Reviewerly/exHarmony huggingface dataset:
Artificial Intelligence based Models for Screening of Hematologic Malignancies using Cell Population Data

Abstract Cell Population Data (CPD) provides various blood cell parameters that can be used for differential diagnosis. Data analytics using Machine Learning (ML) have been playing a pivotal role in revolutionizing medical diagnostics. This research presents a novel approach of using ML algorithms for screening hematologic malignancies using CPD. The data collection was done at Konkuk University Medical Center, Seoul. A total of (882 cases: 457 hematologic malignancy and 425 hematologic non-malignancy) were used for analysis. In our study, seven machine learning models, i.e., SGD, SVM, RF, DT, Linear model, Logistic regression, and ANN, were used. In order to measure the performance of our ML models, stratified 10-fold cross validation was performed, and metrics, such as accuracy, precision, recall, and AUC were used. We observed outstanding performance by the ANN model as compared to other ML models. The diagnostic ability of ANN achieved the highest accuracy, precision, recall, and AUC ± Standard Deviation as follows: 82.8%, 82.8%, 84.9%, and 93.5% ± 2.6 respectively. ANN algorithm based on CPD appeared to be an efficient aid for clinical laboratory screening of hematologic malignancies. Our results encourage further work of applying ML to wider field of clinical practice.
Product quantization with dual codebooks for approximate nearest neighbor search

Product quantization (PQ) is a powerful technique for approximate nearest neighbor (ANN) search. In this paper, to improve the accuracy of ANN search, we propose a new PQ-based method named product quantization with dual codebooks (DCPQ). Different from traditional PQ-based methods, we analyze quantization errors after learning the first PQ codebook, and then part of training vectors with larger quantization errors are found and selected to relearn a second PQ codebook. When encoding the database offline, all database vectors are firstly quantized using both of dual codebooks in each subspace, and the encoding mode of a database vector is determined after comparing the two quantization errors based on dual codebooks. Moreover, database vectors with the same encoding mode are grouped as a sub-database and can be more efficiently searched. Experimental results demonstrate that our proposed dual codebooks solution can achieve higher accuracy compared with the standard PQ and its variants.
CompostNet: An Image Classifier for Meal Waste

Many businesses, cafes, and outdoor spaces provide trash, recycling, and composting bins, requiring consumers to decipher instructional text, icons, or images in order to sort their waste accurately. It can be confusing to know what pieces of waste go in which bin. Moreover, different areas may have different rules for how to separate waste, and people often inadvertently throw their trash in the wrong bin. Machine learning solutions can help us more quickly and accurately choose the proper receptacle for our waste by classifying a photograph of the waste. This paper presents a novel image classification model that categorizes the types of waste produced after eating a meal, which can be used in mobile applications to encourage users to correctly sort waste. Building on recent work in deep learning and waste classification, we introduce CompostNet, a convolutional neural network that classifies images according to how they should be appropriately discarded. We provide details about the design and development of CompostNet, along with an evaluation of its effectiveness in classifying images of waste. Further, we discuss two different approaches to the design of our system, one using a custom model and the other augmenting a pre-trained image classification model (MobileNet) through transfer learning, and and how we achieved greater success with the transfer learning approach. To the best of our knowledge, CompostNet is the first waste classification system that uses a deep learning network to identify compostable, recyclable, and landfill materials. CompostNet is an application of machine learning for social good, and supports United Nations Sustainable Development Goal 12: Responsible Consumption and Production [1].
Measure distance locating nearest public facilities using Haversine and Euclidean Methods

Abstract The application of finding the nearest public facility using 2 methods to measure the distance between 2 points, i.e. the Euclidean Method and the Haversine Formula. Euclidean is a heuristic function obtained based on direct distance without obstacles such as to get the value of the length of a diagonal line on a triangle. Whereas Haversine is an equation that looks for the distance of an arc between two points on longitude and latitude. The results of the calculation of the average distance Euclidean deviations with an average value of data 2.539764, and Haversine 2.536912. This shows that the comparison of the measurements of the distance between Euclidean and Haversine has a difference of 0.002852 or the percentage of the distance between the two methods is 99.89 percent. Of the two methods, which yield values almost by measurements on Google maps is Haversine. For Euclidean, it is used to measure the distance between two points on a flat plane so that the results have differences when compared to the Haversine formula.
Optimisation‐based training of evolutionary convolution neural network for visual classification applications

Training of the convolution neural network (CNN) is a problem of global optimisation. This study proposed a hybrid modified particle swarm optimisation (MPSO) and conjugate gradient (CG) algorithm for efficient training of CNN. The training involves MPSO–CG to avoid trapping in local minima. Particularly, improvements in the MPSO by introducing a novel approach for control parameters, improved parameters updating criteria, a novel parameter in the velocity update equation, and fusion of the CG allows handling the issues in training CNN. In this study, the authors validate the proposed MPSO algorithm on three benchmark mathematical test functions and also compared with three different variants of the baseline particle swarm optimisation algorithm. Furthermore, the performance of the proposed MPSO–CG is also compared with other training algorithms focusing on the analysis of computational cost, convergence, and accuracy based on a standard problem specific to classification applications on CIFAR‐10 dataset and face and skin detection dataset.
Eagle-Eyed Multitask CNNs for Aerial Image Retrieval and Scene Classification

In recent years, convolutional neural networks (CNNs) have become the predominant method for content-based aerial image retrieval (CBAIR) and aerial scene classification (ASC) due to their overwhelming performance advantages. However, existing CNN-based models have the following shortcomings: first, they do not deal with large intraclass variations, thereby overlooking the possibility of fine-grained retrieval and classification; second, all similarity learning methods for CBAIR consider similarity between two images as a constant, neglecting the fact that image similarity is uncertain in nature; third, similarity learning is separated from ASC, ignoring the advantages of joint optimization. To address these issues, we propose a novel metric learning method called center-metric learning, and couple it with a new kind of loss called positive-negative center loss, which, with the help of several “experts,” enables CNNs to cope successfully with within-class variations. Besides, we propose similarity distribution learning, making the first attempt to embed uncertainty regarding similarity into the training process. The resulting fine-grained similarity predictions can further strengthen CNNs' fine discrimination ability. Furthermore, three tasks, that is, center-metric learning, similarity distribution learning, and ASC, are incorporated into one CNN, benefitting from one another and leading to a better generalization capability. Just like an eagle, our model is able to discriminate subtle differences among aerial images, hence the name “eagle-eyed multitask CNN.” We carry out extensive experiments over four publicly available aerial image sets and achieve a performance better than all existing methods.
Video Multitask Transformer Network

In this paper, we propose the Multitask Transformer Network for multitasking on untrimmed video. To analyze the untrimmed video, it needs to capture important frame and region in the spatio-temporal domain. Therefore, we utilize the Transformer Network, which can capture the useful features from CNN representations through an attention mechanism. Motivated by the Action Transformer Network, which is a repurposed model of the Transformer for video, we modified the concept of query which was specialized only for action recognition on the trimmed video to fit the untrimmed video. In addition, we modified the structure of the Transformer unit to the pre-activation structure for identity mapping on residual connections. We also utilize the class conversion matrix (CCM), one of the feature fusion methods, to share the information of different tasks. Combining our Transformer structure and CCM, the Multitask Transformer Network is proposed for multitasking on untrimmed video. Eventually, our model evaluated on CoVieW 2019, and we enhanced the performance through post-processing based on prediction results that suitable to the CoVieW 2019 evaluation metric. In CoVieW 2019 challenge, we placed fourth on final rank while first on scene and action score.
On detecting cherry-picked trendlines

Poorly supported stories can be told based on data by cherry-picking the data points included. While such stories may be technically accurate, they are misleading. In this paper, we build a system for detecting cherry-picking, with a focus on trendlines extracted from temporal data. We define a support metric for detecting such trendlines. Given a dataset and a statement made based on a trendline, we compute a support score that indicates how cherry-picked it is. Studying different types of trendlines and formalizing terms, we propose efficient and effective algorithms for computing the support measure. We also study the problem of discovering the most supported statements. Besides theoretical analysis, we conduct extensive experiments on real-world data, that demonstrate the validity of our proposed techniques.
High Quality Candidate Generation and Sequential Graph Attention Network for Entity Linking

Entity Linking (EL) is a task for mapping mentions in text to corresponding entities in knowledge base (KB). This task usually includes candidate generation (CG) and entity disambiguation (ED) stages. Recent EL systems based on neural network models have achieved good performance, but they still face two challenges: (i) Previous studies evaluate their models without considering the differences between candidate entities. In fact, the quality (gold recall in particular) of candidate sets has an effect on the EL results. So, how to promote the quality of candidates needs more attention. (ii) In order to utilize the topical coherence among the referred entities, many graph and sequence models are proposed for collective ED. However, graph-based models treat all candidate entities equally which may introduce much noise information. On the contrary, sequence models can only observe previous referred entities, ignoring the relevance between the current mention and its subsequent entities. To address the first problem, we propose a multi-strategy based CG method to generate high recall candidate sets. For the second problem, we design a Sequential Graph Attention Network (SeqGAT) which combines the advantages of graph and sequence methods. In our model, mentions are dealt with in a sequence manner. Given the current mention, SeqGAT dynamically encodes both its previous referred entities and subsequent ones, and assign different importance to these entities. In this way, it not only makes full use of the topical consistency, but also reduce noise interference. We conduct experiments on different types of datasets and compare our method with previous EL system on the open evaluation platform. The comparison results show that our model achieves significant improvements over the state-of-the-art methods.
Privacy-preserving AI Services Through Data Decentralization

User services increasingly base their actions on AI models, e.g., to offer personalized and proactive support. However, the underlying AI algorithms require a continuous stream of personal data—leading to privacy issues, as users typically have to share this data out of their territory. Current privacy-preserving concepts are either not applicable to such AI-based services or to the disadvantage of any party. This paper presents PrivAI, a new decentralized and privacy-by-design platform for overcoming the need for sharing user data to benefit from personalized AI services. In short, PrivAI complements existing approaches to personal data stores, but strictly enforces the confinement of raw user data. PrivAI further addresses the resulting challenges by (1) dividing AI algorithms into cloud-based general model training, subsequent local personalization, and community-based sharing of model updates for new users; by (2) loading confidential AI models into a trusted execution environment, and thus, protecting provider's intellectual property (IP). Our experiments show the feasibility and effectiveness of PrivAI with comparable performance as currently-practiced approaches.
Dynamic Sampling and Selective Masking for Communication-Efficient Federated Learning

Federated learning (FL) is a novel machine learning setting that enables on-device intelligence via decentralized training and federated optimization. Deep neural networks’ rapid development facilitates the learning techniques for modeling complex problems and emerges into federated deep learning under the federated setting. However, the tremendous amount of model parameters burdens the communication network with a high load of transportation. This article introduces two approaches for improving communication efficiency by dynamic sampling and top-$k$k selective masking. The former controls the fraction of selected client models dynamically, while the latter selects parameters with top-$k$k largest values of difference for federated updating. Experiments on convolutional image classification and recurrent language modeling are conducted on three public datasets to show our proposed methods’ effectiveness.
Mobile Augmented Reality (AR) Marker-based for Indoor Library Navigation

Abstract This paper presents the development of Augmented Reality (AR) for smart campus urbanization using library as the environment for the demonstration of the AR prototype. The main goal of the AR development is to help users to get information and direction easily using AR based mobile application when walking inside the library. In normal circumstances, users typically walk around and explore the library area before reaching their targeted destinations. Depending on the library size and number of reading corners, exploration and walking in the library can be time consuming. Therefore, an AR technology is introduced in this paper to improve the user experience inside the library in the right direction and information instantly. This application is developed using Vuforia software to set the image marker-based and process the output into Unity3D software, Android Studio for the Main Menu interface and IBM Watson for voice recognition. The final form of the application is successfully generated from the development of Augmented Reality (AR) application for the smart campus by using a library for the demonstration of the AR prototype. A series of application tests are conducted in each corner of the library to evaluate the effectiveness of developed AR.
RLPer: A Reinforcement Learning Model for Personalized Search

Personalized search improves generic ranking models by taking user interests into consideration and returning more accurate search results to individual users. In recent years, machine learning and deep learning techniques have been successfully applied in personalized search. Most existing personalization models simply regard the search history as a static set of user behaviours and learn fixed ranking strategies based on the recorded data. Though improvements have been observed, it is obvious that these methods ignore the dynamic nature of the search process: search is a sequence of interactions between the search engine and the user. During the search process, the user interests may dynamically change. It would be more helpful if a personalized search model could track the whole interaction process and update its ranking strategy continuously. In this paper, we propose a reinforcement learning based personalization model, referred to as RLPer, to track the sequential interactions between the users and search engine with a hierarchical Markov Decision Process (MDP). In RLPer, the search engine interacts with the user to update the underlying ranking model continuously with real-time feedback. And we design a feedback-aware personalized ranking component to catch the user’s feedback which has impacts on the user interest profile for the next query. Experimental results on the publicly available AOL search log verify that our proposed model can significantly outperform state-of-the-art personalized search models.
An idea to explore: Use of augmented reality for teaching three‐dimensional biomolecular structures

Abstract Biology and biochemistry students must learn to visualize and comprehend the complex three‐dimensional (3D) structures of macromolecules such as proteins or DNA. However, most tools available for teaching biomolecular structures typically operate in two dimensions. Here, we present protocols and pedagogical approaches for using immersive augmented reality (AR) visors, specifically the Microsoft HoloLens, to reinforce learning with large scale 3D holographic structures. We developed a novel workflow to render vividly colored custom biomolecules in AR visors. In addition, we developed AR exercises to review concepts relevant to protein or DNA structure and then implemented the exercises in four different biology and biochemistry courses. Surveys showed that students reported greater interest in biomolecular structures after the exercise. We also highlight some of the advantages and disadvantages of the software and hardware of this upcoming technology.
Dynamic Graph Convolutional Networks for Entity Linking

Entity linking, which maps named entity mentions in a document into the proper entities in a given knowledge graph, has been shown to be able to significantly benefit from modeling the entity relatedness through Graph Convolutional Networks (GCN). Nevertheless, existing GCN entity linking models fail to take into account the fact that the structured graph for a set of entities not only depends on the contextual information of the given document but also adaptively changes on different aggregation layers of the GCN, resulting in insufficiency in terms of capturing the structural information among entities. In this paper, we propose a dynamic GCN architecture to effectively cope with this challenge. The graph structure in our model is dynamically computed and modified during training. Through aggregating knowledge from dynamically linked nodes, our GCN model can collectively identify the entity mappings between the document and the knowledge graph, and efficiently capture the topical coherence among various entity mentions in the entire document. Empirical studies on benchmark entity linking data sets confirm the superior performance of our proposed strategy and the benefits of the dynamic graph structure.
Directional and Explainable Serendipity Recommendation

Serendipity recommendation has attracted more and more attention in recent years; it is committed to providing recommendations which could not only cater to users' demands but also broaden their horizons. However, existing approaches usually measure user-item relevance with a scalar instead of a vector, ignoring user preference direction, which increases the risk of unrelated recommendations. In addition, reasonable explanations increase users' trust and acceptance, but there is no work to provide explanations for serendipitous recommendations. To address these limitations, we propose a Directional and Explainable Serendipity Recommendation method named DESR. Specifically, we extract users' long-term preferences with an unsupervised method based on GMM (Gaussian Mixture Model) and capture their short-term demands with the capsule network at first. Then, we propose the serendipity vector to combine long-term preferences with short-term demands and generate directionally serendipitous recommendations with it. Finally, a back-routing scheme is exploited to offer explanations. Extensive experiments on real-world datasets show that DESR could effectively improve the serendipity and explainability, and give impetus to the diversity, compared with existing serendipity-based methods.
Differential Privacy in Deep Learning: An Overview

Nowadays, deep learning has many applications in our daily life such as self-driving, product recommendation, advertisements and healthcare. In the training phase, deep learning models are trained on dataset of which the information privacy is stored locally through model parameters. The information privacy can in some cases be inferred from the parameters of model, and then traced back to uncover sensitive information. The privacy challenges are solved with many different anonymization methods such as k-anonymity, l-diversity, and t-closeness, which may not be sufficient anymore with unstructured data and inference attack. However, we argue that this problem can be solved by differential privacy. Differential privacy provides a mathematical framework that can be used to understand the extent to which a deep learning algorithm remembers information about individuals and be able to evaluate deep learning for privacy guarantees. In this paper, we review the threats and defenses on privacy models in deep learning, especially the differential privacy. We classify threats and defenses, and identify the points in deep learning to add random noises to input samples, gradient or function to protect privacy model.
BimodalGaze: Seamlessly Refined Pointing with Gaze and Filtered Gestural Head Movement

Eye gaze is a fast and ergonomic modality for pointing but limited in precision and accuracy. In this work, we introduce BimodalGaze, a novel technique for seamless head-based refinement of a gaze cursor. The technique leverages eye-head coordination insights to separate natural from gestural head movement. This allows users to quickly shift their gaze to targets over larger fields of view with naturally combined eye-head movement, and to refine the cursor position with gestural head movement. In contrast to an existing baseline, head refinement is invoked automatically, and only if a target is not already acquired by the initial gaze shift. Study results show that users reliably achieve fine-grained target selection, but we observed a higher rate of initial selection errors affecting overall performance. An in-depth analysis of user performance provides insight into the classification of natural versus gestural head movement, for improvement of BimodalGaze and other potential applications.