Reviewerly/exHarmony huggingface dataset:
Rectified Cross-Entropy and Upper Transition Loss for Weakly Supervised Whole Slide Image Classifier

Convolutional neural network (CNN) has achieved promising results in classifying histopathology images so far. However, most clinical data only has label information for the whole tissue slide and annotating every region of different tissue type is prohibitively expensive. Hence, computer aided diagnosis of whole slide images (WSIs) is challenging due to: (1) a WSI contains tissues with different types but it is classified by the most malignant tissue; (2) the gigapixel size of WSIs makes loading the whole image and end-to-end CNN training computationally infeasible. Previous works tended to classify WSI patch-wisely using the whole slide label and overlooked one useful information: it is an error to classify a patch as higher-grade classes. To address this, we propose a rectified cross-entropy loss as a combination of soft pooling and hard pooling of discriminative patches. We also introduce an upper transition loss to restrain errors. Our experimental results on colon polyp WSIs showed that, the two new losses can effectively guide the CNN optimization. With only WSI class information available for training, the patch-wise classification results on the testing set largely agree with human experts' domain knowledge.
Optical Rectifying Linear Units for Back-Propagation Learning in a Deep Holographic Convolutional Neural Network

We present the design for a bidirectional coherent optical Rectifying Linear Unit (ReLU) device capable of phase thresholding and appropriately rectifying forward-propagating optical-neuron activity and gating optically back-propagating error that will enable the construction of an all-optical deep learning system. The ReLU device can be fabricated in large arrays using high-speed liquid-crystal-on-Silicon (LCoS) smart-pixel technology capable of implementing arrays of feature planes needed for convolutional neural networks. Interferometric detection of the phase of a split-off fraction of the forward-propagating neuron input is used to set the state of the bidirectional switch and gate both the rest of the forward-propagating neuron input as well as the back-propagating error signals. We show how the array of convolutional adaptive interconnections needed for deep learning can be physically implemented and learned in an all-optical multistage dynamic holographically-interconnected architecture using lenslet arrays addressing thick Fourier-plane dynamic holograms. This optical architecture is self-aligned, phase-calibrated, and aberration compensated by using phase-conjugate mirrors to record the dynamic-holographic interconnections in each layer. This system has the potential to achieve a computational throughput approaching that of supercomputer clusters at a much lower energy cost by synergistically combining the analog computational properties of coherent Fourier optics with the hardware fault-tolerance provided by error-driven deep-learning algorithms.
Generation of personalized video summaries by detecting viewer’s emotion using electroencephalography

Video summaries produced by low level features are unaware of the viewer’s requirements and result in a semantic gap. Video content evokes certain emotions in a viewer, which can be measured and act as a strong source of information to generate summaries meeting viewer’s expectation. In this paper, we propose a personalized video summarization framework that classifies viewer’s emotion based on electroencephalography (EEG) signals, while watching a video to extract keyframes. Features are extracted from recorded EEG signals in time, frequency and wavelet domain to classify viewer’s emotions. Those frames are selected as keyframes from the video, where different emotions of viewer are evoked. Experiments are performed on 50 viewers and 50 video sequences to validate the effectiveness and efficiency of the proposed framework. It is evident from the results that the proposed method generates summaries with high precision, recall, F-measure, accuracy, and low error, hence reducing the semantic gap.
An Efficient Social Attribute Inference Scheme Based on Social Links and Attribute Relevance

Social network is a critical component in mobile multimedia systems, where users share their videos, photos, and other media. However, the information (e.g., posts, user profiles, etc.) shared on the social network platforms usually reflects many users’ personal (private) information, which could be mined and abused for malicious purposes. To address privacy concerns, many social network service providers adopted privacy-preserving mechanisms, e.g., anonymizing user identity, hiding users’ profiles, etc. As a result, the attributes in user profiles are usually set up to be accessed only by friends to prevent privacy leakage. Several attacks have been proposed to infer the hidden attributes to Several the efficiency of current privacy-protecting mechanisms. Most of these solutions are based on the social links among users or their behaviors. In this paper, we systematically analyze the social features related to user privacy inference and found that there are relevances among social attributes, which has a great impact on inferring users’ hidden attributes. According to our findings, we propose an efficient social attribute inference scheme based on social links and attribute relevance properties. We develop a relevance attribute inference method (ReAI) using random walks with restart. We analyze attribute relevance on inference performance and use Kulczynski measure to quantify attribute relevance as edge weights of attribute nodes in an improved social-attribute network. We evaluate our method and compare it with the traditional attribute inference method. The results show that our method performs better than the traditional method. We also use Kulczynski measure and Information Gain Ratio to evaluate the improvements. The results show that the bigger relevance between attributes contributes to higher improvements.
Upper gastrointestinal anatomy detection with multi‐task convolutional neural networks

Esophagogastroduodenoscopy (EGD) has been widely applied for gastrointestinal (GI) examinations. However, there is a lack of mature technology to evaluate the quality of the EGD inspection process. In this Letter, the authors design a multi-task anatomy detection convolutional neural network (MT-AD-CNN) to evaluate the EGD inspection quality by combining the detection task of the upper digestive tract with ten anatomical structures and the classification task of informative video frames. The authors' model is able to eliminate non-informative frames of the gastroscopic videos and detect the anatomies in real time. Specifically, a sub-branch is added to the detection network to classify NBI images, informative and non-informative images. By doing so, the detected box will be only displayed on the informative frames, which can reduce the false-positive rate. They can determine the video frames on which each anatomical location is effectively examined, so that they can analyse the diagnosis quality. Their method reaches the performance of 93.74% mean average precision for the detection task and 98.77% accuracy for the classification task. Their model can reflect the detailed circumstance of the gastroscopy examination process, which shows application potential in improving the quality of examinations.
PRICHAIN: A Partially Decentralized Implementation of UbiPri Middleware Using Blockchain

With the popularization of the Internet-of-Things, various applications have emerged to make life easier. These applications generate a large amount of user data. Analyzing the data obtained from these applications, one can infer personal information about each user. Considering this, it is clear that ensuring privacy in this type of application is essential. To guarantee privacy various solutions exist, one of them is UbiPri middleware. This paper presents a decentralized implementation of UbiPri middleware using the Ethereum blockchain. Smart contracts were used in conjunction with a communication gateway and a distributed storage service to ensure users privacy. The results obtained show that the implementation of this work ensures privacy at different levels, data storage security, and performance regarding scalability in the Internet of Things environments.
Boosting Agile by Using User-Centered Design and Lean Startup: A Case Study of the Adoption of the Combined Approach in Software Development

Background: Despite the claimed benefits of an agile transformation, user involvement and added value are still reported as challenges in software development. A combined use of User-Centered Design and Lean Startup with Agile Development has been pointed out as a means to minimize these challenges. Aims: This paper aimed to characterize how a transformation to such combined approach takes place in a multinational company, as well as to point out benefits and challenges to undergo such transformation. Method: We performed a multiple case study. We collected data through semi-structured interviews, focus group session, questionnaire and observations. Results: Our study revealed two major strategies to conduct the transformation: to create a dedicated Transformation Team to lead it and to make a Transformation Package Toolkit available to team members. Also, introducing a Product Designer role to a cross-functional team and using experimentation are among the most important benefits. Managerial mindset change and team autonomy are among the most concerning challenges. Conclusions: Our results bring implications to industry practitioners: organizational and cultural changes involve role and responsibilities definitions, introduction of new working processes, and are dependent upon user engagement and managerial support. These sound paramount to organizations that aim to succeed in this yet-to-be-further-understood transformation.
Towards Long-Term Deployment of a Mobile Robot for at-Home Ambient Assisted Living of the Elderly

In a social and economic context characterized by a constantly aging population, the research for new technologies able to assist elderly people is becoming a hot topic. In this paper we illustrate the main components of the European project MoveCare, a multi-actor framework designed to assist pre-frail elders living alone. The main component of the system is an assistance mobile robot that provides the user with a set of functionalities to support cognitive and social stimulation, assistance, and transparent monitoring. In view of the long-term deployment of the autonomous robotic system to be carried out for three months inside the houses of end-users, we present in this paper a preliminary experimental evaluation of the system within an apartment, focusing on the evaluation of the platform under the perspective of long-term autonomy (LTA).
A Direct Regression Scene Text Detector With Position-Sensitive Segmentation

Direct regression methods have demonstrated their success on various multi-oriented benchmarks for scene text detection due to the high recall rate for small targets and the direct regression for text boxes. However, too many false positive candidates and inaccurate position regression still limit the performance of these methods. In this paper, we propose an end-to-end method by introducing position-sensitive segmentation into the direct regression method to overcome these shortcomings. We generate the ground truth of position-sensitive segmentation maps based on the information of text boxes so that the position-sensitive segmentation module can be trained synchronously with the direct regression module. Besides, more information about the relative position of text is provided for the network through the training of position-sensitive segmentation maps, which improves the expressiveness of the network. We also introduce spatial pyramid of position-sensitive segmentation into the proposed method considering the huge differences in sizes and aspect ratios of scene texts and we propose position-sensitive COI(Corner area of Interest) pooling into the proposed method to speed up the inference. Experiments on datasets ICDAR2015, MLT-17 and COCO-Text demonstrate that the proposed method has a comparable performance with state-of-the-art methods while it is more efficient. We also provide abundant ablation experiments to demonstrate the effectiveness of these improvements in our proposed method.
Classification of plastic bottles based on visual and physical features for waste management

This paper presents an approach to identify the type of plastics depending upon it's material, it can be concluded whether the plastic can be recycled or not. In this approach, visual and physical properties were used to classify the plastic materials. It makes use of the fact that recycled plastics are having some similar features like weight, pressure and color. Given an image of a plastic object, these features will form a dataset to train different classifiers which will classify the given plastic into recycled or non-recycled. A color based segmentation algorithm is used to detect color and KNN classifier is used to predict the color of plastic. A tactile touch sensor is used to calculate the pressure that can be applied on the plastic object. Different types of plastics are having inconsistent set of features. Therefore, perceptively we are using four different classifiers for the classification namely SVM, KNN, Decision tree and Logistic Regression.
Arithmetic operations of non-normal fuzzy sets using gradual numbers

This paper will study the arithmetic operations of non-normal fuzzy sets using the concept of gradual numbers that can be regarded as the elements of gradual sets. We shall present the idea in which the fuzzy sets can be formulated as consisting of gradual elements like the usual set consisting of usual elements. When the universal set is taken to be the real number system, the gradual element is also called a gradual number. In this case, the arithmetic operations of non-normal fuzzy sets can be defined by using the gradual numbers.
The effect of aggregation methods on sentiment classification in Persian reviews

ABSTRACT One of the most essential parts of every sentiment analysis application is the aggregation mechanism used to combine results obtained from a lower granularity level into an overall result. In this paper, the effects of the sentiment lexicon, aggregation level, and aggregation method on the sentiment polarity and rating classification of Persian reviews are investigated. To this aim, a new sentiment aggregation method based on the cross-ratio operator is proposed. The results on four Persian review data sets show that the review-level aggregation can improve rating classification, although this approach does not have a positive impact on polarity classification.
VR creation experience in cultural heritage education: A preliminary exploration

ABSTRACT Despite an increasing adoption of virtual reality (VR) in education, few studies have explored VR creation in cultural heritage education. This study aims to investigate students' experience of creating VR content featuring cultural heritage in an undergraduate general education course repurposed from a digital collection course. A survey was conducted with 87 students, collecting both close‐ended and open‐ended responses. A coding framework was designed to analyze students' open‐ended responses. Preliminary findings show that students were largely satisfied with the experience, which helped learners from diverse academic backgrounds toward acquiring technological skills that are essential for the 21 st century workforce. The VR creation experience also motivated the students to learn more about cultural heritage. Technical issues regarding spherical photo‐taking and the online VR creation tool were identified, which may call for new alternative tools and devices. Findings offer empirical evidence on the value of integrating VR creation into cultural heritage education, as well as implications for pedagogical design and educational applications of VR creation.
Drugs Reviews Sentiment Analysis using Weakly Supervised Model

The enormous amounts of vital data available in social media and health forums containing the expression of customers comments have recently provided resources for the research of Adverse Drug Reactions (ADRs) identification. However, in the existing methods, the neglect of noise data and the need for manual labeled data reduce the accuracy of prediction results and greatly increase human labor when applying social comment data. Based on the drug reviews of patients’ feedback, our objective is to recognize the sentiment inclination (positive/negative) of drug reviews by sentiment analysis for ADRs identification. Firstly, this paper proposes a weakly supervised mechanism (WSM) that applies the weakly labeled data to pre-train the parameters of the model then use labeled data to fine-tune initialed parameters, which reduces the effect of noise data on the consequences. Secondly, we present a novel architecture applys the WSM and combines the strength of Convolutional Neural Network (CNN) and Bi-directional Long Short-term Memory (Bi-LSTM) named as WSM-CNN-LSTM to complete the task for ADR reviews sentiment classification. Finally, experimental results demonstrate that our model marginally out-performes the comparison models in ADR identification and a small quantity of ADR-labeled examples are needed to attain optimal performance, which declines the influence of noise and reduces the manual labeled data.
A Pay-as-you-go Methodology to Design and Build Enterprise Knowledge Graphs from Relational Databases

Business users must answer business questions quickly to address Business Intelligence (BI) needs. The bottleneck is to understand the complex databases schemas. Only few people in the IT department truly understand them. A holy grail is to empower business users to ask and answer their own questions with minimal IT support. Semantic technologies, now dubbed as Knowledge Graphs, become useful here. Even though the research and industry community has provided evidence that semantic technologies works in the real world, our experience is that there continues to be a major challenge: the engineering of ontologies and mappings covering enterprise databases containing thousands of tables with tens of thousands of attributes. In this paper, we present a novel and unique pay-as-you-go methodology that addresses the aforementioned difficulties. We provide a case study with a large scale e-commerce company where Capsenta’s Ultrawrap has been deployed in production for over 3 years.
Fusing absolute and relative information for augmenting the method of nearest neighbors for ordinal classification

Ordinal classification is a special case of multiclass classification in which there exists a natural order on the set of class labels. Due to the nature of the problem, datasets for ordinal classification are typically rather small, having a negative impact on performance. A possible way out is to look for additional information. In this paper, firstly, we make use of order relations for unlabeled examples to generate relative information. Secondly, we incorporate this relative information into the method of k nearest neighbors, thus exploiting absolute and relative information at the same time. More specifically, we bring together notions from the fields of information fusion and machine learning to integrate both types of information. Finally, we test the proposed method on some classical machine learning datasets. The experimental results show the effectiveness of our approach.
On the Analysis and Improvement of Min-Entropy Estimation on Time-Varying Data

Widely used as fundamental security components in most cryptographic applications, random number generators (RNGs) rely mainly on randomness provided by entropy sources. If the provided randomness is less than expected, RNGs may be compromised and thus impair the security of the whole cryptographic applications. However, the common assumptions (e.g., outputs are independent and identically distributed, i.e., IID) may not always hold. For example, many entropy sources are based on some physical phenomena that are fragile and sensitive to external factors (e.g., temperature), which means the distributions of these entropy sources' outputs are continuously changing. As important tools to measure the quality of entropy sources, existing entropy estimation methods may provide false estimations against these time-varying data, because they cannot detect the changes of data distributions. In this paper, we firstly review and analyze the existing typical entropy estimators including the NIST SP 800-90B (90B for short) estimators and the lately proposed neural network based (NN-based) estimators, especially, their limitations on the aforementioned time-varying data. Second, we propose an entropy estimation framework adopting change detection techniques to address this problem. In contrast to the NN-based estimators, the proposed estimator under this framework employs a change detection method to preprocess the tested data and adds additional distribution features to each data sample, which makes it possible to learn the distribution changes and estimate the entropy more accurately. Finally, we evaluate the performance of our estimator using various kinds of simulated data and real world data, and compare our estimator with the 90B estimators and the NN-based estimators. Extensive evaluations demonstrate that the proposed estimator provides similar or more accurate entropy estimation than the other estimators, especially for time-varying data.
PrivateTalk: Activating Voice Input with Hand-On-Mouth Gesture Detected by Bluetooth Earphones

We introduce PrivateTalk, an on-body interaction technique that allows users to activate voice input by performing the Hand-On-Mouth gesture during speaking. The gesture is performed as a hand partially covering the mouth from one side. PrivateTalk provides two benefits simultaneously. First, it enhances privacy by reducing the spread of voice while also concealing the lip movements from the view of other people in the environment. Second, the simple gesture removes the need for speaking wake-up words and is more accessible than a physical/software button especially when the device is not in the user's hands. To recognize the Hand-On-Mouth gesture, we propose a novel sensing technique that leverages the difference of signals received by two Bluetooth earphones worn on the left and right ear. Our evaluation shows that the gesture can be accurately detected and users consistently like PrivateTalk and consider it intuitive and effective.
Cascade of encoder-decoder CNNs with learned coordinates regressor for robust facial landmarks detection

Convolutional Neural Nets (CNNs) have become the reference technology for many computer vision problems. Although CNNs for facial landmark detection are very robust, they still lack accuracy when processing images acquired in unrestricted conditions. In this paper we investigate the use of a cascade of Neural Net regressors to increase the accuracy of the estimated facial landmarks. To this end we append two encoder-decoder CNNs with the same architecture. The first net produces a set of heatmaps with a rough estimation of landmark locations. The second, trained with synthetically generated occlusions, refines the location of ambiguous and occluded landmarks. Finally, a densely connected layer with shared weights among all heatmaps, accurately regresses the landmark coordinates. The proposed approach achieves state-of-the-art results in 300W, COFW and WFLW that are widely considered the most challenging public data sets.