Reviewerly/exHarmony huggingface dataset:
Multi-scale Gem Pooling with N-Pair Center Loss for Fine-Grained Image Search

Most existing fine-grained image retrieval schemes are built based upon deep feature learning paradigms, which typically leverage the feature maps of the last convolutional layer as features. However, such representation focuses only on the global information of the object, leaving the local details unexploited, which is however crucial to identifying subtle differences for fine-grained retrieval. In this paper, we have discovered that the mid-level feature map roles as local salient regions, which well complements the existing global feature representations. To this end, a multi-layer framework is proposed to integrate both local and global representations with generalized mean (GeM) pooling and attention mechanism, trained with the proposed N-pair Center loss to learn more discriminative features. By doing so, state-of-the-art performance can be achieved without using the hard or negative example minings. In the experiments, our approach outperforms favourably compared to the current state-of-the-art methods on the CUB-200-2011, CARS196 and In-shop Clothes Retrieval datasets.
Application Advantages and Prospects of Web-Based AR Technology in Publishing

Web-based Augmented Reality technology (WebAR) is a new hot spot in the development of AR technology. Its lightweight, easy-to-propagate and cross-platform communication features will break through the limitations of the existing Application-based Augmented Reality (APP AR) mode. The paper first analyzes the development status and technical advantages of WebAR. Next, the publishing mode of WebAR application in publishing is compared with the current popular APP AR publishing mode to expound the characteristics of WebAR publishing mode. Then, combined with WebAR's technical advantages and characteristics of publishing and distribution mode, this paper analyzes the advantages its application in publishing brings to publishers and readers. Finally, the application prospect of WebAR in the publishing of three-dimensional textbooks and sci-tech periodicals is briefly discussed.
Improved Consensus Mechanisms Against Censorship Attacks

The censorship attack is ubiquitous in prevailing blockchains, such as Bitcoin and Ethereum, yet has not been resolved well so far. According to known vulnerabilities of different consensus algorithms, censorship attacks can always achieve great benefits at low costs by various means. In this paper, we propose improvements to existing POS and POW algorithms to resolve censorship attacks in two methods. The first method improves the Tendermint consensus mechanism by introducing three new types of messages and an auxiliary role of network nodes. The method can automatically defend against censorship attacks and organize an honest chain. The second method is based on the CasperFFG consensus mechanism and can accurately identify the attackers by evaluating the suspicious scores of all validators. However, an honest validator has to manually choose the honest chain. Theoretical analyses show their effectiveness.
Support for User Generated Evolutions of Goal Models

Goal models are used in early phase requirements engineering to elicit stakeholders' intentions, analyze dependencies, and help stakeholders make trade-off decisions about the project and its interaction with the environment. The Evolving Intentions framework extended goal model analysis to evaluate how models change over time, by creating simulation paths showing possible evolutions of the model. More recently, we extended this analysis to allow users to explore states along the path and generate their own simulation paths. However, this approach is limited by users' ability to comprehend the state space, which grows exponentially with the size of the model. In this paper, we explore using filters to reduce the number of viewable solutions enabling users to create their own simulation results. We present our approach and initial validation, including an analysis of prior models and a review of expert feedback.
An Integrated Camera Parameters Calibration Approach for Robotic Monocular Vision Guidance

A core problem in robotics is to determine the position and orientation of an industrial robot in its working environment. The basic principle of vision-based robotic positioning technology relies on optical sensors that can provide tremendous amount of data. Monocular vision guidance of the robot requires firstly computing the intrinsic and extrinsic camera parameters from a set of physical features. In this paper, a camera calibration method combining Zhang Zhengyou's calibration method and PnP solution together is introduced to establish the three dimensional position and orientation of camera. Compared with other methods, this method does not require too much experimental data, it only requires the aid of a checkerboard and a white paper with several feature points. Moreover, the obtained camera parameters have high precision that can meet the requirements of a general robot monocular vision guiding system.
Music as language: putting probabilistic temporal graph grammars to good use

Music composers have long been attracted by the idea of an automated tool for music generation, that is able to aid them in their day-to-day compositional process. We focus on algorithmic composition techniques that do not aim to produce complete music pieces, but rather provide an expert composer with a source of copious amounts of musical ideas to explore.
Discovering reliable evidence of data misuse by exploiting rule redundancy

Big Data offers opportunities for in-depth data analytics and advanced personalized services. Yet, while valuable, data analytics might rely on data that should not have been used due to, e.g., privacy constraints from the data subject or regulations. As decision makers and data controllers often act outside any control mechanism and with no requirement of transparency, it is challenging to verify whether constraints on data usage are actually satisfied. In this work, we relate the problem of finding evidence of data misuse to the identification of unique decision rules, i.e. rules that have likely been used for decision making. Accordingly, we propose an approach to find reliable evidence of data misuse in the context of classification problems using association rule mining, along with novel metrics to assess the level of redundancy among decision rules. Our proposed approach is able to identify the use of sensitive information in decisional processes along with their context. We evaluated our approach through both controlled experiments and two case studies using real-life event data. The results show that our approach finds more reliable evidence of data misuse compared to previous work.
Hierarchical Persian Text Categorization in Absence of Labeled Data

Hierarchical text categorization is used in many real-world applications, such as webpage topic classification and product categorization. Large quantities of labeled training data are needed to build an accurate hierarchical classification model. However, labeled samples are difficult and very time-consuming to obtain. On the other hand, due to the expansion of the Internet, plenty of unlabeled documents are available. In this paper, we propose a top-down method to hierarchically categorize partially labeled documents (having labeled documents only at first few levels of the hierarchy) using “local classifier per parent node” approach. We utilize a classification algorithm for the parent nodes with training data available. We use a labeling strategy for other parent nodes that do not have labeled data to be able to train a classifier. In our knowledge, this is the first study on hierarchical Persian text categorization, and our experiments show that the proposed approach achieves an acceptable performance.
Underwater De-scattering Range-Gated Imaging Based on Numerical Fitting and Frequency Domain Filtering

One of the major challenges of underwater imaging is its sensitivity to water scattering. In this paper, a method of underwater de-scattering range-gated imaging based on numerical fitting and frequency domain filtering is developed to suppress scatter effect and improve image quality. The backscatter noise is eliminated by using its numerical fitting value after measuring a sequence of water scattering maps. The Jaffe-McGlamery computer model and the light propagation property in water are introduced, and forward-scatter noise is reduced by filtering process in the frequency domain. After removing the backscatter and forward-scatter noise, the resolution and contrast of gated images are improved. A range-gated imaging system is established, and experiments are conducted in pools to prove the effectiveness and superiority of the proposed method. Compared with traditional range-gated imaging, the results demonstrate that the proposed method increases the image entropy by about 69%. The research is beneficial for enhancing underwater range-gated active imaging.
Design and Implementation of Vehicle Unlocking System Based on Face Recognition

There are some shortcomings in both safety and convenience for existing vehicle unlocking methods, mostly due to the separation between the vehicle and its key. As an improvement, we propose a vehicle unlocking system based on face recognition. The system includes hardware and software. The hardware scheme adopts a modular design, and try to make full use of existing devices of the autonomous driving system and ADAS. The software uses two different deep learning algorithms: FaceNet to verify facial identity, and modified ResNet to distinguish between real faces and secondary faces. A prototype is implemented using the embedded platform Raspberry Pi, and a series of tests are carried out on it. The prototype verifies the feasibility of the hardware scheme and the effectiveness of the algorithms. Test results show that the system is available day and night, quick in unlocking, safe and reliable.
Private Data Aggregation in Decentralized Networks

Privacy-preserving data aggregation is growing in popularity due to the increasing amount of online services depending on user data. This information is privacy-sensitive, warranting the need for protection during data-processing. A wide variety of approaches have been considered to achieve privacy during the processing. Examples include differential privacy, masking, cryptographic techniques (e.g. using homomorphic encryption which enables data processing under encryption). In recent works, several approaches employing the latter privacy-preserving technique has been proposed that is proven to be secure in terms of sensitive data protection. However, the research mainly focuses mostly on efficiency rather than on the selected network topology. In contrast to existing work, we consider a decentralized network, where data can be aggregated without the presence of a central authority, such as an aggregator. We propose two novel protocols based on homomorphic encryption and secret sharing, respectively. Our analyses confirm our claims regarding high efficiency, scalability, and security.
Risk Prediction as a Service: a DSS Architecture Promoting Interoperability and Collaboration

Clinical research and practice are rapidly changing mostly due to Information and Communication Technology, especially, as Machine Learning (ML) offers great potential for predictive and personalised medicine. Nevertheless, barriers are still existing for widespread adoption of ML tools, as highlighted by studies from the European Union. In this paper, we propose an architecture for a Decision Support System assisting clinicians in assessing health risk of patients by delivering "Risk Prediction as a Service". By leveraging standard web technologies as well as the PMML and PFA formats for exchange of trained models, we achieve ubiquitous access to predictions, ease of deployment, and seamless interoperability, while promoting collaboration.
An Online Motion Planning Approach of Mobile Robots in Distinctive Homotopic Classes by a Sparse Roadmap

This paper presents a novel approach for online motion planning of mobile robots in distinctive homotopic classes. The approach contains two parts. Firstly, a local planner called TC-SQP (timed control and sequential quadratic programming) is proposed. An optimal trajectory is deformed from an initial path from a global planner, considering collision avoidance, time optimality and kino-dynamic constraints. Our approach takes the control variables rather than position variables as optimization variables in a time horizon. Hence, TC-SQP requires fewer optimization variables (3/4 comparing to the state-of-art method) and parameters. And the transition time is reduced by 14% from the global path. Then, to overcome the issue that there exist several trajectories of local minimum cost, a candidate of global paths of different homotopic classes is maintained by using a sparse graph spanner. These global paths are optimized in parallel, and the lowest cost trajectory is selected as the final trajectory. Our method can generate enough homotopic candidates, and it needs much fewer queries comparing with a Probability Road Map. Besides the simulations, some results are also proved theoretically.
A Parallel Multi-keyword Top-k Search Scheme over Encrypted Cloud Data

With searchable encryptions in the cloud computing, users can outsource their sensitive data in ciphertext to the cloud that provides efficient and privacy-preserving multi-keyword top-k searches. However, most existing top-k search schemes over encrypted cloud data are the centralize schemes which are limited in large scale data environment. To support scalable searches, we propose a parallel multi-keyword top-k search scheme over encrypted cloud data. In this scheme, the fragment-based encrypted inverted index is designed, which is indistinguishable and can be used for parallel searching. On the basis of such indexes, a Map-Reduce-based distributed computing framework is adopted to implement the parallel multi-keyword top-k search algorithms. Security analysis and experiment evaluation show that the proposed scheme is privacy-preserving, efficient and scalable.
The Design of Typical Balinese Food Recommendation System Using Hybrid Method of Collaborative Filtering and Slope One Algorithm on Mobile Device Platform

As a result of globalization in Indonesia especially Bali, the existence of fast food and food stalls owned by people from outside Bali, causing food stalls typical of Bali to be marginalized. Previous research by (Darmaja, 2016) succeded in making a recommendation system for Balinese food stalls on mobile platforms. The weakness seen from previous research documentation report, one of them is the use of Collaborative Filtering method which only use rating item as recommendation parameter of Balinese typical food stalls, causing new food stalls which do not have rating are less accessed in the system because they can not be recommended by system. The purpose of this study is that the typical Balinese food stalls that rarely get a rating can be recommended evenly with the help of ICHM (Item-based Clustering Hybrid Method) and Slope One algorithm. The result of the research is recommendation system of Bali food stalls in website and mobile platform by using throwaway prototype system development method, with recommendation accuracy based on the lowest MAE value of 0,11 and the highest is 1,06 and new items entered on the system (not yet rated) may be recommended based on item content in the ICHM method.
Learning Transmission Filtering Network for Image-Based Pm2.5 Estimation

PM2.5 is an important indicator of the severity of air pollution and its level can be predicted through hazy photographs caused by its degradation. Image-based PM2.5 estimation is thus extensively employed in various multimedia applications but is challenging because of its ill-posed property. In this paper, we convert it to the problem of estimating the PM2.5-relevant haze transmission and propose a learning model called the transmission filtering network. Different from most methods that generate a transmission map directly from a hazy image, our model takes the coarse transmission map derived from the dark channel prior as the input. To obtain a transmission map that satisfies the local smoothness constraint without regional boundary degradation, our model performs the edge-preserving smoothing filtering as the refinement on the map. Moreover, we introduce the attention mechanism to the network architecture for more efficient feature extraction and smoothing effects in the transmission estimation. Experimental results prove that our model performs favorably against the state-of-the-art dehazing methods in a variety of hazy scenes.
Logo Recognition by Combining Deep Convolutional Models in a Parallel Structure

In this paper, a new approach is proposed for logo recognition using deep convolutional neural networks. Precise recognition of logos is of high importance in several applications like intelligent traffic control systems and copyright infringement. To enhance the efficiency of logo recognition, we have employed several strategies. In the first strategy, pre-trained deep models are employed for deep feature extraction and classification using the Support Vector Machine (SVM) classifiers. In the second strategy, existing pre-trained deep models are modified for logo recognition after transfer learning and fine-tuning. Finally, fine-tuned models are employed in a parallel structure to enhance the efficiency of logo recognition. We tested the proposed structures with Logos32-plus dataset and the results showed that combining fine-tuned deep models using a voting algorithm gives rise to the best recognition rate of 98.4%. The comparison of results of the proposed structure with a state-of-art deep approach for logo recognition shows the efficiency of the proposed approach.
Survey of Smart Grid Network Using Drone &amp; PTZ Camera

Currently Drones become a brand new issue for Grid monitoring & security. As extremely innovative Drones area unit offered at the high value. Drones are facing various analysis & physical threats. The threats that may be caused by the attacks of drone has been enhanced. Recent object detection are dramatically improved in accuracy by victimization convolution neural networks. Sometimes GPS information will become defective underneath conduits, within subways, or close to high voltage (HV) power lines, that may result in flight inaccuracies. To optimize that, we ARE emerging a drone for whole grid structure checkups. Drones essentially use Global Positioning System (GPS) for independent control & monitoring, through image processing. This article defines a way for examine smart grid victimizing drones & employing a Pan-Tilt-Zoom (PTZ) camera. With the ambition of emerging associate self-directed flight & secure power lines for better Grid Performance & sort fault clearance with decreasing human life risk.
Generating of Sign System for Bahasa Indonesia (SIBI) Root Word Gestures Using Deep Temporal Sigmoid Belief Network

Sign language is a language that uses a combination of hand gestures and lip movements for people with hearing impairment to communicate. In Indonesia there are two sign language systems used, Sign System for Indonesian Language known as SIBI (Sistem Isyarat Bahasa Indonesia) recognizes as the official sign language system by the Indonesian Government. This research is focused on the generation process of skeleton sequence; in which represent a SIBI hand gesture excluding the finger joints. The hand skeleton that will be generated from the generation process is limited to root-word gestures only. Some researchers were using a Restricted Boltzmann Machine model and its variant known as Deep Belief Networks (DBN) to solve the sequence modelling problems. One of DBN variants is Sigmoid Belief Network (SBN). An SBN is a Bayesian network that models a binary visible vector. Deep Temporal Sigmoid Belief Network (DTSBN) is a sequence of SBNs (with deep architecture) arranged in such way that at any given time step has a fully generative process capability, where data are readily generated from the model using ancestral sampling. Since, DTSBN performance is quite novel for this particular case, we decided to implement the DTSBN model using the SIBI dataset from the previous research to construct generated hand-skeleton gestures which represent SIBI's root-word gestures. Based on the success of the experimental DTSBN model that has been successfully generated new skeleton sequences, which represent a SIBI hand gesture. Some of the inputs to the model include cartesian coordinates from shoulder joints, elbow joints, and wrist joints and the newly generated data are proven have no significant difference with the actual data set.
Monocular Vision-Based Dynamic Moving Obstacles Detection and Avoidance

In this paper, we proposed an UAV system for obstacle avoidance depending of a ground station processing based on monocular vision. To accomplish detection, tracking, proximity estimation and dynamic obstacles avoidance that are approaching the UAV, a series of methods and techniques are implemented. To detect movement, frame differentiation was applied to consecutive frames, once the moving object is detected, we detect Shi-Tomasi feature points to track the object using optical flow method Lucas-Kanade. To make possible proximity estimation, a linear regression method based on the area covered by the object was used. A fuzzy logic controller was designed to avoid the moving object, we considered the approach rate and the area of the object to control UAV’s position in relation to the object as fuzzy inputs.
Research on radar clutter recognition method based on LSTM

Considering the increasingly complex electromagnetic environment of radar detection, it still contains a lot of clutter after target detection, which is a challenging problem for subsequent target tracking. A radar clutter recognition method based on long short-term memory (LSTM) neural network is proposed here. This method can further identify clutter points after target detection and improve the quality of target tracking. LSTM neural network is trained and compared with K nearest neighbour method and support vector machine. It is found that the clutter recognition accuracy of this method is up to 86.3%, which is 12.2 and 4.1% higher than the latter two methods. At the same time, the target loss rate of this method is only 16.6%, which is 18.3 and 5.7% lower than the latter two methods respectively. The experimental results show that the radar clutter recognition method based on LSTM neural network is effective.
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Let Fq be the finite field of order q, where q is a power of an odd prime p, and p,ℓ are distinct odd primes, and m,n,K are positive integers. In this paper, the multiplicative group Fq∗=Fq∖{0} is decomposed into mutually disjoint union of gcd(2Kℓm,q−1) cosets of the cyclic group generated by ξ2Kℓmpn, where ξ is a primitive element of Fq. With the help of this decomposition, all constacyclic codes of length 2Kℓmpn over Fq are classified into gcd(2Kℓm,q−1) disjoint classes. Accordingly, generator polynomials of constacyclic codes of length 2Kℓmpn over the finite field Fq and their duals are explicitly determined in the cases, when q≡±1(mod2K). And also some complementary-dual, self-orthogonal, self-dual, dual-containing constacyclic codes of length 2Kℓmpn over Fq are determined.