Reviewerly/exHarmony huggingface dataset:
A Low-Cost Image Encryption Method to Prevent Model Stealing of Deep Neural Network

Model stealing attack may happen by stealing useful data transmitted from embedded end to server end for an artificial intelligent systems. In this paper, we are interested in preventing model stealing of neural network for resource-constrained systems. We propose an Image Encryption based on Class Activation Map (IECAM) to encrypt information before transmitting in embedded end. According to class activation map, IECAM chooses certain key areas of the image to be encrypted with the purpose of reducing the model stealing risk of neural network. With partly encrypted information, IECAM can greatly reduce the time overheads of encryption/decryption in both embedded and server ends, especially for big size images. The experimental results demonstrate that our method can significantly reduce time overheads of encryption/decryption and the risk of model stealing compared with traditional methods.
One does not Simply RSVP: Mental Workload to Select Speed Reading Parameters using Electroencephalography

Rapid Serial Visual Presentation (RSVP) has gained popularity as a method for presenting text on wearable devices with limited screen space. Nonetheless, it remains unclear how to calibrate RSVP display parameters, such as spatial alignments or presentation rates, to suit the reader's information processing ability at high presentation speeds. Existing methods rely on comprehension and subjective workload scores, which are influenced by the user's knowledge base and subjective perception. Here, we use electroencephalography (EEG) to directly determine how individual information processing varies with changes in RSVP display parameters. Eighteen participants read text excerpts with RSVP in a repeated-measures design that manipulated the Text Alignment and Presentation Speed of text representation. We evaluated how predictive EEG metrics were of gains in reading speed, subjective workload, and text comprehension. We found significant correlations between EEG and increasing Presentation Speeds and propose how EEG can be used for dynamic selection of RSVP parameters.
A Novel Multi-Scale Feature Fusion Method for Region Proposal Network in Fast Object Detection

Neural network models have been widely used in the field of object detecting. The region proposal methods are widely used in the current object detection networks and have achieved well performance. The common region proposal methods hunt the objects by generating thousands of the candidate boxes. Compared to other region proposal methods, the region proposal network (RPN) method improves the accuracy and detection speed with several hundred candidate boxes. However, since the feature maps contains insufficient information, the ability of RPN to detect and locate small-sized objects is poor. A novel multi-scale feature fusion method for region proposal network to solve the above problems is proposed in this article. The proposed method is called multi-scale region proposal network (MS-RPN) which can generate suitable feature maps for the region proposal network. In MS-RPN, the selected feature maps at multiple scales are fine turned respectively and compressed into a uniform space. The generated fusion feature maps are called refined fusion features (RFFs). RFFs incorporate abundant detail information and context information. And RFFs are sent to RPN to generate better region proposals. The proposed approach is evaluated on PASCAL VOC 2007 and MS COCO benchmark tasks. MS-RPN obtains significant improvements over the comparable state-of-the-art detection models.
Continuous Prefetch for Interactive Data Applications

Interactive data visualization and exploration (DVE) applications, such as the one in Figure 1, have rapidly grown in popularity with use cases in numerous sectors [2, 4, 9, 11, 15]. Like typical web services, DVE applications may be run on heterogeneous client devices and networks, with users expecting fast response times under 100 ms [12]. However, the resource demands of DVE applications are magnified and highly unpredictable, making it difficult to achieve such interactivity.
Reconstructing Traditional Chinese Paintings with Immersive Virtual Reality

This paper describes the design of a virtual reality learning environment that reconstructs a traditional Chinese painting, Spring Morning in the Han Palace, using a head-mounted platform. The issues of art styles, enhanced interpretation and interaction design strategies that may impact learning experience are considered. We discuss the future plan for conducting user studies with young undergraduate students.
WithYou: Automated Adaptive Speech Tutoring With Context-Dependent Speech Recognition

Learning to speak in foreign languages is hard. Speech shadowing has been rising as a proven way to practice speaking, which asks a learner to listen and repeat a native speech template as simultaneously as possible. However, shadowing can be hard to do because learners can frequently fail to follow the speech and unintentionally interrupt a practice session. Worse, as a technical way to evaluate shadowing performance in real-time has not been established, no automated solutions are available to help. In this paper, we propose a technical framework with context-dependent speech recognition to evaluate shadowing in real-time. We propose a shadowing tutor system called WithYou, which can automatically adjust the playback and the difficulty of a speech template when learners fail, so shadowing becomes smooth and tailored. Results from a user study show that WithYou provides greater speech improvements (14%) than the conventional method (2.7%) with a lower cognitive load.
Better-than-Demonstrator Imitation Learning via Automatically-Ranked Demonstrations.

The performance of imitation learning is typically upper-bounded by the performance of the demonstrator. While recent empirical results demonstrate that ranked demonstrations allow for better-than-demonstrator performance, preferences over demonstrations may be difficult to obtain, and little is known theoretically about when such methods can be expected to successfully extrapolate beyond the performance of the demonstrator. To address these issues, we first contribute a sufficient condition for better-than-demonstrator imitation learning and provide theoretical results showing why preferences over demonstrations can better reduce reward function ambiguity when performing inverse reinforcement learning. Building on this theory, we introduce Disturbance-based Reward Extrapolation (D-REX), a ranking-based imitation learning method that injects noise into a policy learned through behavioral cloning to automatically generate ranked demonstrations. These ranked demonstrations are used to efficiently learn a reward function that can then be optimized using reinforcement learning. We empirically validate our approach on simulated robot and Atari imitation learning benchmarks and show that D-REX outperforms standard imitation learning approaches and can significantly surpass the performance of the demonstrator. D-REX is the first imitation learning approach to achieve significant extrapolation beyond the demonstrator's performance without additional side-information or supervision, such as rewards or human preferences. By generating rankings automatically, we show that preference-based inverse reinforcement learning can be applied in traditional imitation learning settings where only unlabeled demonstrations are available.
Perceptions of human shadow manipulation as an ambient display

This paper explores the concept of using human shadows as an ambient information display. As shadows are an integral part of the physical world, consisting of non-illuminated and dynamic shapes, they provide a potential basis for the design of unobtrusive ambient displays. We created a set of artificial human shadows to explore the concept, and conducted a user study (n = 12) to chart perceptions on the idea. As salient findings, the concept was perceived as interesting, and provoked strong reactions during the user study, e.g. surprise, laughter, and, at times, the feeling of spookiness. Synchronisation of movements between the manipulated shadows and the physical world and the ability to visually recognize the human shape, even when distortions are applied, are essential for the user experience.
Continuous-Variable Assisted Thermal Quantum Simulation

Simulation of a quantum many-body system at finite temperatures is crucially important but quite challenging. Here we present an experimentally feasible quantum algorithm assisted with continuous-variable for simulating quantum systems at finite temperatures. Our algorithm has a time complexity scaling polynomially with the inverse temperature and the desired accuracy. We demonstrate the quantum algorithm by simulating finite temperature phase diagram of the Kitaev model. It is found that the important crossover phase diagram of the Kitaev ring can be accurately simulated by a quantum computer with only a few qubits and thus the algorithm may be readily implemented on current quantum processors. We further propose a protocol implementable with superconducting or trapped ion quantum computers.
Encrypted video traffic clustering demystified

Cyber threat intelligence officers and forensics investigators often require the behavioural profiling of groups based on their online video viewing activity. It has been demonstrated that encrypted video traffic can be classified under the assumption of using a known subset of video titles based on temporal video viewing trends of particular groups. Nonetheless, composing such a subset is extremely challenging in real situations. Therefore, this work exhibits a novel profiling scheme for encrypted video traffic with no a priori assumption of a known subset of titles. It introduces a seminal synergy of Natural Language Processing (NLP) and Deep Encoder-based feature embedding algorithms with refined clustering schemes from off-the-shelf solutions, in order to group viewing profiles with unknown video streams. This study is the first to highlight the most computationally effective, accurate combinations of feature embedding and clustering using real datasets, thereby, paving the way to future forensics tools for automated behavioural profiling of malicious actors.
The Speed and Accuracy Evaluation of Random Forest Performance by Selecting Features in the Transformation Data

Random Forest is a machine learning method by building several trees in a forest, and getting the results of the classification by voting. The method of taking features to build a tree is done randomly, so there is a possibility that the feature chosen is not necessarily informative. Feature selection is needed to speed up the process. The feature selection used in this study is Correlation-based Feature Selection with the best first method. Based on the results of trials using six high-dimensional datasets, it was found that the selected features decreased by 15% to 96%. The average time needed to execute a Random Forest is less than that of a Random Forest execution on a dataset that has not been selected for features. This applies to datasets that have been transformed using Fast Fourier Transform, and returned using the Inverse Fast Fourier Transform. The average accuracy value for the dataset that has been transformed, accuracy has increased 0.03 to 0.08% compared to the dataset that has not been transformed. FFT is used to test the performace enhancement of the tranformed data of the Radom Forest.
High Performance Distributed OLAP on Property Graphs with Grasper

Achieving high performance OLAP over large graphs is a challenging problem and has received great attention recently because of its broad spectrum of applications. Existing systems have various performance bottlenecks due to limitations such as low parallelism and high network overheads. This Demo presents Grasper, an RDMA-enabled distributed graph OLAP system, which adopts a series of new system designs to overcome the challenges of OLAP on graphs. The take-aways for Demo attendees are: (1)~a good understanding of the challenges of processing graph OLAP queries; (2)~useful insights about where Grasper's good performance comes from; (3)~inspirations about how to design an efficient graph OLAP system by comparing Grasper with existing systems.
Cascaded Multi-Task Learning of Head Segmentation and Density Regression for RGBD Crowd Counting

In this paper we propose a novel regression based RGBD crowd counting method. Compared with previous RGBD crowd counting methods which mainly exploit depth cue to facilitate person/head detection, our approach adopts density map regression and is more robust to severe occlusion under dense crowded scenarios. We develop a cascaded depth-aware counting network that jointly performs head segmentation and density map regression. Our network explicitly feeds depth map at each stage so that depth cues are sufficiently exploited. The multi-task strategy allows the network to explicitly attent to foreground regions of a crowd scene and improve density regression. To generate the ground truth of head segmentation and density map, we propose a head scale estimation method according to the basic geometric assumption and camera projection function. Experiments on two public RGBD crowd counting benchmarks, ShanghaiTechRGBD dataset and MICC dataset show that the proposed method achieves new state-of-the-art on both datasets. Further, our method can be easily extended to RGB datasets and achieves comparable performances on WorldExpo'10 dataset and UCF-QNRF dataset.
Toward Adversarial Robustness by Diversity in an Ensemble of Specialized Deep Neural Networks

We aim at demonstrating the influence of diversity in the ensemble of CNNs on the detection of black-box adversarial instances and hardening the generation of white-box adversarial attacks. To this end, we propose an ensemble of diverse specialized CNNs along with a simple voting mechanism. The diversity in this ensemble creates a gap between the predictive confidences of adversaries and those of clean samples, making adversaries detectable. We then analyze how diversity in such an ensemble of specialists may mitigate the risk of the black-box and white-box adversarial examples. Using MNIST and CIFAR-10, we empirically verify the ability of our ensemble to detect a large portion of well-known black-box adversarial examples, which leads to a significant reduction in the risk rate of adversaries, at the expense of a small increase in the risk rate of clean samples. Moreover, we show that the success rate of generating white-box attacks by our ensemble is remarkably decreased compared to a vanilla CNN and an ensemble of vanilla CNNs, highlighting the beneficial role of diversity in the ensemble for developing more robust models.
A New Adaptive Weighted Deep Forest and Its Modifications

A new adaptive weighted deep forest algorithm which can be viewed as a modification of the confidence screening mechanism is proposed. The main idea underlying the algorithm is based on adaptive weigting of every training instance at each cascade level of the deep forest. The confidence screening mechanism for the deep forest proposed by Pang et al., strictly removes instances from training and testing processes to simplify the whole algorithm in accordance with the obtained random forest class probability distributions. This strict removal may lead to a very small number of training instances at the next levels of the deep forest cascade. The presented modification is more flexible and assigns weights to instances in order to differentiate their use in building decision trees at every level of the deep forest cascade. It overcomes the main disadvantage of the confidence screening mechanism. The proposed modification is similar to the AdaBoost algorithm to some extent. Numerical experiments illustrate the outperformance of the proposed modification in comparison with the original deep forest. It is also illustrated how the proposed algorithm can be extended for solving the transfer learning and distance metric learning problems.
Improved Correlated Sampling for Join Size Estimation

Recent research on sampling-based join size estimation has focused on a promising new technique known as correlated sampling. While several variants of this technique have been proposed, there is a lack of a systematic study of this family of techniques. In this paper, we first introduce a framework to characterize its design space in terms of five parameters. Based on this framework, we propose a new correlated sampling based technique to address the limitations of existing techniques. Our new technique is based on using a discrete learning method for estimating the join size from samples. We experimentally compare the performance of multiple variants of our new technique and identify a hybrid variant that provides the best estimation quality. This hybrid variant not only outperforms the state-of-the-art correlated sampling technique, but it is also more robust to small samples and skewed data.
Three-level binary tree structure for sentiment classification in Arabic text

The advent of web 2.0 platforms allowed users to generate and share textual content. This results in an explosive increase of online personal opinion. Sentiment Analysis, which is a recent field of Natural Language Processing, aims to predict the orientation of sentiment present on this massive textual data. This plays a vital role in many applications, such as recommender systems, customer intelligence, information retrieval and psychological study of crowd. Most existing approaches in sentiment analysis trait only positive, negative and neutral classes, ignoring the class strength (weak or strong positive/negative). In this paper, we propose an innovative approach for multi-class hierarchical sentiment classification in Arabic text based on a three-level binary tree structure. Experimental results show that our approach gives significant improvements over other classification methods.
Privacy-Preserving Krawtchouk Moment feature extraction over encrypted image data

Resource-constrained users outsource the massive image data to the cloud to reduce storage and computation overhead locally, but security and privacy concerns seriously hinder the applications of outsourced image processing services. Besides, existing image processing solutions in the encrypted domain still bring high computation overhead, and even lead to characteristic loss. To this end, we propose a Privacy-Preserving Krawtchouk Moment (PPKM) feature extraction framework over encrypted image data by utilizing the Paillier cryptosystem. First, a mathematical framework for PPKM implementation and image reconstruction is presented in the encrypted domain. Then, the detailed expanding factor and upper bound analysis shows that plaintext Krawtchouk moment and plaintext image reconstruction can be realized over encrypted image with PPKM. Furthermore, the computation complexity of PPKM can be significantly reduced with the block-based parallel algorithm. Experimental results verify that the PPKM is feasible and acceptable in practice in terms of image reconstruction capability and image recognition accuracy.
Application expert system career guidance for students

Abstract The use of information technology and communication (ICT) is widely applied in student career guidance services. In addition to increasing student accessibility in obtaining career information, the use of ICT can also be used as a reference system in decision making known as expert systems. The focus of the research in this paper is to analyze the results of research related to the impact of the application of expert systems in student guidance on aspects of educational guidance, educational evaluation and aspects of academic career guidance and work guidance. Overall, review findings on the topics and sub-topics analyzed show that the application of expert systems in educational guidance greatly helps students achieve: learning success, specialization in education and training, student performance, achievement and self evaluation. Given the lack of development of expert systems in the field of student career guidance, it is very necessary for continuous development, especially in aspects of uncertainty arising from application and user factors.
Parijoto Fruits Classification using K-Nearest Neighbor Based on Gray Level Co-Occurrence Matrix Texture Extraction

Abstract Parijoto fruit is a typical fruit that grows around Mount Muria, Kudus Regency and Mount Merapi in Yogyakarta, Indonesia. This fruit has many health benefits, especially for pregnant women. This fruit production is not much because of its limited growth around Mount Muria. So parijoto fruit is made into powder drink products and syrup, so that it can be consumed in a longer period of time and not only during the harvest. To get a good processed product requires good quality ingredients. Parijoto fruit needs to be sorted and classified. Current technology allows classification to be done by digital image processing. The Gray Level Cooccurrence Matrix (GLCM) method is proposed to extract the texture features from the parijoto fruit and then classify them using the K-Nearest Neighbor (KNN) method. GLCM can describe a spatial linear relationship of the frequency at which gray values are determined by other gray values in one area of investigation. It can simply use the statistical approach of appearance or histogram of the image matrix. In this way, information will be easily relative position of neighboring pixels that are suitable for the classification process using KNN. KKN was chosen because this method was proven to be used for relatively few datasets, but a normalization process was needed to increase accuracy. Based on the results of the implementation of the GLCM and KNN methods for parijoto fruit classification the classification accuracy was 80%.
Towards Trustworthy and Independent Data Marketplaces

Data is the new oil. In the past years the awareness about benefits of data has increased. A growing number of sectors recognize the opportunities from data. On the one hand it is very difficult for many researchers and enterprises to obtain data and on the other hand for those who collect data, the problem is, how to draw to additional profit from the data beyond its obvious purpose. To tackle this problem, we propose a common data sharing platform, where the data producers can sell data and the others can consume it. Just like any other online marketplace a data marketplace is a platform which enables convenient buying and selling of products- in this case "data". Blockchain enables businesses to be decentralized and more secure. Thus, in this paper we explore an approach to combine data marketplaces and blockchain for fair and independent data marketplaces.
A Natural Language Interface for Database: Achieving Transfer-learnability Using Adversarial Method for Question Understanding

Relational database management systems (RDBMSs) are powerful because they are able to optimize and execute queries against relational databases. However, when it comes to NLIDB (natural language interface for databases), the entire system is often custom-made for a particular database. Overcoming the complexity and expressiveness of natural languages so that a single NLI can support a variety of databases is an unsolved problem. In this work, we show that it is possible to separate data specific components from latent semantic structures in expressing relational queries in a natural language. With the separation, transferring an NLI from one database to another becomes possible. We develop a neural network classifier to detect data specific components and an adversarial mechanism to locate them in a natural language question. We then introduce a general purpose transfer-learnable NLI that focuses on the latent semantic structure. We devise a deep sequence model that translates the latent semantic structure to an SQL query. Experiments show that our approach outperforms previous NLI methods on the WikiSQL [49] dataset, and the model we learned can be applied to other benchmark datasets without retraining.