Reviewerly/exHarmony huggingface dataset:
Dynamic Programming Algorithms for Computing Optimal Knockout Tournaments

We study competitions structured as hierarchically shaped single-elimination tournaments. We define optimal tournaments by maximizing attractiveness such that the topmost players will have the chance to meet in higher stages of the tournament. We propose a dynamic programming algorithm for computing optimal tournaments and we provide its sound complexity analysis. Based on the idea of the dynamic programming approach, we also develop more efficient deterministic and stochastic sub-optimal algorithms. We present experimental results obtained with the Python implementation of all the proposed algorithms regarding the optimality of solutions and the efficiency of the running time.
Integrated sulfide alkali wastewater treatment using spent galvanic solutions

Abstract We identified the efficiency of spent Ni 2+ -containing galvanic solutions for removing sulfide ions from sulfide alkali wastewater. It was found that the removal of sulfide ions led to achieving 89.7 % of purification degree by the chemical oxygen demand value. We also studied how various concentrations of nickel (II) and copper (II) ions affect the oxidation intensity of pollutants in mixed wastewater with ambient oxygen and with ozone-air mixture. Optimal conditions were identified for cleaning sulfide alkali wastewater from hydrocarbons: Oxidation of organic components by ozone-air mixture for 30 minutes, containing 300 mg/dm 3 of nickel (II) ions; implementing such conditions resulted in the greatest reduction of the chemical oxygen demand value (down to 798 mg of O/dm 3 . A comprehensive method for the purification of sulfur-alkaline wastewater is proposed, which includes the stages of removing readily volatile hydrocarbons by simple distillation, sulfide ions by reagent deposition using spent Ni 2+ -containing galvanic solutions, and soluble hydrocarbons by oxidation of ozone-air mixture with adding 300 mg/dm 3 of nickel (II) ions contained in galvanic wastewater. Implementation of this technique led to achieving 96% of the wastewater purification degree by the chemical oxygen demand value, which allows discharging wastewater into biological treatment.
Through the Zoom Glass: Drawing Design Inspiration from Mediated Playful Interactions with Food and Child Personas

In this paper we describe our experiences in running a workshop aimed at exploring the use of playful food interactions as a means of uncovering design insights for child oriented food technologies and experiences. The workshop was originally planned as an in-person event in which we could jointly experience the gustatory and olfactory elements of the food items at hand and interact with their tactile properties. However, as a result of the COVID-19 (coronavirus) pandemic, we converted the workshop into a virtual event in which participants had to convey their multisensory food experiences and ideas through the limited sight and sound capabilities offered by online platforms such as Zoom and Miro. We report on the methods used and reflect on how the constraints of digital mediation and the desire of participants to overcome these constraints shaped the experience and outcome of the workshop.
Direct Oriented Ship Localization Regression in Remote Sensing Imagery with Curriculum Learning

Accurate and efficient ship detection in remote sensing images still remains a challenging task due to the large variations of scales, orientations and distributions. In this paper, we propose an anchor-free ship detector that directly regresses ship localization parameters, offering a simpler pipeline over the previous methods. The detection network is then trained in a multi-task fashion which contains not only the ship center-point maps and oriented bounding boxes but the ship masks. Instead of fixing the weights among the multiple task losses, we adopt a curriculum learning strategy which gradually adapts the loss weights during the training process so that the network can learn the discriminative ship features at the early stage and obtain more localization information while training continues. Experimental results on real dataset demonstrate the effectiveness and efficiency of our proposed method.
Unsupervised morphological analysis of minority languages with NPYLM: –Consideration for situations where training data is too small–

In this study, we propose a method based on NPYLM to support the segmentation of speech information into words in the archive work performed by linguists. Due to limited data and prior linguistic knowledge in minority languages, training data for unsupervised morphological analysis was not large enough to efficiently construct NPYLM. We propose two methods to improve the accuracy of analysis with NPYLM for application to small data. The first is replacing all the words obtained in the previous steps with different symbols, and the second is replacing only the uncommon words based on TF-IDF with other symbols. Our experiments show that both of these two methods worked effectively. Therefore we confirm that unsupervised morphological analysis with NPYLM supports the segmentation of speech information into word units even when the available data size is small.
A three-layer architecture to support disparity map construction in stereo vision systems

By adequately locating objects of interest, a computer vision-driven machine can avoid obstacles and collisions, set safe paths to move autonomously, and reconstruct the geometric properties of a scene. In this sense, three-dimensional information is required to perform these tasks properly. As digital images provide information about the scene, they are often used to estimate objects’ spatial position and depths concerning image acquisition machinery. Thus, disparity maps can be obtained, and the differences between similar features from two or more images can be used to encode depth information. Although the disparity map construction has been extensively surveyed, some concepts such as code reuse and compartmentalization need further investigation to optimize application development. Therefore, based on the major stages of disparity calculation, we present a framework to support disparity map construction. This study’s main contributions include a delimitation of elements that integrate stereo vision scope and the modeling of an architecture that integrates the main components of disparity calculation. The proposal is evaluated in different scenarios which demand sparse and dense disparity maps. The standard concepts shared between them are discussed and used to implement a multifaceted application, useful in evaluating stereo vision methods and providing disparity maps as inputs for three-dimensional reconstruction and point cloud elaboration. The code prepared by the authors is publicly available1.
Sally&amp;Molly: A Children’s Book with Real-Time Multiplayer Mobile Augmented Reality

The book ’Sally&Molly’ is for children aged five to ten, a phase where children transition from a self-centered view of the world to social understanding. Via mobile AR we offer a second layer to the book and playful activities with it. We describe the design and design rationale, and explore cooperative real-time multiplayer mechanics for children in mobile AR.
Investigating Parody from Social Media Accounts

Parody is a widespread comic on different social media platforms. As the usage of social media increases, the number of parody accounts increases as well. The main purpose of parody accounts is to spread fake news or to criticize someone in comic ways. In this paper, we investigate parody for different social media accounts. For this study, a customized tweets dataset is created related to different Pakistani personalities and companies. The experiment is carried out using different machine learning and deep learning methods. Our results indicate an accuracy of 92% using RoBERTa.
BIT-Event at NLPCC-2021 Task 3: Subevent Identification via Adversarial Training

This paper describes the system proposed by the BIT-Event team for NLPCC 2021 shared task on Subevent Identification. The task includes two settings, and these settings face less reliable labeled data and the dilemma about selecting the most valid data to annotate, respectively. Without the luxury of training data, we propose a hybrid system based on semi-supervised algorithms to enhance the performance by effectively learning from a large amount of unlabeled corpus. In this hybrid model, we first fine-tune the pre-trained model to adapt it to the training data scenario. Besides, Adversarial Training and Virtual Adversarial Training are combined to enhance the effect of a single model with unlabeled in-domain data. The additional information is further captured via retraining using pseudo-labels. On the other hand, we apply Active Learning as an iterative process that starts from a small number of labeled seeding instances. The experimental results suggest that the semi-supervised methods fit the low-resource subevent identification problem well. Our best results were obtained by an ensemble of these methods. According to the official results, our approach proved the best for all the settings in this task.
A Novel Android Based Application for Collaborative Learning in Colleges

Technology has taken a long leap with the introduction of social platforms like instant messaging applications. The easy accessibility of smartphones and internet connectivity has drastically changed the way in which we communicate today. Apart from making instant communication possible, it also has an untapped potential of providing a platform for collaborative learning. Collaborative learning involves the use of social networking tools to enhance the learning process between teachers and students by enabling the sharing of valuable information, study materials, etc.In this paper, we propose a new Android-based application tailored to fulfill the needs of real-time information dissemination in our college as well as for effective sharing of study material between teachers and students by overcoming the drawbacks of the existing systems. Traditionally, all information pertaining to studentâ€™s academics are displayed on notice boards or carried out manually which is both time-consuming and laborious. Moreover, there is a lack of a proper channel for sending urgent updates to students. This paper discusses the design and benefits of the proposed Android application and how it successfully caters to the need of academic information dissemination in our college.
Interpretable Phenotyping for Electronic Health Records

Datasets from Electronic Health Records (EHRs) are increasingly large and complex, creating challenges in their use for predictive modeling. The two major challenges are large-scale and high-dimensionality. One of the common way to address the large-scale challenge is through use of data phenotypes: clinically relevant characteristic groupings that can be expressed as logical queries (e.g., “senior patients with diabetes”). With the increasing use of machine learning across the continuum of care, phenotypes play an important role in modeling for population management, clinical trials, observational and interventional research, and quality measures. Yet, phenotype interpretation can often be difficult and require post-hoc clarifications from experienced clinicians. For example, detailed analysis may be needed to find that all patients in a a phenotype are diabetic seniors with complications from previous surgery. Moreover, the high-dimensionality problem is often addressed either separately or simultaneously with phenotyping by dimension reduction methods that may further hinder interpretability. In this paper, we introduce the notion of interpretable data phenotypes generated by an unsupervised learning technique. Methods are designed to disambiguate relative feature memberships, thus facilitating general clinical validation, and alleviating the problem of high-dimensionality. The empirical study applies the proposed unsupervised interpretable phenotyping method to a real world healthcare dataset (MIMIC), then uses hospital length of stay as a reference prediction task. The results demonstrate that the proposed method produces phenotypes with improved interpretability and without diminishing the quality of prediction results.
Faster-PPN: Towards Real-Time Semantic Segmentation with Dual Mutual Learning for Ultra-High Resolution Images

Despite recent progress on semantic segmentation, there still exist huge challenges in high or ultra-high resolution images semantic segmentation. Although the latest collaborative global-local semantic segmentation methods such as GLNet [4] and PPN [18] have achieved impressive results, they are inefficient and not fit for practical applications. Thus, in this paper, we propose a novel and efficient collaborative global-local framework on the basis of PPN named Faster-PPN for high or ultra-high resolution images semantic segmentation which makes a better trade-off between the efficient and effectiveness towards the real-time speed. Specially, we propose Dual Mutual Learning to improve the feature representation of global and local branches, which conducts knowledge distillation mutually between the global and local branches. Furthermore, we design the Pixel Proposal Fusion Module to conduct the fine-grained selection mechanism which further reduces the redundant pixels for fusion resulting in the improvement of inference speed. The experimental results on three challenging high or ultra-high resolution datasets DeepGlobe, ISIC and BACH demonstrate that Faster-PPN achieves the best performance on accuracy, inference speed and memory usage compared with state-of-the-art approaches. Especially, our method achieves real-time and near real-time speed with 36 FPS and 17.7 FPS on ISIC and DeepGlobe, respectively.
Comparative Analysis of Normalizing Techniques Based on the Use of Classification Quality Criteria

The paper presents a comparative analysis of various types of normalization techniques. The accuracy of data classification which was carried out after data normalizing was used as the main criterion for evaluating the quality of the appropriate normalizing method. Four various types of datasets downloaded from the UCI Machine Learning Repository were used as the experimental data during the simulation process. Various normalization techniques available from package clusterSim of R software were applied to the experimental data. The quality of the data normalizing procedure was evaluated based on the use of data classification by the calculation of the accuracy of the objects distribution into classes. The neural network multilayer perceptron was used as the classifier at this step. The simulation results have shown that the data normalizing stage significantly influences the classification accuracy and selection of the normalization method depends on the type of data and, consequently, the selection of the normalizing technique should be carried out in each of the cases separately.
Adaptive Binarization Method for Binary Neural Network

In order to make convolutional neural network (CNN) run more effectively on embedded devices, many studies about binary neural networks have appeared. The traditional binary neural networks adopt the STE-based binarization method. This method has the problem of gradient mismatch during the training process. It will cause an enormous loss of accuracy to the network. For this reason, this paper proposes an adaptive binarization method. The method selects hardtanh function as the basic binarization function. This function changes alternately as the training epoch increases. It will eventually approach the sign function. And the method adjusts the threshold of binarization adaptively by introducing a learnable parameter. Based on the two CNN models of VGG-Small and ResNet-20, this paper conducts training with the adaptive binarization method on the CIFAR-10 dataset. The results show that the proposed method is better than traditional methods (BC, BWN). And Its accuracy is very close to some advanced methods, while these methods have lower running speed. Therefore, the adaptive binarization method has higher accuracy, and it can achieve faster running speed in the application of binary neural network.
CTRD: A Chinese Theme-Rheme Discourse Dataset

Discourse topic structure is the key to the cohesion of the discourse and reflects the essence of the text. Current Chinese discourse corpus are constructed mainly based on rhetoric and semantic relations, which ignore the functional information in discourse. To alleviate this problem, we introduce a new Chinese discourse analysis dataset called CTRD, which stands for Chinese Theme-Rheme Discourse dataset. Different from previous discourse banks, CTRD was annotated according to a novel discourse annotation scheme based on the Chinese theme-rheme theory and thematic progression patterns from Halliday’s systemic functional grammar. As a result, we manually annotated 525 news documents from OntoNotes 4.0 with a Kappa value greater than 0.6. And preliminary experiments on this corpus verify the computability of CTRD. Finally, we make CTRD available at https://github.com/ydc/ctrd.
IoT Equipped Intelligent Distributed Framework for Smart Healthcare Systems

Now days, the fundamental aim of the healthcare sector is to incorporate different technologies to observe and keep a track of the various clinical parameters of the patients in day-to-day life. Distant patient observation applications are becoming popular as economical healthcare services are facilitated by these apps. The process of data management gathered through these applications also requires due attention. Although cloud-facilitated healthcare applications cater a variety of solutions to store patients' record and deliver the required data as per need of all the stakeholders but are affected by security issues, more response time and affecting the continues availability of the system. To overcome these challenges, an intelligent IoT-based distributed framework to deploy remote healthcare services is proposed in this chapter. In the proposed model, various entities of the system are interconnected using IoTs and Distributed Database Management Systems (DDBMS) is used to cater secure and fast data availability to the patients and health care workers. The concept of Blockchain is used to ensure the security of the patients' medical records. The proposed model will comprise of intelligent analysis of the clinical records fetched from DDBMS secured with Blockchain. The proposed model is tested with true clinical data and results are discussed in detail. At last, the future scope of the work is presented in the Conclusions section.
Multi-branch Recurrent Attention Convolutional Neural Network with Evidence Theory for Fine-Grained Image Classification

Fine-grained image classification (FGIC) aims to classify subordinate classes belonging to the same meta category. One of the existing FGIC methods is to use attention mechanism to localize and crop a discriminative region from the input image, and this process can be executed recurrently. In this way, the cropped image will progressively focus on a smaller local region containing the object part. However, this may cause the contour information of the object to be incomplete at the finest-scale and thereby the accuracy of the finest-scale is affected. In addition, the fusion strategy of these methods, which generally concatenates the outputs of multiple scales for the final classification, is not sufficient. To tackle the problems, based on the backbone of RA-CNN we first construct a multi-branch attention proposal network (APN) at middle scale of RA-CNN to jointly localize a most discriminative region where multiple APNs can complement each other's incomplete contour information. Moreover, in addition to concatenating the outputs of all scales, we also use the Dempster's combination rule to combine the outputs of all scales. Then, the features of these two parts are further combined for the final classification. Experimental results on the real-world datasets clearly validate the effectiveness of the proposed method.
Cryptocurrencies: Analysis of the Technology and Need for Its Regulation

Cryptocurrencies have been subject to much controversy across the world. So much so that many countries, including India, have made attempts to ban the cryptocurrency business altogether, considering it an economical threat which must be nipped in the bud. However, the crazy money that hangs in the air and concerns from the fin-tech entrepreneurs have made these governments sit up and take notice of the need for regulating the crypto market.The purpose of the present research is to delve into the evolution of cryptocurrencies, to analyze the legal issues surrounding the cryptocurrencies and their trading and the circumstances necessitating their regulation at the highest level. The research also covers the current international setup in cryptocurrency regulations and developing trends. The research gives a special emphasis on the Indian scenario – the developments in the past, regulatory actions, hits and misses, judicial pronouncements and emerging trends in the country.
Anomaly Detection on Bitcoin Values

Bitcoin has received a lot of attention from investors, researchers, regulators, and the media. It is a known fact that the Bitcoin price usually fluctuates greatly. However, not enough scientific research has been done on these fluctuations. In this study, long short-term memory (LSTM) modeling from Recurrent Neural Networks, which is one of the deep learning methods, was applied on Bitcoin values. As a result of this application, anomaly detection was carried out in the values from the data set. With the LSTM network, a time-dependent representation of Bitcoin price can be captured, and anomalies can be selected. The factors that play a role in the formation of the model to be applied in the detection of anomalies with the experimental results were evaluated.
Massively parallel binary neural network inference for detecting ships in FPGA systems on the edge

This paper presents the development of a ship-detecting edge-processing system for deployment on an aerial FPGA platform. A ship detection chain was developed with imager-specific pre-processing algorithms, massively parallel FPGA neural network inference, and host post-processing procedures. The ship detection binary neural network implemented in combinational logic enables high frame and detection rates, and achieves 98.40% patch classification accuracy. A new algorithm for optimizing a combinational binary neural network circuit is presented that merges multiple neurons in a network layer taking advantage of similarities between neuron weights, which leads to lower logic size and power consumption. Thus, state-of-the-art performance is achieved in comparison to similar previous works using combinational binary neural networks, achieving 38 ns inference latency, 0.425 W of power dissipation, and only 19 k FPGA slices.
Classification of Customer Service Tickets in Power System Based on Character and Word Level Semantic Understanding

Recently, a large amount of electric power data has been accumulated in the power system because of the development of information technology. Customer service ticket data is one of the most important types of electric power data. Classifying customer service tickets according to the text content is conducive to understanding customer demands accurately. So it is of great significance to improve the quality and efficiency of power system operation. In this paper, we propose a classification model of power system customer service tickets based on hierarchical semantic understanding. The deep learning method is used to simultaneously model the words and characters in the tickets. And then the hierarchical semantic representations can be used to classify tickets. The contrast experiments on real dataset prove that the proposed method can accurately learn the hidden semantics of tickets, and has better classification accuracy than the comparative approaches.