Reviewerly/exHarmony huggingface dataset:
Towards Trustworthiness in the Context of Explainable Search

Explainable AI (XAI) is currently a vibrant research topic. However, the absence of ground truth explanations makes it difficult to evaluate XAI systems such as Explainable Search. We present an Explainable Search system with a focus on evaluating the XAI aspect of Trustworthiness along with the retrieval performance. We present SIMFIC 2.0 (Similarity in Fiction), an enhanced version of a recent explainable search system. The system retrieves books similar to a selected book in a query-by-example setting. The motivation is to explain the notion of similarity in fiction books. We extract hand-crafted interpretable features for fiction books and provide global explanations by fitting a linear regression and local explanations based on similarity measures. The Trustworthiness facet is evaluated using user studies, while the ranking performance is compared by analysis of user clicks. Eye tracking is used to investigate user attention to the explanation elements when interacting with the interface. Initial experiments show statistically significant results on the Trustworthiness of the system, paving way for interesting research directions that are being investigated.
User-Centric Path Reasoning towards Explainable Recommendation

There has been significant progress in the utilization of heterogeneous knowledge graphs (KG) as auxiliary information in recommendation systems. Reasoning over KG paths sheds light on the user's decision-making process. Previous methods focus on formulating this process as a multi-hop reasoning problem. However, without some form of guidance in the reasoning process, such a huge search space results in poor accuracy and little explanation diversity. In this paper, we propose UCPR, a user-centric path reasoning network that constantly guides the search from the aspect of user demand and enables explainable recommendations. In this network, a multi-view structure leverages not only local sequence reasoning information but also a panoramic view of the user's demand portfolio while inferring subsequent user decision-making steps. Experiments on five real-world benchmarks show UCPR is significantly more accurate than state-of-the-art methods. Besides, we show that the proposed model successfully identifies users' concerns and increases reason-ing diversity to enhance explainability
Efficient and Secure File Transfer in Cloud Through Double Encryption Using AES and RSA Algorithm

With recent advances in Cloud computing, information is being contracted by cloud services. Dropbox and Google Drive provide cloud services to users with low-cost storage. Here we present a protection method by encrypting and decrypting the files which offer an enhanced level of protection. To encrypt the file that we upload in cloud, we make use of Double encryption technique. The file is being encrypted twice using the two algorithms one after the other. The file is first encrypted using AES algorithm and then by RSA algorithm. The corresponding keys are being generated during the execution of the algorithm. This technique increases the security level. The various parameters that we have considered here are security level, speed, data confidentiality, data integrity and cipher text size. Our method is more efficient as it satisfies all the parameters where the conventional methods failed to do so. The cloud we used to store the content of the file is DropBox, which is in the encrypted format using AES and RSA algorithms.
Simulation-derived best practices for clustering clinical data

Clustering analyses in clinical contexts hold promise to improve the understanding of patient phenotype and disease course in chronic and acute clinical medicine. However, work remains to ensure that solutions are rigorous, valid, and reproducible. In this paper, we evaluate best practices for dissimilarity matrix calculation and clustering on mixed-type, clinical data.We simulate clinical data to represent problems in clinical trials, cohort studies, and EHR data, including single-type datasets (binary, continuous, categorical) and 4 data mixtures. We test 5 single distance metrics (Jaccard, Hamming, Gower, Manhattan, Euclidean) and 3 mixed distance metrics (DAISY, Supersom, and Mercator) with 3 clustering algorithms (hierarchical (HC), k-medoids, self-organizing maps (SOM)). We quantitatively and visually validate by Adjusted Rand Index (ARI) and silhouette width (SW). We applied our best methods to two real-world data sets: (1) 21 features collected on 247 patients with chronic lymphocytic leukemia, and (2) 40 features collected on 6000 patients admitted to an intensive care unit.HC outperformed k-medoids and SOM by ARI across data types. DAISY produced the highest mean ARI for mixed data types for all mixtures except unbalanced mixtures dominated by continuous data. Compared to other methods, DAISY with HC uncovered superior, separable clusters in both real-world data sets.Selecting an appropriate mixed-type metric allows the investigator to obtain optimal separation of patient clusters and get maximum use of their data. Superior metrics for mixed-type data handle multiple data types using multiple, type-focused distances. Better subclassification of disease opens avenues for targeted treatments, precision medicine, clinical decision support, and improved patient outcomes.
Gazetteer Enhanced Named Entity Recognition for Code-Mixed Web Queries

Named entity recognition (NER) for Web queries is very challenging. Queries often do not consist of well-formed sentences, and contain very little context, with highly ambiguous queried entities. Code-mixed queries, with entities in a different language than the rest of the query, pose a particular challenge in domains like e-commerce (e.g. queries containing movie or product names). This work tackles NER for code-mixed queries, where entities and non-entity query terms co-exist simultaneously in different languages. Our contributions are twofold. First, to address the lack of code-mixed NER data we create EMBER, a large-scale dataset in six languages with four different scripts. Based on Bing query data, we include numerous language combinations that showcase real-world search scenarios. Secondly, we propose a novel gated architecture that enhances existing multi-lingual Transformers with a Mixture-of-Experts model to dynamically infuse multi-lingual gazetteers, allowing it to simultaneously differentiate and handle entities and non-entity query terms in multiple languages. Experimental evaluation on code-mixed queries in several languages shows that our approach efficiently utilizes gazetteers to recognize entities in code-mixed queries with an F1=68%, an absolute improvement of +31% over a non-gazetteer baseline.
Design of an energy‐efficient IoT device with optimized data management in sports person health monitoring application

The improvements in health habits, technological advances, and the proliferation of healthy living activities have contributed to the comprehensive extension of sports person health assessment. Since the internet of things (IoT) device requires energy‐optimized wearable devices, it has been observed that the demanding factors include energy efficiency factor in Sports Person Health Monitoring wearable device. Hence in this paper, IoT‐based Hierarchical Health Monitoring Model (IoT‐HHMM) is proposed to improve the efficiency factor by minimizing the energy consumption to achieve effective assessment of sports person health monitoring wearables. The complexity of limited resources and usage of energy is optimized by introducing the Optimal Energy‐Efficient Resource Assignment Algorithm. Likewise, a cloud computing technique is implemented using Probabilistic Radial Basis Function Neural Network to ensure effective prediction and classification in healthcare data management, which is considered as a significant factor in wearable IoT devices for Sports Person Health Monitoring. The result indicates that the proposed IoT‐HHMM achieves a high accuracy ratio of 98.4%, a sensitivity ratio of 92.5%, a performance ratio of 96.7% when compared to traditional approaches.
CoinPrune: Shrinking Bitcoin’s Blockchain Retrospectively

Popular cryptocurrencies continue to face serious scalability issues due to their ever-growing blockchains. Thus, modern blockchain designs began to prune old blocks and rely on recent snapshots for their bootstrapping processes instead. Unfortunately, established systems are often considered incapable of adopting these improvements. In this work, we present CoinPrune, our block-pruning scheme with full Bitcoin compatibility, to revise this popular belief. CoinPrune bootstraps joining nodes via snapshots that are periodically created from Bitcoin's set of unspent transaction outputs (UTXO set). Our scheme establishes trust in these snapshots by relying on CoinPrune-supporting miners to mutually reaffirm a snapshot's correctness on the blockchain. This way, snapshots remain trustworthy even if adversaries attempt to tamper with them. Our scheme maintains its retrospective deployability by relying on positive feedback only, i.e., blocks containing invalid reaffirmations are not rejected, but invalid reaffirmations are outpaced by the benign ones created by an honest majority among CoinPrune-supporting miners. Already today, CoinPrune reduces the storage requirements for Bitcoin nodes by two orders of magnitude, as joining nodes need to fetch and process only 6 GiB instead of 271 GiB of data in our evaluation, reducing the synchronization time of powerful devices from currently 7 h to 51 min, with even larger potential drops for less powerful devices. CoinPrune is further aware of higher-level application data, i.e., it conserves otherwise pruned application data and allows nodes to obfuscate objectionable and potentially illegal blockchain content from their UTXO set and the snapshots they distribute.
Hierarchical Adaptive Pooling by Capturing High-Order Dependency for Graph Representation Learning

Graph neural networks (GNN) have been proven to be mature enough for handling graph-structured data on node-level graph representation learning tasks. However, the graph pooling technique for learning expressive graph-level representation is critical yet still challenging. Existing pooling methods either struggle to capture the local substructure or fail to effectively utilize high-order dependency, thus diminishing the expression capability. In this paper we propose HAP, a hierarchical graph-level representation learning framework, which is adaptively sensitive to graph structures, i.e., HAP clusters local substructures incorporating with high-order dependencies. HAP utilizes a novel cross-level attention mechanism MOA to naturally focus more on close neighborhood while effectively capture higher-order dependency that may contain crucial information. It also learns a global graph content GCont that extracts the graph pattern properties to make the pre- and post-coarsening graph content maintain stable, thus providing global guidance in graph coarsening. This novel innovation also facilitates generalization across graphs with the same form of features. Extensive experiments on ten datasets show that HAP significantly outperforms twelve popular graph pooling methods on graph classification task with an maximum accuracy improvement of 20.18%, and exceeds the performance of state-of-the-art graph matching and graph similarity learning algorithms by over 3.42% and 16%.
Using nursing notes to improve clinical outcome prediction in intensive care patients: A retrospective cohort study

Abstract Objective Electronic health record documentation by intensive care unit (ICU) clinicians may predict patient outcomes. However, it is unclear whether physician and nursing notes differ in their ability to predict short-term ICU prognosis. We aimed to investigate and compare the ability of physician and nursing notes, written in the first 48 hours of admission, to predict ICU length of stay and mortality using 3 analytical methods. Materials and Methods This was a retrospective cohort study with split sampling for model training and testing. We included patients ≥18 years of age admitted to the ICU at Beth Israel Deaconess Medical Center in Boston, Massachusetts, from 2008 to 2012. Physician or nursing notes generated within the first 48 hours of admission were used with standard machine learning methods to predict outcomes. Results For the primary outcome of composite score of ICU length of stay ≥7 days or in-hospital mortality, the gradient boosting model had better performance than the logistic regression and random forest models. Nursing and physician notes achieved area under the curves (AUCs) of 0.826 and 0.796, respectively, with even better predictive power when combined (AUC, 0.839). Discussion Models using only nursing notes more accurately predicted short-term prognosis than did models using only physician notes, but in combination, the models achieved the greatest accuracy in prediction. Conclusions Our findings demonstrate that statistical models derived from text analysis in the first 48 hours of ICU admission can predict patient outcomes. Physicians’ and nurses’ notes are both uniquely important in mortality prediction and combining these notes can produce a better predictive model.
Session-based news recommendations using SimRank on multi-modal graphs

Recommender systems are among the most widespread applications of artificial intelligence techniques. For instance, news recommender systems serve users in managing the overload of information they come across when accessing news portals. Obviously, in the news domain time-awareness of recommendation approaches are crucial. However, most of these approaches missed to consider user sessions, which group the items that a user interacted with. In this paper, we study the problem of session-based recommendations by running SimRank on time-evolving heterogeneous graphs. In particular, we construct a dynamic heterogeneous multi-partite graph and adjust SimRank to run on it by using different (i) sliding time window sizes, (ii) sub-graphs used for model learning and (iii) sequential article weighting strategies. We evaluate our algorithms on two real-life datasets, and we show that our method outperforms other state-of-the-art methods in terms of accuracy and diversity. The significance and impact of this work is important because it introduces to the research community of expert and intelligent systems, for the first time, a stream-based version of SimRank algorithm, which is able to run over time-evolving graphs.
Utilizing Blockchain Technology to Improve WSN Security for Sensor Data Transmission

This paper proposes a method for improving the data security of wireless sensor networks based on blockchain technology. Blockchain technology is applied to data transfer to build a highly secure wireless sensor network. In t... | Find, read and cite all the research you need on Tech Science Press
Using Cloud-Based Augmented Reality to 3D-Enable the 2D Drawings of AISC Steel Sculpture: A Plan-Reading Educational Experiment

Two-dimensional (2D) drawing is one of the most common communication approaches in the civil and construction engineering domain. However, there are limits to the traditional classroom setting for training in 2D drawing and interpretation skills. New visualization techniques exist for integrating the third dimension (depth) to 2D drawings, providing more accurate and natural representations of objects. This helps students to better understand 2D drawings and develop required spatial skills. This study employed cloud-based augmented reality (AR) technology to 3D-enable 2D drawings through virtually superimposing interactive 3D building information models onto 2D paper-based drawings. This research aims to understand if AR-enabled 3D augmentation can be used to facilitate students’ understanding of complex 2D drawings. An experiment was conducted to evaluate how AR could facilitate understanding 2D drawings against the conventional paper-only approach. The results revealed that AR was positively rated by students and could improve the accuracy of a plan-reading task.
Strategic Creation and Placement of Landmarks for Robot Navigation in a Partially-known Environment

Navigation of autonomous robots in uncharted or uncertain, and constrained or hazardous environment is a common problem but integrated with multiple challenges. This paper considers a subsection of such an environment and essays a feasible yet innovative solution for accurate parking and obstacle avoidance of a car-like mobile robot. The new method is based on strategic creation and positioning of landmarks in a bounded workspace that will aid or guide the robot to safely navigate the workspace and finally park correctly inside of the designated parking bay. By autonomously controlling its translational velocity and the steering angle, the car-like robot navigates from one (newly fixed) landmark to another (newly fixed) and finally converges to a target with a pre-defined posture. Attaining accurate posture is very important in real-life situations which involve tasks such as loading or off-loading and deliveries in constrained spaces. The paper establishes practical posture stability of the system. The computer simulations verify the effectiveness of the proposed method and the proposed control inputs.
Universal pooling – A new pooling method for convolutional neural networks

Pooling is one of the key elements in a convolutional neural network. It reduces the feature map size, thereby enabling training with a limited amount of computation. The most common pooling methods are average pooling, max pooling, and stride pooling. The common pooling methods, however, have the disadvantage that they can perform only specified and fixed pooling functions and thus have limited expressive power. In this paper, we propose a new pooling method named universal pooling (UP). UP performs different pooling functions depending on the training samples. UP is a general pooling and includes the previous common pooling methods as special cases. The structure of UP is inspired by attention methods. UP can actually be considered as a channel-wise local spatial attention module. It is quite different from attention-based feature reduction methods. We insert UP into a couple of popular networks and apply the networks to benchmark sets in two applications, namely, image recognition and semantic segmentation. The experiment results show that complex poolings are trained in the proposed UP and that UP achieves better performance than the previous pooling methods.
TNNL: A novel image dimensionality reduction method for face image recognition

Face image recognition technology plays an important role in biometric recognition field. Among the present face recognition methods, the method based on subspace learning have aroused wide concern due to their favorable properties, such as convenience for computation and effectiveness for identification. However, existing methods based on subspace learning are out of work when the sample-specific corruptions and outliers come along. To solve this problem, we build a novel model for face image recognition named truncated nuclear norm on low rank discriminant embedding (TNNL). The TNNL can mitigate the negative impact of noise and enhance the discriminability of features. Furthermore, we propose two iterative algorithms to extract the robust low dimensional image feature. To testify the effectiveness and robustness of TNNL, we conduct experiments on two benchmark face image databases for low dimensional feature extraction. The experimental results show that TNNL is better than the existing methods.
Blockchain enabled Digitization of Land Registration

In India, the Land Registry System is a very time-consuming procedure that requires many intermediaries, thus increasing the number of fraudulent cases. The use of Blockchain Technology for Land Registry Management can eliminate these problems. Blockchain is simply a data structure where each block is connected to another block. It is a distributed data ledger with an immutable public record of digital transactions. The key features of this technology are, the data in the block are immutable which is achieved by using hashing algorithms, cryptography techniques and consensus mechanisms which are done before adding a block into the blockchain. Even though there are limitations in Blockchain technology like, it is complex to implement and not as much fast as the centralized system. Also, there is a need of miners to validate the transactions but these can be overcome by using appropriate consensus mechanisms. In India, the Land registration process is one of the tedious process and most often people are not aware of the entire rules to be followed during registration process. Also, more documents need to be verified and thus it takes delay in completing registration. In addition to this, the middlemen collect bribes to complete this process. Mistakes also may occur while processing land records. The aim of this work is to develop a land registry system using Blockchain with a detailed and user-friendly feature with high reliability and good interface. It mainly focuses to cover the rules and procedures stated by the Indian Government regarding land registration. It ensures the enhanced security and accuracy of records.
Investigating the impact of site management on distress in refugee sites using Fuzzy Cognitive Maps

Refugees and migrants (Persons of Concern, PoC) may reside in temporary sites. In the context of site management, protection refers also to preventing or mitigating mental health problems and psychosocial distress, as well as tensions, in order to ensure the well-being including psychosocial well-being of PoC. Besides overcrowding and problems in meeting basic needs, social factors could also affect the well-being and distress of PoC. This paper investigates the influence of site management response in diverse operational sectors on distress and negative emotions of PoC. Therefore, it also aims to assist in providing a tool for measuring the performance of humanitarian response in terms of PoC psychological and social costs. The proposed methodology is based on Fuzzy Cognitive Maps (FCM) and uses a psychological model of emotion in order to forecast emotions of PoC. The FCM concepts include site management indicators as well as past PoC stressors and the output concepts represent the forecasted emotions. The proposed methodology has been applied to a case study of PoC sites in Greece based on historical UNHCR data.
A Two-Phase Fashion Apparel Detection Method Based on YOLOv4

Object detection is one of the important technologies in the field of computer vision. In the area of fashion apparel, object detection technology has various applications, such as apparel recognition, apparel detection, fashion recommendation, and online search. The recognition task is difficult for a computer because fashion apparel images have different characteristics of clothing appearance and material. Currently, fast and accurate object detection is the most important goal in this field. In this study, we proposed a two-phase fashion apparel detection method named YOLOv4-TPD (YOLOv4 Two-Phase Detection), based on the YOLOv4 algorithm, to address this challenge. The target categories for model detection were divided into the jacket, top, pants, skirt, and bag. According to the definition of inductive transfer learning, the purpose was to transfer the knowledge from the source domain to the target domain that could improve the effect of tasks in the target domain. Therefore, we used the two-phase training method to implement the transfer learning. Finally, the experimental results showed that the mAP of our model was better than the original YOLOv4 model through the two-phase transfer learning. The proposed model has multiple potential applications, such as an automatic labeling system, style retrieval, and similarity detection.