from simulation show promising behavior in achieving the trade-off between the aggregate cost and the QoS.
UniAdapt

The Resource Description Framework (RDF), which has become the standard data model to represent web data, is facing the exploding size of the web resources leading to difficulties in terms of maintaining and querying the data. Distributed RDF triple stores and their storage layers were already under research for a decade. While multiple systems tried to employ the workload to guide partitioning and replicating the data set, they are not able to find optimal levels for both the replication and local index storage as well as the main memory cached indexes.
Study on pairwise LDA for x‐vector‐based speaker recognition

In typical x-vector-based speaker recognition systems, standard linear discriminant analysis (LDA) is used to transform the x-vector space with the aim of maximising the between-speaker discriminant information while minimising the within-speaker variability. For LDA, it is customary to use all the available speakers in the speaker recognition development dataset. In this study, the authors investigate if it would be more beneficial to estimate the between-speaker discriminant information and the within-speaker variability using the most confusing samples and the most distant samples (from the target speaker mean), respectively, in the LDA-based channel compensation. The between-speaker variance is estimated using a pairwise approach where the most confusing non-target speaker samples are found based on the Euclidean distance between the speaker mean and adjacent speaker's samples. The within-speaker variance is estimated using the mean of each speaker and the furthermost samples in the speaker sessions. Experimental results demonstrate the proposed LDA approach for an x-vector-based speaker recognition system achieves over 17% relative improvement on equal error rate over standard LDA-based x-vector speaker recognition systems on the NIST2010 corext–corext condition.
State Transfer Management in Adaptive Software: An Approach from Design to Runtime

Dynamic adaptation of software is the change in the structure or behavior of the software at runtime without stopping whole system. In component-based adaptive software, an adaptation process includes actions such as stopping and removing components, adding and starting component, etc. A challenge when realizing the adaptation process is to ensure the system consistency. One of the system consistency aspects, the state of the system must be guaranteed. The maintenance of state is a challenge when reconfiguring the system, specially the inhomogeneity of system versions before and after adaptation. To cope with this challenge, some approaches use state transfer functions which are manually implemented to realize the state transfer. However, they are ad-hoc approaches. They do not offer a general solution to transfer state in the adaptive system which can be applied from design time to runtime in the development process. Therefore, we propose an approach based on a state transfer model that is specified at design time and used at runtime during adaptation process for transferring state.
Evaluating Acoustic Feature Maps in 2D-CNN for Speaker Identification

This paper presents a study evaluating different acoustic feature map representations in two-dimensional convolutional neural networks (2D-CNN) on the speech dataset for various speech-related activities. Specifically, the task involves identifying useful 2D-CNN input feature maps for enhancing speaker identification with an ultimate goal to improve speaker authentication and enabling voice as a biometric feature. Voice in contrast to fingerprints and image-based biometrics is a natural choice for hands-free communication systems where touch interfaces are inconvenient or dangerous to use. Effective input feature map representation may help CNN exploit intrinsic voice features that not only can address the instability issues of voice as an identifier for textindependent speaker authentication while preserving privacy but can also assist in developing efficacious voice-enabled interfaces. Three different acoustic features with three possible feature map representations are evaluated in this study. Results obtained on three speech corpora shows that an interpolated baseline spectrogram performs best compared to Mel frequency spectral coefficients (MFSC) and Mel frequency cepstral coefficient (MFCC) when tested on a 5-fold cross-validation method using 2D-CNN. On both textdependent and text-independent datasets, raw spectrogram accuracy is 4% better than the traditional acoustic features.
Solving feature selection problem by hybrid binary genetic enhanced particle swarm optimization algorithm

In this paper, a new hybrid binary version of Genetic algorithm (GA) and enhanced particle swarm optimization (PSO) algorithm is presented in order to solve feature selection (FS) problem. The proposed algorithm is called Hybrid Binary Genetic Enhanced PSO Algorithm (HBGEPSO). In the proposed HBGEP SO algorithm, the GA is combined with its capacity for exploration of the data through crossover and mutation and enhanced version of the PSO with its ability to converge to the best global solution in the search space. In order to investigate the general performance of the proposed HBGEPSO algorithm, the proposed algorithm is compared with the original optimizers and other optimizers that have been used for FS in the past. A set of assessment indicators are used to evaluate and compare the different optimizers over 20 standard data sets obtained from the UCI repository. Results prove the ability of the proposed HBGEPSO algorithm to search the feature space for optimal feature combinations.
A Face Replacement Neural Network for Image and Video

We propose a method to solve the problem of face replacing for image and video. This approach is enabled to transform an input identity into a target identity, including the facial expression, facial organs and the facial skin colour. To this end, we make the following contributions. (a)We elaborately design a simple auto encoder network to reconstruct the face. (b)Building on recent research in this area, we integrate a weight mask into the loss function to improve the performance of the network during training. (c)Unlike the previous work, we can transform the face not only in image, but also merging video after we adjust the results. We make it easier to replace a people's face with another one in image or video by combining neural networks with simple processing steps.
EraseMe

Graphics Processing Units (GPU) play a major role in speeding up computational tasks of the users, especially in applications such as high volume text and image processing. Recent works have demonstrated the security problems associated with GPU that do not erase the remnant data left behind by previous applications prior to OS context switching. In these attacks, adversaries are able to allocate their memory region on the same memory region used by previous applications and are able to steal their secrets. To overcome this problem, one needs to erase every modified memory page, and this process incurs very high latencies (order of several seconds to even minutes). In this work, we propose EraseMe, a lightweight, content-aware memory-cleansing framework that identifies and erases the sensitive memory pages left behind by victim applications. Our preliminary evaluation shows that EraseMe is able to increase the difficulty of image reconstruction by over 10× for the attacker.
TaxVis: a Visual System for Detecting Tax Evasion Group

The demo presents TaxVis, a visual detection system for tax auditor. The system supports tax evasion group detection based on a two-phase detection approach. Different from the pattern matching based methods, this two-phase method can analyze the suspicious groups automatically without artificial extraction of tax evasion patterns. In the first phase, we use a network embedding method node2vec to learn representations that embed corporations from a Corporation Associated Network (CANet), and use LightGBM to calculate a suspicious score for each corporation. In the second phase, the system use three detection rules to analyze the transaction anomaly around the suspicious corporations. According to these transaction anomalies, we can discover potential suspicious tax evasion groups. We demonstrate TaxVis on tax data of Shaanxi province in China to verify the usefulness of the system.
Reasoning about socio-economic data: a visual analytics approach to Bayesian network

The visual analytics approach has become an important tool for analytical reasoning in the fields of information visualization, scientific visualization and cartography. Much research effort has been made to embed statistical methods in the interactive environment of visual analytics where analysts are supported by data visualization tools for the understanding and exploration of complex datasets. This has stimulated further demands on a more collaborative and co-creative human-computer reasoning. In this study, we have developed a model-based visual analytics prototype in which a probabilistic graphical model, namely Bayesian Network, is integrated within a geospatial visualization tool. Such a visual analytics environment aims to empower the joint human-computer reasoning under conditions of uncertainty, where domain experts may interactively assess multiple geospatial datasets. The proposed approach is demonstrated by means of a scenario with heterogeneous socio-economic data in Munich, Germany. By developing a Bayesian Network-enabled visual analytics, we establish a novel system that supports prior knowledge awareness and user involvement in socio-economic data discovery using interactive visualization. The proposed system uses data to uncover structure of the relationships and dependencies among demographic, social and economic factors.
Creation of Adversarial Examples with Keeping High Visual Performance

The accuracy of the image classification by the convolutional neural network is exceeding the ability of human being and contributes to various fields. However, the improvement of the image recognition technology gives a great blow to security system with an image such as CAPTCHA. In particular, since the character string CAPTCHA has already added distortion and noise in order not to be read by the computer, it becomes a problem that the human readability is lowered. Adversarial examples is a technique to produce an image letting an image classification by the machine learning be wrong intentionally. The best feature of this technique is that when human beings compare the original image with the adversarial examples, they cannot understand the difference on appearance. However, Adversarial examples that is created with conventional FGSM cannot completely misclassify strong nonlinear networks like CNN. Osadchy et al. have researched to apply this adversarial examples to CAPTCHA and attempted to let CNN misclassify them. However, they could not let CNN misclassify character images. In this research, we propose a method to apply FGSM to the character string CAPTCHAs and to let CNN misclassified them.
Professional Training: Challenges in the Digital Economy Context

The paper covers the problem of changes in professional training that are demandable for the digital economy. The high level of country digitalization, detected by several indicators, is interpreted as a change of intellectual tools with transition from the use of signs to the use of sign systems. Challenges of digital economy are discussed in close relation with the psychological changes in all components of professional activity. New goals, needs, and motives in professional activities are considered as necessary requirements for innovative achievements. It is emphasized that the changes in professional activity should be dynamically reflected in professional training. The results of a study to assess educational conditions that academic teachers provide for students in the e-environment are presented. Three categories of educational conditions in the e-environment are explored: acquisition of learning content, network communication with learning goals, and self-management and self-realization in learning activity. It is concluded that training should occur within the e-environment that simulates trends of the real digital professional environment.