Reviewerly/exHarmony huggingface dataset:
A CNN Based HEp-2 Specimen Image Segmentation and Identification of Mitotic Spindle Type Specimens

In the proposed work, an effective framework for identification of mitotic type staining patterns is demonstrated, integrated with a segmentation approach, in order to detect the autoimmune disorders using HEp-2 based cell substrates. It is shown that the segmentation approach obviates the requirement of DAPI channels in Indirect Immuno-Fluorescence (IIF) imaging process. Moreover, the segmentation is required for cell-based processing of staining patterns, which is in turn required, due to the rare appearance and occurrence of mitotic type cell patterns. The segmentation involves a pixel labeling strategy, using U-Net, a Deep Convolutional Neural Network (D-CNN) based approach. The effectiveness of such segmentation approach is shown by the subsequent performance for detection of Mitotic Spindle (MS) type staining patterns, framed as mitotic v/s non-mitotic/interphase classification problem. This classification task is effectively addressed using features extracted from a traditional filter bank and CNN based feature representation. After identification of individual MS cells in specimens, a threshold-based MS detection criteria has been used specifically for declaration of specimens, consisting of MS patterns. The current study demonstrates a comparative analysis of proposed segmentation masks, with given DAPI-based masks with encouraging results for segmentation, as well as classification, over a publicly available dataset.
Document-Level Named Entity Recognition with Q-Network

Named entity recognition (NER) is typically viewed as a sequence labeling problem, where the solution is optimized in sentence level. In this paper, we explore utilizing document-level label consistency to improve NER performance by reinforcement learning. The process entails searching similar mentions and reconciling the labels, which are repeated until sufficient evidence is collected. We employ a Q-network, trained to maximize the total reward that reflects labeling accuracy while penalizing extra effort. On three publicly available datasets, our approach achieves an F $$_1$$ of 90.75%, outperforming the ensemble method (89.80%) by 0.95% and the best base tagger (88.16%) by 2.59% (The source code is available at https://github.com/KrisWentaoWong/dqnner/ ).
Deep Neural Networks for Breast Cancer Diagnosis: Fine Needle Biopsy Scenario

In this study, we focus on the problem of computer-aided diagnosis of breast cancer using cytological images of fine needle biopsies. We explore the potential of modern deep neural network architectures by comparing five different convolutional neural networks trained to classify the specimen as either benign or malignant. For experimentation, we use 550 cytological images of fine needle biopsies from 50 patients, balanced between benign and malignant cases, acquired at the University Hospital in Zielona Góra, Poland. We found that the convolutional neural network Inception-v3 is the best model, reaching 91.86% accuracy and 0.97 value for area under the curve (AUC).
Indoor Positioning System on Identification of Neurodegenerative Disease: A Comparative Study of BLE versus RFID

This paper introduces the feasibility examination of indoor positioning system using the Bluetooth low energy (BLE) and the radio-frequency identification (RFID) devices to further apply in the diagnosis of neurodegenerative disease leading an unusual human body motion. Experimentally, a BLE module and a RFID tag are simulated to be the wearable devices mounted to human body, moving with common body motions. Their movements affecting on position tracking efficiency are investigated through smartphone and ultra-high frequency RFID reader, respectively. The results show that a BLE device presents the best position sensing ability.
Multilinear Subspace Method Based on Geodesic Distance for Volumetric Object Classification

Organs, cells in organs and microstructures in cells are mathematically spatial textures. Tensors allow us to directly analyse, manipulate and recognise such volumetric data in medical image computing. Tensor-based data expression provides a classification method for temporal morphogenesis of spatiotemporal volumetric sequences using geodesic distances between tensor subspaces. Geodesic measures are introduced both for the Grassmann and Stiefel manifolds in multilinear space. Experimental evaluations of cardiac MRI dataset for 17 patients show the validity of the method for discrimination and classification.
AutoEncoder Guided Bootstrapping of Semantic Lexicon

Mutual bootstrapping is a commonly used technique for many natural language processing tasks, including semantic lexicon induction. Among many bootstrapping methods, the Basilisk algorithm achieved successful applications through two key iterative steps: scoring context patterns and candidate instances. In this work, we improve Basilisk by modifying its two scoring functions. By incorporating AutoEncoder to the scoring functions of patterns and candidates, we can reduce the bias problems and obtain more balanced results. The experimental results demonstrate that our proposed methods for guiding bootstrapping of a semantic lexicon with AutoEncoder can boost overall performance.
Sensor Based Affective Learning Information Process System for Intelligent Tutoring System

Abstract As smartphones like televisions or cars are used every day and everybody, smartphones are used to access to digital content and various ICT services. Especially, smartphone is equipped with many types of sensors so that various multimedia content can use smartphone sensors. If users’ information through sensors would be analysed, users’ movement, emotion, and states can be inferred. Especially, on e-learning environment, students’ learning states can be decided by students’ information that are collected by smartphone sensors, since more than 90% of students of Korea National Open University have access to learning contents through their smartphones in mobile learning environment. However, previous researches focused on interactions between students and learning contents. And there are few methods, technology, and decision models for students to track their learning activity, learning interests, concentration and emotions. In this paper, we propose a learning reaction analysis state model and Student Activity Analysis System. In this research, sensed learning activity information is collected by sensors of smartphone. Students’ learning activity information can be classified into mandatory and optional information. In Student Activity Analysis System, students’ learning emotional state is decided by learning reaction analysis state model. The students’ learning emotional state could be used for intelligent tutoring system (ITS) that construct personal learning strategy. Since students’ learning activity information is collected and analyzed from the information technology viewpoint, it is possible to measure the concentration and interest of learning contents of students regardless of pedagogical model.
The analysis on applicability of hourly horizontal diffuse radiation estimation model of Shanghai Zone

Abstract For shading design and energy consumption simulation, hourly diffuse radiation data is necessary. In order to estimate the total solar radiation on surfaces with different spatial orientations, the knowledge of all components on the horizontal plane is required. Available measured data is, however, typically less. The 2004-2013 year’s measured data of Shanghai were localized revising of three categories models, comparing the measured data and estimation data by the correlation coefficient and error index. The results show that the three types of models have high accuracy at higher solar altitude and the accuracy of multi parameter logistic function is best. For stations with only daily solar radiation data, the third types of models can be used to calculate hourly diffuse solar radiation.
Implementation of Elliptic Curve25519 in Cryptography

Bernstein's design implementation of elliptic Curve25519 in key exchange is claimed to be highly secure and efficient. This curve is, for example, used in the key exchange scheme of TextSecure for Instant Messaging. In this paper, we present an implementation of elliptic Curve25519 in the simplified Elliptic Curve Integrated Encryption Scheme, thus showing that elliptic Curve25519 can also serve other purposes than key exchange. The curve is in Montgomery form, which makes it possible to use Montgomery ladder. Point compression, point decompression, encryption, and decryption algorithms are presented for the simplified Elliptic Curve Integrated Encryption Scheme.
Recent Advances of Affect Detection from Arabic Text

Emotion Detection (ED) from text has been an active research field recently. It has attracted the attention of researchers as it can measure the emotional contexts while humans interact with computers. Humans could express their emotion in various ways; using typed text, facial expressions, speech, gestures, and physiological measures. ED is considerably different from sentiment analysis SA, where SA goal is to detect polarity from text such as positive, negative or neutral. On the other hand, ED aims to recognize emotions from input text. Emotions can be modeled as discrete categories, e.g. Ekmans six basic emotions (angry, fear, joy, disgust, surprise and sadness). On the other hand there is the dimensional model that express emotions as valence, arousal and dominance values. Social media provides a rich source of emotional text, e.g. Twitter and Facebook. In this paper, we provide a review of recent work on ED from Arabic text. We discuss approaches (lexicons, machine learning, deep neural networks and ensemble approaches), tools for text processing, and We also discuss description of the most popular datasets in this domain.
A Semi-Automatic Soccer Video Annotation System based on Ontology Paradigm

Currently, the video annotation process becomes a very necessary task to overcome the problem of finding the adequate information on a huge database. To achieve this, it will be necessary to translate low level analysis results to a semantic meaning related to the video sequence, generally known as problem of semantic gap. Many researchers have worked on this issue in the aim to find new solutions, where the ontology paradigm is represented as one of them. In this paper, we consider this problem by developing a soccer ontology, that is used in our OSAS annotation system (Ontology Soccer Annotation System). This one is based on a set of Semantic Web Rule Language (SWRL) rules that bridges the semantic gap problem. Our idea show new directions and perspectives for future developments.
An End-to-End Structure Aware Graph Convolutional Network for Modeling Multi-relational Data

Low-dimensional embeddings of entities and relations in large scale knowledge graphs have been proved extremely beneficial in variety of downstream tasks, e.g. entity classification and knowledge graph completion. Most of existing approaches incorporate both textual information and relation paths of triple facts for knowledge graph representation. However, they ignore rich structural information in a knowledge graph, i.e., connectivity patterns in neighboring entities and relations around a given entity. In this work, we propose a novel knowledge representation model, denoted Structure Aware Graph Convolutional Network (SAGCN), which leverages structural information for modeling the highly multi-relational data characteristic of realistic knowledge graphs. Specifically, we sample multi-hop neighboring entities and relations of a given entity as its local graph, which depicts the neighborhood topology structure. To encode features from the local graph, we introduce localized graph convolutions as a neighborhood structure encoder to generate embeddings. We further design distinct decoders for entity classification and knowledge graph completion. The proposed approach are evaluated on three public datasets and substantially outperforms state-of-the-arts.
A Contribution Algorithm from LDRI to HDRI

High dynamic range image (HDRI) which is combined with low dynamic range image (LDRI) needs to be mapped to a low dynamic area to display. In the process of mapping, it is impossible to determine the contribution of low dynamic image sequences in the display images, so that it results in a problem that the low dynamic images cannot be accurately selected. In this paper, for the first time, a contribution algorithm from LDRI to HDRI according to the corresponding response curve of the camera is proposed.
The Strength of Weak Randomization: Easily Deployable, Efficiently Searchable Encryption with Minimal Leakage

Efficiently searchable and easily deployable encryption schemes enable an untrusted, legacy service such as a relational database engine to perform searches over encrypted data. The ease with which such schemes can be deployed on top of existing services makes them especially appealing in operational environments where encryption is needed but it is not feasible to replace large infrastructure components like databases or document management systems. Unfortunately all previously known approaches for efficiently searchable and easily deployable encryption are vulnerable to inference attacks where an adversary can use knowledge of the distribution of the data to recover the plaintext with high probability. We present a new efficiently searchable, easily deployable database encryption scheme that is provably secure against inference attacks even when used with real, low-entropy data. We implemented our constructions in Haskell and tested databases up to 10 million records showing our construction properly balances security, deployability and performance.
A Click Prediction Model Based on Residual Unit with Inception Module

The explosion in online advertisement urges to better estimate the click prediction of ads. For click prediction on single ad impression, we have access to pairwise relevance among elements in an impression, but not to global interaction among key features of elements. Moreover, the existing method on sequential click prediction treats propagation unchangeable for different time intervals. In this work, we propose a novel model, Convolutional Click Prediction Model (RES-IN), based on residual unit with inception module. RES-IN can extract local-global key feature interactions from an input instance with varied elements, which can be implemented for not only single ad impression but also sequential ad impression. Experiment results on three public large-scale datasets indicate that RES-IN is effective on click prediction.
Ustaad: A Mobile Platform for Teaching Illiterates

According to a recent statistical analysis conducted in 2018, more than 40% of the population has no reading or writing skills especially in rural areas of Pakistan. On the contrary, the mobile phone users have grown at a very steep rate even with a stagnant literacy rate. We formed a user-driven approach to research, develop and test a prototype mobile application that could be used to teach illiterates basic reading, writing and counting skills without using traditional schooling techniques. This first of a kind application provided the user the ability to customize their own learning plan. Focusing on native language Urdu, the application teaches them the required skill they need for daily life activities such as writing their own name, scenario-based calculations, identifying commonly used words.
Learning a Subclass of Deterministic Regular Expression with Counting

In this paper, we propose a subclass of single-occurrence regular expressions with counting (cSOREs) and give a learning algorithm of cSOREs. First, we learn a SORE. Then, we construct a countable finite automaton (CFA) by traversing the syntax tree of the obtained SORE. Next, the CFA runs on the given finite sample to obtain the minimum and maximum number of repetitions of the subexpressions under the iteration operators. Finally we obtain a cSORE by traversing the syntax tree and introducing the counting operators. Our algorithm not only can learn a cSORE, which is expressive enough to cover more XML data, but also has better generalization ability for smaller sample.
Enhancing Low Quality Face Image Matching by Neurovisually Inspired Deep Learning

Computerized human face matching from low quality images is an active area of research in deformable pattern recognition especially in non-cooperative security, surveillance, authentication and multi-camera tracking. In low resolution and motion-blurry face images captured from surveillance cameras, it is challenging to get good match of faces and even extracting suitable feature vectors both in classical signal/image processing based and deep learning based approaches. In the current work, we have proposed a novel low quality face image matching algorithm in the light of a neuro-visually inspired method of figure-ground segregation (NFGS). The said framework is inspired by the non-linear interaction between the classical receptive field (CRF) and its non-classical extended surround, comprising of the non-linear mean increasing and decreasing sub-units. The current work demonstrates not only better detection of low quality face images in NFGS enabled deep learning framework, but also it prescribes an efficient way of low quality face image matching addressing low contrast, low resolution and motion blur which are prime responsible factors of making image low quality. The experimental results shows the effectiveness of proposed algorithm not only quantitatively but also qualitatively in terms of psycho-visual experiments and its statistical analysis outcome.
Enhanced Model-Free Deep-Q Network based PTZ Camera Control Method

Recently, the algorithm based on reinforcement learning for controlling PTZ(Pan/Tilt/Zoom) of a camera has been significantly increasing. To improve the accuracy of video analysis in a video surveillance environment, it is essential to get good video sources from a camera such as an appropriate object size or an object not covered by the other object. In this paper, we propose a model-free reinforcement learning based PTZ control method to support improving the accuracy of video analysis. The proposed method fine-tunes the DQN (Deep-Q Network) for smoothly controlling PTZ camera. The simulation results show that the proposed method can automatically and seamlessly manage the PTZ camera according to moving objects.
A Study of Outbound Automated Call Preferences for DOTS Adherence in Rural India

Outbound automated calls present an excellent opportunity to deliver messages among low-technology literate users from a resource-constrained environment, such as rural India. While automated calls have been used for various purposes in rural settings, sufficient research has not been done to understand the motivation for attending the calls, preferred contents, call duration, time, preferred gender of voice, content learnability and preference of automated call over SMS for information delivery. In this paper, we present a study conducted among 40 early-stage Tuberculosis (TB) positive patients to investigate content learnability and preferences of outbound automated calls aimed to increase DOTS adherence. The results indicate the demand for easily actionable contents, evenings as preferred time and less than 5 min as ideal call duration for automated calls. We found the preference of automated calls over SMS for information delivery. We also observed a significant increase in learnability among participants who listened to the complete call as compared to participants who did not. We present these findings in detail and suitable recommendations.
A Digital Coupling Coordination Degree Model for Docking the Specialty Structure and Industrial Structure in Vocational Education

Vocational education and industrial development, as the two key elements, have important contributions to social development, and the coordination of the two will have a positive effect. By establishing a digital coupling model, the coordinated development for specialty structure of vocational education and industrial structure can be expressed in a quantitative manner. In this paper, the specialty structure of vocational education and industrial structure are treated as two systems, and their representative elements are selected respectively. According to the coupling theory, a digital coupling coordination degree model for specialty structure of vocational education and industrial structure is constructed.
The future of tangible user interfaces

Envisioning, designing, and implementing the user interface require a comprehensive understanding of interaction technologies. In this forum we scout trends and discuss new technologies with the potential to influence interaction design. --- Albrecht Schmidt, Editor
Training-Less Multi-label Text Classification Using Knowledge Bases and Word Embeddings

Traditional multi-label text classifiers suffer from the high dimensionality of feature space, label imbalance, and training overhead. In this work, we depart from traditional approaches with intensive feature engineering and linguistic analysis by introducing a novel ontology-based training-less multi-label text classifier. We transform the classification task into a graph matching problem to have a training-less classifier. The experiment results, using the EUR-Lex dataset, proved that our method offers competitive performance with respect to the above-mentioned approaches in terms of $$F1_{macro}$$ giving fair performance over the different labels despite of the training-less configurations.
Utilizing Local Outlier Factor for Open-Set Classification in High-Dimensional Data - Case Study Applied for Text Documents

In this paper, we focus on the utilization of Local Outlier Factor (LOF) algorithm in the task of performing open-set classification on high-dimensional data. Concerning the application on text documents, we research the fastText method for extraction of feature vectors. Then we build a classifier and evaluate its accuracy (precision, recall) on prepared test data, containing both subject categories known during training and completely new categories. Next we attempt to identify incorrect outcomes related to assigning the documents of new categories to one of the trained classes; for this we use the Local Outlier Factor algorithm. We show how decision function threshold in LOF influences the precision and recall of this open-set classification procedure.
CAPER: A Connectivity-Aware Path Planner with Regulatory Compliance for UAVs

Well-connected, regulatory compliant flight paths are crucial for UAVs to be adopted in mission-critical applications. In this paper, we present the Connectivity-Aware Path plannEr with Regulatory compliance (CAPER): a solution for planning safe, cellular-connected UAV paths in environments with heterogeneous connectivity regions, such that the planned paths comply with regulatory no-fly zones and height constraints. CAPER builds on the sampling-based planner Rapidly-exploring Random Trees (RRT), and makes a number of algorithmic modifications both in the planner and the collision detector. RRT has seen widespread use in planning paths in robotics, due to its ability to quickly search high dimensional spaces for feasible paths. However, several challenges exist in adopting RRTs for the connectivity-aware path planning problem in realistic spaces, which CAPER seeks to alleviate. In this paper we detail CAPER, and present results of its implementation in two realistic urban environments in Stockholm and Los Angeles. Since CAPER is built on the randomized algorithm RRT, we also present a brief analysis of multiple runs within the same environment.