Reviewerly/exHarmony huggingface dataset:
Adaptive bits selection for DRM application on high-quality audio

Consumption of lossless audio sources such as FLAC is on the rise as rapid increase in the Internet access speed and people&#x0027;s demand for high-quality audio. In addition to this, consumption of MQS (Mastering Quality Sound) sources such as 24bit and 192KHz audio format has been increasing recently. In order to prevent illegally-sharing audio contents with copyrights, DRM technology has been widely used. However, the large file size of MQS quality audio format makes an increase the amount of computation and time taken for decryption when conventional DRM (Digital Rights Management) is applied. This paper presents an effective DRM model that allows for low-quality preview and minimizes battery drain on mobile devices. The proposed method has the advantage of providing the constant audio quality at low-quality pre-listening. This model has been implemented on the Android platform to evaluate its usability and effectiveness.
B2B Artificial Intelligence Transactions: A Framework for Assessing Commercial Liability

Business to business (“B2B”) artificial intelligence (“AI”) transactions raise challenging private law liability issues because of the distinctive nature of AI systems and particularly the new relational dynamics between AI solutions providers and procurers. This article advances a three-stage framework comprising data management, system development and implementation, and external threat management. The purpose is to unpack AI design and development processes involving the relational dynamics of providers and procurers in order to understand the parties’ respective responsibilities. Applying this framework to English commercial law, this article analyses the potential liability of AI solutions providers and procurers under the Supply of Goods and Services Act and the Sale of Goods Act. The assumption that only AI solutions providers will be subject to liability, or that no party will be liable due to the “autonomous” nature of AI systems, is rejected.
Optical and Control Design of Bright-pupil Microsaccadic Artificial Eye

An artificial eye is a useful tool for evaluating the accuracy and precision of eye trackers, but it has been difficult to design an optical system with a fast response time that can be used to evaluate the measurement performance of dynamic characteristics such as microsaccades. In this paper, we propose an optical and control design for the artificial eye that can reproduce microsaccadic behavior. The optical design provides bright pupil that can be available for eye tracking and enables quick response using a galvano motor. The control design generates a response waveform based on a saccadic parametric model considering peak velocity in microsaccade detection methods. We have experimentally confirmed that the designed artificial eye produced images with bright pupil and corneal reflection of sufficient contrast, ability to move with the designed peak velocity, and sufficient validation performance of conventional microsaccade detection in 500-fps images.
Toward Collaborative Game Commentating Utilizing Pre-Trained Generative Language Models

In this paper, we propose a novel task of collaborative game commentating, an artificial intelligence agent capable of collaboratively commentating with a human commentator in Live-Streaming of Esports. To this end, we propose a collaborative game commentating system that employs a pre-trained language model trained using commentaries by professional commentators, along with metadata including title and tags. The conducted experiments show that (1) fine-tuned Text-to-Text Transfer Transformer (T5) model, a state-of-the-art generative language model, could produce more clearer and precise commentary and better recall the words from the reference commentary, as it effectively improves the scores on evaluation metrics that are widely used for concise text generation task after tuning the model. (2) The more information used for the current method fusion of information, the clearer and more precise generated commentary is. However, it performs worse to recall the words from reference commentary.
A High-Precision and Wide Range Method for Inner Diameter Measurement

Inner diameter measurement technology with high precision and wide measuring ranges is a difficult area to master. It is challenging to achieve high precision and a wide measuring range for inner diameter measurement. The dispersive confocal technique is used to describe a high accuracy, large range approach for measuring inner diameter. The inner diameter is measured utilizing a rotating scanning approach that combines the dispersive confocal technique with least squares. Meanwhile, a plane mirror deflects the optical path of a dispersive confocal sensor, focusing monochromatic light of a certain wavelength on the object’s surface in the radial direction. The measuring range of the device may be modified to fit different objects by modifying the distance between the dispersive confocal sensor and the plane mirror, as well as the eccentricity of the dispersive confocal sensor. Furthermore, this study examines the method’s accuracy and suggests a calibration technique. As a consequence, the procedure may ensure measurement precision as well as a measurement range. Finally, experiments are utilized to test the validity of the strategy. To accomplish a 3 μm inner diameter measurement precision and a measuring range of 76 mm to 150 mm, a dispersive confocal sensor with a range of just 20 mm was utilized.
U-Net based mitosis detection from H&amp;E-stained images with the semi-automatic annotation using pHH3 IHC-stained images

We propose a new U-Net-based method for mitosis detection and a semi-automatic image processing algorithm to generate datasets from the H&E- and pHH3- stained tissue images. Instead of manual annotation, which requires not only specialized knowledge but also a lot of labor and time, our dataset generation algorithm is capable of generating precisely labeled datasets that can be easily used as a data expansion for training various kinds of models. Moreover, the proposed U-Net-based mitosis detection model, called GaussUNet, can learn the features of mitotic figures from the images by using novel two-dimensional-Gaussian-distribution-based labels created from the centroid coordinates given by annotations. In addition, we tried to improve the performance of the model by adding false positives obtained from the trained model as the mitosis look-alikes (MLAs) class to the training data. In the experiments, we confirmed the high performance of the proposed method with a simple and efficient model compared to conventional methods.
We're not puffer fish doctors: the road to the first spine emoji

I hate to be that guy. You know—that old guy who bemoans all the new stuff and mocks the younger generation for not knowing what life was like with a cathode-ray television, let alone without email or the internet. However, I am that guy. One of the earliest memories I have of “technology” was watching Sesame Street on a 13-inch black and white TV in the small kitchen of a tiny apartment in Brooklyn in the early 1970s. The set was poised on a tall cart with wheels so it could be moved around to get the best reception. Let's not forget about the art of positioning the rabbit ear antennae just right.
A Cloud Based Breast Cancer Risk Prediction (BCRP) System

Breast cancer is one of the main causes of cancer across the world for women. Early diagnostics of cancer considerably increases the chances of correct treatment and survival. However, this diagnosis process is tedious and often leads to a disagreement between pathologists. Machine learning algorithms and computer aided tools have significant potential for improvement in diagnosis process. It is a need of time to implement an Artificial Intelligence based breast cancer risk prediction system. In this paper our aim to develop a cloud based model that is capable of detecting the breast cancer at an early stages. In the pre-processing stage, missing values are replaced by mean and one hot encoding method is used for converting categorical attributes of dataset into numeric. This paper also analyzes the performance of various classification models i.e., Logistic Regression, Decision Tree, Naïve Bayes, Gradient Boosting, Random Forest, KNN, Linear Discriminant Analysis and Multilayer Perceptron. The implementation of a machine learning model is done in python using jupyter notebook. This model is deployed in cloud using IBM cloud services. This application will be useful to doctors for making decision whether a patient is benign or malignant.
Causal Networks

Directed graphs suggest themselves for an additional interpretation: Directed edges between nodes are intuitive and are often used to indicate causal relationships between a cause and an effect. It is a common practice to have domain experts draft causal influences as a directed graph and then transforming it to a directed independence map. We will discuss the question whether it is possible to use graphical models that were induced by some learning algorithm to reflect (at least some of) the causal relations in the data.
Digital MemComputing machines

Abstract This Chapter introduces the formal definition of digital MemComputing machines (DMMs), designed to solve combinatorial optimization problems. It then shows the difference between their test and solution modes of operation, and discusses how to transition from the mathematical definition of DMMs to their physical realization.
A new person‐fit method based on machine learning in CDM in education

Cognitive diagnosis models have become popular in educational assessment and are used to provide more individualized feedback about a student's specific strengths and weaknesses than traditional total scores. However, if the testing data are contaminated by certain biases or aberrant response patterns, such predictions may not be accurate. The current research objective is to develop a new person-fit method that is based on machine learning and improves the functionality of existing person-fit methods. Various simulations were designed under three aberrant conditions: cheating, sleeping and random guessing. Simulation results showed that the new method was more powerful and effective than previous methods, especially for short-length tests.
Model Predictive Obstacle Avoidance Control for an Electric Wheelchair in Indoor Environments Using Artificial Potential Field Method

In this paper, a model predictive control (MPC) based obstacle avoidance control scheme in the indoor environment is proposed. Since the mobile area is limited, indoor vehicles need to take a path with minimum avoidance while estimating states of obstacles and vehicles sequentially. With the combination of the artificial potential field method, an MPC-based control scheme is introduced to deal with the above problem. To estimate the self-position and obstacle position, a 3D point cloud is clustered, and the Normal Distribution Transform-Simultaneous Localization And Mapping (NDT-SLAM) and the Kalman filter are employed. A Gazebo simulation is conducted to evaluate and compute path and velocity considering obstacles in an environment with multiple unknown stationary obstacles. As a result, the avoidance of many obstacles occluded by other objects can be realized. In addition, an experiment in an actual indoor environment is conducted to verify the motion of the experimental vehicle. It is confirmed that it is possible to drive with MPC using the estimated position even in a narrow environment.
A Simultaneous Object Detection and Tracking Framework Based on Point Cloud

Achieving accurate and effective detection and multi-object tracking plays a very important role in the field of automatic driving. Accurate detection of pedestrians, cars, cyclists, and other objects can improve the performance of the downstream tasks, such as obstacle avoidance and path planning. Based on the object detection results, multi-object tracking can identify and track moving targets in a period of time. The traditional tracking-by-detection methods can not handle the occlusion situation or long-term tracking problem well. In this paper, we propose a simultaneous detection and tracking method based on spatial temporal map, which has superior performance of trajectory connectivity and tracking accuracy in a long period of time. In the detection module, we use RANSAC algorithm to extract and segment the plane for each frame of lidar data, and then, cluster and classify different objects using the Euclidean Clustering algorithm. In the tracking module, we propose a two-frame and multi-frame switchable architecture for online tracking. In two-frame tracking, we use Kalman filter to update the object location measurements to match the same object in the current frame. For long-term and multi-frame tracking, the trajectories of objects are directly represented on spatial temporal map, and we fit polylines in the map to find the best data association. Experiment results on the KITTI dataset show that our 3D object detection and tracking framework is elegant and complete, the tracking module is robust, widely applicable, and low in cost.
Improve Representation for Cross-Language Clone Detection by Pretrain Using Tree Autoencoder

With the rise of deep learning in recent years, many code clone detection (CCD) methods use deep learning techniques and achieve promising results, so is cross-language CCD.However, deep learning techniques require a dataset to train the models.The dataset is typically small and has a gap between real-world clones due to the difficulty of collecting datasets for cross-language CCD.This creates a data bottleneck problem: data scale and quality issues will cause that model with a better design can still not reach its full potential.To mitigate this, we propose a tree autoencoder (TAE) architecture.It uses unsupervised learning to pretrain with abstract syntax trees (ASTs) of a large-scale dataset, then finetunes the trained encoder in the downstream CCD task.Our proposed TAE contains a tree Long Short-Term Memory (LSTM) encoder and a tree LSTM decoder.We design a novel embedding method for AST nodes, including type embedding and value embedding.In the training of TAE, we present an "encode and decode by layers" strategy and a node-level batch size design.For the CCD dataset, we propose a negative sampling method based on probability distribution.The experimental results on two datasets verify the effeteness of our embedding method, as well as that TAE and its pretrain enhance the performance of the CCD model.The node context information is well captured, and the reconstruction accuracy of the node-value reaches 95.45%.TAE pretrain improves the performance of CCD with a 4% increase in F1 score, which alleviates the data bottleneck problem.
Design and Implementation of Blockchain-enabled Immutable and Interoperable Data Management System for Enterprise Systems

As each organization has different enterprise systems, securely sharing and managing data between organizations is not an easy task. Blockchain technologies are a powerful tool to enable this, and many blockchain-based data storing methods have been proposed. However, they often take a rather tailor-made approach, meaning that a blockchain system needs to be developed to a specific underlying enterprise system. To fill this gap, we propose a flexible, immutable, and interoperable data storage system with blockchain. We demonstrated our system with construction companies’ contracts and progressive claims use-case.
Software Development for Building Simulation Models of Digital Twins of TPP Equipment in Diagnostic Systems

This paper deals with the development of mathematical, algorithmic and software for building simulation models of digital twins of thermal power plant (TPP) equipment and thermal circuits for use as part of diagnostic and predictive analytics systems. The database is created, which allows storing thermal and technical parameters of the object and data of the technical state of equipment. An interface for working with digital models of a low-pressure heater, steam cylinder and condenser is developed, which allows creating various schemes of equipment mutual arrangement as a part of the thermal scheme. The functional capabilities of the software are described and the results of its operation are given, as well as the estimations of the technical condition of the equipment at the given parameters.
LA-U2Net: location-aware U2Net for salient object detection

Salient object detection(SOD) is particularly important especially for applications like autonomous driving which requires real-time inference speed and high performance. Most of the previous works however focus on global object accuracy but not on the connection of local objects. In this paper, we first process the cityscapes dataset into a saliency detection dataset, which focuses on distinguishing between moving objects on the road and moving objects on the sidewalk. In order to enable the saliency detection network to learn the connection between the target categories, we propose a gated convolution(GCov), which can control the input of the feature layer. For the evaluation of SOD, we combine a variety of loss functions to form a mixed loss. Equipped with the GCov and mixed loss, the proposed architecture is able to effectively distinguish the difference in the semantics of the location for the targets of the same category. Experimental results on the dataset show that our method has competitive results compared with other saliency detection networks.
Comparison on video object segmentation: methods and results

Video object segmentation (VOS) is a fundamental research area in the computer vision field in which the goal is to separate the target object(s) from the background in continuous frames of a video. It has huge application value and demand in human-computer interaction, video analysis, compression, re-creation, and numerous fields. Current VOS research in CV academia is mainly classified into four main categories: semi-supervised VOS, unsupervised VOS, weakly supervised VOS, and interactive VOS. In this paper, we give an overview of the latest methods in the first three categories. We summarized problems in each area and features of different methods aiming to solve them. Then we compare these methods to find out the performance in different test environments.
Big Data Edge on Consumer Devices for Precision Medicine

Consumer electronics like smartphones and wearable computers are furthering precision medicine significantly, through capturing/leveraging big data on the edge towards real-time, interactive healthcare applications. Here we propose a big data edge platform that can, not only capture/manage different biomedical dynamics, but also enable real-time visualization of big data. The big data can also be uploaded to cloud for long-term management. The system has been evaluated on the real-world biomechanical data-based application, and demonstrated its effectiveness on big data management and interactive visualization. This study is expected to greatly advance big data-driven precision medicine applications.
Unsupervised concept identification from a large corpus of research documents

Research documents play a crucial role in data-driven research. Identifying concepts in a corpus of research documents can lead to a better understanding of the current stage of research. It can reveal fruitful concepts hidden inside the corpus. However, manually analyzing the corpus is laborious and inefficient. Automating the process is challenging due to the lack of background knowledge to fill the semantic gap that exists between humans and machines. To address this issue, we introduce a novel method that leverages information from an online open resource, namely Wikipedia, to build background knowledge automatically. An experiment on a set of 13,636 research documents shows that the framework can effectively and efficiently identify broad range of concepts within a large text corpus by exploiting only Wikipedia categories and documents' titles.
Age Estimation and Gender Recognition Using Biometric Modality

Recently, age estimation and gender recognition have become important tasks in various popular applications, such as human-machine interaction, child safety, marketing, and demographic analysis. However, research in this area remains limited and very preliminary. We present in this paper our proposed system that aims to recognize the gender of persons and estimate their age range using a finger vein print. A large set of experiments are conducted on MMCBNU_6000 and UTFVP finger vein datasets showing that the proposed system identifies the age range with 92% and 90% for gender recognition.
Few-shot Learning via Non-negative Representation

Due to the uncertainty caused by using a small number of labeled samples, few-shot classification is a challenging problem. In the past few years, many methods have been proposed to solve few-shot classification, among which the method based on transduction has been proved to be the best. According to this idea, in this paper, we propose a new transduction-based method, which is based on the nonnegative representation. In order to obtain the classification results, we minimize the objective function with two items: (1) The first item assigns representation coefficients for each class prototype to obtain the minimum reconstruction error; (2) The second item encourages similar query samples to have consistent label allocation. At the same time, we make non-negative constraints on the representation coefficient to make the representation sparse and discriminative. Using standardized visual benchmarks, we prove that the proposed method can achieve high accuracy in various data sets, inductive and transductive settings, and is robust to the situation that the number of unlabeled samples per class is unbalanced.
Research on thermal infrared face alignment method based on regional location

Face alignment is a research hotspot in face recognition technology, and face alignment can help improve the recognition accuracy of face recognition. Many face alignment methods have been proposed, such as the visible light face alignment technology based on key facial feature points. However, it is not applicable to thermal infrared face alignment technology. This paper proposes a thermal infrared face alignment technology based on regional positioning, that is, the key feature area of the thermal infrared face image is extracted by the generation confrontation network, and then the key feature area is converted into key feature points, so as to realize the thermal infrared face alignment technology. The data set used in this method is the thermal infrared face data set created by the team of Professor Zhang Tianxu of Huazhong University of Science and Technology. The thermal infrared face image after the alignment operation using this method is input to the FaceNet face recognition network, and the recognition accuracy rate is 95.46%. Compared with the input of misaligned thermal infrared face images, the recognition accuracy is improved by 1.62%. The regional positioning thermal infrared face alignment method proposed in this paper can effectively improve the accuracy of thermal infrared face recognition.