Reviewerly/exHarmony huggingface dataset:
MP19-20 STATE-OF-THE-ART WEAKLY SUPERVISED AUTOMATED CLASSIFICATION OF PROSTATE CANCER TISSUE MICROARRAYS VIA DEEP LEARNING: CAN SUFFICIENT ACCURACY BE ACHIEVED WITHOUT MANUAL PATCH LEVEL ANNOTATION?

INTRODUCTION AND OBJECTIVE: Prostate sarcoma is a rare malignancy with a series of different pathological types.The prognosis of prostate sarcoma patients was not fully understood due to its rareness.We reported the survival and characteristics of prostate sarcoma treated at a single high-volume institution.METHODS: The medical records of 39 adult patients from January 2000 to March 2017 with a diagnosis of primary prostate sarcoma were retrieved.Univariate and multivariable Cox regression analysis were applied to identify the predictors of overall survival (OS), progression-free survival (PFS) and metastasis-free survival (MFS).RESULTS: The median OS was 15.2 months (range 2.3-171.8),and the OS rate at 1, 3 and 5 years was 69.2%, 23.1% and 10.3%, respectively.Patients were divided into 4 pathology types based on the prognosis (stromal sarcoma, undifferentiated pleomorphic sarcoma and others rare sarcomas; rhabdomyosarcoma; leiomyosarcoma; carcinosarcoma and gastrointestinal stromal tumor (GIST)).The most common histological type was rhabdomyosarcoma (10 cases, 25.6%).Multivariable Cox regression reveled that invasive disease at diagnosis (yes vs no, hazard ratio (HR) 5.69, p [ 0.019), pathological type and adjuvant therapy (yes vs no, HR 0.30, p [ 0.069) was associated with OS.Pathological type was associated with PFS (carcinosarcoma and GIST vs stromal sarcoma, HR 0.09, p [ 0.011), and adjuvant therapy (yes vs no HR 8.66, p [ 0.098) was associated with MFS.CONCLUSIONS: Different pathological types of prostate sarcoma were associated with distinctive prognosis.Patients with carcinosarcoma and GIST of prostate had the longer OS, followed by leiomyosarcoma, rhabdomyosarcoma and stromal sarcoma and others.Pathological type like leiomyosarcoma, carcinosarcoma and GIST, no invasive disease and the application of adjuvant therapy were associated with overall long-term survival.The application of adjuvant therapy may improve the overall survival of these patients.Further studies are needed to identify better treatment strategies for prostate sarcoma especially in the age of precision medicine.
Predictions from data analytics: Does Malaysian data protection law apply?

As data analytics become prevalent in industries in Malaysia to draw predictions about individuals’ habits and behaviour, it is important that certainty exists about the legal status of predictions vis-à-vis data protection law. The predictions can be privacy-intrusive and threaten individuals’ autonomy, although this may not always be so. The Malaysian Personal Data Protection Act 2010 is silent on the legal status of predictions. This paper examines whether the Malaysian Parliament should extend the Act to provide control to individuals over predictions about themselves. In doing so, the paper explores the position in the EU, Japan, Australia and the USA. The finding is that in those jurisdictions, predictions are within the remit of data protection law. It is argued that this is an over-generalisation and is inconsonant with commercial realities. The author advocates that a different approach be adopted to achieve a balance between individuals’ interest to control their data and commercial needs to use predictions without undue hindrance.
Need of iSchools in Developing Countries

Information is one of the valuable and important facets these days. It is responsible for the development of the society many ways. Information has created the Digital Revolution and that has changes the science of mechanics, and whole electronic technology. It is important to note that the digital electronics and its importance were started in 1950s and later grown in the 1970s. Moreover the development and proliferation of digital computers changes the entire information systems and process. Gradually digital record keeping and information management lead the organizations, institutions etc. for their information and technological solutions. The integration of Computing with the Management, Electronics, Information Studies lead the development of many information related domains internationally in many universities under various departments and faculties. That continues to the present day with more other interdisciplinary subjects. Information treated as power these days and also for the development. Information is for everything. Today most of the organizations, institutions are moving towards healthy and better knowledge and technology management; and in all cases Information Sciences and Technologies play a lead role. Internationally many academic departments, which are deals with the information and computing subjects moving towards a new academic innovation for solid and real development. That academic model is called iSchools or Information Schools. There are many opportunities of iSchools in business, healthcare, management and corporations, education etc. and there are huge possibilities of offering such subjects in the universities around the world especially in the developing countries. This paper discusses about the development projects of the iSchools and its potentialities in the developing countries in strategic and educational and cognitive context.
Rethinking Classification and Localization for Cascade R-CNN.

We extend the state-of-the-art Cascade R-CNN with a simple feature sharing mechanism. Our approach focuses on the performance increases on high IoU but decreases on low IoU thresholds--a key problem this detector suffers from. Feature sharing is extremely helpful, our results show that given this mechanism embedded into all stages, we can easily narrow the gap between the last stage and preceding stages on low IoU thresholds without resorting to the commonly used testing ensemble but the network itself. We also observe obvious improvements on all IoU thresholds benefited from feature sharing, and the resulting cascade structure can easily match or exceed its counterparts, only with negligible extra parameters introduced. To push the envelope, we demonstrate 43.2 AP on COCO object detection without any bells and whistles including testing ensemble, surpassing previous Cascade R-CNN by a large margin. Our framework is easy to implement and we hope it can serve as a general and strong baseline for future research.
On Domain Transfer When Predicting Intent in Text

In many domains, especially enterprise text analysis, there is an abundance of data which can be used for the development of new AI-powered intelligent experiences to improve people’s productivity. However, there are strong-guarantees of privacy which prevent broad sampling and labeling of personal text data to learn or evaluate models of interest. Fortunately, in some cases like enterprise email, manual annotation is possible on certain public datasets. The hope is that models trained on these public datasets would perform well on the target private datasets of interest. In this paper, we study the challenges of transferring information from one email dataset to another, for predicting user intent. In particular, we present approaches to characterizing the transfer gap in text corpora from both an intrinsic and extrinsic point-of-view, and evaluate several proposed methods in the literature for bridging this gap. We conclude with raising issues for further discussion in this arena.
Real-Time Monitoring of a Vacuum Gripper Pick-Up Operation in a Box Packaging Process by CNN based the Box Surface Deviation Image Analysis

Due to smart factories that can produce various types of products quickly, the ability to pick up various objects within a single manufacturing process has become important. Accordingly, many general-purpose vacuum grippers have been used, however, the performance of the pick-up operation depends on the degree of uniformness in a contact surface of a target object. Therefore, we aim to prevent abnormal conditions in the manufacturing process by real-time inspecting a surface of a target product before a pick-up/gripping process is performed. In particular, in order not to increase the manufacturing cost, the surface deviation data of a product was collected using an ultrasonic sensor, and a quality inspection was performed in real time by applying a convolution neural network to the collected data. The performance of the proposed method is verified through pick-up operation using the 6-axis robot with a vacuum gripper in the box packaging process.
A Light Implementation of a 3D Convolutional Network for Online Gesture Recognition

With the advancement of machine learning techniques and the increased accessibility to computing power, Artificial Neural Networks (ANNs) have achieved state-of-the-art results in image classification and, most recently, in video classification. The possibility of gesture recognition from a video source enables a more natural non-contact human-machine interaction, immersion when interacting in virtual reality environments and can even lead to sign language translation in the near future. However, the techniques utilized in video classification are usually computationally expensive, being prohibitive to conventional hardware. This work aims to study and analyze the applicability of continuous online gesture recognition techniques for embedded systems. This goal is achieved by proposing a new model based on 2D and 3D CNNs able to perform online gesture recognition, i.e. yielding a label while the video frames are still being processed, in a predictive manner, before having access to future frames of the video. This technique is of paramount interest to applications in which the video is being acquired concomitantly to the classification process and the issuing of the labels has a strict deadline. The proposed model was tested against three representative gesture datasets found in the literature. The obtained results suggest the proposed technique improves the state-of-the-art by yielding a quick gesture recognition process while presenting a high accuracy, which is fundamental for the applicability of embedded systems.
Heterogeneous gold metallogeny in the North China Craton

Geological JournalVolume 55, Issue 8 p. 5641-5645 PREFACE Heterogeneous gold metallogeny in the North China Craton Sheng-Rong Li, Corresponding Author Sheng-Rong Li lisr@cugb.edu.cn State Key Laboratory of Geological Processes and Mineral Resources, China University of Geosciences, Beijing, China School of Earth Sciences and Resources, China University of Geosciences, Beijing, China Correspondence Sheng-Rong Li, State Key Laboratory of Geological Processes and Mineral Resources, China University of Geosciences, 29 Xueyuan Road, Beijing 100083, China.Search for more papers by this authorM. Santosh, M. Santosh School of Earth Sciences and Resources, China University of Geosciences, Beijing, China Department of Earth Sciences, University of Adelaide, Adelaide, South Australia, AustraliaSearch for more papers by this authorCheng-Xue Yang, Cheng-Xue Yang School of Earth Sciences and Resources, China University of Geosciences, Beijing, ChinaSearch for more papers by this author Sheng-Rong Li, Corresponding Author Sheng-Rong Li lisr@cugb.edu.cn State Key Laboratory of Geological Processes and Mineral Resources, China University of Geosciences, Beijing, China School of Earth Sciences and Resources, China University of Geosciences, Beijing, China Correspondence Sheng-Rong Li, State Key Laboratory of Geological Processes and Mineral Resources, China University of Geosciences, 29 Xueyuan Road, Beijing 100083, China.Search for more papers by this authorM. Santosh, M. Santosh School of Earth Sciences and Resources, China University of Geosciences, Beijing, China Department of Earth Sciences, University of Adelaide, Adelaide, South Australia, AustraliaSearch for more papers by this authorCheng-Xue Yang, Cheng-Xue Yang School of Earth Sciences and Resources, China University of Geosciences, Beijing, ChinaSearch for more papers by this author First published: 03 May 2020 https://doi.org/10.1002/gj.3855 Funding information: State Key Reserch and Development Program, Grant/Award Number: 2016YFC0600106 Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinkedInRedditWechat Volume55, Issue8Special Issue: Gold metallogeny in the North China CratonAugust 2020Pages 5641-5645 RelatedInformation
A distance-based method for outlier detection on high dimensional datasets

Tasks such as classification, clustering, and regression require the identification and elimination of outliers as part of the preprocessing of data. Without an adequate processing of outliers the results of data analysis will be biased and inexact. This article proposes a distance-based method for the detection of outliers in multivariate datasets. The proposed method takes advantage of the principal components for avoiding problems of collinearity in datasets and the high concentration of variance is used to increase the separation between outliers and inliers. The proposed method was compared against four outlier detection methods from the literature, two deterministic and two stochastic. The datasets used in the comparison were generated according to different works in the literature. These datasets follows different distributions and contains different amount of outliers and inliers, and different number of variables and instances. Per each distribution, low and high dimensionality were considered. Unlike to other methods in the state-of-the-art, the proposed method does not require apriori the definition of any value for its operation. Also, the calculation of distances of elements in the dataset takes lower processing time. According to the experiments, the proposed method is suitable to deal with dataset contaminated with low and high proportions of outliers, low and high dimensions, and simmetric and assimetric distributions. Also supports colineality in data.
Age Estimation via Selecting Discriminated Features and Preserving Geometry

Human apparent age estimation has become a popular research topic and attracted great attention in recent years due to its wide applications, such as personal security and law enforcement.To achieve the goal of age estimation, a large number of methods have been pro-posed, where the models derived through the cumulative attribute coding achieve promised performance by preserving the neighbor-similarity of ages.However, these methods afore-mentioned ignore the geometric structure of extracted facial features.Indeed, the geometric structure of data greatly affects the accuracy of prediction.To this end, we propose an age estimation algorithm through joint feature selection and manifold learning paradigms, so-called Feature-selected and Geometry-preserved Least Square Regression (FGLSR).Based on this, our proposed method, compared with the others, not only preserves the geometry structures within facial representations, but also selects the discriminative features.Moreover, a deep learning extension based FGLSR is proposed later, namely Feature selected and Geometry preserved Neural Network (FGNN).Finally, related experiments are conducted on Morph2 and FG-Net datasets for FGLSR and on Morph2 datasets for FGNN.Experimental results testify our method achieve the best performances.
Active Learning Using Margin Sampling Strategy for Entity Recognition

Different machine learning techniques require large amount of labeled training data to work well. It is very costly and difficult to label the unlabelled data. The active learning approach can be used in this case as it selects the most appropriate data from the unlabeled data pool. In this work, we have applied active learning using CRF classifier over biomedical disease names and adverse effect corpus using margin sampling strategy. Experiment have been carried for selecting the most uncertain samples for labeling by defining different threshold values for the margin used to measure the uncertainty. The active learning approach performs well with much less data requirement and its performance increases with the increase in threshold values of the margin.
TF-IDF vs Word Embeddings for Morbidity Identification in Clinical Notes: An Initial Study.

Today, we are seeing an ever-increasing number of clinical notes that contain clinical results, images, and textual descriptions of patient's health state. All these data can be analyzed and employed to cater novel services that can help people and domain experts with their common healthcare tasks. However, many technologies such as Deep Learning and tools like Word Embeddings have started to be investigated only recently, and many challenges remain open when it comes to healthcare domain applications. To address these challenges, we propose the use of Deep Learning and Word Embeddings for identifying sixteen morbidity types within textual descriptions of clinical records. For this purpose, we have used a Deep Learning model based on Bidirectional Long-Short Term Memory (LSTM) layers which can exploit state-of-the-art vector representations of data such as Word Embeddings. We have employed pre-trained Word Embeddings namely GloVe and Word2Vec, and our own Word Embeddings trained on the target domain. Furthermore, we have compared the performances of the deep learning approaches against the traditional tf-idf using Support Vector Machine and Multilayer perceptron (our baselines). From the obtained results it seems that the latter outperforms the combination of Deep Learning approaches using any word embeddings. Our preliminary results indicate that there are specific features that make the dataset biased in favour of traditional machine learning approaches.
Adaptive Contrast Enhancement of Optical Imagery Based on Level of Detail (LOD)

The viewing and sharing of remote sensing optical imagery through the World Wide Web is an efficient means for providing information to the general public and decision makers. Since clouds and hazes inevitably limit the contrast and deteriorate visual effects, only cloudless scenes are usually included and presented in existing web mapping services. This work proposes a level-of-detail (LOD) based enhancement approach to present satellite imagery with an adaptively enhanced contrast determined by its viewing LOD. Compared to existing web mapping services, this new approach provides a better visual effect as well as spectral details of satellite imagery for cases partially covered with clouds or cirrocumulus clouds. The full archive of global satellite imagery, either the existing one or the one collected in the future, can be utilized and shared through the Web with the processing proposed in this new approach.
Using Conditional Random Field in Named Entity Recognition for Crime Location Identification

Electronic data or information comes in different forms, some are structured data and others unstructured data. The act of collecting such data is known as data mining. This paper will discuss the mining of crime data from electronic news sources in Malaysia, and how this data is further transformed to extract meaningful information from it. Furthermore, the paper will demonstrate how crime locations can be identified within the various news articles. This is significant because there are cases where a location name is mentioned in the news article but that is not the true crime location. To help achieve this, the system makes use of Named Entity Recognition (NER) algorithms. They are task with identifying locations in various sentences. To bring more accuracy to the work, the system will employ machine learning technique known as Conditional Random Field (CRF) to recognize if a sentence is referring to a crime location.
Analysis of the effectiveness of evacuation paths in terms of mall visitors evacuation speed (Case Study: Mall Palladium)

Abstract The Mall is a shopping center that focuses on a department store consisting of retail and restaurants with the shop typologies facing a corridor that serves to connect all shops. Generally, a mall has three floors or more and has various human activities inside. Fire Protection System is a system consisting of equipment, supplementary, and facilities in building to protect the building and their environment against fire disaster. Fire Passive Protection System can prevent mortality and fatal damage to the building so that the security and safety of the mall users will be more secure. Evacuation is a way to survive from the dangers that happened around so that the components to support evacuation must be well maintained. The observation method is used to determine the effectiveness of the evacuation path based on the speed of evacuation of the user and to determine the current condition. The simulation method uses Arena software that is used to determine the speed of evacuation of occupants (in this case, Case Study: Palladium Mall). The final results of this study are in the form of recommendations for the design of evacuation lines that are optimal for fire protection in buildings.
Optimizing Image Compression With Deep Super-Resolution Techniques

Efficient image/video storage and transmission on mobile devices is becoming today an important challenge, since smartphones have become the most popular image acquisition devices, progressively replacing traditional cameras. However, most of the time, the acquired pictures are displayed on small screens and for a limited time. In order to manage this kind of oversized (with respect to the usage) data, it is mandatory to employ dedicated compression techniques. The solution considered in this article consists of storing solely low resolution versions of the images that can be efficiently compressed with standardized solutions. The challenge is then to restore high quality, full resolution images, while dealing with the complex artifacts that are inherently introduced by modern codecs. In this article, we introduce a two-stage approach, which consists of applying a deep superresolution technique upon images compressed with state-of-the-art codecs. The experimental results obtained demonstrate that the proposed method outperforms, in terms of perceptual quality, existing compression standards, in particular at very low bitrates.
A Software Process Gap Analysis Methodology for Very Small Entity

Nowadays, the demand for information technology and software are increasing. Many software development units are established. The most of units are very small entity (VSE.) which a software development unit include not over 25 people. They encounter many problems in their work. The software process improvement (SPI) is a methodology to reduce the problem. This paper focuses on to evaluate and analyze the processes of VSEs reference by ISO/IEC 29110 standard. The standard consists of two processes, which are Project management and Software implementation. The main objective of this research is to design a methodology for displaying the gap between the current process and standard. The result of the evaluation can lead to the VSE. prioritize the risk when they implement standard to the organization.
FIGHTING ONLINE DEFAMATION, DOXING, AND IMPERSONATION

Online defamation, doxing, and impersonation are three of the major problems of the Internet age.As technology advances, defamation, doxing, and impersonation become greater problems.Online defamation, doxing, and impersonation can cause serious damages to victims.The laws badly need to be updated to deal with defamation, doxing, and impersonation in the information age.This research paper makes 12 recommendations to effectively combat online defamation, doxing, and impersonation in different countries.