Reviewerly/exHarmony huggingface dataset:
Graph-based particular object discovery

Severe background clutter is challenging in many computer vision tasks, including large-scale image retrieval. Global descriptors, which are popular due to their memory and search efficiency, are especially prone to corruption by such a clutter. Eliminating the impact of the clutter on the image descriptor increases the chance of retrieving relevant images and prevents topic drift due to actually retrieving the clutter in the case of query expansion. In this work, we propose a novel salient region detection method. It captures, in an unsupervised manner, patterns that are both discriminative and common in the dataset. Saliency is based on a centrality measure of a nearest neighbor graph constructed from regional CNN representations of dataset images. The proposed method exploits recent CNN architectures trained for object retrieval to construct the image representation from the salient regions. We improve particular object retrieval on challenging datasets containing small objects.
Aberration-reduced spherical concave grating holographically recorded by a spherical wave and a toroidal wave for Rowland circle mounting

We propose a new recording system that employs a spherical wave and a toroidal wave to holographically record an aberration-reduced Rowland-circle spherical grating. Two more variables in this recording system are available for the design compared with a conventional symmetric dual-plane-wave recording system, which allows both the astigmatism and sagittal coma of the grating to be corrected to minimum. We derive the aberration coefficients of such grating and give the universal design principles for the recording system. An aberration-reduced grating used in a spectrometer equipped with linear array CCDs is then specifically designed and fabricated. The performances of the aberration-reduced grating and that of the conventional grating recorded by dual plane waves are compared in terms of the ray-tracing spot diagrams, and the photographed spectral images. The experimental results show that the spectral intensity of the aberration-reduced grating is about 4.5 times that of the conventional grating, while the resolution of the aberration-reduced grating is close to that of the conventional grating.
Machine Learning, Data Mining for IoT-Based Systems

This chapter will addresses challenges with the internet of things (IoT) and machine learning (ML), how a bit of the trouble of machine learning executions are recorded here and should be recalled while arranging the game plan, and the decision of right figuring. Existing examination in ML and IoT was centered around discovering how garbage in will convey garbage out, which is extraordinarily suitable for the extent of the enlightening list for machine learning. The quality, aggregate, availability, and decision of data are essential to the accomplishment of a machine learning game plan. Therefore, the point of this section is to give an outline of how the framework can utilize advancements alongside machine learning and difficulties get a kick out of the chance to understand the security challenges IoT can be bolstered. There are a few extensively unmistakable counts open for ML use. In spite of the way that counts can work in any nonexclusive conditions, there are specific standards available about which figuring would work best under which conditions.
AFCoin: A Framework for Digital Fiat Currency of Central Banks Based on Account Model

Currently, the technique choices of issuing digital fiat currencies include RSCoin, Corda and Quorum etc. RSCoin is specially designed for central banks based on Bitcoin. Corda and Quorum are permissioned distributed ledger techniques based on Bitcoin and Ethereum, respectively. There lacks a framework specially for central banks based on Ethereum. We here introduce AFCoin: a framework based on the account model and smart contract of Ethereum. AFCoin shows a possible way to issue and manage fiat currencies by central banks. It can be deployed in an evolutionary way and enjoys good efficiency, regulation and privacy properties.
Walking Simulation for VR Game Character Using Remote Sensoring Device Based on AHRS-Motion Recognition

In this paper, the remote sensoring device for controlling the avatar in the virtual reality (VR) game which is being worn by the head-mounted display (HMD) was investigated. This remote sensoring device, which people use while sitting on a swivel chair, minimizes sensor attachment, and removes parameters for pelvic rotation angle in standing position. The rotating chair, which also functions as the center-weighted stationary belt of the conventional treadmill-type input device, has the advantage that the head trekking function mounted on the HMD can be utilized freely and the balance feeling of the user can be stabilized in a sitting position on the chair. In this posture, the sensoring input device was developed using Bluetooth wireless communication to freely turn and walk. Using the data from the inertial measurement device, which was a combination of the speedometer and the accelerometer, a sensoring input device for recognizing the user's leg motion was created. The running speed of the recognized leg was designed to be close to the average walking speed and the running speed of the person, so that the running and walking in the game can be separated and implemented. The motion velocity of the leg would be calculated through each speedometer measurement, the value would be integrated, and the error would be corrected by the Kalman filter to obtain the user's knee angle. In other words, applying a Kalman filter to data obtained from sensors, such as accelerometers, angular speedometers, and geomagnetic sensor can determine the direction of motion of the legs. This integrated system is called the attitude and heading references system. The knee joint angle and the value of the pressure sensor were used as threshold values to determine the state of the leg when walking, and the angular velocity derived from the movement of the leg was used to determine the moving speed of the avatar in the VR game. The recognition rate was calculated by comparing the number of times the user attempts to move the avatar by moving the avatar and the number of times the avatar of the game was moved, and the distance of the avatar was measured to evaluate the performance of the proposed system.
The Impact of Pleasure Reading on Enhancing Writing Achievement and Reading Comprehension

Reading opens up wide-reaching paths for knowledge as it deepens the feelings of empathy.Therefore, it represents a unique skill consists of a mixture of several procedures that encourage the interaction with printed words for content and pleasure.The current study tries to identify the impact of pleasure reading on enhancing writing achievement and reading comprehension.The study has employed different data collection methods, such as published research, articles, books, theses, and dissertations that are related to the study matter to provide answers to the following question: ' Do pleasure reading have an impact on enhancing writing achievement and reading comprehension?'.The results reveal that; pleasure reading has a positive impact on enhancing writing achievement and reading comprehension.In addition to, pleasure reading provides readers with entertainment, relaxation, comfort, as it also offers them a creative outlet and means of escape, which, in turn, affect positively effect on the writing performance.Moreover, reading helps learners develop their writing experience by inspiring them, expanding their vocabulary, and improving their grammatical structure.The current study recommends applying more reading programs into schools to encourage reading.It also suggests conducting more studies in order to realize the true potential of reading and its impact on learners' written works and reading comprehension.
Pruning filters with L1-norm and standard deviation for CNN compression

Convolution Neural Networks (CNN) have evolved to be the state-of-art technique for machine learning tasks. However, CNNs bring a significant increase in the computation and parameter storage costs, which makes it difficult to deploy on embedded devices with limited hardware resources and a tight power budget. In recent years, people focus on reducing these overheads by compressing the CNN models, such as pruning weights and pruning filters. Compared with the method of pruning weights, the method of pruning filters does not result in sparse connectivity patterns. And it is conducive to the parallel acceleration on hardware platforms. In this paper, we proposed a new method to judge the importance of filters. In order to make the judgement more accurate, we use the standard deviation to represent the amount of information extracted by the filter. In the process of pruning, the unimportant filters can be removed directly without loss in the test accuracy. We also proposed a multilayer pruning method to avoid setting the pruning rate layer by layer. This holistic pruning method can improve the pruning efficiency. In order to verify the effectiveness of our algorithm, we do experiments with simple network VGG16 and complex networks ResNet18/34. We re-trained the pruned CNNs to compensate the accuracy loss caused by the pruning process. The results showed that our pruning method can reduce inference cost by up to 50% for VGG16 and 35% for ResNet18/34 on CIFAR10 with little accuracy loss.
An Online Calibration Method for a Galvanometric System Based on Wavelet Kernel ELM

The online calibration method of a two-dimensional (2D) galvanometer requires both high precision and better real-time performance to meet the needs of moving target position measurement, which presents some challenges for traditional calibration methods. In this paper, a new online calibration method is proposed using the wavelet kernel extreme learning machine (KELM). Firstly, a system structure is created and its experiment setup is established. The online calibration method is then analyzed based on a wavelet KELM algorithm. Finally, the acquisition methods of the training data are set, two groups of testing data sets are presented, and the verification method is described. The calibration effects of the existing methods and wavelet KELM methods are compared in terms of both accuracy and speed. The results show that, for the two testing data sets, the root mean square errors (RMSE) of the Mexican Hat wavelet KELM are reduced by 16.4% and 38.6%, respectively, which are smaller than that of the original ELM, and the standard deviations (Sd) are reduced by 19.2% and 36.6%, respectively, indicating the proposed method has better generalization and noise suppression performance for the nonlinear samples of the 2D galvanometer. Although the online operation time of KELM is longer than ELM, due to the complexity of the wavelet kernel, it still has better real-time performance.
Public-Key Encryption With Keyword Search via Obfuscation

Public-key encryption with keyword search (PEKS) enables users to search on encrypted data, which is applicable to the scenario of sharing data in the cloud storage.In this paper, we focus on how to construct a PEKS scheme via obfuscation.Our basic scheme is built on the differing-inputs obfuscation (diO) and can be considered as an initial attempt to apply diO in the PEKS field.The scheme supports searching on encrypted data by providing to the cloud server an obfuscated simple ''decrypt-then-compare'' circuit with the secret key and the queried keyword hardwired in it.More interestingly, the scheme can be simply improved to resist off-line keyword guessing attacks (KGAs) as the standard PEKS scheme rather than a designated tester one.For complex search conditions, our scheme can be easily extended to multiple functionalities, such as conjunctive and fuzzy keyword search.Furthermore, it can be extended to the PEKS scheme in the multi-user setting.
Learning Hierarchical Teaching in Cooperative Multiagent Reinforcement Learning.

Heterogeneous knowledge naturally arises among different agents incooperative multiagent reinforcement learning. As such, learning can be greatlyimproved if agents can effectively pass their knowledge on to other agents.Existing work has demonstrated that peer-to-peer knowledge transfer, a processreferred to as action advising, improves team-wide learning. In contrast toprevious frameworks that advise at the level of primitive actions, we aim tolearn high-level teaching policies that decide when and what high-level action(e.g., sub-goal) to advise a teammate. We introduce a new learning to teachframework, called hierarchical multiagent teaching (HMAT). The proposedframework solves difficulties faced by prior work on multiagent teaching whenoperating in domains with long horizons, delayed rewards, and continuousstates/actions by leveraging temporal abstraction and deep functionapproximation. Our empirical evaluations show that HMAT accelerates team-widelearning progress in difficult environments that are more complex than thoseexplored in previous work. HMAT also learns teaching policies that can betransferred to different teammates/tasks and can even teach teammates withheterogeneous action spaces.
Approximated computing for low power neural networks

This paper investigates about the possibility to reduce power consumption in Neural Network using approximated computing techniques. Authors compare a traditional fixed-point neuron with an approximated neuron composed of approximated multipliers and adder. Experiments show that in the proposed case of study (a wine classifier) the approximated neuron allows to save up to the 43% of the area, a power consumption saving of 35% and an improvement in the maximum clock frequency of 20%.
A Novel Features Set for Internet Traffic Classification using Burstiness

Traffic classification is an essential tool for network management and security. Traditional techniques such as port-based and payload analysis are ineffective as major Internet applications use dynamic port numbers and encryption. Recent studies have used statistical properties of flows to classify traffic with high accuracy, minimising the overhead limitations associated with other schemes such as deep packet inspection (DPI). Classification accuracy of statistical flow-based approaches, however, depends on the discrimination ability of the traffic features used. To this effect, the present paper customised the popular tcptrace utility to generate classification features based on traffic burstiness and periods of inactivity (idle time) for everyday Internet usage. An attempt was made to train a C5.0 decision tree classifier using the proposed features for eleven different Internet applications, generated by ten users. Overall, the newly proposed features reported a significant level of accuracy (~98%) in classifying the respective applications.
Positive Data Modeling Using a Mixture of Mixtures of Inverted Beta Distributions

Finite mixture models based on the symmetric Gaussian distribution have been applied broadly in data analysis. However, not all the data in real-world applications can be safely supposed to have a symmetric Gaussian form. This paper presents a new mixture model that includes the inverted Beta mixture model (IBMM) as a special case to analyze the positive non-Gaussian data. The advantage of the proposed model is that the number of the model parameters is variable and infinite. Consequently, the proposed model is adaptable to the size of the data. On the basis of the recently proposed extended variational inference (EVI) framework, we develop a closed-form solution to approximate the posterior distributions. The performance and the effectiveness of the proposed model are demonstrated with the real data generated from two challenging applications, namely, image classification and object detection.
Peran Kepuasan Belajar Dalam Mengukur Mutu Pembelajaran Dan Hasil Belajar

&lt;p&gt;&lt;em&gt;Student’s learning satisfaction is one of measuring tools of the success of learning process which would be realized through learning outcomes. This statement has been widely proven through various studies that have been done related to the learning satisfaction and learning outcomes. This article aims to describe three main points relating to the vital position of learning satisfaction as a determinant factor of student learning outcomes namely; (1) factors affecting learning satisfaction, (2) the influence of learning satisfaction on learning outcomes, and (3) how to measure learning satisfaction as well as examples of instruments that have been developed and used by researchers. The main source of this article was the research results from around the world that raised the learning satisfaction as the main topic. This paper was expected to provide an additional knowledge about learning satisfaction variables, so it can be utilized to carry out more in-depth research.&lt;/em&gt;&lt;/p&gt;
The profile of the international cyber offender in the U.S.

This study explores the characteristics of international cyber offenders prosecuted in the U.S. Our findings to a large extent correspond with general studies about cyber offenders with a few important exceptions. First, the average age of the offenders in our study is slightly higher than others that do not focus exclusively on international offenders. Second, while this research confirms that China is among the leading country in committing cybercrimes when it comes to committing particular types of cybercrimes, the offenders come from other countries as well such as Romania, Estonia, Ukraine, South Africa, and Nigeria. Third, our results show that in each of the cases from the sample, the international offenders received prison sentences alone, or complemented with a fine or restitution. In addition, the sentence length of citizens of African countries is significantly higher than the ones of citizens of other geographic regions. Prison sentences for cyber frauds and identity thefts were also found to be much lengthier than sentences for other types of cybercrimes. Implications are provided.
Location-Aware Web Service Composition Based on the Mixture Rank of Web Services and Web Service Requests

Web service composition is widely used to extend the function of web services. Different users have different requirements of QoSs (Quality of Services) making them face many problems. The requirement of a special QoS may be a hard requirement or a soft requirement. The hard requirement refers to the QoS which must be satisfied to the user, and the soft one means that the requirement is flexible. This paper tries to solve the service composition problem when there are two kinds of requirements of QoSs. To satisfy various kinds of requirement of the QoS, we propose a composition method based on our proposed framework. We give an analysis from composition models of services and from related QoE (Quality of Experience) of web services. Then, we rank the service candidates and the service requests together. Based on the ranking, a heuristics is proposed for service selection and composition-GLLB (global largest number of service requests first, local best fit service candidate first), which uses “lost value” in the scheduling to denote the QoE. Comparisons are used to evaluate our method. Comparisons show that GLLB reduces the value of NUR (Number of Unfinished service Requests), FV (Failure Value), and AFV (Average Failure Value).
APPLICATION OF A GROUNDWATER MODELLING SYSTEM IN GROUNDWATER ENVIRONMENTAL IMPACT ASSESSMENT OF RIVER AND LAKE CONNECTION IN WESTERN JILIN REGION

Whereas groundwater numerical simulation is an essential part of groundwater environmental impact analysis, the authors propose a more accurate and suitable method for identifying and predicting the groundwater environment, laying a scientific basis for groundwater environmental impact evaluation.Targeted at the river and lake connection project in Jilin Province, this paper establishes a groundwater prediction model based on the groundwater modelling system (GMS), and considers the land salinization risk posed by the variation in groundwater level after the water diversion of the project.The results show that the depth of phreatic water in each area is greater than the upper limit of ecological water level after the implementation of the project plan, leaving a small chance for soil salinization.
A public announcement separation logic

Abstract We define a Public Announcement Separation Logic (PASL) that allows us to consider epistemic possible worlds as resources that can be shared or separated, in the spirit of separation logics. After studying its semantics and illustrating its interest for modelling systems, we provide a sound and complete tableau calculus that deals with resource, agent and announcement constraints and give also a countermodel extraction method.
Lexicalized Neural Unsupervised Dependency Parsing

Unsupervised dependency parsing is the task of learning a dependency grammar from a set of unannotated sentences. This paper describes a neural network (NN) based probabilistic model, Neural Dependency Model with Valence (NDMV), that combines the dependency parsing with the rich nonlinear featurization of NN approaches. NDMV follows the traditional DMV model and computes the probability of a grammar rule via a feed-forward NN. A strength of this proposed approach is the ability to learn the underlying features of input tokens. In our experiments, this capability leads to gains in both NDMV and its extension version, Lexicalized Neural Dependency Model with Valence (L-NDMV): NDMV achieves better performance on WSJ and datasets of eight additional languages in comparison with previous approaches in the basic setting; L-NDMV achieves a result that is competitive with the current state-of-the-art.1
Object Tracking Based on Vector Convolutional Network and Discriminant Correlation Filters

Due to the fast speed and high efficiency, discriminant correlation filter (DCF) has drawn great attention in online object tracking recently. However, with the improvement of performance, the costs are the increase in parameters and the decline of speed. In this paper, we propose a novel visual tracking algorithm, namely VDCFNet, and combine DCF with a vector convolutional network (VCNN). We replace one traditional convolutional filter with two novel vector convolutional filters in the convolutional stage of our network. This enables our model with few memories (only 59 KB) trained offline to learn the generic image features. In the online tracking stage, we propose a coarse-to-fine search strategy to solve drift problems under fast motion. Besides, we update model selectively to speed up and increase robustness. The experiments on OTB benchmarks demonstrate that our proposed VDCFNet can achieve a competitive performance while running over real-time speed.