Reviewerly/exHarmony huggingface dataset:
Blind Packet-Based Receiver Chain Optimization Using Machine Learning

The selection of the most appropriate equalization-detection-decoding algorithms in wireless receivers is a challenging task due to the diversity of application requirements, algorithm performance-complexity trade-offs, numerous transmission modes, and channel properties. Typically, the fixed receiver-chain is employed for specific application scenario that may support iterative processing for better adaptation to variable channel conditions. We propose a novel method for optimizing receiver efficiency in the sense of maximizing packet transmission reliability while minimizing receiver processing complexity. We achieve this by packet-wise dynamic selection of the least complex receiver that enables error-free packet reception out of set of available receivers. The scheme employs convolutional neural network (CNN) and supervised deep learning approach for packet classification and subsequent prediction of the optimum receiver using raw baseband signals. The proposed scheme aims to approach a packet error rate close to the rate of the most complex receiver architecture while using a combination of both low and high complexity architectures. This is achieved by employing the neural network based classifier to dynamically select packet-specific optimum architecture; i.e. instead of using the most complex receiver for all packets, the approach dynamically assigns the packet to the most appropriate receiver in terms of equalization-detection-decoding capability and the least possible complexity. We analyze the performance of the proposed scheme considering various channel scenarios. The system demonstrates excellent packet classification performance resulting in the significant performance increase and the reduction of the usage of the functional blocks that can go up to 96% of the time in different scenarios.
Models of Two-Wheeled Mobile Robots with Experimental Validation

Two wheeled mobile robots are fully autonomous and self-balancing vehicles. As these systems are nonlinear and open-loop unstable, various and complex control algorithms are needed to guarantee the dynamic system stability. One requirement for the development of a suitable controller is the availability of a reliable mathematical model. This paper treats the mathematical modeling of Two-wheeled Unstable Transporter (UnTrans). The mathematical model of UnTrans is identified using the input/output collected data after a real-time experiment and Grey Wolf Optimizer algorithm.
Melanoma Disease Detection Using Convolutional Neural Networks

There are different forms of cancers out of which skin cancer is the most common one, Usually, Every year the people infected by Skin Cancer will be more than the number of people infected by all other types of cancer combined. Mortality rates of skin cancer in the world have risen. According to the World Health Organization, the early finding of transformations of the skin significantly improve the chances of good medication and treatment so that the patient can be saved. The Computer system integrated with the software developed from deep learning, namely convolutional neural networks (CNN), is good at detecting skin cancer than experienced dermatologists, so now We had extended this Deep Learning Architecture to develop a model that categorizes the given infected skin image of patient as Malignant (Melanoma or Harmful) or Benign (Harmless) By using various libraries in Python. This model is trained and tested by using dataset taken from International Skin Imaging Collaboration(ISIC). The main aim of this model is to detect skin cancer for patients in earlier stages and treat them effectively so that we can reduce the mortality rate.
Clinical questionnaire filling based on question answering framework

Electronic Health Records (EHR) are the foundation of much medical research. However, analyzing the text data of EHRs directly is an challenging task. Therefore, physicians often use questionnaires to first convert text data to structured data. Filling in these questionnaires requires a considerable amount of time and medical knowledge. It is a significant task to develop an algorithm to make computers fill out these questionnaires automatically. This research aims to build a deep learning model that can automatically complete questionnaires with given medical text. This task is a part of Information Extraction (IE), but it differs from the existing tasks of medical IE. Because of the questions in questionnaires are closed-end type, which refers to making a selection among given options, we could treat this task as a classification problem. However, conventional classification algorithms are resource-consuming when filling out one entire questionnaire with one model. They also could not use the question information to guide the questionnaire filling task. To handle these issues, we propose a neural network model based on question answering (QA) framework in this paper. With this framework, our neural network can fill out one complete questionnaire using only one model. We perform experiments on three real-world Chinese medical datasets and related clinical questionnaires. Our model respectively achieves F1 scores of 0.9273, 0.8834, and 0.9846. The results outperform several baseline models. The strong performance of our QA model will allow us to build a system which can help physicians to fill out questionnaires and convert text data to structured data. This system can reduce the workload of physicians.
Incremental Refinement of Page Ranking of Web Pages

Web-based information retrieval systems called search engines have made things easy for information seekers, but still do not provide guarantees about the relevance of the information provided to the users. Information retrieval systems provide the information to the user based on certain retrieval criteria. Due to the large size of the WWW, it is very common that a large number of documents get identified related to a particular domain. Therefore, to help users towards finding the best matching documents, a ranking mechanism is employed by the search engine. In this article, an improved architecture for an information retrieval system is proposed. The proposed system makes a query log for each user query and stores the results retrieved to the user for that query. The system also provides relevant results by analyzing the content of the pages retrieved for the user query.
Age and Gender Recognition on Imbalanced Dataset of Face Images with Deep Learning

The paper describes usage of deep neural networks based on ResNet and Xception architectures for recognition of age and gender of imbalanced dataset of face images. Described dataset collection process from open sources. Training sample contains more than 210000 images. Testing sample have more 1700 special selected face images with different ages and genders. Training data has imbalanced number of images per class. Accuracy for gender classification and mean absolute error for age estimation are used to analyze results quality. Age recognition is described as classification task with 101 classes. Gender recognition is solved as classification task with two categories. Paper contains analysis of different approaches to data balancing and their influence to recognition results. The computing experiment was carried out on a graphics processor using NVidia CUDA technology. The average recognition time per image is estimated for different deep neural networks. Obtained results can be used in software for public space monitoring, collection of visiting statistics etc.
The 128-bit AES design by using FPGA

Abstract Advanced Encryption Standard (AES) is a common symmetric encryption algorithm and widely implemented in Wireless Local Area Network (WLAN) and Bluetooth controller for security services in its application. This paper presents a 128-bit data path with 128-bit, 192-bit or 256-bit key size. The purpose of the design is high throughput and low area design of AES. The AES methodology is by using Field-Programmable Gate Array (FPGA) and Xilinx Virtex-7 XC7VX485T as a tool to obtain simulation results through Verilog Hardware Description Language (HDL). This design utilized a 2730 Slices with the throughput of 12.9 Gbps. The design is suitable for the portable device application which requires data security with a high throughput and speed.
Learning to Visualize Music Through Shot Sequence for Automatic Concert Video Mashup

An experienced director usually switches among different types of shots to make visual storytelling more touching. When filming a musical performance, appropriate switching shots can produce some special effects, such as enhancing the expression of emotion or heating up the atmosphere. However, while the visual storytelling technique is often used in making professional recordings of a live concert, amateur recordings of audiences often lack such storytelling concepts and skills when filming the same event. Thus a versatile system that can perform video mashup to create a refined high-quality video from such amateur clips is desirable. To this end, we aim at translating the music into an attractive shot (type) sequence by learning the relation between music and visual storytelling of shots. The resulting shot sequence can then be used to better portray the visual storytelling of a song and guide the concert video mashup process. To achieve the task, we first introduces a novel probabilistic-based fusion approach, named as multi-resolution fused recurrent neural networks (MF-RNNs) with film-language, which integrates multi-resolution fused RNNs and a film-language model for boosting the translation performance. We then distill the knowledge in MF-RNNs with film-language into a lightweight RNN, which is more efficient and easier to deploy. The results from objective and subjective experiments demonstrate that both MF-RNNs with film-language and lightweight RNN can generate attractive shot sequences for music, thereby enhancing the viewing and listening experience.
TEAN: Timeliness enhanced attention network for session-based recommendation

Session-based recommendation task attracts more researchers’ attention in recent years. However, previous approaches suffer from limited timeliness since they overlook dynamic features of items and temporal semantic information, which results in inappropriate prediction. In this study, we propose an attention-based model named Timeliness Enhanced Attention Network (TEAN). It first extracts features of user and item from static and dynamic perspectives and then employs temporal semantic information by a time-cross mechanism. Our model is capable of ranking items based on timeliness enhanced features. Besides, we apply a pre-training method based on word2vec to learn embedding vector for users, items and temporal semantic information in an elegant way. Experiments on three datasets of different domains demonstrate that our approach improves performance opposed to other methods.
On the Potential of the Nature-Inspired Algorithms for Pure Binary Classification

With the advent of big data, interest for new data mining methods has increased dramatically. The main drawback of traditional data mining methods is the lack of comprehensibility. In this paper, the firefly algorithm was employed for standalone binary classification, where each solution is represented by two classification rules that are easy understandable by users. Implicitly, the feature selection is also performed by the algorithm. The results of experiments, conducted on three well-known datasets publicly available on web, were comparable with the results of the traditional methods in terms of accuracy and, therefore, the huge potential was exhibited by the proposed method.
Machine learning methods in Smartphone-Based Activity Recognition

In this paper, we present a system for human physical Activity Recognition (AR) using smartphone with embedded sensors. This paper addresses the question whether there is a comfortable way to predict human activities based on collected data from smartphone embedded gyroscope and accelerometer. Computational background of this work based on self-learning machine learning methods. In order to train the machine learning algorithms, The University of California, Irvine (UCI) dataset was used and the different models were compared. After selecting the best model further modifications were suggested in order to improve the accuracy of the model. At the end 96.88% accuracy was reached.
Blockchain based Data Security for Financial Transaction System

There is exponential growth in the Internet of Things (IoT) research but still it faces potential privacy and security challenges. For safe and secure transaction blockchain technology is the best-known for powering cryptocurrencies, for making secure and safe transactions from a person to another. Blockchain is a technology that is booming for a decade and many areas must go under improvisation despite its numerous advancements. The proposed system predominantly focuses on providing security to the blockchain system using various mechanisms. the proposed model consists of the financial transaction-based system which works on the RFID technology. The data obtained from the system can be only accessed by the clients who are authorized hence providing the first level of security by providing authentication to the valid client using M2M authentication. Once the user is authenticated, he can then have access to the transaction system. The transaction data that is stored in the local system is guarded by using blockchain technology by using hashing. The hash generated is again guarded by dividing and keeping the hash in two different places. Experiment results indicate that the application of various security mechanisms for the proposed scheme does indeed improve the privacy of the generated hash, authentication speed while also satisfying requirements of data security.
Removing mixture of Gaussian and Impulse noise of images using sparse coding

Real images contain different types of noises and a very difficult process is to remove mixed noise in any type of them. Additive White Gaussian Noise (AWGN) coupled with Impulse Noise (IN) is a typical method. Many mixed noise removal methods are based on a detection method that generates artificial products in case of high noise levels. In this article, we suggest an active weighted approach for mixed noise reduction, defined as Weighted Encoding Sparse Noise Reduction (WESNR), encoded in sparse non-local regulation. The algorithm utilizes a non-local self-similarity feature of image in the sparse coding framework and a pre-learned Principal Component Analysis (PCA) dictionary. Experimental results show that both the quantitative and the visual quality, the proposed WESNR method achieves better results of the other technique in terms of PSNR.
Image Watermarking by Q Learning and Matrix Factorization

Today, with the advancement of technology and the widespread use of the internet, watermarking techniques are being developed to protect copyright and data security. The methods proposed for watermarking can be divided into two main categories: spatial domain watermarking, and frequency domain watermarking. Often matrix transformation methods are merged with another method to select the right place to hide. In this paper, a non-blind watermarking id presented. In order to embed watermark Least Significant Bit (LSB) replacement and QR matrix factorization are exploited. Q learning is used to select the appropriate host blocks. The Peak Signal-to-Noise Ratio(PSNR) of the watermarked image and the extracted watermark image is considered as the reward function. The proposed method has been improved over the algorithms mentioned above with no learning methods and achieved a mean PSNR values of 56.61 dB and 55.77 dB for QR matrix factorization and LSB replacemnet embedding method respectively.
Indigenous Worldviews to Inform Participatory Creativity

This focused reflection explores how Mesoamerican worldviews can inform participatory work. Purépecha accounts of creation are examined here to discover insights and entailments that sustain other ways of creating. With this, we wish to formulate deep questions about the core beliefs and views of what design(s) can be in a more inclusive world.
Probabilistic Sequential Multi-Objective Optimization of Convolutional Neural Networks

With the advent of deeper, larger and more complex convolutional neural networks (CNN), manual design has become a daunting task, especially when hardware performance must be optimized. Sequential model-based optimization (SMBO) is an efficient method for hyperparameter optimization on highly parameterized machine learning (ML) algorithms, able to find good configurations with a limited number of evaluations by predicting the performance of candidates before evaluation. A case study on MNIST shows that SMBO regression model prediction error significantly impedes search performance in multi-objective optimization. To address this issue, we propose probabilistic SMBO, which selects candidates based on probabilistic estimation of their Pareto efficiency. With a formulation that incorporates error in accuracy prediction and uncertainty in latency measurement, probabilistic Pareto efficiency quantifies a candidate's quality in two ways: its likelihood of being Pareto optimal, and the expected number of current Pareto optimal solutions that it will dominate. We evaluate our proposed method on four image classification problems. Compared to a deterministic approach, probabilistic SMBO consistently generates Pareto optimal solutions that perform better, and that are competitive with state-of-the-art efficient CNN models, offering tremendous speedup in inference latency while maintaining comparable accuracy.
Research and Design of Meteorological Disaster Early Warning System for Power Grid Based on Big Data Technology

A large number of overhead transmission lines and other power transmission and transformation equipment in the power grid have been exposed to the atmospheric environment for a long time, and its operation safety is directly related to the operation safety of the power grid. Under the background of climate change, the intensity and frequency of severe weather and extreme weather events are increasing. How to deal with meteorological disasters is one of the key problems that need to be solved in the power system. The related theories and technologies such as meteorological disaster early warning and big data analysis have become a long-term research hotspot in the field of electrical engineering. This paper analyzes the big data technology and GIS (Geographic Information System) technology of power grid, and puts forward the design scheme of meteorological disaster early warning system of power grid. Through the analysis of power grid data and meteorological data, combined with GIS technology and visualization technology, the system can realize the functions of meteorological data monitoring and display, meteorological analysis and forecast, disaster dynamic simulation, transfer route plotting, emergency plan generation, personnel and material scheduling, emergency information release, disaster impact assessment, etc., so as to realize the seamless collection of emergency command business among multiple departments Share with resources.
Connecting Isolated Communities

What determines the success of community cellular networks? We leverage unique circumstances where all households in seven localities were interviewed before the launch of cellular networks. We observed substantial differences in network adoption across communities. Four communities displayed high and regular usage, while usage dissipated shortly after the network launch in three sites. Sixty-five percent of households made or received at least one call or text message. We find that a one standard deviation increase in household wealth is correlated with a three percentage point increase in network adoption and 43 additional cellular network transactions. Agricultural households were ten percentage points more likely to adopt the network than other households and female-headed households were five percentage points more likely to use the network at least once.
A novel key-frame selection-based sign language recognition framework for the video data

Sign language is a medium of communication for people with hearing disabilities. Static and dynamic gestures are identified in a video-based sign language recognition and translated them into humanly understandable phrases to achieve the communication objective. However, videos contain redundant Key-frames which require additional processing. Number of such Key-frames can be reduced. The selection of particular Key-frames without losing the required information is a challenging task. The Key-frame extraction algorithm is used which helps to speed-up the sign language recognition process by extracting essential key-frames. The proposed framework eliminates the computation overhead by picking up the distinct Key-frames for the recognition process. Discrete Wavelet Transform (DWT), Discrete Cosine Transform (DCT), and Histograms of Oriented Gradient (HOG) are used for unique features extraction. We used the bagged tree, boosted tree ensemble method, Fine KNN, and SVM for classification. We tested methodology on video-based datasets of Pakistani Sign Language. It achieved an overall 97.5% accuracy on 37 Urdu alphabets and 95.6% accuracy on 100 common words.
Entity disambiguation with context awareness in user-generated short texts

Conceptualization is to obtain the most appropriate concepts for noun terms (entities) under different contexts, which plays an important role in human knowledge understanding. However, in natural language, entities are often ambiguous, which creates difficulties in conceptualization. To accurately conceptualize, we must eliminate the ambiguity of entities. Existing methods mainly rely on similar or related entities in context for disambiguation. However, due to the sparsity of user-generated short texts, the number of entities that can be extracted from them is limited. In this paper, we propose an entity disambiguation method, which consists of three steps. (1) Measuring the correlation between terms, which uses both corpus and knowledge information to capture the specific semantic relationship. (2) Selecting informative terms, which considers various types of contextual terms, not just entities, thereby mitigating the effects of text sparsity. (3) Prioritizing informative terms to highlight their discriminative power, which reduces noise interference. Finally, the target entity is disambiguated based on informative terms. Experimental results on ground-truth datasets demonstrate that the proposed method outperforms baseline methods.
Android Live Text Recognition and Translation Application using Tesseract

There are thousands of languages in the world. Even though English is the most popular business language, only about twenty percent of the world population can speak English, moreover, people always prefer to use their language. Hence, translation is very important for advancements in literature, technology, and international politics. In this proposed work, we have made an application that can be used to take pictures of text in the paper, screen, signboards, or anywhere and the application will translate the text right there. The goal is to be able to understand any text in any language and translate it in real-time to any other language of preference. To achieve this, we have used Android as my preferred platform as it is a widely used mobile OS around the globe. We have also used many open-source projects to help us achieve our goal, such as Tesseract Leptonica for image computation, google-API-translate to translate the recognized text. We can recognize text from printed papers or screens and translate them into any language of preference. The accuracy in recognizing handwritten text is low and needs additional work. The proposed work has a scope to be used by visually impaired to recognize the text from anywhere and read the translated text to them.