Reviewerly/exHarmony huggingface dataset:
Federated Learning of Deep Neural Decision Forests

Modern technical products have access to a huge amount of data and by utilizing machine learning algorithms this data can be used to improve usability and performance of the products. However, the data is likely to be large in quantity and privacy sensitive, which excludes the possibility of sending and storing all the data centrally. This in turn makes it difficult to train global machine learning models on the combined data of different devices. A decentralized approach known as federated learning solves this problem by letting devices, or clients, update a global model using their own data and only sending changes of the global model, which means that they do not need to communicate privacy sensitive data. Deep neural decision forests (DNDF), inspired by the versatile algorithm random forests, combine the divide-and-conquer principle together with the property representation learning. In this paper we further develop the concept of DNDF to be more suited for the framework of federated learning. By parameterizing the probability distributions in the prediction nodes of the forest, and include all trees of the forest in the loss function, a gradient of the whole forest can be computed which some/several federated learning algorithms utilize. We demonstrate the inclusion of DNDF in federated learning by an empirical experiment with both homogeneous and heterogeneous data and baseline it against a convolutional neural network with the same architecture as the DNDF. Experimental results show that the modified DNDF, consisting of three to five decision trees, outperform the baseline convolutional neural network.
Imitation Learning-Based Unmanned Aerial Vehicle Planning for Multitarget Reconnaissance Under Uncertainty

This paper proposes a planning framework for a multitarget unmanned aerial vehicle (UAV) reconnaissance mission under target position uncertainty based on the imitation learning of a policy network. A problem in planning the flight path and task sequence for the reconnaissance of given targets is formulated as a Markov decision process (MDP). A deep neural classifier is introduced as a policy network to predict the probability over admissible immediate actions at each mission state. A set of MDPs with specified target locations (training set) are constructed and solved offline to generate the segments of underlying optimal policy, which are used to train the policy network. The trained policy networks are used for online (runtime) planning of the UAV conducting reconnaissance for arbitrary target locations by providing the near-optimal action corresponding to specified target locations and the current state. A numerical case study has been conducted to demonstrate the effectiveness of the proposed approach from the perspective of its classification performance and the expected return in comparison with a baseline online planning algorithm.
CEPV: A Tree Structure Information Extraction and Visualization Tool for Big Knowledge Graph

A large amount of data with rich semantic and structural information has been accumulated in many real-world applications. In order to effectively describe the concepts and connections in these data sets, knowledge graph was proposed as a tool to handle it. The genealogy is a typical tree structure data and can be stored in the knowledge graph. However, due to the complexity and increasing volume of the data, how to efficiently extract and visualize the customized information from the big knowledge graph is hence a challenge and worthy of in-depth study. Motivated by this, we propose a novel user-specified information extraction and visualization tool, named CEPV (the Customized information Extracting, Processing and Visualization tool), for converting the big graph structure data into a specified tree structure display. The main steps of CEPV are as follows: firstly, according to the requirements of users, extracting the specified data from the massive, complex, heterogeneous data as fewer times as possible, which can reduce the frequency of database access and improve the overall efficiency of the algorithm. Secondly, the fault tolerance mechanism and attribute judgment rules are executed to ensure the correctness during the data processing. Finally, the processed data with a complex relationship is presented to the user in multiple visualization models. The high availability and effectiveness of our proposed tool is verified on a big knowledge graph dataset.
Facial Gender Classification — Analysis using Convolutional Neural Networks

Automatic gender classification is an important and challenging problem. The challenges are magnified by low resolution of input images and partial occlusion of the face in existing datasets. In recent years, using facial components to conduct gender classification and using deeper convolutional neural networks has both achieved high accuracy and recognition. This analysis paper examines the effect of using deeper convolutional neural networks trained on separate facial components and the results are compared with the state-of-the-art gender classification techniques. We also investigate the effects of network settings and parameters surrounding convolutional neural networks, how they affect the overall classification and provide insights into age-related gender classification. The results show that the proposed technique is promising and performs better with larger crop sizes. Our experiments suggest that the proposed technique can classify gender well from mouth, nose and face (less eyes) only.
Real-Time Application of Document Classification Based on Machine Learning

This research has been performed, keeping a real-time application of document (multi-page, varying length, scanned image-based) classification in mind. History of property title is captured in various documents, recorded against the said property in all the countries across the world. Information of the property, starting from ownership to the conveyance, mortgage, refinance etc. are buried under these documents. This is by far a human driven process to manage these digitized documents. Categorization of the documents is the primary step to automate the management of these documents and intelligent retrieval of information without or minimal human intervention. In this research, we have examined a popular, supervised machine learning technique called, SVM (support vector machine) with a heterogeneous data set of six categories of documents related to property. The model obtained an accuracy of 88.06% in classifying over 988 test documents.
Classification of Forest Cover Type Using Random Forests Algorithm

Natural resource planning isKumar, Arvind anSinha, Nishant important aspect for any society. Knowing forest cover type is one of them. Multiple statistical and machine learning approaches are already proposed in past for classification. In the current work, publicly available dataset of Forest Cover type (FC) from UCI repository was taken and classified for the forest cover type, using random forests machine learning algorithm. On ten-fold cross validation, we got accuracy of 94.6% over 70.8% accuracy of original work presented at UCI repository. The result is also compared with existing work done in past and it have been shown that random forests algorithm performed better than most of existing works.
Web Application for Screening Resume

This paper focuses majorly on the design of the web application which will be used to screen resumes (Curriculum Vitae) for a particular job posting. In the proposed system, a web application will encourage the job applicant candidates as well as the recruiters to use it for job applications and screening of resumes. Recruitment is a tedious process wherein the first task for any recruiter is to screen the resumes. The proposed web application is designed in such a way that job applicant as well as recruiters can use it with ease for applying for job openings and screening respectively. The recruiters from various companies can post the details of the job openings available in their respective companies. The interactive web application will allow the job applicants to submit their resume and apply for their job postings they may still be interested in. The resumes submitted by the candidates are then compared with the job profile requirement posted by the company recruiter by using techniques like machine learning and Natural Language Processing (NLP). Scores can then be given to the resumes and they can be ranked from highest match to lowest match. This ranking is made visible only to the company recruiter who is interested to select the best candidates from a large pool of candidates.
Motion Segmentation via Synchronization

In this paper we consider the problem of segmenting points in a collection of images that contain multiple moving objects.Our contribution is three-fold: (i) we propose a matrix representation of segmentation that permits to formulate the problem in terms of "synchronization" of binary matrices; (ii) we derive a spectral solution to solve such a problem, which is inspired by previous works on synchronization of rotations, homographies, rigid motions and permutations; (iii) we explain how our solution can be interpreted in terms of spectral clustering.The proposed approach is validated on both synthetic and real scenarios, in addition to the Hopkins benchmark.
Free gap information from the differentially private sparse vector and noisy max mechanisms

Noisy Max and Sparse Vector are selection algorithms for differential privacy and serve as building blocks for more complex algorithms. In this paper we show that both algorithms can release additional information for free (i.e., at no additional privacy cost). Noisy Max is used to return the approximate maximizer among a set of queries. We show that it can also release for free the noisy gap between the approximate maximizer and runner-up. This free information can improve the accuracy of certain subsequent counting queries by up to 50%. Sparse Vector is used to return a set of queries that are approximately larger than a fixed threshold. We show that it can adaptively control its privacy budget (use less budget for queries that are likely to be much larger than the threshold) in order to increase the amount of queries it can process. These results follow from a careful privacy analysis.
Generalized Multiscale RBF Networks and the DCT for Breast Cancer Detection

The use of the multiscale generalized radial basis function (MSRBF) neural networks for image feature extraction and medical image analysis and classification is proposed for the first time in this work. The MSRBF networks hold a simple and flexible architecture that has been successfully used in forecasting and model structure detection of input-output nonlinear systems. In this work instead, MSRBF networks are part of an integrated computer-aided diagnosis (CAD) framework for breast cancer detection, which holds three stages: an input-output model is obtained from the image, followed by a high-level image feature extraction from the model and a classification module aimed at predicting breast cancer. In the first stage, the image data is rendered into a multiple-input-single-output (MISO) system. In order to improve the characterisation, the nonlinear autoregressive with exogenous inputs (NARX) model is introduced to rearrange the available input-output data in a nonlinear way. The forward regression orthogonal least squares (FROLS) algorithm is then used to take advantage of the previous arrangement by solving the system as a model structure detection problem and finding the output layer weights of the NARX-MSRBF network. In the second stage, once the network model is available, the feature extraction takes place by stimulating the input to produce output signals to be compressed by the discrete cosine transform (DCT). In the third stage, we leverage the extracted features by using a clustering algorithm for classification to integrate a CAD system for breast cancer detection. To test the method performance, three different and well-known public image repositories were used: the mini-MIAS and the MMSD for mammography, and the BreaKHis for histopathology images. A comparison exercise was also made between different database partitions to understand the mammogram breast density effect in the performance since there are few remarks in the literature on this factor. Classification results show that the new CAD method reached an accuracy of 93.5% in mini-Mammo graphic image analysis society (mini-MIAS), 93.99% in digital database for screening mammography (DDSM) and 86.7% in the BreaKHis. We found that the MSRBF networks are able to build tailored and precise image models and, combined with the DCT, to extract high-quality features from both black and white and coloured images.
From “newbie” to professional: Identity building and literacies in an online affinity space

Reading and writing online has become more frequent for young people all over the world. Non-English speakers use English in order to enter international Internet spaces and to interact with foreign audiences. This ethnographic case study explores identity building and second language literacy development in a Russian fan of the show My Little Pony: Friendship is Magic who was engaged in the practice of crafting pony figurines. In order to present his work to an international audience, he had to write in English online using specific crafting terminology while building his identity as a professional crafter. Over a two-year period, he gained online fame as a crafter and at the same time managed to develop his writing by appropriating new linguistic, technological, and multimodal resources. This study shows how the support of the affinity space of a fandom encouraged this “newbie” to build his online identity as a “recognized expert” in crafting and also to become a more confident and competent English writer.
ROBO: Robust, Fully Neural Object Detection for Robot Soccer

Deep Learning has become exceptionally popular in the last few years due to its success in computer vision [1–3] and other fields of AI [4–6]. However, deep neural networks are computationally expensive, which limits their application in low power embedded systems, such as mobile robots. In this paper, an efficient neural network architecture is proposed for the problem of detecting relevant objects in robot soccer environments. The ROBO model's increase in efficiency is achieved by exploiting the peculiarities of the environment. Compared to the state-of-the-art Tiny YOLO model, the proposed network provides approximately 35 times decrease in run time, while achieving superior average precision, although at the cost of slightly worse localization accuracy.
Application of ARIMA and LSTM in Relative Humidity Prediction

Relative humidity is one of the important indicators of precipitation forecasting. Therefore, the prediction of relative humidity plays an important role in improving the accuracy of weather forecasting. This paper used time series and deep neural network to take meteorological data of a county in Gansu as an example, and established the ARIMA and LSTM models respectively to predict the relative humidity. By comparing the two prediction results, it was found that ARIMA had a better prediction effect on relative humidity, which could provide a favorable reference for the actual forecasting work.
Design and Development of MLERWS: A User-Centered Mobile Application for English Reading and Writing Skills

This study aimed to design and develop a mobile learning application for the subject English Reading and Writing Skills of senior high school. Five-Design Sheet (FDS) methodology and a modified evolutionary prototyping was utilized in designing and developing that resulted to a user-centered mobile learning application. The said application underwent three iterations with user evaluation to ensure its functionalities and usability. The application contains translations from English to Tausug (the local dialect in Bongao) to guide students while studying skimming and scanning lessons along with other features. Furthermore, MLERWS (Mobile Learning application for English Reading and Writing Skills) utilized offline database to make the mobile app usable for students with or without internet connectivity.
Scientific Image Restoration Anywhere

The use of deep learning models within scientific experimental facilities frequently requires low-latency inference, so that, for example, quality control operations can be performed while data are being collected. Edge computing devices can be useful in this context, as their low cost and compact form factor permit them to be co-located with the experimental apparatus. Can such devices, with their limited resources, can perform neural network feed-forward computations efficiently and effectively? We explore this question by evaluating the performance and accuracy of a scientific image restoration model, for which both model input and output are images, on edge computing devices. Specifically, we evaluate deployments of TomoGAN, an image-denoising model based on generative adversarial networks developed for low-dose x-ray imaging, on the Google Edge TPU and NVIDIA Jetson. We adapt TomoGAN for edge execution, evaluate model inference performance, and propose methods to address the accuracy drop caused by model quantization. We show that these edge computing devices can deliver accuracy comparable to that of a full-fledged CPU or GPU model, at speeds that are more than adequate for use in the intended deployments, denoising a 1024x1024 image in less than a second. Our experiments also show that the Edge TPU models can provide 3x faster inference response than a CPU-based model and 1.5x faster than an edge GPU-based model. This combination of high speed and low cost permits image restoration anywhere.
An Efficient Miner Strategy for Selecting Cryptocurrency Transactions

Cryptocurrencies like Bitcoin use the blockchain technology to record transactions in a distributed and secure way. Each block contains a cryptographic hash of the previous block in addition to a set of transactions that it records. The first step for a miner to add a new block to the blockchain is to select a set of pending transactions from a mempool. The total size of selected transactions should not exceed the fixed capacity of blocks. If a miner completes the computationally-hard task of finding the cryptographic hash of the formed block, the block can be added to the blockchain in which case the transactions in that block will become complete. Transaction might have a fee that is granted to the miner upon being complete. As such, when forming a new block, miners tend to select transactions that offer the best fees. Finding a set of transactions with maximum total fee that fit into a block translates to the classic knapsack problem, which is an NP-hard problem. Meanwhile, miners are in fierce competition for mining blocks and hence cannot dedicate too much computational power for selecting the best set of transactions. Most existing solutions to tackle this problem are based on sorting the set of pending transactions by the ratio between their fee and their size. While the sorting step is not a bottleneck in normal situations, transaction can grow explosively in case of a market turbulence like that of 2017. Meanwhile, the total number of transactions increases over time. As such, it is desirable to have an efficient strategy that does not rely on sorting transactions before forming a block. In this paper, we review some of the existing strategies for miners to select transactions from the mempool. We also introduce a robust solution called Size-Density Table (SDT) for selecting transactions that does not use sorting. We perform a theoretical and experimental analysis of our solutions to compare it with other strategies. Our results indicate that our algorithm runs faster than previous approaches while the quality of its solutions (the total fees collected in its blocks) is also slightly better than the best existing strategies.
3D-CNN-SPP: A Patient Risk Prediction System From Electronic Health Records via 3D CNN and Spatial Pyramid Pooling

The problem of extracting useful clinical representations from longitudinal electronic health record (EHR) data, also known as the computational phenotyping problem, is an important yet challenging task in the health-care academia and industry. Recent progress in the design and applications of deep learning methods has shown promising results towards solving this problem. In this paper, we propose 3D-CNN-SPP (3D Convolutional Neural Networks and Spatial Pyramid Pooling), a novel patient risk prediction system, to investigate the application of deep neural networks in modeling longitudinal EHR data. Particularly, we propose a 3D CNN structure, which is featured by SPP. Compared with 2D CNN methods, our proposed method can capture the complex relationships in EHRs more effectively and efficiently. Furthermore, previous works handle the issue of variable length in patient records by padding zeros to all vectors so that they have a fixed length. In our work, the proposed spatial pyramid pooling divides the records into several length sections for respective pooling processing, hence handling the variable length problem easily and naturally. We take heart failure and diabetes as examples to test the performance of the system, and the experiment results demonstrate great effectiveness in patient risk prediction, compared with several strong baselines.
An Improved Speech Emotion Recognition Algorithm Based on Deep Belief Network

In order to improve the characterization ability of speech signal and recognition accuracy of speech emotion recognition, a speech emotion recognition model based on improved Deep Belief Network (DBN) is proposed. The method is to replace the traditional DBN activation function with a Rectified Linear Unit(Relu). And the reconstruction error is used to determine the depth of the DBN network. The short-time energy, short-time zero crossing rate, the fundamental frequency, formants and 24 dimensional MFCC parameters of emotional speech signal are extracted as the basic features. Using these basic features as input to the DBN, automatic recognition of the 6 emotions, anger, fear, joy, calmness, sadness and surprise can be achieved. Compared with the traditional DBN model and the BP model, a better recognition result is achieved by using the improved DBN discussed in this paper, and the recognition rate can reach 84.94%.
An Effective Differential Power Attack Method for Advanced Encryption Standard

In order to improve the efficiency of side-channel attack and reduce attack costs, an effective differential power analysis (DPA) method for advanced encryption standard (AES) is presented. By building the power acquisition platform based on current main stream EDA tools, a lot of power consumption traces can be achieved with ease. In addition, a critical distinguishing function is investigated so as to the key recovering can be more efficient and practical. Finally, two attacking experiments using our analytical method for AES are performed based on the developed DPA platform. Simulation results show that the key of the AES can be cracked correctly illustrating our analytical method has high effectiveness.