Reviewerly/exHarmony huggingface dataset:
Masked Graph Auto-Encoder Constrained Graph Pooling

The node drop pooling is a significant type of graph pooling that is required for learning graph-level representations. However, existing node drop pooling models still suffer from the information loss problem, impairing their effectiveness in graph classification. To mitigate the detrimental effect of the information loss, we propose a novel and flexible technique called Masked Graph Auto-encoder constrained Pooling (MGAP), which enables vanilla node drop pooling methods to retain sufficient effective graph information from both node-attribute and network-topology perspectives. Specifically, MGAP reconstructs the original node attributes of the graph using a graph convolutional network and the node degree of the graph (i.e., structural information) using a feedforward neural network with exponential neurons from the pooled (masked) graphs generated by the vanilla node drop pooling models. Notably, MGAP is a plug-and-play technique that can be directly adopted in the current node drop pooling methods. To evaluate the effectiveness of MGAP, we conduct extensive experiments on eleven real-world datasets by applying MGAP to three commonly-used methods, i.e., TopKPool, SAGPool, and GSAPool. The experimental results reveal that MGAP has the capacity to consistently improve the performance of all the three node drop pooling models in the graph classification task.
The effect of luminance on depth perception in augmented reality guided laparoscopic surgery

Depth perception is a major issue in surgical Augmented Reality (AR) with limited research conducted in this scientific area. This study establishes a relationship between luminance and depth perception. This can be used to improve visualization design for AR overlay in laparoscopic surgery, providing surgeons a more accurate perception of the anatomy intraoperatively. Two experiments were conducted to determine this relationship. First, an online study with 59 participants from the general public, and second, an in-person study with ten surgeons as participants. We developed two open-source software tools utilizing SciKit-Surgery libraries to enable these studies and any future research. Our findings demonstrate that the higher the relative luminance, the closer a structure is perceived to the operating camera. Furthermore, the higher the luminance contrast between the two structures, the higher the depth distance perceived. The quantitative results from both experiments are in agreement, indicating that online recruitment of the general public can be helpful in similar studies. An observation made by the surgeons from the in-person study was that the light source used in laparoscopic surgery plays a role in depth perception. This is due to its varying positioning and brightness which could affect the perception of the overlaid AR. We found that luminance directly correlates with depth perception for both surgeons and the general public, regardless of other depth cues. Future research may focus on comparing different colors used in surgical AR and using a mock Operating Room (OR) with varying light sources and positions.
An Adaptive Algorithm for Polysemous Words in Natural Language Processing

In this research article the supervised adaptive approach for word sense disambiguation is discussed. The most critical and identified problem of natural language understanding is the lexical ambiguity. Lexical ambiguity is introduced by the polysemy words. This paper describes different state-of-the-art techniques to detect ambiguous words from the ambiguous sentence. The word embedding is an important phase of word sense disambiguation and which is succeeded by ambiguous word detection and processed by classification. The different embedding techniques are discussed here and the uniqueness of adaptive word embedding has been proved. There are two standard available datasets OMSTI and WordNet are used for the data processing. This article elaborates the newly generated dataset Adaptive-Lex for disambiguation. There are two important challenges in word sense disambiguation: identifying polysemy words without context information and constructing word embedding for polysemy words with highest sense values. These challenges are addressed with a complex network approach and adaptive word embedding technique. The performance evaluation classifiers are supervised neural network approaches such CNN and DNN.
Time Varied Self-Reliance Aerial Ground Traffic Monitoring System With Pre-Recognition Collision Avoidance

In this paper, we introduce a time varied self-reliance aerial ground traffic monitoring system which provides pre-recognition collision avoidance among mobile robots and smart UAVs for virtual emotion security. Then, with ILP (Integer Linear Programming), we make a formal definition of the problem whose objective is to minimize a total spent time by smart UAVs (Unmanned Aerial Vehicles) and mobile robots without conflicts on condition that the demanded number of self-reliance security barriers are formed. To solve the defined problem, we develop two approaches, time-differentiated pre-stop movement and approximated equal segments movement. Then, those schemes are implemented through expanded experiments and are evaluated based on numerical results.
Benefits and Challenges of Isomorphism in Single-page Applications: Case Study and Review of Gray Literature

An isomorphic web application shares code between the server and the client by cleverly combining suitable parts of server-rendered applications and single-page applications. In this article, we study the benefits and challenges of isomorphism in single-page applications in terms of a gray literature review and a case study. The case study was conducted as a developer interview, where developers familiar with isomorphic web applications were interviewed. The results of both studies are then compared and the key findings are compared together. The results show that isomorphism in single-page applications brings benefits to both the developers and the end-users. Isomorphism in single-page applications is challenging to implement and has some downsides, but they mostly affect developers. Implementing isomorphism enables sharing code between the server and the client, but it increases the complexity of the application. Framework and library compatibility are issues that must be addressed by the developers.
Made to Measure: A Workshop on Human-Centred metrics for information seeking

Measurement is a core tool for improving information interaction. What is measured and how it is measured influences how we perceive the efficacy of retrieval and other interactions. While some measures have become ubiquitous, such as precision and recall, there are many other facets of information interaction we either immature, weakly accepted or only used occasionally. This workshop seeks to develop our metrics for information interaction and retrieval, finding novel measures, or innovative adaptations of existing measures, to create a better experimental toolset to improve our understanding of information interaction, and help develop more effective systems and interactions that support it.
NosWalker: A Decoupled Architecture for Out-of-Core Random Walk Processing

Out-of-core random walk system has recently attracted a lot of attention as an economical way to run billions of walkers over large graphs. However, existing out-of-core random walk systems are all built upon general out-of-core graph processing frameworks, and hence do not take advantage of the unique properties of random walk applications. Different from traditional graph analysis algorithms, the sampling process of random walk can be decoupled from the processing of the walkers. It enables the system to reserve only pre-sample results in memory, which are typically much smaller than the entire edge set. Moreover, in random walk, it is not the number of walkers but the number of steps moved per second that dominates the overall performance. Thus, with independent walkers, there is no need to process all the walkers simultaneously.
A Generative Model Based Chatbot Using Recurrent Neural Networks

Conversational modeling is an important task in natural language understanding and machine intelligence. It makes sense for natural language to become the primary way in which we interact with devices because that is how humans communicate with each other. Thus, the possibility of having conversations with machines would make our interaction much more smooth and human-like. The natural language techniques need to be evolved to match the level of power and sophistication that users expect from virtual assistants. Although previous approaches exist, they are often restricted to specific domains and require handcrafted rules. The obvious problem lies in their inability to answer questions for which the rules were not written. To overcome this problem, we build a generative model neural conversation system using a deep LSTM Sequence to Sequence model with an attention mechanism. Our main emphasis is to build a generative model chatbot in open domain which can have a meaningful conversation with humans. We consider Reddit conversation datasets to train the model and applied turing test on the proposed model. The proposed chatbot model is compared with Cleverbot and the results are presented.
Bearing Fault Diagnosis Using Extreme Learning Machine Based on Artificial Gorilla Troops Optimizer

Bearing diagnosis is important to ensure smooth machinery operation and safety. Machine learning methods have been used widely in bearing diagnosis study, and one of the recent methods used is an extreme learning machine (ELM). The ELM method offers ease of implementation, rapid learning rate, and better generalization performance. However, the ELM method may lead to inaccurate diagnosis due to inappropriate value selection for neuron number, input weight, and hidden layer bias. Therefore, this paper proposed a new bearing diagnosis using ELM-based gorilla troops optimizer (GTO) method, named as GTO-ELM. The GTO method was used to select an optimized parameter for the ELM method. Two sets of bearing datasets from experimental work and online database were used in this study for evaluation on the proposed method. Both datasets have four different type of operation condition which are normal (baseline), inner race fault, outer race fault, and ball fault. Based on diagnosis result, the proposed GTO-ELM was able to surpass whale optimization algorithm (WOA)–ELM in term of convergence speed and conventional ELM methods in term of diagnosis performance with almost 10–12% better performance.
Federal learning edge network based sentiment analysis combating global COVID-19

As one of the important research topics in the field of natural language processing, sentiment analysis aims to analyze web data related to COVID-19, e.g., supporting China government agencies combating COVID-19. There are popular sentiment analysis models based on deep learning techniques, but their performance is limited by the size and distribution of the dataset. In this study, we propose a model based on a federal learning framework with Bert and multi-scale convolutional neural network (Fed_BERT_MSCNN), which contains a Bidirectional Encoder Representations from Transformer modules and a multi-scale convolution layer. The federal learning framework contains a central server and local deep learning machines that train local datasets. Parameter communications were processed through edge networks. The weighted average of each participant’s model parameters was communicated in the edge network for final utilization. The proposed federal network not only solves the problem of insufficient data, but also ensures the data privacy of the social platform during the training process and improve the communication efficiency. In the experiment, we used datasets of six social platforms, and used accuracy and F1-score as evaluation criteria to conduct comparative studies. The performance of the proposed Fed_BERT_MSCNN model was generally superior than the existing models in the literature.
Removing the Field Size Loss from Duc et al.’s Conjectured Bound for Masked Encodings

At Eurocrypt 2015, Duc et al. conjectured that the success rate of a side-channel attack targeting an intermediate computation encoded in a linear secret-sharing, a.k.a. masking with $$d+1$$ shares, could be inferred by measuring the mutual information between the leakage and each share separately. This way, security bounds can be derived without having to mount the complete attack. So far, the best proven bounds for masked encodings were nearly tight with the conjecture, up to a constant factor overhead equal to the field size, which may still give loose security guarantees compared to actual attacks. In this paper, we improve upon the state-of-the-art bounds by removing the field size loss, in the cases of Boolean masking and arithmetic masking modulo a power of two. As an example, when masking in the AES field, our new bound outperforms the former ones by a factor $$256$$ . Moreover, we provide theoretical hints that similar results could hold for masking in other fields as well.
Pedestrian Detection and Avoidance at Night Using Multiple Sensors and Machine Learning

We develop a machine learning-based pedestrian detection and alert system that can operate both during the day and at night using a visual camera, an infrared camera, and a radar sensor. The visible camera is used to detect pedestrians during the daytime while the infrared camera during the nighttime. Whereas the radar sensor is utilized to detect the presence of pedestrians including their range and directions of motion. We have developed and conducted actual experimentation of the system in a vehicle. We achieved an average accuracy of 98% based on our proposed multi-sensor data analysis using a deep learning algorithm that classifies a pedestrian’s presence during the day and at night and alerts the driver in a real-time monitoring system.
3D network with channel excitation and knowledge distillation for action recognition

Modern action recognition techniques frequently employ two networks: the spatial stream, which accepts input from RGB frames, and the temporal stream, which accepts input from optical flow. Recent researches use 3D convolutional neural networks that employ spatiotemporal filters on both streams. Although mixing flow with RGB enhances performance, correct optical flow computation is expensive and adds delay to action recognition. In this study, we present a method for training a 3D CNN using RGB frames that replicates the motion stream and, as a result, does not require flow calculation during testing. To begin, in contrast to the SE block, we suggest a channel excitation module (CE module). Experiments have shown that the CE module can improve the feature extraction capabilities of a 3D network and that the effect is superior to the SE block. Second, for action recognition training, we adopt a linear mix of loss based on knowledge distillation and standard cross-entropy loss to effectively leverage appearance and motion information. The Intensified Motion RGB Stream is the stream trained with this combined loss (IMRS). IMRS surpasses RGB or Flow as a single stream; for example, HMDB51 achieves 73.5% accuracy, while RGB and Flow streams score 65.6% and 69.1% accuracy, respectively. Extensive experiments confirm the effectiveness of our proposed method. The comparison with other models proves that our model has good competitiveness in behavior recognition.
Python script fuzzy time series Markov chain model for forecasting the number of diseases cocoa plant in Bendungan district

Cocoa is a plantation commodity whose role is essential for the economy, so it is necessary to be aware of its health to maximize production. Cocoa plant disease data is a time series data because it is observed continuously. One of the time series forecasting models is the Fuzzy Time Series Markov Chain (FTS-MC), a combination of the Fuzzy Time Series (FTS) and Markov Chain models. The model uses the principle of fuzzy logic by transferring FTS data to fuzzy logic and using the obtained fuzzy logic groups to derive the Markov chain transition matrix. In this research, a Python script of the FTS-MC model was built in the Google Colaboratory to forecast the number of cocoa plant diseases in Bendungan District to simplify and speed up the data processing. Python was used in this research because of its easy-to-use, flexible, and open-source syntax. In building Python scripts, libraries and functions are needed by utilizing loop processes and if-else statements. Based on the processing results, forecasting with the FTS-MC model using Python only takes less than 1 minute.
Fighting crime through crime analysis: The experience of using innovative technologies in European Union countries

The aim of this article was to study innovative technologies and tools in the context of the introduction of crime analysis tools used in the countries of the European Union EU, to the Ukrainian practice. The research involved the following methods: statistical analysis, induction and deduction, classification and comparison. The study described the legally enshrined powers of the Ukrainian police with respect to information and analytical activities. Current trends in the number of criminal offenses committed in Ukraine under separate articles were determined, and these trends were compared before and after the adoption of the Law of Ukraine “On National Police”. The introduction of the latest means of criminal analysis into Ukrainian practice was also studied. The conclusions established that the technologies used by EU countries can increase the effectiveness of law enforcement agencies in Ukraine, provided that certain adaptation measures are implemented. Moreover, the results obtained during the research can be used by law enforcement agencies to improve and optimize crime analysis.
LinSets.zip: Compressing Linear Set Diagrams

Linear diagrams are used to visualize set systems by depicting set memberships as horizontal line segments in a matrix, where each set is represented as a row and each element as a column. Each such line segment of a set is shown in a contiguous horizontal range of cells of the matrix indicating that the corresponding elements in the columns belong to the set. As each set occupies its own row in the matrix, the total height of the resulting visualization is as large as the number of sets in the instance. Such a linear diagram can be visually sparse and intersecting sets containing the same element might be represented by distant rows. To alleviate such undesirable effects, we present LinSets.zip, a new approach that achieves a more space-efficient representation of linear diagrams. First, we minimize the total number of gaps in the horizontal segments by reordering columns, a criterion that has been shown to increase readability in linear diagrams. The main difference of LinSets.zip to linear diagrams is that multiple non-intersecting sets can be positioned in the same row of the matrix. Furthermore, we present several different rendering variations for a matrix-based representation that utilize the proposed row compression. We implemented the different steps of our approach in a visualization pipeline using integer-linear programming, and suitable heuristics aiming at sufficiently fast computations in practice. We conducted both a quantitative evaluation and a small-scale user experiment to compare the effects of compressing linear diagrams.
AN EFFICIENT HIDING METHOD FOR PRIVACY PRESERVING UTILITY MINING

Due to the rapid evolution of data saved in electronic form, data mining technologies have become critical and indispensable in looking for nontrivial, implicit, hidden, and possibly beneficial information in enormous volumes of data. High Utility Pattern Mining (HUPM), among the most intriguing data mining techniques, is broadly leveraged to analyze business interactions in market data based on the notion of economic utilities. These economic utilities can be used to examine the factors influencing a customer's purchasing behavior or to come up with new tailored selling and promotion tactics. This in turn has made utility-driven techniques an essential operation and vital activity for many data analysts since they can lead to proper decision-making processes. Nevertheless, such techniques can also lead to major threats regarding privacy and information security if they were misused. Privacy-Preserving Utility Mining (PPUM), also known as High Utility Pattern Hiding (HUPH), has recently emerged to mitigate the security and privacy issues that could happen in the utility framework. In this paper, we propose a heuristic PPUM method, named HUP-Hiding, to protect the results when mining sensitive data using a utility mining algorithm. The proposed method employs a dataset projection mechanism and a new victim item selection technique to efficiently perform the sanitization process. Experiments were performed to verify the reliability of the suggested algorithm. Our experimental results on different datasets confirm that HUP-Hiding has reasonable performance and fewer side effects compared to existing approaches.
The Adoption of a COVID-19 Contact-Tracing App: Cluster Analysis

Background During the COVID-19 pandemic, there was limited adoption of contact-tracing apps (CTAs). Adoption was particularly low among vulnerable people (eg, people with a low socioeconomic position or of older age), while this part of the population tends to have lesser access to information and communication technology and is more vulnerable to the COVID-19 virus. Objective This study aims to understand the cause of this lagged adoption of CTAs in order to facilitate adoption and find indications to make public health apps more accessible and reduce health disparities. Methods Because several psychosocial variables were found to be predictive of CTA adoption, data from the Dutch CTA CoronaMelder (CM) were analyzed using cluster analysis. We examined whether subgroups could be formed based on 6 psychosocial perceptions (ie, trust in the government, beliefs about personal data, social norms, perceived personal and societal benefits, risk perceptions, and self-efficacy) of (non)users concerning CM in order to examine how these clusters differ from each other and what factors are predictive of the intention to use a CTA and the adoption of a CTA. The intention to use and the adoption of CM were examined based on longitudinal data consisting of 2 time frames in October/November 2020 (N=1900) and December 2020 (N=1594). The clusters were described by demographics, intention, and adoption accordingly. Moreover, we examined whether the clusters and the variables that were found to influence the adoption of CTAs, such as health literacy, were predictive of the intention to use and the adoption of the CM app. Results The final 5-cluster solution based on the data of wave 1 contained significantly different clusters. In wave 1, respondents in the clusters with positive perceptions (ie, beneficial psychosocial variables for adoption of a CTA) about the CM app were older (P&lt;.001), had a higher education level (P&lt;.001), and had higher intention (P&lt;.001) and adoption (P&lt;.001) rates than those in the clusters with negative perceptions. In wave 2, the intention to use and adoption were predicted by the clusters. The intention to use CM in wave 2 was also predicted using the adoption measured in wave 1 (P&lt;.001, β=–2.904). Adoption in wave 2 was predicted by age (P=.022, exp(B)=1.171), the intention to use in wave 1 (P&lt;.001, exp(B)=1.770), and adoption in wave 1 (P&lt;.001, exp(B)=0.043). Conclusions The 5 clusters, as well as age and previous behavior, were predictive of the intention to use and the adoption of the CM app. Through the distinguishable clusters, insight was gained into the profiles of CM (non)intenders and (non)adopters. Trial Registration OSF Registries osf.io/cq742; https://osf.io/cq742