Reviewerly/exHarmony huggingface dataset:
Crypto Key Protection Generated from Images and Chaotic Logistic Maps

Cryptography is a mechanism used for protection of valuable information from unacceptable users in the networking world. It’s a challenging task to protect such assets from illegal users. This necessitated for the development of Cryptographic techniques to provide security as large volumes of digital data traveling through the shared media. These techniques range from traditional security systems to crypto image techniques. This brings forward to focus on cryptographic techniques that have strong algorithm and strong key. The strength of any crypto algorithm depends on the strongness of the crypto key used. Therefore, both the algorithm technique and key generation methodology are equally important. This motivated to propose and concentrate on a novel scheme that includes crypto key generation and crypto key protection where the protected key and its methodology cannot be traced by an ineligible user.
Research on Usability of the Toolbar of Teaching Interactive Smart Tablet

Abstract The work aims to find out the usability problems in the design of the toolbar of teaching interactive smart tablet, and provide an improvement basis and direction for improving the user experience of the teaching interactive smart tablet. Usability testing and user interview were used to research the toolbar of Seewo teaching interactive smart tablet. Quantitative analysis of task completion time, task success level and task completion efficiency were measured in usability testing. The thesis combines the results of user interviews with the results of usability testing to find out the specific factors that affect the usability of the toolbar of teaching interaction smart tablet. The icon design of the toolbar of teaching interactive smart tablet has a great impact on usability. The hiding of frequently used functions may make it difficult for users to find them. The larger screen makes it difficult to find icons outside the field of view. The usability of interactive smart tablet can be improved by improving icon recognition, optimizing common interactive processes, and setting important functions in the best visual field.
Privacy-Preserving Outsourced Nash Equilibrium Computation in Cloud Computing

In a non-cooperative game, the Nash equilibrium is computed by the game players with the payoff matrix. With the obtained Nash equilibrium, game players can analyze equilibrium strategies and make optimum decisions. In cloud computing, the computation of Nash equilibrium may be outsourced to the cloud due to the constrained resources of players. However, the payoff matrix may be sensitive and needs to protect against the cloud server. In this paper, we propose a cloud-based framework to secure outsourcing the task of computing mixed-strategy Nash equilibria. In our framework, the payoff matrix is encrypted with additive homomorphic encryption before uploaded and stored in the cloud. By combining secure multi-party computing techniques, we enable the cloud server to compute Nash Equilibria on the encrypted data without disclosing sensitive users' data. We conduct experiments to verify the effectiveness, evaluate the precision, and then analyze the computational complexities.
A robust and accurate structured light pattern for dynamic 3D reconstruction

A new method based on single shot using color coded image sequence for 3D reconstruction is proposed. The previous spatial neighborhood coding methods often need to determine the center of the pattern, but inaccurate positioning of pattern feature points will bring errors to 3D reconstruction. In this paper, a color-coded pattern is designed as two opposite triangular patterns to directly establish the center point of the pattern, thus avoiding the error caused by the inaccurate positioning of the center point after the pattern distortion. We designed a corner detector for our feature point detection, it is efficient and accurate. The pattern is coded in four colors of red, blue, green and pale yellow. We use a quaternary sixth order primitive polynomial to generate the pseudorandom sequence, which is then regenerated into the M-array. The final size of the code pattern is 65×63, and the unique code window size is 2×3. Experiments show that the 3D reconstruction owns high measurement precision and the decoding has strong robustness.
Enriching Portuguese Word Embeddings with Visual Information

This work focuses on the enrichment of existing Portuguese word embeddings with visual information in the form of visual embeddings. This information was extracted from images portraying given vocabulary terms and imagined visual embeddings learned for terms with no image data. These enriched embeddings were tested against their text-only counterparts in common NLP tasks. The results show an increase in performance for several tasks, which indicates that visual information fusion for word embeddings can be useful for word embedding based NLP tasks.
Going Beyond Saliency Maps: Training Deep Models to Interpret Deep Models

Interpretability is a critical factor in applying complex deep learning models to advance the understanding of brain disorders in neuroimaging studies. To interpret the decision process of a trained classifier, existing techniques typically rely on saliency maps to quantify the voxel-wise or feature-level importance for classification through partial derivatives. Despite providing some level of localization, these maps are not human-understandable from the neuroscience perspective as they often do not inform the specific type of morphological changes linked to the brain disorder. Inspired by the image-to-image translation scheme, we propose to train simulator networks to inject (or remove) patterns of the disease into a given MRI based on a warping operation, such that the classifier increases (or decreases) its confidence in labeling the simulated MRI as diseased. To increase the robustness of training, we propose to couple the two simulators into a unified model based on conditional convolution. We applied our approach to interpreting classifiers trained on a synthetic dataset and two neuroimaging datasets to visualize the effect of Alzheimer’s disease and alcohol dependence. Compared to the saliency maps generated by baseline approaches, our simulations and visualizations based on the Jacobian determinants of the warping field reveal meaningful and understandable patterns related to the diseases.
Tracking Ground Targets with Road Constraint Using a Gaussian Mixture Road-Labeled PHD Filter

The general focus of this paper is the improvement of state-of-the-art Bayesian tracking filters specialized to the domain of ground moving target tracking to obtain high-quality track information by incorporation of road-map information into a Gaussian mixture probability hypothesis density (GM-PHD) filtering scheme. In this paper, we propose a road-labeled GM-PHD (GM-RL-PHD) filter for ground targets with road-segment constrained dynamics and the recursive equations of the filter is derived. The proposed filter is validated with a ground target tracking example. The simulation results show that the proposed algorithm can improve the performance of ground target tracking algorithm by fusing road map information.
Instant Graph Query Recovery on Persistent Memory

Persistent memory (PMem) - also known as non-volatile memory (NVM) - offers new opportunities not only for the design of data structures and system architectures but also for failure recovery in databases. However, instant recovery can mean not only to bring the system up as fast as possible but also to continue long-running queries which have been interrupted by a system failure. In this work, we discuss how PMem can be utilized to implement query recovery for analytical graph queries. Furthermore, we investigate the trade-off between the overhead of managing the query state in PMem at query runtime as well as the recovery and restart costs.
Recognizing Hate-prone Characteristics of Online Hate Speech Targets

Recognizing the characteristics of online hate speech targets can provide important information, which can help predict potential targets and protect them. Understanding the targets of online hate characteristics in the context of social media platforms where the hate is dramatically increasing lately can improve awareness of how different people react to online hate, which we anticipate will be manifested differently for different demographic groups. Targets of online hate play an important role in providing an extensive and informative explanation of the online hate event. This has been barely studied in previous research. In this PhD research, we propose a hate prone characteristics recognition framework for online hate targets, which consists of several modules, including data collection, data pre-processing, feature extraction, contextualisation and the hate prone characteristics recognition model that has the ability to recognise the common online hate prone characteristics to enhance the online hate prevention services, and finally, the hateful replies prediction model. This online hate prediction model has the potential to be personalised/adaptive in future applications.
A Quality-Oriented Reconfigurable Convolution Engine Using Cross-Shaped Sparse Kernels for Highly-Parallel CNN Acceleration

Computational imaging CNNs are computationally intensive and need complexity reduction to support high-throughput applications. However, conventional compact model reduction tends to degrade image quality severely as models become too shallow. On the other hand, irregular pruning-based techniques induce considerable circuit overheads and imbalanced workloads, especially for highly-parallel accelerators. In this paper, we propose cross-shaped sparse kernels to regularly reduce model complexity while preserving image quality well. They improve PSNR (peak signal-to-noise ratio) by 0.03-0.31 dB on classic denoising and super-resolution networks compared to compact depth reduction. Moreover, we design a highly-parallel reconfigurable convolution engine to support three sparsity configurations (0%, 50% and 75% of sparsity) for our complexity-saving method. It can achieve high-quality inference for a wide complexity range with full utilization of MACs. With TSMC 40nm technology, the engine uses 9.85M of logic gates for delivering 8.2 TOPS of inference capability, and only needs 8.4% logic overheads and 14.9% additional power consumption for the quality-oriented reconfigurability. Finally, we do a case study on ERNets for real-time inference, and this work can achieve 10.114.8x higher area efficiency in terms of Mpixel/s/mm <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> compared to SparTen.
Universal Fast Marching Method to Identify Liver Image

Abstract Liver segmentation is of prime importance in modern liver disease diagnosis and analysis. In our paper, random section of liver image is chosen and its histogram is achieved. From histogram, liver pixel intensity range is obtained. Using the range value, threshold segmentation is carried out which detaches the liver from its adjoining organs. Median filter is employed to curtail the noise. The sigmoidal function is applied to improve anatomical structures of image. Then the image is converted into binary called as speed function. The novel algorithm is designed to locate the start points within speed function without user intervention. These start point evolved outwardly using Fast Marching Method till complete periphery of liver is reached. The proposed algorithm is compared with popularly used segmentation algorithms. The results show that proposed segmentation algorithm is robust in approach.
End-To-End Deep Learning for pNN50 Estimation Using a Spatiotemporal Representation

Various industries widely use emotion estimation to evaluate their consumer satisfaction towards their products. Generally, emotion can be estimated based on observable expressions such as facial expression, or unobservable expressions such as biological signals. Although used by many research, the Facial Expression Recognition has a lack of precision for expressions that are very similar to each other or a situation where the shown expression differs from the real subject’s emotion. On the other hand, biological signal indexes such as pNN50 can act as a supportive mechanism to improve emotion estimation from observable expressions such as facial expression recognition method. pNN50 is a reliable index to estimate stress-relax, and it originates from unconscious emotions that cannot be manipulated. In this work, we propose a method for pNN50 estimation from facial video using a Deep Learning model. Transfer learning technique and a pre-trained Image recognition Convolutional Neural Network (CNN) model are employed to estimate pNN50 based on a spatiotemporal map created from a series of frames in a facial video. The model which trained on low, middle, and high pNN50 values, shows an accuracy of about 80%. Therefore, it indicates the potential of our proposed method, and we can expand it to categorize the more detailed level of pNN50 values.
Deep Learning Image Transfer by Simulation

The image classification is a classical problem of image processing, computer vision and machine learning fields. In this paper we study the image classification using deep learning with Neural style transfer that has been a high risk application for deep learning, make attention from and advertising the effectiveness to both the academic prisons and the general public. However, we have found by removal experiments that optimizing an image in the way neural style transfer does, we can even factor out the deepness (multiple layers of exchange linear and nonlinear transformations) all together and have neural style transfer working to a certain range. We present the VGG-19 Artistic Type Neural Algorithm, which can convert and recombine the image quality and style of natural images. This algorithm allows us to produce new images of high perceptual quality that combine the content of an arbitrary photograph with the appearance of numerous well known portrait. Our results provide new insights into the deep image representations learned by Convolutional Neural Networks (CNN) and demonstrate their potential for high level image synthesis and manipulation.
A Conceptual Framework of Future Home Smart Kitchen System with Multiple Experience Modes

With the rapid development of artificial intelligence, home kitchen is facing intelligent upgrade. This paper proposes a conceptual framework of future home kitchen system with multi-experience mode. By setting different experience modes, the design variables of kitchen equipment are extracted, and the mapping relationship between experience mode and kitchen equipment design variables is established. According to the cooking process, we draw the dynamic diagram of the cooperative work of cooking robot, AGV smart car and kitchen three-dimensional warehouse in different experience modes. Finally, the overall layout scheme and VR display model of digital smart kitchen system are constructed, which provides technical route for further development of future smart kitchen system.
Automatic Question Answering Method Based on IMGRU-Seq2seq

Aiming at the problem of gradient disappearance and network degradation caused by using of recurrent neural networks in traditional question answering models, an automatic question answering model based on IMGRU-Seq2seq is proposed. First, based on the gated recurrent unit, the batch normalization technology and the rectified linear unit activation function are combined and the identity mapping is added to construct the IMGRU model; then, the text is represented by weighted word vectors through the TF-IDF method; finally, as the semantic extraction unit of the question answering model, the bidirectional IMGRU introduced attention mechanism and the beam search algorithm to realize automatic question and answer. The experimental results showed that the proposed method is 16.55% and 8.36% higher than the existing methods BLEU and ROUGE-L respectively.
Comparative Analysis of Watermarking Methods on CFRP Sample Thermal Images

Digital watermarking became indispensable in today’s scenarios, and a lot of digital data have become distributed worldwide. Internet connectivity has made it very convenient and inexpensive. Due to rapid technological progress in the past 15 years, current users. There has been a rise in the likelihood of various digital attacks, such as forgery, damaging digital data, downloading, copying and altering digital knowledge. The use of a privacy mechanism that can protect digital data from the owner of digital data and its rights has been relevant in these situations. In this paper, on comparing the various watermarking techniques such as LSB, DWT-SVD hybrid, and reversible blind watermarking on CFRP samples to identify the efficacy of each method. The reversible bind watermarking is better for recovering the layout information of the CFRP sample than the LSB and hybrid method. The watermarking strategies of thermal images layout can become more secure.
Coverless Information Hiding Method Based on Combination Morse Code and Double Cycle Application of Starter

With the continuous development of modern network technology and data informatization, information security has been paid more and more attention by the society, and coverless information hiding has been becoming a new and important research field of information security. At present, the capacity of text coverless information hiding is very limited, generally 3–4 words per text. In order to increase the capacity of text coverless information hiding, this paper proposed a novel method of text coverless information hiding. In this method, the punctuation combination modified by Morse code is combined with the use of starter, list and loop statement to realize the hiding of a number of words in a normal text without modified. The experiments' results show that this new method can hide more words and express all the words composed of representable characters.
Implementation of hybrid cryptosystem using Rabin-p algorithm and One Time Pad to secure images

Abstract Cryptography is a study of mathematical techniques to secure messages by encoding the messages so it cannot be read by a third party. There are two types of algorithms in cryptography, symmetric algorithm and asymmetric algorithm. Asymmetric algorithms, such as Rabin-p cryptosystem, are known to be secure if the key sizes are long enough. However, asymmetric algorithms are ineffective to encrypt large data since the calculations consume CPU time and the resulting ciphertext is multiple times bigger than the original data. In this research, we combine Rabin-p cryptosystem and One Time Pad (OTP) in a hybrid scheme to solve this problem. The OTP is used to encrypt the data (images) and Rabin-p algorithm is used to encrypt the OTP key. In this research, we demonstrate how to secure a *.bmp file with this hybrid scheme.
Identification of the Words Most Frequently Used by Different Generations of Twitter Users

Text data constitutes a significant part of all data generated on the Internet, including the social network users’ comments and posts. Each website offers its users different functionalities. LinkedIn mainly focuses on the labor market as well as professional and business contacts, and Facebook offers the possibility of creating groups as well as photo and message sharing with friends, while Twitter allows short text message posting and tracking. One type of information researchers would like to obtain about the users of these portals is their age. Such information is crucial from the perspective of marketing, social and economic research. Each of the social networks, however, has different rules regarding the privacy policy and the publishing of information about the date of birth. This poses a problem for the researchers who would like to obtain such information. The aim of the research presented is to attempt characterization of the words typically used in the messages published by Twitter users. This social networking site was chosen due to the possibility of downloading data without additional user consent. Text mining methods and techniques were used to carry out the research, which was mainly focused on the analysis of individual words and collocations occurring in the users’ tweets.
A Study on the Factors Influencing the Professional Adaptation School Environment of Interdisciplinary Graduate Students in Local Universities

Rooted theory approach was applied to explore the influence of school environment factors on professional adaptation of interprofessional master's degree students in local universities and propose countermeasures to provide theoretical references for enhancing professional adaptation. Semi-structured interviews were conducted with 17 Interdisciplinary Masters from four local universities in Tianjin. And three-level coding was used to extract and summarize the problems of school environment factors. Through the three-level coding, 163 concepts, 14 main categories and 4 core categories were sorted out, 4 veins were formed: mentor guidance, management services, curriculum situation, and research environment veins. The study found that mentoring is the leading factor, management service provides institutional support, curriculum situation plays a direct role in influencing, and research environment provides platform support. In this regard, suggestions are made to strengthen the financial support of the subject and optimize the support of the research platform environment; strengthen the construction of the tutor team and improve the tutor multiple evaluation system; optimize the curriculum structure setting and improve the feedback mechanism of teaching quality; promote the construction of management system and improve the support system of management service.
Predicting How CNN Training Time Changes on Various Mini-Batch Sizes by Considering Convolution Algorithms and Non-GPU Time

Convolutional neural networks (CNN) drive successful machine learning applications in a growing number of areas. However, training a CNN may take a massive amount of time and expensive high-end GPU resources. CNN training time may change significantly depending on training parameters and GPU type. Therefore, an accurate estimation of CNN training time can help in selecting training parameters and GPU type, which minimise training time and cost. We focus on one training parameter, which has a particularly significant effect on the training time-the mini-batch size. Predicting CNN training time on a wide range of mini-batch sizes is challenging because a small variation in a mini-batch size can change the selection of convolution algorithms and cause abrupt changes in training time, which is also affected by non-GPU operations. This paper shows our approach to predicting CNN training time over a wide range of mini-batch sizes by utilising a proxy application to benchmark convolutional and dense layers and considering non-GPU time. In contrast to prior works, which build one prediction model for all possible CNN configurations, we build simple models that would each make highly accurate predictions for one particular CNN. We evaluate our approach using several CNN samples and GPU types and demonstrate that it can yield highly accurate predictions on unseen mini-batch sizes with a mean percentage error averaged over all experiments equal to 1.38% (the minimum is 0.21% and the maximum is 5.01%).