Reviewerly/exHarmony huggingface dataset:
Differential privacy-based trajectory community recommendation in social network

Trajectory community recommendation (TCR) is a location-based social network (LBSN) service whereby LBSN server recommends a user a community in which the trajectories have similar movement patterns with the user's trajectory. Due to privacy concerns, the trajectory should be protected. However, the data availability of traditional privacy-preserving schemes is limited, and previous differential privacy (DP) methods cannot achieve high data utility in TCR and rely on a fully trusted third party. In this paper, we propose a DP-based trajectory community recommendation (DPTCR) scheme to perform effective TCR service while protecting trajectory privacy by the user himself. First, DPTCR transits the actual trajectory's locations into noisy feature locations based on private semantic expectation method, which ensures the semantic similarity between noisy locations and the actual locations. Second, DPTCR uses a private geographical distance method to construct a noisy trajectory that has the smallest geographical distance with the actual trajectory. Finally, DPTCR uses a semantic-geographical distance model to cluster a community in which the trajectories have high similarity with the constructed noisy trajectory. Security analysis proves that our DPTCR scheme achieves ϵ-DP, and experimental results show that our scheme achieves high efficiency and data utility.
Communication-efficient outsourced privacy-preserving classification service using trusted processor

Machine learning (ML) classification has been one of the most important techniques of popular Internet services that aim to provide accurate predictions of data by means of a classifier model. In a machine-learning-as-a-service (MLaaS) system, the service provider allows classifier owners to upload their classifier models and charge other users for access on a pay-per-query basis, so that a user can query a classifier with data instances and then obtain their classification results. However, in a traditional way, either stored classifiers or queried data, which are potentially sensitive and economical, will be exposed by the service. Due to privacy concerns, both the classifiers and the data should remain confidential. In this paper, we propose a novel scheme to enable a classifier owner to outsourcely store his/her classifier model on a cloud server for users' queries, while protecting the confidentiality of classifier and data. We adopt a trusted processor to design efficient classification protocols for two concrete classifiers respectively. For the communicational efficiency, users only need to interact with the server no more than twice for each query in our scheme. We implement the prototype of the scheme and conduct experiments in an Intel SGX enclave. The experimental results show that the scheme is practical.
Networked Learning in, for, and with the World

This chapter proposes a framework for networked learning in, for, and with the world at mode 3 universities. First, a theoretical overview of the configuration and development of the mode 1 university (the ivory tower), mode 2 university (the factory), and the mode 3 university (the network) is provided. Second, the framework for the networking mode 3 university is developed through presenting and integrating organisational guidelines, pedagogical formats, and learning principles. Then, two categories of educational patterns for learning in and with the world at the networking university are introduced and described: (1) bringing education into the public (learning in the world) and (2) bringing the public into education (learning with the world). Examples of concrete educational design patterns are also given. Finally, three dimensions for students’ learning for the world through hybrid networks at the mode 3 university are developed: networked learning for the world as citizenship, networked learning for the world as trust, and networked learning for the world as ecology. The main contribution of the chapter is to develop the notion of the networking university along with its implicated teaching and learning practices.
Collective Scenario Understanding in a Multivehicle System by Consensus Decision Making

In recent years, unmanned vehicles (UVs) have been largely employed in many applications. They, enhanced with computer vision and artificial intelligence, can autonomously recognize targets in an environment and detect events occurring in a real-world scenario. The employment of cooperative UVs can provide multiple interpretations supporting a multiperspective view of the scene. However, UV multiple interpretations often diverge, therefore, UVs need to find an agreed interpretation of the scenario. To this purpose, this paper proposes a novel consensus-based approach to lead multi-UV systems to find agreement on what they observe and build a group situation-based description of the scenario. UVs are modeled as experts in a group decision making problem that must decide on which situations best describe the scenario. First, the approach allows each UV to build high-level situations from the detected events through a fuzzy-based event aggregation. The event aggregation is modeled with a fuzzy ontology which allows each UV to express preferences on the situations. Then, a collective interpretation of situations is achieved by consensing each UV interpretation. Finally, consensus and proximity measures support the evaluation of the final group decision reliability. The assessed consensus reflects how much the collective scenario interpretation fits each UV perspective. The proximity measures support the detection of reliable and unreliable UVs to serve many tasks (i.e., mission replanning, damaged UV detection, etc.).
Locally Directional and Extremal Pattern for Texture Classification

An image texture was defined in terms of pixel intensities and directionality. However, most of the current texture representation methods did not consider the two key factors simultaneously. To effectively capture the directional and pixel intensity information of texture, in this paper, we propose a novel and robust local descriptor, named locally directional and extremal pattern (LDEP), for texture classification. It extracts directional local difference count pattern (DLDCP) being made up of DLDCP in the odd positions and DLDCP in the even positions to express directional information in the local area in the first place. Furthermore, to acquire the extremum information remained by DLDCP, by concatenating extremum location pattern (ELP), extremum difference pattern (EDP), and extremum compression pattern (ECP) from the sampling points, we extract a neighbors extremum related local pattern (NERLP). The experimental results obtained from four representative texture databases (Prague, Stex, UIUC, Kth-tips2-a, Brodatz, and CUReT) demonstrate that our proposed LDEP descriptor can achieve comparable accurate classification rates in different conditions (rotation, illumination, scale variation, viewpoint variation, and noise) with ten typical texture classification methods.
Scene Categorization From Contours: Medial Axis Based Salience Measures

The computer vision community has witnessed recent advances in scene categorization from images, with the state of the art systems now achieving impressive recognition rates on challenging benchmarks. Such systems have been trained on photographs which include color, texture and shading cues. The geometry of shapes and surfaces, as conveyed by scene contours, is not explicitly considered for this task. Remarkably, humans can accurately recognize natural scenes from line drawings, which consist solely of contour-based shape cues. Here we report the first computer vision study on scene categorization of line drawings derived from popular databases including an artist scene database, MIT67 and Places365. Specifically, we use off-the-shelf pre-trained Convolutional Neural Networks (CNNs) to perform scene classification given only contour information as input, and find performance levels well above chance. We also show that medial-axis based contour salience methods can be used to select more informative subsets of contour pixels, and that the variation in CNN classification performance on various choices for these subsets is qualitatively similar to that observed in human performance. Moreover, when the salience measures are used to weight the contours, we find that these weights boost our CNN performance above that for unweighted contour input. That is, the medial axis based salience weights appear to add useful information that is not available when CNNs are trained to use contours alone.
MJaya-ELM: A Jaya algorithm with mutation and extreme learning machine based approach for sensorineural hearing loss detection

Sensorineural hearing loss (SNHL) is a common hearing disorder or deafness which accounts for about 90% of the reported hearing loss. Magnetic resonance imaging (MRI) has been found to be an effective neuroimaging technique for detecting SNHL. However, manual detection methods, mainly based on the visual inspection of MRI, are cumbersome, time-consuming and need skilled supervision. Hence, there is a great need to design a computer-aided detection system for fast, accurate and automated detection of SNHL. This paper presents a new method for automated diagnosis of SNHL through brain MR images. Fast discrete curvelet transform is employed for image decomposition. The features are extracted from various decomposed subbands at different scales and orientations. A set of discriminant features is then derived using PCA+LDA algorithm. A hybrid classifier is suggested by integrating extreme learning machine and Jaya optimization with mutation (MJaya-ELM) to distinguish hearing loss images from healthy MR images. The proposed hybrid method overcomes the drawbacks of traditional ELM and other learning algorithms for single layer feedforward neural network. The concept of mutation is introduced to conventional Jaya optimization (MJaya) for improving the global search ability of the solutions by providing additional diversity. The proposed system is evaluated on a well-studied database. The comparison results demonstrate that the proposed scheme outperforms the existing schemes in terms of overall accuracy and sensitivity over different classes. The effectiveness of the proposed MJaya-ELM algorithm is also compared with its counterparts such as PSO-ELM, DE-ELM, and Jaya-ELM, and the results indicate the superiority of MJaya-ELM.
Off the Chain: Blockchain Technology—An Information Organization System

Blockchain technology allows data to be effectively organized, stored, and shared. Its unique decentralized authentication system means no central database exists to store information. All information is shared by a network of nodes and is continuously updated. Blockchain is currently being used across several industries due to its unique features: tamper-proof records, authenticity, transparency, and elimination of third-party brokers. These features offer potential for applications in libraries, archives, and museums as well.
Information needs of drug users on a local dark Web marketplace

This study examines the nature of context-sensitive information needs by focusing on the articulations of need for disnormative information among drug users. To this end, the sample of 9300 messages posted to Sipulitori, a Finnish dark web site were examined by means of descriptive statistics and qualitative content analysis. The theoretical framework of the study was developed by drawing on Tom Wilson´s idea of information need as a phenomenon fundamentally triggered by physiological, affective and cognitive factors indicating basic human needs. To examine the contextual features of needs for disnormative information, the study made use of Chatman’s theory of information poverty characteristic of small worlds and Savolainen´s model for way of life. The findings indicate that about 72% of the information need topics related to drugs dealt with the usage, availability and price of narcotics. The articulations of drug-related information needs reflected the users´ ways of life dominated by the activities of buying, selling and using illegal narcotics. Drug-related information needs are typically triggered by physiological factors, because of the centrality of the physical dependence on drugs. Our study also revealed the simultaneous existence of physiological, affective and cognitive factors especially in messages in which the information need was articulated in greater detail.
A core model for choreographic programming

Choreographic Programming is a paradigm for developing concurrent programs that are deadlock-free by construction, as a result of programming communications declaratively and then synthesising process implementations automatically. Despite strong interest on choreographies, a foundational model that explains which computations can be performed with the hallmark constructs of choreographies is still missing. In this work, we introduce Core Choreographies (CC), a model that includes only the core primitives of choreographic programming. Every computable function can be implemented as a choreography in CC, from which we can synthesise a process implementation where independent computations run in parallel. We discuss the design of CC and argue that it constitutes a canonical model for choreographic programming.
Pinpoint Rowhammer

In recent studies, sophisticated attack vectors that use a Rowhammer bug have been developed. These attacks are dangerous, given that they can corrupt data stored in arbitrary memory rows without accessing them. Successful Rowhammer attacks require to flip data of the target cell. However, non-target cells are also corrupted by the attacks. Such unwanted bit flips can lead to unexpected consequences such as an attack failure and a system crash. We propose a novel Rowhammer method, namely, Pinpoint rowhammer, which flips the target bit while suppressing unwanted bit flips. The basic idea is the use of an effective data pattern for the target bit and ineffective data patterns for non-target bits. We evaluate the proposed method by conducting 107,965 attack instances on four different dynamic random-access memory (DRAM) modules. The proposed method increases the attack success rate from 28.9% to 72.4%, when compared with the state-of-the-art method (double-sided Rowhammer). In addition, the proposed method suppresses 99.7% of the unwanted vulnerable cells.
Implementing the NewHope-Simple Key Exchange on Low-Cost FPGAs

Lattice-based cryptography is one of the most promising candidates being considered to replace current public-key systems in the era of quantum computing. In 2016 Alkim, Ducas, Pöppelmann, and Schwabe proposed the lattice-based key exchange scheme NewHope. The scheme has gained some popularity in the research community as it is believed to withstand attacks by quantum computers with a comfortable security margin and provides decent efficiency and low communication cost. In this work, we evaluate the efficiency of NewHope on reconfigurable hardware. We provide the up to our knowledge first field-programmable gate array (FPGA) implementation of NewHope-Simple that is a slight modification of NewHope proposed by the authors themselves in 2016. NewHope-Simple is basically NewHope with different error correction mechanism. Our implementation of the client-side scheme requires 1,483 slices, 4,498 look-up tables (LUTs), and 4,635 flip-flops (FFs) on low-cost Xilinx Artix-7 FPGAs. The implementation of the server-side scheme takes 1,708 slices, 5,142 LUTs, and 4,452 FFs. Both cores use only two digital signal processors (DSPs) and four 18 Kb block memories (BRAMs). The implementation has a constant execution time to prevent timing attacks. The server-side operations take 1.4 ms and the client-side operations take 1.5 ms.
Histopathological Image Retrieval Based on Asymmetric Residual Hash and DNA Coding

Histopathological image retrieval is a key technology for computer-aided diagnosis. However, patients are reluctant to reveal their privacy in histopathological image retrieval. In order to further improve the effectiveness and safety of histopathological image retrieval, this paper proposes a new histopathological retrieval scheme based on asymmetric residual hash (ARH) and DNA coding techniques. In this paper, we first present a novel ARH for histopathological image retrieval to improve the effectiveness of histopathological search scheme, and then we use the 5-D hyperchaotic system to protect patient privacy. Specifically, the contribution consists of four aspects: 1) A histopathology ciphertext domain search scheme was proposed to improve the performance of computer-aided diagnosis. 2) An asymmetric approach was implemented to process histopathological query points and database points, and a novel asymmetric residual hash algorithm was first proposed to improve the accuracy and speed of histopathological image retrieval. 3) The 5-D hyperchaotic system and DNA coding technique are applied to histopathological image retrieval to protect patient privacy. 4) The loss function is constructed and optimized to learn network parameters and hash codes. The simulation experiment was performed on three datasets (Kimia Path24, Kimia Path960, and Malaria), and the results proved the effectiveness of the ARH algorithm. In addition, our proposed search method can resist common types of attacks during histopathological data transmission. Specifically, the MAP of the ARH is 0.9678 on the KIMIA Path24 with the value of hyperparameter is 125 and the length of hash code is 32. The MAP of the ARH is 0.966 on the KIMIA Path960 with the value of hyperparameter is 225 and the length of hash code is 32. The MAP of the ARH is 0.9482 on the Malaria with the value of hyperparameter is 10 and the length of hash code is 24.
WordNet2Vec: Corpora agnostic word vectorization method

The complex nature of big data resources requires new structuring methods, especially for textual content. WordNet is a good knowledge source for the comprehensive abstraction of natural language as it offers good implementation for many languages. Since WordNet embeds natural language in the form of a complex network, a transformation mechanism, WordNet2Vec, is proposed in this paper. This creates vectors for each word from WordNet. These vectors encapsulate a general position — the role of a given word related to all other words in the given natural language. Any list or set of such vectors contains knowledge about the context of its components within the whole language. This type of word representation can be easily applied to many analytic tasks such as classification or clustering. The usefulness of the WordNet2Vec method is demonstrated in sentiment analysis including the classification of an Amazon opinion text dataset with transfer learning.
Base sizes of primitive groups: Bounds with explicit constants

We show that the minimal base size b(G) of a finite primitive permutation group G of degree n is at most 2(log |G|/ log n) + 24.This bound is asymptotically best possible since there exists a sequence of primitive permutation groups G of degrees n such that b(G) = 2(log |G|/ log n) -2 and b(G) is unbounded.As a corollary we show that a primitive permutation group of degree n that does not contain the alternating group Alt(n) has a base of size at most max{ √ n, 25}.
Bendlets: A second-order shearlet transform with bent elements

We introduce bendlets, a shearlet-like system that is based on anisotropic scaling, translation, shearing, and bending of a compactly supported generator. With shearing being linear and bending quadratic in spatial coordinates, bendlets provide what we term a second-order shearlet system. As we show in this article, the decay rates of the associated transform enable the precise characterization of location, orientation and curvature of discontinuities in piecewise constant images. These results yield an improvement over existing directional representation systems where curvature only controls the constant of the decay rate of the transform. We also detail the construction of shearlet systems of arbitrary order. A practical implementation of bendlets is provided as an extension of the ShearLab toolbox, which we use to verify our theoretical classification results.
SPNet: Deep 3D Object Classification and Retrieval Using Stereographic Projection

We propose an efficient Stereographic Projection Neural Network (SPNet) for learning representations of 3D objects. We first transform a 3D input volume into a 2D planar image using stereographic projection. We then present a shallow 2D convolutional neural network (CNN) to estimate the object category followed by view ensemble, which combines the responses from multiple views of the object to further enhance the predictions. Specifically, the proposed approach consists of four stages: (1) Stereographic projection of a 3D object, (2) view-specific feature learning, (3) view selection and (4) view ensemble. The proposed approach performs comparably to the state-of-the-art methods while having substantially lower GPU memory as well as network parameters. Despite its lightness, the experiments on 3D object classification and shape retrievals demonstrate the high performance of the proposed method.
Look, Investigate, and Classify: A Deep Hybrid Attention Method for Breast Cancer Classification

One issue with computer based histopathology image analysis is that the size of the raw image is usually very large. Taking the raw image as input to the deep learning model would be computationally expensive while resizing the raw image to low resolution would incur information loss. In this paper, we present a novel deep hybrid attention approach to breast cancer classification. It first adaptively selects a sequence of coarse regions from the raw image by a hard visual attention algorithm, and then for each such region it is able to investigate the abnormal parts based on a soft-attention mechanism. A recurrent network is then built to make decisions to classify the image region and also to predict the location of the image region to be investigated at the next time step. As the region selection process is non-differentiable, we optimize the whole network through a reinforcement approach to learn an optimal policy to classify the regions. Based on this novel Look, Investigate and Classify approach, we only need to process a fraction of the pixels in the raw image resulting in significant saving in computational resources without sacrificing performances. Our approach is evaluated on a public breast cancer histopathology database, where it demonstrates superior performance to the state-of-the-art deep learning approaches, achieving around 96% classification accuracy while only 15% of original image pixels are required for computation.
Deep Laplacian Pyramid Network for Text Images Super-Resolution

Convolutional neural networks have recently demonstrated interesting results for single image super-resolution. However, these networks were trained to deal with super-resolution problem on natural images. In this paper, we adapt a deep network, which was proposed for natural images super-resolution, to single text image super-resolution. To evaluate the network, we present our database for single text image super-resolution. Moreover, we propose to combine Gradient Difference Loss (GDL) with L1/L2 loss to enhance edges in super-resolution image. Quantitative and qualitative evaluations on our dataset show that adding the GDL improves the super-resolution results.