Reviewerly/exHarmony huggingface dataset:
Proxy Re-Encryption and Re-Signatures from Lattices

Proxy re-encryption (PRE) and Proxy re-signature (PRS) were introduced by Blaze, Bleumer and Strauss [Eurocrypt '98]. Basically, PRE allows a semi-trusted proxy to transform a ciphertext encrypted under one key into an encryption of the same plaintext under another key, without revealing the underlying plaintext. Since then, many interesting applications have been explored, and constructions in various settings have been proposed. On the other hand, PRS allows a semi-trusted proxy to transform Alice's signature on a message into Bob's signature on the same message, but the proxy cannot produce new valid signature on new messages for either Alice or Bob. In this work, we first point out a subtle mistake in the security proof of the work by Kirshanova (PKC '14), who proposed a lattice-based CCA1 PRE. Thus, this reopens the direction of lattice-based CCA1-secure constructions, even in the single-hop setting. Then we construct a single-hop PRE scheme that is proven secure in our new tag-based CCA-PRE model. Next, we construct the first multi-hop PRE construction. Lastly, we also construct the first PRS scheme from lattices that is proved secure in our proposed unified security model.
TEDLESS – Text detection using least-square SVM from natural scene

Text detection from the natural scene is considered to be a challenging problem due to the complex background, varied light intensity at different locations, a large variety of colors, diverse font style and size. This paper focusses on detecting candidate text objects from the scene. The image is initially preprocessed to remove the noise and enhance the contrast. Then the various objects of the scene are marked and extracted forming a pool of objects. A set of candidate text objects are extracted from this pool of objects and given as output. In order to locate text candidates among these objects, we use Least-Square Support Vector Machine Technique, which trains the model using Char 74K character dataset and CIFAR 10 non-text image dataset. Finally, the trained model was applied to perform a binary classification of text and non-text objects. The results were evaluated over ICDAR 2015 scene images, MSRA500 and SVT datasets and also have been compared to other approaches acquiring encouraging results.
A modified approach to data hiding in Microsoft Word documents by change-tracking technique

Steganography is a hidden way of communication, where one person communicates with the other by using some medium under the garb of a cover, without any suspicion created on the part of any unauthorized intermediary about the existence of the secret message. Linguistic steganography is about hiding information in natural language text. Synonym substitution is an important transformation used in linguistic steganography. In this paper, we are going to show a method of redesigning the change tracking technique for hiding the secret message in Microsoft Word document to increase the embedding capacity and to decrease the suspicion of any intermediary about the message’s existence. The secret message in bit form is concealed by performing linguistic steganography using synonym substitution method. In conventional change tracking technique for information hiding in Microsoft Word document, the generated Huffman’s codes of rare words are lengthier as compared to common words, leading to more suspicion in the stego-document due to the usage of rare words as compared to common words as synonyms. To overcome this problem, the proposed algorithm is designed in such a way that familiar synonyms are used more often to hide more bits and avoid suspicions.
Following the cyber money trail

Purpose The purpose of this paper is to show how global regulation of cryptocurrencies and other cybercurrencies can assist in addressing the challenges of attribution when investigating ransomware attacks and other types of cybercrime using these payment methods. Design/methodology/approach A literature review, looking at current academic research and discourse on the topic cryptocurrency regulation, is conducted to highlight current thinking and perceived difficulties in implanting a global regulatory framework. In addition, the research explores how governments have addressed the risks posed by cryptocurrencies and how regulation has been implemented. The research focuses on the regulatory approaches of Australia, Europe and the Americas to determine whether they could feasibly address the risks posed by cryptocurrencies and be implemented on a global scale. Findings To date, few sustained efforts have been made to regulate Bitcoin or other cybercurrencies. Where regulation has been introduced, it has often proven too costly to implement, thereby, stifling Bitcoin industry growth, or too ad hoc to function effectively. These regulatory pitfalls are substantiated by the continuing difficulty faced by law enforcement agencies, in identifying individual Bitcoin users and separating those that are using them for nefarious purposes from those that are using them for legitimate ones. These challenges appear to grow exponentially when it comes to prosecuting criminals for Bitcoin-related offences, due to the enormous lack of agreement within the justice system of most countries as to the appropriate legal definition for Bitcoin. This research highlights three characteristics that will be vital to the success of any global regulatory framework. These are consistency, clarity and cost-effective implementation. A regulatory framework for Bitcoin that lacks any one of these elements will fail to meet the requirements of every stakeholder in the regulatory process. A framework that is too costly to implement will stifle fintech innovation, subsequently depriving national economies of the multitude of potential benefits promised by fostering fintech entrepreneurship. Equally, a framework that is inconsistent will hamper the global cooperation necessary to combat Bitcoin-related crime. Originality/value This research evaluates research, discourse and regulatory responses from academic and governmental sources and discusses how a global response to cryptocurrency regulation will help address the growing problem of attribution when it comes to ransomware attacks, which has experienced a considerable spike in recent months.
Bayesian parametric and semiparametric factor models for large realized covariance matrices

Summary This paper introduces a new factor structure suitable for modeling large realized covariance matrices with full likelihood‐based estimation. Parametric and nonparametric versions are introduced. Because of the computational advantages of our approach, we can model the factor nonparametrically as a Dirichlet process mixture or as an infinite hidden Markov mixture, which leads to an infinite mixture of inverse‐Wishart distributions. Applications to 10 assets and 60 assets show that the models perform well. By exploiting parallel computing the models can be estimated in a matter of a few minutes.
Self-Adaptive Framework for Efficient Stream Data Classification on Storm

In this era of big data, stream data classification which is one of typical data stream applications has become more and more significant and challengeable. In these applications, it is obvious that data classification is much more frequent than model training. The ratio of stream data to be classified is rapid and time-varying, so it is an important problem to classify the stream data efficiently with high throughput. In this paper, we first analyze and categorize the current data stream machine learning algorithms according to their data structures. Then, we propose stream data classification topology (SDC-Topology) on Storm. For the classification algorithms based on the matrix, we propose self-adaptive stream data classification framework (SASDC-Framework) for efficient stream data classification on Storm. In SASDC-Framework, all the data sets arriving at the same unit time are partitioned into subsets with the nearly best partition size and processed in parallel. To select the nearly best partition size for the stream data sets efficiently, we adopt bisection method strategy and inverse distance weighted strategy. Extreme learning machine, which is a fast and accurate machine learning method based on matrix calculating, is used to test the efficiency of our proposals. According to evaluation results, the throughputs based on SASDC-Framework are 8-35 times higher than those based on SDC-Topology and the best throughput is more than 40000 prediction requests per second in our environment.
Robust quantum control for adiabatic quantum computation

Properly designed control has been shown to be particularly advantageous for improving AQC accuracy and time complexity scaling. Here, an \emph{in situ} quantum control optimization protocol is developed to indirectly optimize state fidelity without knowledge of the instantaneous spectral gap or the computational solution. The protocol is shown to converge to analytically-derived time-optimal controls for Grover's search algorithm. Furthermore, the protocol is utilized to explore optimized control trajectories for the Maximum 2-bit Satisifiability (MAX 2-SAT) problem, where appreciable improvement in fidelity and the minimum spectral gap over a linear schedule is observed. The approach is also shown to be robust against system model uncertainties (unitary control errors). This method is designed to enable robust control optimization on existing quantum annealing hardware and future AQC processors.
A Holistic Optimization Framework for Mobile Cloud Task Scheduling

Mobile cloud computing (MCC) is extensively ubiquitous in the mobile Internet era and embraces complex environments because of the heterogeneity of devices and complexity of communications. Balancing the costs of different influencing objectives (e.g., energy consumption, system reliability, and quality of experience (QoE)) in MCC faces great challenges. This paper focuses on reasonably allocating computational tasks to suitable cores of mobile devices or cloud in MCC to minimize the total energy consumption, and maximize the system reliability and QoE. Concretely, this paper 1 proposes a holistic mobile cloud optimization model including energy consumption, system reliability, and QoE; 2 presents a DVFS-enabled and thermal-aware global energy consumption model which simultaneously considers the synergy of multiple factors concerning mobile devices, cloud, and networks; 3 constructs a tensor-based representation model to comprehensively reflect the complex relationship of multiple influencing factors and cope with their heterogeneity; and 4 proposes a customized optimization framework and two heuristic single-objective optimization (SOO) and triple-objective optimization (TOO) algorithms based on simulated annealing. Experimental results demonstrate that the proposed scheme outperforms the state-of-the-art scheduling schemes in SOO and the Pareto front in TOO can provide appropriate solutions to satisfy different application requirements.
The effect of contrasting cases during problem solving prior to and after instruction

Abstract Research on productive failure suggests that attempting to solve a problem prior to instruction facilitates conceptual understanding compared to receiving instruction prior to problem solving. The assumptions are that during the problem-solving phase, students activate their prior knowledge, become aware of their knowledge gaps, and discover deep features of the target content, which prepares them to better process the subsequent instruction. Unclear is whether this effect results from merely changing the order of the learning phases (i.e., instruction or problem solving first) or from additional features, such as presenting problem-solving material in the form of cases that differ in one feature at a time. Contrasting such cases may highlight the deep features and provide grounded feedback to students’ problem-solving attempts. In addition, the effect of the order of instruction and problem solving on procedural fluency is still unclear. The present experiment ( N = 181, mean age = 14.53) investigated in a 2 × 2 design the effects of order (instruction or problem solving first) and of contrasting cases in the problem-solving material (yes/no) on conceptual understanding and procedural fluency. Additionally, the quality and quantity of students’ solution attempts from the problem-solving phase were coded. Regarding the learning outcomes, the ANOVA results suggest that for procedural fluency instruction prior to problem solving was more beneficial than problem solving prior to instruction. Merely delaying instruction did not increase conceptual understanding. The contrasting cases did not affect the quality of solution attempts, nor the posttest results. As expected, students who received instruction first generated fewer, but higher-quality solution attempts.
Provably secure key-aggregate cryptosystems with auxiliary inputs for data sharing on the cloud

In the applications of Internet of Things (IoT), users usually do not believe that the cloud server is doing a good job of confidentiality, and thus the private data/documents are encrypted and maintained in the cloud storage. And then, fine-grained data sharing of these encrypted data for increased efficiency is an important requirement. Key-aggregate cryptosystem (KAC) has been proposed to address this problem, which can realize efficient delegation of decryption rights of any subset of ciphertexts. However, the end-devices of IoT may not have the special trusted hardware, and the stored keys are apt to be leaked by side-channel attacks. To solve this problem, we design two leakage-resilient KAC schemes, which can be proved secure with auxiliary input. We also implement these schemes over two platforms to test their applicability for the resource-constrained devices.
A signaling game-based approach for Data-as-a-Service provisioning in IoT-Cloud

The impressive progress in sensing technology over the last few years has contributed to the proliferation and popularity of the Internet of Things (IoT) paradigm and to the adoption of Sensor Clouds for provisioning smart ubiquitous services. Also, the massive amount of data generated by sensors and smart objects led to a new kind of services known as Data-as-a-Service (DaaS). The quality of these services is highly dependent on the quality of sensed data (QoD), which is characterized by a number of quality attributes. DaaS provisioning is typically governed by a Service Level Agreement (SLA) between data consumers and DaaS providers. In this work, we propose a game-based approach for DaaS Provisioning, which relies on signaling based model for the negotiation of several QoD attributes between DaaS providers and data consumers. We consider that these entities are adaptive, rational, and able to negotiate the QoD offering even in the case of incomplete information about the other party. We use in the negotiation between the two parties a Q-learning algorithm for the signaling model and a Multi Attributes Decision Making (MADM) model to select the best signal. Moreover, we empirically validate the MADM model using Shannon’s entropy. The results obtained in the case of a multi-stages negotiation scenario show the convergence towards the pooling equilibrium.
Head and Body Orientation Estimation Using Convolutional Random Projection Forests

In this paper, we consider the problem of estimating the head pose and body orientation of a person from a low-resolution image. Under this setting, it is difficult to reliably extract facial features or detect body parts. We propose a convolutional random projection forest (CRPforest) algorithm for these tasks. A convolutional random projection network (CRPnet) is used at each node of the forest. It maps an input image to a high-dimensional feature space using a rich filter bank. The filter bank is designed to generate sparse responses so that they can be efficiently computed by compressive sensing. A sparse random projection matrix can capture most essential information contained in the filter bank without using all the filters in it. Therefore, the CRPnet is fast, e.g., it requires to process an image of pixels, due to the small number of convolutions (e.g., 0.01 percent of a layer of a neural network) at the expense of less than 2 percent accuracy. The overall forest estimates head and body pose well on benchmark datasets, e.g., over 98 percent on the HIIT dataset, while requiring without using a GPU. Extensive experiments on challenging datasets show that the proposed algorithm performs favorably against the state-of-the-art methods in low-resolution images with noise, occlusion, and motion blur.
Subspace Clustering Under Complex Noise

In this paper, we study the subspace clustering problem under complex noise. A wide class of reconstruction-based methods model the subspace clustering problem by combining a quadratic data-fidelity term and a regularization term. In a statistical framework, the data-fidelity term assumes to be contaminated by a unimodal Gaussian noise, which is a popular setting in most current subspace clustering models. However, the realistic noise is much more complex than our assumptions. Besides, the coarse representation of the data-fidelity term may depress the clustering accuracy, which is often used to evaluate the models. To address this issue, we propose the mixture of Gaussian regression (MoG Regression) for subspace clustering. The MoG Regression seeks a valid way to model the unknown noise distribution, which approaches the real one as far as possible, so that the desired affinity matrix is better at characterizing the structure of data in the real world, and furthermore, improving the performance. Theoretically, the proposed model enjoys the grouping effect, which encourages the coefficients of highly correlated points are nearly equal. Drawing upon the ideal of the minimum message length, a model selection strategy is proposed to estimate the numbers of the Gaussian components that shows a way how to seek the number of Gaussian components besides determining it by empirical value. In addition, the asymptotic property of our model is investigated. The proposed model is evaluated on the challenging datasets. The experimental results show that the proposed MoG Regression model significantly outperforms several state-of-the-art subspace clustering methods.
Leveraging Official Content and Social Context to Recommend Software Documentation

For an unfamiliar Application Programming Interface (API), software developers often access the official documentation to learn its usage, and post questions related to this API on social question and answering (Q&A) sites to seek solutions. The official software documentation often captures the information about functionality and parameters, but lacks detailed descriptions in different usage scenarios. On the contrary, the discussions about APIs on social Q&A sites provide enriching usages. Moreover, existing code search engines and information retrieval systems cannot effectively return relevant software documentation when the issued query does not contain code snippets or API-like terms. In this paper, we present CnCxL2RCnCxL2R, a software documentation recommendation strategy incorporating the content of official documentation and the social context on Q&A into a learning-to-rank schema. In the proposed strategy, the content, local context and global context of documentation are considered to select candidate documents. Then four types of features are extracted to learn a ranking model. We conduct a large-scale automatic evaluation on Java documentation recommendation. The results show that CnCxL2RCnCxL2R achieves state-of-the-art performance over the eight baseline models. We also compare the CnCxL2RCnCxL2R with Google search. The results show that CnCxL2RCnCxL2R can recommend more relevant software documentation, and can effectively capture the semantic between the high-level intent in developers' queries and the low-level implementation in software documentation.
Direction Selective Contour Detection for Salient Objects

The active contour model is a widely used technique for automatic object contour extraction. Existing methods based on this model can perform with high accuracy, even in the case of complex contours, but challenging issues remain, like the need for precise contour initialization for high curvature boundary segments or the handling of cluttered backgrounds. To deal with such issues, this paper presents a salient object extraction method, the first step of which is the introduction of an improved edge map that incorporates edge direction as a feature. The direction information in the small neighborhoods of image feature points is extracted, and the images' prominent orientations are defined for direction-selective edge extraction. Using such improved edge information, we provide a highly accurate shape contour representation, which we also combine with texture features. The principle of the paper is to interpret an object as the fusion of its components: its extracted contour and its inner texture. Our goal in fusing textural and structural information is twofold: it is applied for automatic contour initialization, and it is also used to establish an improved external force field. This fusion then produces highly accurate salient object extractions. We performed extensive evaluations, which confirm that the presented object extraction method outperforms parametric active contour models and achieves higher efficiency than the majority of the evaluated automatic saliency methods.
Exploring Variability within Ensembles of Decadal Climate Predictions

Ensemble simulations are used in climate research to account for natural variability. For medium-term decadal predictions, each simulation run is initialized with real observations from a different day resulting in a set of possible climatic futures. Understanding the variability and the predictive power in this wealth of data is still a challenging task. In this paper, we introduce a visual analytics system to explore variability within ensembles of decadal climate predictions. We propose a new interactive visualization technique (clustering timeline) based on the Sankey diagram, which conveys a concise summary of data similarity and its changes over time. We augment the system with two additional visualizations, filled contour maps and heatmaps, to provide analysts with additional information relating the new diagram to raw data and automatic clustering results. The usefulness of the technique is demonstrated by case studies and user interviews.
Asymmetric Release Planning: Compromising Satisfaction against Dissatisfaction

Maximizing satisfaction from offering features as part of the upcoming release(s) is different from minimizing dissatisfaction gained from not offering features. This asymmetric behavior has never been utilized for product release planning. We study Asymmetric Release Planning (ARP) by accommodating asymmetric feature evaluation. We formulated and solved ARP as a bi-criteria optimization problem. In its essence, it is the search for optimized trade-offs between maximum stakeholder satisfaction and minimum dissatisfaction. Different techniques including a continuous variant of Kano analysis are available to predict the impact on satisfaction and dissatisfaction with a product release from offering or not offering a feature. As a proof of concept, we validated the proposed solution approach called Satisfaction-Dissatisfaction Optimizer (SDO) via a real-world case study project. From running three replications with varying effort capacities, we demonstrate that SDO generates optimized trade-off solutions being (i) of a different value profile and different structure, (ii) superior to the application of random search and heuristics in terms of quality and completeness, and (iii) superior to the usage of manually generated solutions generated from managers of the case study company. A survey with 20 stakeholders evaluated the applicability and usefulness of the generated results.