Reviewerly/exHarmony huggingface dataset:
Recommender Systems with Condensed Local Differential Privacy

Recommender systems aim at predicting users’ future behaviors by learning the users’ personal information and historical behaviors. Unfortunately, training by the user’s raw data will inevitably cause the user’s private information to leak. Moreover, exiting privacy-preserving recommendation methods are based on matrix factorization with Local Differential Privacy (LDP). Because LDP performs poorly in small or multi-dimensional data sets, these methods do not work well with sparse or multi-dimensional features. Motivated by these questions, we propose a privacy-preserving recommendation model based on Deep neural networks and Factorization Machines (DeepFM), which can provide users with recommendations under the promise of protecting users’ privacy. In our recommendation model, each user randomly perturbs the calculated gradients to satisfy Condensed Local Differential Privacy (CLDP). The recommender system collects the perturbed gradients to train a recommendation model, which guarantees the safety of users’ private information and has excellent accuracy for the recommendation. Experiments on a real-world dataset show that the recommendation accuracy of our algorithm performs better than the existing methods.
Preserving Low-Quality Video through Deep Learning

Abstract Lossy video stream compression is performed to reduce the bandwidth and storage requirements. Moreover also image compression is a need that arises in many circumstances.It is often the case that older archive are stored at low resolution and with a compression rate suitable for the technology available at the time the video was created. Unfortunately, lossy compression algorithms cause artifact. Such artifacts, usually damage higher frequency details also adding noise or novel image patterns. There are several issues with this phenomenon. Low-quality images can be less pleasant to persons. Object detectors algorithms may have their performance reduced. As a result, given a perturbed version of it, we aim at removing such artifacts to recover the original image. To obtain that, one should reverse the compression process through a complicated non-linear image transformation. We propose a deep neural network able to improve image quality. We show that this model can be optimized either traditionally, directly optimizing an image similarity loss (SSIM), or using a generative adversarial approach (GAN). Our restored images have more photorealistic details with respect to traditional image enhancement networks. Our training procedure based on sub-patches is novel. Moreover, we propose novel testing protocol to evaluate restored images quantitatively. Differently from previously proposed approaches we are able to remove artifacts generated at any quality by inferring the image quality directly from data. Human evaluation and quantitative experiments in object detection show that our GAN generates images with finer consistent details and these details make a difference both for machines and humans.
Data-Enhanced Bayesian MIMO-OFDM Channel Estimation Strategy with Universal Noise Model

Model-based methods are dominant in current systems for their optimal designs under given models, but may suffer from inaccurate modeling assumptions. Recently, data-based deep learning methods have achieved remarkable performances by training a large amount of data but encounter some challenges such as, lack of available training data and explainability. In this paper, we propose a novel hybrid idea to integrate the strengths of both data and model-driven methods, named model based method enhanced by data, which is training affordable, theoretically interpretable and model flexible. To show the idea more concretely, we consider a multiple-input multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM) channel state information (CSI) acquisition approach. Specifically, we utilize a universal mixture of Gaussian (MoG) model to deal with the nongaussianity of the noise and interference in complex communication environments, which can adaptively adjust involved parameters to fit the true distribution by observed data. We propose a variational Bayesian framework to derive the specific form of minimum mean square error (MMSE) estimator. Simulations are performed to verify the efficiency of our proposed method and the accuracy of our analysis.
ANN-Assisted Multi-cloud Scheduling Recommender

Cloud computing has been widely adopted, in the forms of public clouds and private clouds, for many benefits, such as availability and cost-efficiency. In this paper, we address the problem of scheduling jobs across multiple clouds, including a private cloud, to optimize cost efficiency explicitly taking into account data privacy. In particular, the problem in this study concerns several factors, such as data privacy of job, varying electricity prices of private cloud, and different billing policies/cycles of public clouds, that most, if not all, existing scheduling algorithms do not 'collectively' consider. Hence, we design an ANN-assisted Multi-Cloud Scheduling Recommender (MCSR) framework that consists of a novel scheduling algorithm and an ANN-based recommender. While the former scheduling algorithm can be used to schedule jobs on its own, their output schedules are also used as training data for the latter recommender. The experiments using both real-world Facebook workload data and larger scale synthetic data demonstrate that our ANN-based recommender cost-efficiently schedules jobs respecting privacy.
Classification of objects of natural hyperspectral images

This article describes the process of determination of the classification of objects or zones in a hyperspectral image by classes: forest, water, earth. There are given methods and algorithms for finding a relation of the member to a particular class. Further prospects for the development of the algorithm on large volumes of data and the possibilities for improving the algorithm are described.
Q-Learning-Based Adaptive Bacterial Foraging Optimization

As a common biological heuristic algorithm, Bacterial Foraging Optimization (BFO) is often used to solve optimization problems. Aiming at increasing the solution accuracy and convergence performance while enhancing the capability of individual's self-learning and exploration, a Q-Learning-Based Adaptive Bacterial Foraging Optimization (QABFO) is proposed in this paper. The basic chemotaxis, reproduction and elimination/dispersal operations in standard BFO are redesigned in a Q-learning mechanism, and the Q-table will be updated on the basis of the changed fitness values in each iteration. In chemotaxis operation, modified search direction and secondary cruising mechanism are introduced into tumbling and swimming behaviors, with the purpose of improving the search efficiency and balancing local and global search. Additionally, a generation skipping adaptive reproduction is designed to control the accuracy and convergence of QABFO. Experimental results demonstrate that compared with BFO, PSO and GA, the proposed algorithm performs better in terms of accuracy and stability on most of the test functions and can effectively improve the premature convergence problem due to the original reproduction operation in BFO.
Design and Implementation of Pulse-Coupled Phase Oscillators on a Field-Programmable Gate Array for Reservoir Computing

Reservoir computing (RC) has been viewed as a model of a neurological system. The RC framework constructs a recurrent neural network, which mimics parts of the brain, to solve temporal problems. To construct a neural network inside a reservoir, we adopt the pulse-coupled phase oscillator (PCPO) with neighbor topology connections on a field-programmable gate array (FPGA). Neural spikes for the PCPO are generated by the Winfree model. The low resource consumption of the proposed model in time-series generation tasks was confirmed in an evaluation study. We also demonstrate that on the FPGA, we can expand a 3 $$\times $$ 3 PCPO into a 10 $$\times $$ 10 PCPO, generate spike behavior, and predict the target signal with a maximum frequency of 418.796 MHz.
The Annotation Scheme of English-Chinese Clause Alignment Corpus

A clause complex consists of clauses, which are connected by component sharing relations and logic-semantic relations. Hence, clause-complex level structural transformations in translation are concerned with the expression adjustment of these two types of relations. In this paper, a formal scheme for tagging structural transformations in English-Chinese translation is designed. The annotation scheme include 3 steps operated on two grammatical levels: parsing an English clause complex into constructs and assembling construct translations on the clause complex level; translating constructs independently on the clause level. The assembling step involves 2 operations: performing operation functions and inserting Chinese words. The corpus annotation shows that it is feasible to divide structural transformations in English-Chinese translation into 2 levels. The corpus, which unfolds formally the operations of clause-complex level structural transformations, would help to improve the end-to-end translation of complicated sentences.
Secure data exchange between IoT endpoints for energy balancing using distributed ledger

This paper investigates a secure data exchange between many small distributed consumers/prosumers and the aggregator in the process of energy balancing. It addresses the challenges of ensuring data exchange in a simple, scalable, and affordable way. The communication platform for data exchange is using Ethereum Blockchain technology. It provides a distributed ledger database across a distributed network, supports simple connectivity for new stakeholders, and enables many small entities to contribute with their flexible energy to the system balancing. The architecture of a simulation/emulation environment provides a direct connection of a relational database to the Ethereum network, thus enabling dynamic data management. In addition, it extends security of the environment with security mechanisms of relational databases. Proof-of-concept setup with the simulation of system balancing processes, confirms the suitability of the solution for secure data exchange in the market, operation, and measurement area. For the most intensive and space-consuming measurement data exchange, we have investigated data aggregation to ensure performance optimisation of required computation and space usage.
Regular Expression Learning from Positive Examples Based on Integer Programming

This paper presents a novel method to infer regular expressions from positive examples. The method consists of a candidate’s construction phase and an optimization phase. We first propose multiscaling sample augmentation to capture the cycle patterns from single examples during the candidate’s construction phase. We then use common substrings to build regular expressions that capture patterns across multiple examples, and we show this algorithm is more general than those based on common prefixes or suffixes. Furthermore, we propose a pruning mechanism to improve the efficiency of useful common substring mining, which is an important part of common substring-based expression building algorithm. Finally, in the optimization phase, we model the problem of choosing a set of regular expressions with the lowest cost as an integer linear program, which can be solved to obtain the optimal solution. The experimental results on synthetic and real-life samples demonstrate the effectiveness of our approach in inferring concise and semantically meaningful regular expressions for string datasets.
Enhanced peer-to-peer anonymity approach for privacy preserving in location-based services

Nowadays, Location-based services (LBS) are important services that take benefits from the revolution in communications. Nevertheless, user’s privacy is considered as significant challenge that could impede the use of this type of services. Current Privacy-preserving techniques mainly preserve location not query privacy (i.e., query issuer identification). The untrusted LBS provider (adversary) can breach user privacy in case that it has some user background knowledge and caches queries from more than one user in the same anonymity region (group). These types of attacks use users’ profiles and cached queries to predict semantically the issuer of each query. In this paper, a peer-to-peer privacy-preserving model is presented to protect the user privacy against these types of attacks taking into account the users’ profiles and cached queries in the LBS server. Using this model, an inference algorithm for predicating semantically the issuer of each query and her/his underlying location is presented to check the probability that a query privacy could be breached. A set of experiments is performed to check the effectiveness of the proposed privacy-preserving model. The results show that the cached queries with semantic matching affect negatively in breaching the query and location privacy.
Extracting Advertisements from Content Marketing Articles based on TopicCNN

With the rapid development of Internet technology, online social networks have been an essential part of people's life. With the increase in the number of users on various social platforms, more and more companies begin to value the importance of social media marketing. One of the most common ways is Content marketing (CM), which inserts advertisements into regular articles in a roundabout and covert way. However, CM articles often contain false and exaggerated information, misleading consumers, and harming their economic interests. The advertisement also affects the reading experience of users and affects the accuracy of data analysis. Extracting advertisements from CM articles is beneficial to both users and social platforms. Generally speaking, the topics of the inserted advertisement are very different from that of the regular content. Moreover, advertisement usually contains more positive words and phrases to attract more attention. According to these characteristics, we propose a method for identifying the content of advertisements based on the fusion of topic and semantic features, which are used to train a supervised classifier based on a manually labeled dataset. We also conduct extensive experiments to validate the effectiveness of the proposed method.
Microscheduling and Randomization Strategies in Distributed and Cloud Computing

In this work, we study heuristic and randomized approaches for parallel jobs execution in high performance computing environments with heterogeneous resources. Traditional process of a job-flow scheduling implies many requirements for jobs execution criteria and priority and, thus, usually cannot be changed. However, special microscheduling heuristics may be used in such scenarios as a secondary optimization based on the primary scheduling procedure. Coordinated and randomized microscheduling approaches simulate different strategies for the whole job queue scheduling in order to select the most resource-efficient scenarios. Based on a conservative backfilling scheduling procedure we study how different resources allocation heuristics affect integral job-flow scheduling characteristics in a dedicated simulation environment.
Method for Construction of Highly Robust Watermarks in the Task of Copyright Protection for Digital Images

In this paper, the method for construction of highly-robust digital watermarks is proposed. The so-called noise-like watermarks are synthesized artificially using a key sequence, serving as verification information during the extraction process. The main goal of the research is to investigate the possibility of the method application for the protection of raster images against illegal distribution. The constructed watermarks are embedded into bitmap images using the least significant bit (LSB) strategy. It is shown that, due to the unique features of the noise-like watermarks, the robustness of LSB schemes can be significantly increased, which allows to provide the possibility of their application in the task of protection against illegal distribution. The results of the conducted experiments demonstrate clear advantages of the proposed watermarking method over existing analogues: the noise-like watermark images possess high robustness against carrier modifications and malicious attacks, while the embedding into the spatial domain does not significantly decrease the carrier quality.
Video-Interfaced Human Motion Capture Data Retrieval Based on the Normalized Motion Energy Image Representation

To retrieve expected clips from human motion capture (MoCap) databases for effective reuse with a user-friendly interface is a challenging task. In this work, we propose an effective video-based human MoCap data retrieval scheme, which lets the user act in front of a video camera to specify the query and searches for similar motion clips in the MoCap database. Specifically, we propose a novel normalized motion energy image (NMEI) representation to bridge the representational gap between video clips and MoCap clips. Then, the discriminative feature of each NMEI is extracted by computing its local augmented Gabor features, constructing its identity vector in a learned variability space, and making a subspace projection by linear discriminative analysis. Finally, effective similarity metric between the extracted features of any two NMEIs is designed. Experimental results demonstrate the promising performance of the proposed video-based MoCap data retrieval approach.
Text Classification Based on A New Joint Network

In order to solve the problem that single convolutional neural network (CNN) lacks the ability to make use of text context information and that simple recurrent neural network (RNN) cannot solve the problem of long-time dependence, this paper proposes the joint network (BL-CN) combine bidirectional long short term memory network (BiLSTM) with capsule network (CapsNet) for text classification. CapsNet is an improved version of CNN, which can retain more text features, increase the diversity of features, and capture the local pluralistic features of sentences. Combined with BiLSTM which has ability to extract context-related features. It can get not only the local phrase features with multiple information, but also the sequence structure features of text and the bidirectional semantic dependence of sentence, which can more comprehensively express the text features. Experiments were carried out on the data sets of IMDB and THUCNews to compare the accuracy and loss of each single network and the combined network respectively. The results show that the model proposed in this paper can improve the accuracy of text classification.
Triple Attention Network for Clothing Parsing

Clothing parsing has been actively studied in the vision community in recent years. Inspired by the color coherence for clothing and the self-attention mechanism, this paper proposes a Triple Attention Network (TANet) equipped with a color attention module, a position attention module and a channel attention module, to facilitate fine-grained segmentation of clothing images. Concretely, the color attention module is introduced for harvesting color coherence, which selectively aggregates the color feature of clothing. The position attention module and the channel attention module are designed to emphasize the semantic interdependencies in spatial and channel dimensions respectively. The outputs of the three attention modules are incorporated to further improve feature representation which contributes to more precise clothing parsing results. The proposed TANet has achieved 69.54 $$\%$$ mIoU - a promising clothing parsing performance on ModaNet, the latest large-scale clothing parsing dataset. Especially, the color attention module is also demonstrated to bring semantic consistency and precision improvement obviously. The source code is made available in the public domain.
Generative approach to unsupervised deep local learning

Most existing feature learning methods optimize inflexible handcrafted features and the affinity matrix is constructed by shallow linear embedding methods. Different from these conventional methods, we pretrain a generative neural network by stacking convolutional autoencoders to learn the latent data representation and then construct an affinity graph with them as a prior. Based on the pretrained model and the constructed graph, we add a self-expressive layer to complete the generative model and then fine-tune it with a new loss function, including the reconstruction loss and a deliberately defined locality-preserving loss. The locality-preserving loss designed by the constructed affinity graph serves as prior to preserve the local structure during the fine-tuning stage, which in turn improves the quality of feature representation effectively. Furthermore, the self-expressive layer between the encoder and the decoder is based on the assumption that each latent feature is a linear combination of other latent features, so the weighted combination coefficients of the self-expressive layer are used to construct a new refined affinity graph for representing the data structure. We conduct experiments on four datasets to demonstrate the superiority of the representation ability of our proposed model over the state-of-the-art methods.
A New Similarity Space Tailored for Supervised Deep Metric Learning

We propose a novel deep metric learning method. Differently from many works in this area, we define a novel latent space obtained through an autoencoder. The new space, namely S-space, is divided into different regions describing positions where pairs of objects are similar/dissimilar. We locate makers to identify these regions and estimate the similarities between objects through a kernel-based Cauchy distribution to measure the markers’ distance and the new data representation. In our approach, we simultaneously estimate the markers’ position in the S-space and represent the objects in the same space. Moreover, we propose a new regularization function to prevent similar markers from collapsing altogether. Our method emphasizes the group property (separability) while preserving instance representativity. We present evidence that our proposal can represent complex spaces, for instance, when groups of similar objects are located in disjoint regions. We compare our proposal to nine different distance metric learning approaches (four of them are based on deep learning) on 28 real-world heterogeneous datasets. According to the four quantitative metrics used, our method overcomes all of the nine strategies from the literature.
Multiplexer-based High-speed S-box Architecture for PRESENT Cipher in FPGA

The Internet of Things (IoT) is the technology that will revolutionize our world. It is a system of interrelated devices linked to the Internet. It comprises large number of resource-constrained devices, machines and sensors. Actualization of IoT requires large amount of devices like sensors and actuators which requires large power but with bounded power supply and area. The aim of lightweight cryptography is to provide security solution for resource-constrained devices. The conventional cryptography algorithms are slow, less responsive and require more energy and storage space as compared to lightweight algorithm. Therefore, researchers are more oriented toward lightweight-based algorithms. This chapter discusses hardware implementation of PRESENT cipher on a field-programmable gate array (FPGA) platform. In this work, our goal is to improve its performance in terms of speed and minimize its area as much as possible. In our proposed design, we have implemented a combinational S-box through multiplexers and some logic gates. In this chapter, various platforms for implementation such as Spartan-6, Virtex-5 and Virtex-4 are used, and as a result, it achieves high throughput per slice for PRESENT cipher as well as highest maximum frequency.