Reviewerly/exHarmony huggingface dataset:
Capturing Numerals and Pronouns at the Morphological Layer in the Prague Dependency Treebanks of Czech

Abstract The paper presents a novel and unified morphological description of numerals and pronouns, as compiled for the newest edition of the Prague Dependency Treebank (Prague Dependency Treebank – Consolidated 1.0) and its integral part the morphological dictionary MorfFlex. On the basis of considerable experience with real data annotation and the use of the morphological dictionary, particular changes were proposed. For both of the parts of speech a new set of subtypes was proposed, based mainly on the morphological criterion and its combination with semantic properties and other relevant features, such as definiteness in numerals and possessivity, reflexivity, and clitichood in pronouns. Each subtype has a specific value at the 2 nd position of the morphological tag, which serves also as an indicator of the applicability of other tag categories.
Attenuation Characteristics of Wusn Signal Under Different Soil Composition

In this paper, we designed low-power wireless underground sensor network nodes, studied and analyzed the attenuation prediction values of two prediction models under different soil components, a fusion optimization model is proposed to make the prediction of signal attenuation under different soil components more accurate. The modified Frisian transmission model and the complex refractive index Fresnel model are used to predict signal attenuation, then use the optimized model for signal prediction, compare the optimized model with the modified Friis prediction model and the complex refractive index Fresnel prediction model, the results show that the complex refractive index Fresnel model is more suitable for soil environments with high water content (>20%) and low clay content (<30%). the modified Friis model is more suitable for soil conditions with lower water content (<20%) and higher clay content (>90%), the proposed optimization model is more suitable for soil environments where the clay content is greater than 30% and less than 90%.
Alpha-trimmed Mean Filter and XOR based Image Enhancement for Embedding Data in Image

In the age of digital content creation and distribution, steganography, that is, hiding of secret data within another data is needed in many applications, such as in secret communication between two parties, piracy protection, etc. In image steganography, secret data is generally embedded within the image through an additional step after a mandatory image enhancement process. In this paper, we propose the idea of embedding data during the image enhancement process. This saves the additional work required to separately encode the data inside the cover image. We used the Alpha-Trimmed mean filter for image enhancement and XOR of the 6 MSBs for embedding the two bits of the bitstream in the 2 LSBs whereas the extraction is a reverse process. Our obtained quantitative and qualitative results are better than a methodology presented in a very recent paper.
Joint Model of Triple Relation Extraction with Label Embeddings

Extracting triple relations from unstructured text is an important task for building a knowledge graph. The method for triple relation extraction can be broadly divided into the pipeline and joint extraction models. Both methods focus only on word embedding, and treating labels as meaningless information. These labels are effective intermediate features that capture the semantic rules between words. In this paper, with the joint extraction mode of relation extraction, a framework for joint extraction of triples based on label embedding is proposed, called TriLab. Specifically, it first uses a pretrained model to convert the text into a vector representation. Then, the decoding part implements the extraction of triples through two two-class classifiers, sharing the encoding layer. The label-word joint embedding problem is regarded as the focus of the classification task. The experiments were conducted on a large-scale Chinese data set. The comparative results show that the TriLab framework outperformed the previous methods in triple extraction.
V-Net Based Liver and Tumor Segmentation Using 3D ConditionalRandom Fields and Graph Cuts Method

Automatic liver segmentation from 3D biomedical images is an essential task for many clinical applications and also helpful to radiologists and doctors to analyze and segment the liver tumor faster and accurately. However, it is still a challenging task due to the complex background of the liver, fuzzy boundary, and different appearance of liver and tumor. The present study was performed to propose more efficient deep learning based approach for liver and tumor segmentation from 3D CT scans using V-Net and two different post-processing methods: Conditional Random Field (CRF) and Graph Cuts. In propose approach, using 3-slice input and ResNet as base network outer performs by applying CRF as a post-processing step. Results are evaluated using different metrics that are used for biomedical images processing and with different combinations of parameters such as 3-slice input using ResNet with CRF and Graph Cuts. Results were significant by the proposed approach as compared to other state-of-the- art approaches. Tumor segmentation part are better than other approaches having 96.60%, 5.10%, -1.5%, 0.96mm, and 12.76mm for DSC, VOE, RVD, ASD, and MSD respectively for liver segmentation and 83.91%, 38.92%, -2.6%, 1.13mm and 5.76mm for DSC, VOE, RVD, ASD and MSD respectively for tumor segmentation.
A Time-Space Exchange Autoencoder for Image Super-Resolution

Due to the constraints of imaging equipment cost, manufacturing process and external environment, the resolution of digital image cannot meet industrial demands. Image Super-Resolution (SR) technology makes up for the shortcomings of low imaging resolution and converts low-resolution images into high-resolution images. This paper proposes an Autoencoder (AE) network, which uses high-resolution images for training and low resolution image for inference. What differentiate our approach from other approaches is the use of iterative inference, which exchanges time with space. During the inference phase, the low resolution input image is iterated a few times to generate a high resolution image. The memory capacity of N neurons increased from 0.15 to N through time-space exchange. Experimental results show that the network used in this paper has more concise structure and fewer neurons, the peak Signal to Noise Ratio of SR image finally output from the AE is 33.1715, confirmed that the methods of this paper achieved a competitive SR performance.
Benefit vs Cost:Examining Factors of Intention to Comply Information Security Policy

Cybersecurity risk remains a challenge since it's attributed from human negligence or error due to lack of compliance with policies and procedures. Information security policy underlines roles, responsibilities, guidelines and rules for employees to protect information and technological resources that encompass the intellectual capital of organizations. Information security policy protects knowledge and information in terms of confidentiality, integrity and availability. This study aims to investigate factors that influence compliance behaviour of information security policy in a public sector agency. Grounded from the theory of planned behaviour, rational choice and innovation diffusion theory, we derived a research model on employee's decision to comply (or not to comply) based on individual-based-belief. We adopted the quantitative design to gather data using survey questionnaire distributed to employees at the agency. The results of study shows that perceived cost of non-compliance and compliance significantly influence attitude towards compliance of information security policy. Future recommendation is further proposed at the end this paper.
Personal Internal Space in Educational Leadership

Summary The space between the subjective and objective fills a person’s identity. It is knowledge about oneself, about the difference between oneself and others / strangers, about one’s own actions, their effects and consequences. It is likewise knowledge of one’s own uniqueness, of the consequences of one’s own actions that define the limits of one’s own identity. Identity is a personal internal space, built on the basis of empathy, autonomy, distance, assertiveness, activity, and tolerance. Identity is different dimensions of individuality, uniqueness and separateness, compatibility of thoughts-feelings-actions, self-esteem and intentionality important for interpersonal and social conflicts. It serves to build and consolidate an image of oneself and knowledge about oneself. Identity allows a person to self-reflect, receive insight, evaluate, make changes, and strengthens the image of his/her person, offering a sense of separateness and being yourself.
Single-axis solar tracking system referring to date and time

Abstract This article is about designing and building a single-axis solar tracking system referring to the sun position database. The objectives are as follows: 1. to design and build a solar tracking system, and, 2. to compare the power produced from the solar tracking system with that from the stationary solar panel. The angle of the solar panel from the solar tracking system is positioned at a constant altitude angle, 15 degrees, facing south, and the moving part was the azimuth, which follows the position of the sun. Latitude and longitude coordinates are identified by an Arduino UNO R3 microcontroller board for processing data, reading coordinates of the sun’s angle degrees from the SD card module, and commanding the servo motor to rotate to adjust the angle of the solar panel in a position perpendicular to the sun. Results from the experiment were collected in October 2020 from 9 AM to 4 PM. The system changed the angle degree every 30 minutes. It is found that the solar tracking system can easily be created and controlled, and can also accurately follow the sun’s position all day long. Moreover, it can generate more electric power than that generated by the stationary solar panel by up to 15%. The system is applicable and can generate more electric power than other tracking systems, although the results were collected during the rainy season when the weather was generally cloudy and rainy throughout the month.
Extended Kalman filter techniques and difference equation for time varying stochastic nonlinearities

In this paper the Extended Kalman filter is designed for time varying dynamic model by linearizing the first order nonlinear system. Taylor's series expansion is applied in the Extended Kalman filter algorithm to identify the dominant element. The projected Extended Kalman filter consists of forecast and data assimilation. The forecast error covariance and posterior error covariance is obtained. The Iterated Extended Kalman filter improves the accuracy by linearizing the most recent estimate with higher computational time. An estimation error and stability conditions of nonlinear system is obtained and shown that estimation error is exponentially bounded with mean square.
Constellation Diagram Analyzer Based on Few Shot Learning

A constellation analyzer based on few shot learning is proposed, which can be used for OSNR estimation and modulation format recognition. 100% accuracy is achieved for modulation format recognition and OSNR estimation (except 128QAM).
Clarity 2.0: Improved assessment of product competitiveness from online content

Abstract Competitive analysis is a critical part of any business. Product managers, sellers, and marketers spend time and resources scouring through an immense amount of online and offline content, aiming to discover what their competitors are doing in the marketplace to understand what type of threat they pose to their business' financial well‐being. Currently, this process is time and labor‐intensive, slow and costly. This paper presents Clarity , a data‐driven unsupervised system for assessment of products, which is currently in deployment in the global technology company, IBM. Clarity has been running for more than a year and is used by over 4,500 people to perform over 200 competitive analyses involving over 1000 products. The system considers multiple factors from a collection of online content: numeric ratings by online users, sentiment of user generated online content for key product performance dimensions, content volume, and topic analysis of content. The results and explanations of factors leading to the results are visualized in an interactive dashboard that allows users to track their product's performance as well as understand main contributing factors. Its efficacy has been tested in a series of cases across IBM's portfolio which spans software, hardware, and services. After initial release and first year of use, improvements to the methodology were implemented to ensure it was relevant to and served the highest impact needs of target users. Moreover, new use cases leveraging the initial ideas and approaches continue to be explored.
Algorithm for Steganographic Hiding of Information Using a File System

This article analyzes a method to hide data by modifying directory index entry information in NTFS file system. By modifying the file names of several index entries to make them duplicated, if the duplicated files are deleted, then the only the original file is deleted, but the modified files are retained in the MFT entry intact. So, the fact that these files are hidden is not exposed. The file contents hiding is to allocate data to be hidden on an empty index record page that is not used. If many files are made in the directory, several 4KB index records are allocated. NTFS leaves the empty index records unchanged after deleting the files. By modifying the run-list of the index record with the cluster number of the file-to-hide, the contents of the file-to-hide are hidden in the index record. By applying the analyses method to the case of hiding two files, the file lists are not exposed in the file explorer and the command prompt window, and the contents of the file-to-hide are hidden in the empty index record. It is proved that the analyzed method has effectiveness and validity.
Predominant Sense Acquisition with a Neural Random Walk Model

Domain-Specific Senses (DSS) acquisition has been one of the major topics in Natural Language Processing (NLP). However, most results from unsupervised learning methods are not effective. This paper addresses the problem and proposes an approach for improving performance based on deep learning. To obtain DSS, we utilize Approximate Personalized Propagation of Neural Predictions (APPNP) consisting of Graph Convolutional Networks (GCN) and PageRank. GCN is a neural network that performs on graphs to learning sense features from neighbors' senses and using Personalized PageRank for propagation. For constructing sense features, we collect glosses from WordNet and obtained sense embedding by using Bidirectional Encoder Representations from Transformers (BERT). Our experimental results show that the approach works well and attain at 0.614 Macro F1-score. In addition, to demonstrate the efficacy that DSS can work well in the NLP task, we apply the results on DSS to text categorization and gain a macro F1-score at 0.920, while the CNN baseline method is 0.776.
A Video-Based Taiwan Sign Language Recognition System Using Deep Learning Techniques

Past research on sign language recognition has mostly been based on physical information obtained via wearable devices or depth cameras. However, both types of devices are costly and inconvenient to carry, making it difficult to gain widespread acceptance by potential users. The goal of this research is to use sophisticated and recently developed deep learning technology to build a recognition model for a Taiwanese version of sign language, with a limited focus on RGB images for training and recognition. It is hoped that this research, which makes use of lightweight devices such as mobile phones and webcams, will make a significant contribution to the communication needs of deaf and hard-of-hearing (DHH) individuals.
Implementation of Prototype Environment of Virtual Reality Platform for Virtual Lab (Use case of ESL)

Virtual learning platforms have extended the learning experience beyond the constraints of space and time. Learning new language skills in virtual environments is becoming more and more convenient and efficient. In this work, learning English as a second language (ESL) is tested. Initial results showed that it is not only efficient to learn ESL with this tool, but it also helped users to develop good interaction skills with the computer-generated 3D world without any fear or shyness. Current virtual learning platforms suffers from distraction inherited by designing learning activities that requires user interaction, such as pressing the keyboard and clicking the mouse to control the avatar and background. In our research work, we employed a new software implementation stack that include a unity virtual environment, an applied Speech Recognition (SR) capability, and a Natural Language Processing (NLP) component to overcome these distractions and improve the Human-Computer Interaction (HCI) capability without the need for a keyboard and mouse for the dynamic avatar’s movement and backgrounds.
Web Analytics and Online Retail

Currently, all major e-retailers and even the start-ups have incorporated web analytics services on their websites to monitor customer behaviour while extracting personal data. However, the web analytics data collection methods and the applications of such collected data have raised a lot of concerns regarding the ethical use of this data. The present work identifies some important ethical challenges and unethical practices that have cropped up with the usage of these techniques. The research also suggests the measures to reduce the volume and type of personal data that can be monitored by the websites/applications at the user level. It also elaborates on measures and requirements that need to be undertaken by the online retailers at the policy level to meet the country and industry standards, while keeping their practices ethical.
Discovery of Object Concepts in Academic Field Based on Deep Learning

With the rapid development of Internet technology, a large amount of information has been generated in academic field. At present, various academic recommendation services aim to improve users' ability to acquire new knowledge through semantic analysis and search. However, due to the lack of cognition analysis of academic concepts, they are unable to provide high-quality and high-precision recommendation results. In this paper, according to the academic concept’s characteristics of long-distance dependence, context dependence and morphological correlation, a deep learning model (CBC) to mine object concepts based on their cognition attributes is proposed. We use BiLSTM to solve the problem of word long-distance dependence and semantic difference in the process of mining object concepts, and use the combination of character embedding and word embedding to enhance the expression ability of words. The experimental results show that the CBC model has achieved a good mining effect on object concepts in the Information Retrieval field and Knowledge Discovery and Data mining filed.
Leveraging Group Annotations in Object Detection Using Graph-Based Pseudo-labeling

We address the problem of dealing with group annotations in object detection where a multitude of items is included in a single bounding box. The standard training protocols in use for most datasets either ignore anchors overlapping with group annotations in the loss function or discard group annotations completely. In this paper, we argue that group annotations contain unexplored potential in many commonly used datasets. We propose to leverage group annotations by generating pseudo-labels on top of them. We develop a novel graph-based pseudo-label generation method that interprets pseudo-label creation as a graph clustering problem that is suited to deal with overlapping objects. In experiments on CityPersons, MS COCO, and a subset of OpenImages we show that our approach outperforms the usual training strategies on the respective datasets when dealing with group annotations as well as other pseudo-label generation methods. We find that the greater the share of group annotations, the larger the increase in performance.
Neural Network Based Intelligent Traffic System

As years pass by, population and vehicular mobility in most of the cities are rapidly growing which leads to a lot of traffic congestion at junctions. This also makes lots of emergency vehicles like Ambulance, Firefighters, etc to stand in traffic snarls which leads to loss of life during the delay of these vehicles. This paper outlines the development of the Intelligent Traffic System (ITS) prototype by providing solutions to these problems which can handle traffic congestion at right time without manual intervention. ITS uses a neural network model and Internet of Things (IoT) unit to calculate the number of vehicles and emergency vehicles in the traffic lanes and then control the traffic signal based on it. A neural model algorithm is designed in such a way that the model is capable of detecting the ambulance from the live video as well as still images. Eventually, the IoT unit will provide the density of the vehicles in each lane and accordingly allocate the timing for signal transition in the following sequence: RED, YELLOW, GREEN, and the cycle repeats.
Persion Pharm. LLC v. Alvogen Malta Operations Ltd. United States Court of Appeals of the Federal Circuit, 2019 945 F.3d 1184

Biotechnology Law ReportVol. 39, No. 2 As presented by Chris HolmanPersion Pharm. LLC v. Alvogen Malta Operations Ltd. United States Court of Appeals of the Federal Circuit, 2019 945 F.3d 1184Edited for Biotechnology Law Report by CHRISTOPHER M. HOLMAN*Published Online:9 Apr 2020https://doi.org/10.1089/blr.2020.29169.cmhAboutSectionsView articleView Full TextPDF/EPUB Permissions & CitationsPermissionsDownload CitationsTrack CitationsAdd to favorites Back To Publication ShareShare onFacebookTwitterLinked InRedditEmail View articleFiguresReferencesRelatedDetails Volume 39Issue 2Apr 2020 InformationCopyright 2020, Mary Ann Liebert, Inc., publishersTo cite this article:Persion Pharm. LLC v. Alvogen Malta Operations Ltd. United States Court of Appeals of the Federal Circuit, 2019 945 F.3d 1184.Biotechnology Law Report.Apr 2020.155-162.http://doi.org/10.1089/blr.2020.29169.cmhPublished in Volume: 39 Issue 2: April 9, 2020PDF download
Development and application of face recognition system based on occlusion

This paper studies the automatic identification of students in and out of the campus. Due to the outbreak of epidemic disease in the past year, it is necessary to timely confirm the students in the school and conduct access supervision. At the same time, it is necessary to improve the identification efficiency and reduce the workload of the staff. On this basis, the face recognition system under occlusion is studied. Research shows that the occlusion face recognition system has high recognition efficiency and high accuracy. Therefore, the development of face recognition system is reasonable and has strong applicability.
Risks of Digital Transformation: Review of Machine Learning Algorithms in Credit Card Fraud Detection

In addition to the advantages of the digital world, there are also disadvantages, which can harm people. With the spread of credit cards with the digital transformation, banks have become the targets of malicious hackers. In this study, firstly, information about artificial intelligence and digital transformation is given. In related studies, some machine learning methods such as Random Forest, Naive Bayes, K-Nearest Neighbor, Logistic Regression, Support Vector Machines, Decision Tree, Artificial Neural Networks, Multilayer Perceptron and Ensemble Learning have been used to detect credit card fraud and their algorithm performance has been demonstrated.