Reviewerly/exHarmony huggingface dataset:
First-person activity recognition from micro-action representations using convolutional neural networks and object flow histograms

A novel first-person human activity recognition framework is proposed in this work. Our proposed methodology is inspired by the central role moving objects have in egocentric activity videos. Using a Deep Convolutional Neural Network we detect objects and develop discriminant object flow histograms in order to represent fine-grained micro-actions during short temporal windows. Our framework is based on the assumption that large scale activities are synthesized by fine-grained micro-actions. We gather all the micro-actions and perform Gaussian Mixture Model clusterization, so as to build a micro-action vocabulary that is later used in a Fisher encoding schema. Results show that our method can reach 60% recognition rate on the benchmark ADL dataset. The capabilities of the proposed framework are also showcased by profoundly evaluating for a great deal of hyper-parameters and comparing to other State-of-the-Art works.
A Novel Approach For Generating One-Time Password With Secure Distribution

Authentication nowadays is an important issue that cannot depend only on static passwords because it is vulnerable and subjected to many types of attacks. Many researchers addressed this problem by introducing a dynamic password framework called a one-time password (OTP). However, this framework still has many limitations. One of these limitations is sending OTP that is done without any encryption. In this paper, we will introduce a new OTP framework that achieves the confidentiality of exchanging the OTP by using a combination of AES-256 bit, RSA, SHA-512. OTP should be delivered to the client in a secure way, as an example is using network traffic to avoid the delay in SMS. In the proposed model the server encrypts OTP using the RSA public-key cryptosystem with the client's public key, in order not to send OTP as a plain text.
Role-Based Authority Control in Management Information System under the Background of Internet

This paper proposes an implementation method of role-based authority management in a management information system under a computer network environment, and gives some application technologies and specific examples of role use and control in combination with practical topics. At the same time, it is based on modern encryption algorithms. The safety of important data in the database was discussed above. Practice has proved that this authority management framework makes authority management more flexible and efficient, and has certain universal applicability.
A Multi-Task Learning Approach to Improve Sentiment Analysis with Explicit Recommendation

When expressing sentiment towards products, customers often explicitly indicate their recommendation status. Nevertheless, most existing literature focuses on sentiment analysis but neglects the rich correlation information that may be brought by explicit recommendation classification. We argue that the two tasks are correlated and hence, the knowledge in explicit recommendation classification can also be beneficial to sentiment analysis. Consequently, in this paper, a novel bidirectional encoder representations from transformers (BERT)-enhanced multi-task learning (BeMTL) approach is proposed to improve sentiment analysis with explicit recommendation classification. Specifically, the proposed MTL approach takes contextualized word embeddings produced by the pre-trained BERT-based embedding layer. Then, it learns the sentence contextual features shared between both tasks with a convolutional multi-head attention neural network. To fully exploit the correlation information between sentiment analysis and explicit recommendation classification tasks, a novel inter-task matching layer (IML) is designed to match their representations. In nutshell, our study reveals the potential of multi-task learning models on such types of problems, and experimental results on two Amazon datasets show that our approach outperforms the state-of-the-art baseline approaches for sentiment analysis.
Exploring the Correlation Between Random Convolutional Architectures and the Trained Equivalent

In this paper we explore the correlation between Convolutional Neural Network (CNN) architectures with random weights in the convolutional layers to the same architectures with trained weights. We show that this correlation extends to deep CNN architectures of up to 10 or even 12 layers to the extent that untrained model accuracy could be a useful proxy for trained model accuracy. We also find that for models with fewer layers much of this relationship comes from the strong correlation between the number of features output from the final CNN layer and final accuracy. With 10 and 12 layers there is a moderate correlation even when the size of the fully connected layer is held constant. We anticipate our findings in extending these correlations to deeper networks will be useful in designing faster Neural Architecture Search (NAS) models. Analytically solving for the weights of the final prediction layer is orders of magnitude faster than training the weights via backpropagation.
Enhancing Neural Networks with Locality-Sensitive Clustering of Internal Representations

Some data exhibit natural divisions where the application of a single neural network leaves some accuracy on the table, thereby making a multi-network approach more appropriate. We develop an approach to preserving knowledge encoded in the hidden layer of several ANN's and assemble that knowledge in new, composite networks based on spatial clustering that more effectively make predictions over subdivisions of the entire dataspace. We show that this method has an accuracy advantage over the single-network approach.
Memory-Augmented Auto-Regressive Network for Frame Recurrent Inter Prediction

Inter prediction is quite important for the modern codecs to remove temporal redundancy. In this paper, we make endeavors in generating artificial reference frames with previous reconstructed frames for inter prediction, to offer a better choice when the traditional block-wise motion estimation fails to find a good reference block. Long-term temporal dynamics are tracked during the whole coding process to generate more accurate and realistic artificial reference frames. Specifically, we propose a Memory-Augmented Auto-Regressive Network (MAAR-Net) for frame prediction in video coding. MAAR-Net regresses the current frame with two nearest frames via an auto-regressive (AR) model to better capture the main spatial and temporal structures. The AR regression coefficients are generated based on adjacent frame information as well as the long-term motion dynamics accumulated and propagated by a convolutional Long Short-Term Memory (LSTM). To generate the target frame with higher quality, a quality attention mechanism is introduced for the temporal regularization between different reconstructed frames. With the well-designed network, our method surpasses HEVC on average 4.0% BD-rate saving and up to 10.6% BD-rate saving for the luma component under the low-delay configuration.
Live Demonstration: Revealing the Secret Parameters of an FPGA-Based “True” Random Number Generator

An FPGA-based chaotic “true” random number generator (RNG) is targeted. In the demonstration, the target system encrypts an image using keys generated by the chaotic system. A new response system using auto-synchronization method is suggested. It tries to decrypt the encrypted image without knowing the secret key. Experimental results prove that it is possible to estimate secret key generated by the target system by only monitoring limited scalar time series from the chaotic RNG.
An Optimized Adaptive Image Enhancement Method under Low Illumination

Due to the harsh environment and the lack of light at night and other reasons, the images we collect often have problems such as low contrast, serious loss of detail information and so on. In order to solve this problem, firstly, we optimize the traditional adaptive gamma correction with weighted distribution algorithm, then propose an adaptive low illuminance image enhancement method based on the optimized adaptive gamma correction with weighting distribution and dark channel prior. We set a threshold value, for the image above the threshold value, we use the optimized adaptive gamma correction with weighting distribution to process, otherwise, we use the dark channel prior to process. Subjective and objective evaluation results show that the algorithm proposed in this paper has a good enhancement effect on low illuminance image, and can effectively improve the contrast of image and highlight details.
An Effective Hierarchical Resolution Learning Method for Low-Resolution Targets Tracking

Suffering from the low-resolution target's visual quality, the precisions of visual object trackers are reduced. This paper proposes an effective hierarchical resolution learning method for low-resolution targets tracking, abbreviated as HRT. We adopt a hierarchical structure to exploit information from different resolution levels. (1) At the high level: the super-resolution (SR) images, determining the target's shape, contains richer image textures and clearer target contours, and transmits the search region to the low level. (2) At the low level: low-resolution (LR) images maintain the spatial structure information of the original target, providing the precise center coordinates of the target. Experimental results demonstrate the effectiveness of the proposed tracker, which HRT achieves 90.3% precision on OTB100 LR sequences and 78.5% precision on LR sequences from UAV123 datasets, gaining 2.0%, 2.4% improvement over state-of-the-art trackers respectively.
Implementation of Augmented Reality in Ablution Learning Based on Android

This study discusses the development of Augmented Reality technology in ablution procedures that can be useful in children's education. The use of Augmented Reality is expected to be able to attract interest in learning and add insight to children in terms of introducing the procedures for ablution to be more effective. This Augmented Reality application uses the Marker Based Tracking method. This method was chosen because it is able to read objects that will later bring up 3-dimensional virtual objects from the procedures for ablution. The initial step in solving this problem is done by making animations using the Blender application, then determining the image that will be used as a Marker and then exported to Unity to become an Augmented Reality application for ablution procedures. The testing method used on the implementation of this application is to use ISO 9126 and a questionnaire with a total of 10 respondents. Based on the results of tests that have been carried out based on functionality, reliability, usability, and efficiency tests, it shows that the application is going well and 88% of respondents said they were satisfied with the benefits of the application of the ablution procedure.
Image retrieval using locality preserving projections

In web surfing, image retrieval algorithms play a vital role. Many algorithms are existing but they have their drawbacks and also low retrieval rates. Locality preserving projections is a novel method for image retrieval proposed in this study. As the method includes principal component analysis, nearest neighbour and eigen features in extracting the hyper parameters for classification, it has better retrieval rate. This novel approach is tested using the Yale facial image dataset and the performance of the algorithm is tested by using different classifiers like Decision Tree etc.
A Neural Network Toolbox for Electricity Consumption Forecasting

The aim of this study is to propose a neural toolbox for electricity consumption prediction. The toolkit covers the implementation of three artificial neural networks, namely: (i) a multi-layered perceptron network; (ii) a radial basis function network; and (iii) a self-organising map. Moreover, the toolbox includes tools known as metamodels, which allow easy use of these networks for forecasting. There are two prediction systems, namely: (i) serially connected models; and (ii) a two-levelled structure containing a classifier and a set of parallel models. They have been validated experimentally in the task of electricity consumption prediction. The results and flexibility of use suggest that the neural toolbox should help users to develop prediction systems of electricity consumption more conveniently, as it is designed for that particular purpose. The toolbox has been developed as open source - no commercial software is required to use it.
Information Design to Save Lives: Visualizing Data in the Design of Overdose Kits

This paper visualizes and discusses the data collected in an iterative design project to improve the design of instructions for how to use naloxone kits. In addition to a discussion about participant demographics, it presents the usability testing data and will use several data visualizations to illustrate the information that could be found, information that was used correctly and accurately, and overall performance level achieved. This paper will also present the observations that were made in the testing and comment on how the variations in design affected the comprehension of the instructions. It will visually explain and discuss the data collected in the design and comment on the importance of data collection and visualization in an interdisciplinary health design project. The project has proven the need for an evidence-based, human centred design approach to the redesign of any kind of procedural instruction that is to be read and understood in an emergency situation.
Estimation Of Impression Associated With Portraits Using Facial Landmarks And Visual Features

Portrait manipulation techniques are important for expressing oneself in social networking or portrait/video posting services. Even though several applications (e.g., smartphone apps) have been developed to facilitate portrait manipulation, it is still difficult to obtain ideal results owing to the large number of user-controllable parameters. To tackle this problem, we propose a novel application that can manipulate portraits using impression words. In this study, we select new portraits and impression words, and we devise new experimental settings to enrich the dataset. Moreover, using human evaluations collected by crowdsourcing, we develop and analyze a novel ranking network that uses portraits and facial landmarks as input for impression estimation of the manipulated images.
Non-conjugate Posterior using Stochastic Gradient Ascent with Adaptive Stepsize

Large scale Bayesian nonparametrics (BNP) learner such as Stochastic Variational Inference (SVI) can handle datasets with large class number and large training size at fractional cost. Like its predecessor, SVI rely on the assumption of conjugate variational posterior to approximate the true posterior. A more challenging problem is to consider large scale learning on non-conjugate posterior. Recent works in this direction are mostly associated with using Monte Carlo methods for approximating the learner. However, these works are usually demonstrated on non-BNP related task and less complex models such as logistic regression, due to higher computational complexity. In order to overcome the issue faced by SVI, we develop a novel approach based on the recently proposed constant stepsize stochastic gradient ascent to allow large scale learning on non-conjugate posterior. Unlike SVI, our new learner does not require closed-form expression for the variational posterior expectatations. Our only requirement is that the variational posterior is differentiable. In order to ensure convergence in stochastic settings, SVI rely on decaying step-sizes to slow its learning. Inspired by SVI and Adam, we propose the novel use of adaptive stepsizes in our method to significantly improve its learning. We show that our proposed methods is compatible with ResNet features when applied to large class number datasets such as MIT67 and SUN397. Finally, we compare our proposed learner with several recent works such as deep clustering algorithms and showed we were able to produce on-par or outperform the state-of-the-art methods in terms of clustering measures.
Deep Photo Cropper And Enhancer

This paper introduces a new type of image enhancement problem. Compared to traditional image enhancement methods, which mostly deal with pixel-wise modifications of a given photo, our proposed task is to crop an image which is embedded within a photo and enhance the quality of the cropped image. We split our proposed approach into two deep networks: deep photo cropper and deep image enhancer. In the photo cropper network, we employ a spatial transformer to extract the embedded image. In the photo enhancer, we employ super-resolution to increase the number of pixels in the embedded image and reduce the effect of stretching and distortion of pixels. We use cosine distance loss between image features and ground truth for the cropper and the mean square loss for the enhancer. Furthermore, we propose a new dataset to train and test the proposed method. Finally, we analyze the proposed method with respect to qualitative and quantitative evaluations.
Creating Corpora for Seq2Seq Tone Rephrasing Using Social Media Posts

We present a methodology to use Twitter posts to create a parallel corpus which can be used to train Seq2Seq neural networks for a tone rephrasing task. Given that people tend to post texts expressing opinions or emotions of varied intensities regarding given real-world events, the main idea is to create corpus containing pairs of posts with opposite tone but about the same topic. By doing so we overcome the main limitation of current tone rephrasing methods: the lack of appropriate parallel training corpora. We explore different methods to create the datasets, including some which require some level of manual labelling. The results show that a completely automatic generation from Twitter data yields training datasets which are better than those with manual interventions, and good enough for Seq2Seq models to outperform non-Seq2Seq models trained with similar data.
Adapted Search Equations of Artificial Bee Colony Applied to Feature Selection

This article proposes a Novel Search Equation of Artificial Bee Colony called NSABC. This study introduces new search equations for employed and onlooker bees involving more parameters for each food source (more features) and avoid local optima. NSABC outperforms various ABC variants and particle swarm optimization PSO in term of reduced size of feature and accuracy. Experimental results validate the efficiency of NSABC method on machine learning UCI data.
Unstructured Road Segmentation Method Based on Super Pixel and Region Growing Algorithm

Given the large randomness and the low color information utilization rate, a region growing algorithm based on super pixel is proposed. It can also eliminate the uncertainty of the initial setting parameters of super pixel segmentation. The method in this paper was proposed by three steps. In the first step, set the most suitable number of super pixel by the lab feature histogram of the image. In the second step, segment the original image by the SLIC super pixel segmentation algorithm. In the third step, merge super pixel blocks by region growing algorithm and then obtain the target unstructured road. This paper uses Jaccard coefficients to evaluate the segmentation accuracy. In a warehouse environment, this method is more accurate than traditional region growing algorithm and normalized segmentation method based on SLIC super pixel.
Using Machine Learning for processing Big Data of Copernicus Satellite Sensors at the Example of the TROPOMI / Sentinel-5 Precursor and Sentinel-4 Cloud Product

&amp;lt;p&amp;gt;The satellites of the Copernicus program show the increasing relevance of properly handling the huge amount of Earth observation data, nowadays common in remote sensing. This is further challenging if the processed data has to be provided in near real time (NRT), like the cloud product from TROPOMI / Sentinel-5&amp;lt;!-- The slash has spaces before &amp;amp;#8220;TROPOMI&amp;amp;#8221; and after &amp;amp;#8220;Sentinel-5&amp;amp;#8221;, which is inconsistent with the lack of spaces in the title. --&amp;gt; Precursor (S5P) or the upcoming Sentinel-4 (S4) mission.&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;In order to solve the inverse problems that arise in the retrieval of cloud products, as well as in similar remote sensing problems, usually complex radiative transfer models (RTMs) are used. These are very accurate, however also computationally very expensive and therefore often not feasible in combination with NRT requirements. With the recent significant breakthroughs in machine learning, easier application through better software and more powerful hardware, the methods of this field have become very interesting as a way to improve the classical remote sensing algorithms.&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;In this presentation we show how artificial neural networks (ANNs) can be used to replace the original RTM in the ROCINN (Retrieval Of Cloud Information using Neural Networks) algorithm with sufficient accuracy while increasing the computational performance at the same time by several orders of magnitude.&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;We developed a general procedure which consists of smart sampling, generation and scaling of the training data, as well as training, validation and finally deployment of the ANN into the operational processor. In order to minimize manual work, the procedure is highly automated and uses latest technologies such as TensorFlow. It is applicable for any kind of RTMs and thus can be used for many retrieval algorithms like it is already done for ROCINN in S5P and will be soon for ROCINN in the context of S4. Regarding the final performance of the generated ANN, there are several critical parameters which have a high impact (e.g. the structure of the ANN). These will be evaluated in detail. Furthermore, we also show general limitations of ANNs in comparison with RTMs, how this can lead to unexpected side effects and ways to cope with these issues.&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;With the example of ROCINN, as part of the operational S5P and upcoming S4 cloud product, we show the great potential of machine learning techniques in improving the performance of classical retrieval algorithms and thus increasing their capability to deal with much larger data quantities. However, we also highlight the importance of a proper configuration and possible limitations.&amp;lt;/p&amp;gt;
Taking the Long View

This paper proposes first steps in the development of practical techniques for the expert evaluation of long-term interactions driven by the need to perform expert evaluation of such systems in a consultancy framework. Some interactions are time-limited and goal-driven, for example withdrawing money at an ATM. However, these are typically embedded within longer-term interactions, such as with the banking system as a whole. We have numerous evaluation and design tools for the former, but long-term interaction is less well served. To fill this gap new evaluation prompts are presented, drawing on the style of cognitive walkthroughs to support extended interaction.