Reviewerly/exHarmony huggingface dataset:
Fairness-aware Maximal Clique Enumeration

Cohesive sub graph mining on attributed graphs is a fundamental problem in graph data analysis. Existing cohesive sub graph mining algorithms on attributed graphs do not consider the fairness of attributes in the subgraph. In this paper, we for the first time introduce fairness into the widely-used clique model to mine fairness-aware cohesive subgraphs. In particular, we propose two novel fairness-aware maximal clique models on attributed graphs, called weak fair clique and strong fair clique respectively. To enumerate all weak fair cliques, we develop an efficient backtracking algorithm called WFCEnum equipped with a novel colorful k-core based pruning technique. We also propose an efficient enumeration algorithm called SFCEnum to find all strong fair cliques based on a new attribute-alternatively-selection search technique. To further improve the efficiency, we also present several non-trivial ordering techniques for both weak and strong fair clique enumeration. The results of extensive experiments on four real-world graphs demonstrate the efficiency and effectiveness of the proposed algorithms.
Highlights on some Robots and Drones for Hazardous Environments

Robots and drones are useful devices where operations are expensive, tedious, or dangerous for humans. Recently the decrease of cost of robotic devices has extended the range of applications of robotic systems particularly in hazardous environments. The cooperation between drones and robots can give further possibilities and opens new challenges. In this work some environments and scenarios where robots and drones and their cooperation could be effective, are shortly reviewed and commented. In particular, four different situations are described with experimental results: the exploration of active volcanic environments, the search of mines for humanitarian demining, the radioactive waste inspections and the assessment on terrains with high landslide risk. Finally, some conclusions and perspectives for future applications conclude the paper.
MSIPA: Multi-Scale Interval Pattern-Aware Network for ICU Transfer Prediction

Accurate prediction of patients’ ICU transfer events is of great significance for improving ICU treatment efficiency. ICU transition prediction task based on Electronic Health Records (EHR) is a temporal mining task like many other health informatics mining tasks. In the EHR-based temporal mining task, existing approaches are usually unable to mine and exploit patterns used to improve model performance. This article proposes a network based on Interval Pattern-Aware, Multi-Scale Interval Pattern-Aware (MSIPA) network. MSIPA mines different interval patterns in temporal EHR data according to the short, medium, and long intervals. MSIPA utilizes the Scaled Dot-Product Attention mechanism to query the contexts corresponding to the three scale patterns. Furthermore, Transformer will use all three types of contextual information simultaneously for ICU transfer prediction. Extensive experiments on real-world data demonstrate that an MSIPA network outperforms state-of-the-art methods.
Sentence transition matrix: An efficient approach that preserves sentence semantics

Sentence embedding is an influential research topic in natural language processing (NLP). Generation of sentence vectors that reflect the intrinsic meaning of sentences is crucial for improving performance in various NLP tasks. Therefore, numerous supervised and unsupervised sentence-representation approaches have been proposed since the advent of the distributed representation of words. These approaches have been evaluated on semantic textual similarity (STS) tasks designed to measure the degree of semantic information preservation; neural network-based supervised embedding models typically deliver state-of-the-art performance. However, these models have limitations in that they have numerous learnable parameters and thus require large amounts of specific types of labeled training data. Pretrained language model-based approaches, which have become a predominant trend in the NLP field, alleviate this issue to some extent; however, it is still necessary to collect sufficient labeled data for the fine-tuning process is still necessary. Herein, we propose an efficient approach that learns a transition matrix tuning a sentence embedding vector to capture the latent semantic meaning. Our proposed method has two practical advantages: (1) it can be applied to any sentence embedding method, and (2) it can deliver robust performance in STS tasks with only a few training examples.
Massive-scale complicated human action recognition: Theory and applications

Recognizing various human actions in video is a challenging task, which is also one of the key tasks in computer vision. It has received extensive attention from AI researchers Bakker et al. (2003), Bruderlin and Williams (1995), Cardle et al. (2003), Carlsson (1996, 1999), Clausen and Kurth (2004). It has important applications in human behavior analysis, artificial intelligence, and video surveillance. Compared with still image classification, the time component of video provides important clues for recognition, so multiple human actions can be recognized based on motion information. In addition, video provides natural data enhancement for individual images. For motion recognition from videos, appearance and temporal dynamics are two key and complementary cues. In this work, we formulate a human motion recognition framework based on 2D spatial feature fusion of Kinect bone data. This method utilizes the human body structure and spatial geometry to represent human body structure and extract features. Meanwhile, it combines the active action and auxiliary action features of two spatial dimensions by layering, and leverages the pervasively used support vector machine and hidden Markov model to classify human body structure. Noticeably, it is difficult to extract its information due to the limitations of background clutter, viewpoint change, scale change, different lighting conditions and camera movement. In this way, while learning the classification information of human behavioral categories, designing effective representations is the key to deal with these challenges. In this work, a method for human motion recognition in video based on ResNext network is proposed. Based on our proposed ResNeXt network, using the data of RGB and optical flow, we can extract more appearance characteristics and temporal features of human actions, so as to better achieve the classification of actions. The utilization of video time segmentation method can capture the long range of time in video, in order to make better use of the longer range of time information in video. Comprehensive experimental results have shown that the proposed method improves the performance of UCF101 and HMDB51 motion recognition data sets to a certain extent.
Evaluation Design for Learning with Mixed Reality in Mining Education Based on a Literature Review

Mixed Reality technologies are on the rise in the educational sector. However, research shows that there is still a lack in knowledge concerning the evaluation of these technologies. In this paper we present a research on current practices in evaluation for Mixed Reality. For this purpose, we selected 94 publications from between 2015 and 2021 and reduced them to 45 which included formal evaluation processes. We then adapted a classification scheme by Duenser et al. [5] and categorized these papers according to their evaluation methods. We present our overall findings and explain some examples more detailed. The results are then compared to previous work outside and within the MiReBooks project and applied on the didactical framework. This allows us to illustrate the development of this sector over the last years and it helps us to enhance our own evaluation approaches. First results also show that there is a rise in evaluation approaches recently and that the overall goals for these processes did not change much from 2008.
Assessing Explainability in Reinforcement Learning

Reinforcement Learning performs well in many different application domains and is starting to receive greater authority and trust from its users. But most people are unfamiliar with how AIs make their decisions and many of them feel anxious about AI decision-making. A result of this is that AI methods suffer from trust issues and this hinders the full-scale adoption of them. In this paper we determine what the main application domains of Reinforcement Learning are, and to what extent research in those domains has explored explainability. This paper reviews examples of the most active application domains for Reinforcement Learning and suggest some guidelines to assess the importance of explainability for these applications. We present some key factors that should be included in evaluating these applications and show how these work with the examples found. By using these assessment criteria to evaluate the explainability needs for Reinforcement Learning, the research field can be guided to increasing transparency and trust through explanations.
Arabic Handwriting Word Recognition Based on Convolutional Recurrent Neural Network

The success of any words-characters recognition system depends on board parameters such as the language (Arabic, Latin, Indi …), the document type (writing or typing), based or free-segmentation, pretreatment, features extraction and classification approaches. Within these fields, Building a robust and viable recognition system for Arabic handwritten has always been a challenging task since a long time. In this study, we propose an end-to-end system based on deep Convolutional Recurrent Neural Network CNN/RNN; we trained our system on IFN/ENIT extended database in order to improve our results.
Fuzzy Control of an Intelligent Electric Wheelchair Using an EMOTIV EPOC Headset

This chapter involves the design and the implementation of an intelligent control method of an electric wheelchair for several disables persons using technologies derived from mobile robotics. The main objective of this work is to improve the performance of the wheelchair which is controlled by the brain without requirements of any physical feedback from the user. A detection system and obstacle avoidance based on the ultrasonic sensors with the integration of fuzzy logic controller is established. Intelligent control, via brain signal based on EMOTIV EPOC headset which can control the wheelchair with expressive, affective and cognitive data using a graphical interface, is achieved.
Government Data Sharing based on Blockchain

With the development of smart city, the cooperation between government sectors is becoming more and more frequent, breaking the information island and big data sharing has become an inevitable trend. The traditional way of government data sharing is mainly based on one unified data center, which has high construction cost and maintenance cost, and fails to fully consider the sectoral interests, authority, security and traceability, so there are still many barriers in government data sharing. Taking advantage of the features of blockchain technology, such as distributed ledger, smart contract, data trust and traceability, this paper proposes an application model of government data sharing based on blockchain, which uses a unified data sharing model and autonomous authority management to provide efficient and safe bus service for distributed government big data sharing.
Character Input Using the Motion Sensor on a Smartwatch

The screen of a smartwatch is small, which creates operational issues such as erroneous input and screen occupancy of the keyboard. Therefore, an input method, that does not depend on screen tapping but instead utilizes taps outside the touch screen and a wrist operation, is proposed herein. Five types of tapping movements were used in this study, which were aimed outside the screen and involved wrist movements. With respect to the tapping movements outside the screen, the tapped parts were placed in different locations, and tapping was performed toward the center of the screen. The wrist movement comprised a series of movements ranging from pronation to supination. These movements were assumed to be the character input. As five types of tap movements were used, five vowels of flat kana can be input in a single movement. Ten selections can be made with one input, depending on the tapping movements outside the screen and the presence or absence of wrist movement. By doing this twice, 100 selections can be made, and QWERTY array keyboard input is possible. Accelerometers and angular velocity sensors, built into the smartwatch, were used to determine the movements. In the experiment, the subjects performed each movement and data were acquired. As a result of the analysis, the off-screen tap movement was determined by the acceleration in the x-axis and y-axis. It was suggested that it is possible to discriminate whether the wrist movement is based on the angular velocity of the x-axis. A real-time discrimination experiment based on these revealed that the average discrimination rate was 81.7%. The proposed motions can determine several choices, and the efficacy of the proposed method as a viable input method for smart watches was clearly demonstrated.
FLM-2A: Towards Automated HCI Modeling of Android Applications Based on a Modified Version of the Keystroke Level Model

Keystroke-Level Model (KLM) is an established HCI model for predicting users’ time on task. KLM was originally developed for desktop systems, but it has been extended for different interaction contexts. Fingerstroke-Level Model (FLM) is such an extension for touch-sensitive interactions with direct finger movements. This paper presents a novel tool, named FLM for Android Apps (FLM-2A), that supports automated FLM modeling of tasks performed in Android apps. The tool aims to support design and evaluation of Android apps in an effective and efficient manner. A study investigated the accuracy of FLM-2A predictions by comparing them to participants’ interaction times for three custom-built Android apps. Results showed that the error rate for the FLM-2A predictions with Fitts’ Law enabled were 0.3% and 2.8% for the first and second apps respectively. However, the error rate for the third custom-built Android app was 40.0%, indicating that additional steps are required to finetune the FLM-2A predictions.
A Real-Time Effective Fusion-Based Image Defogging Architecture on FPGA

Foggy weather reduces the visibility of photographed objects, causing image distortion and decreasing overall image quality. Many approaches (e.g., image restoration, image enhancement, and fusion-based methods) have been proposed to work out the problem. However, most of these defogging algorithms are facing challenges such as algorithm complexity or real-time processing requirements. To simplify the defogging process, we propose a fusional defogging algorithm on the linear transmission of gray single-channel. This method combines gray single-channel linear transform with high-boost filtering according to different proportions. To enhance the visibility of the defogging image more effectively, we convert the RGB channel into a gray-scale single channel without decreasing the defogging results. After gray-scale fusion, the data in the gray-scale domain should be linearly transmitted. With the increasing real-time requirements for clear images, we also propose an efficient real-time FPGA defogging architecture. The architecture optimizes the data path of the guided filtering to speed up the defogging speed and save area and resources. Because the pixel reading order of mean and square value calculations are identical, the shift register in the box filter after the average and the computation of the square values is separated from the box filter and put on the input terminal for sharing, saving the storage area. What’s more, using LUTs instead of the multiplier can decrease the time delays of the square value calculation module and increase efficiency. Experimental results show that the linear transmission can save 66.7% of the total time. The architecture we proposed can defog efficiently and accurately, meeting the real-time defogging requirements on 1920 × 1080 image size.
Automatic Classification of Gastrointestinal Diseases Based on Machine Learning Techniques

Gastrointestinal (GI) diseases are common diseases which affect the GI tract. Treatment of GI diseases is quite expensive, complicated and challenging. Machine learning techniques could considerably reduce the cost of examination processes and improve the speed and quality of diagnosis. Therefore, this paper presents an approach to classify GI diseases using machine learning techniques. The proposed method consists of 4 steps; image pre-processing, feature extraction, feature reduction, and classification. First the images are resized. Afterwards, the discrete wavelets transform (DWT) and histograms of oriented gradient (HOG) are used as feature extraction methods. Next, the feature data were reduced by utilizing principle component analysis (PCA) and single value decomposition (SVD). Finally, the reduced features are used to train different classifiers. Results show that classification with complex decision tree achieved an accuracy of 99.8% using SVD method. The results were compared with recent related work. The comparison showed that the proposed approach is capable of diagnosis and classification of GI diseases compared to other work. Thus, it can be used to help medical experiments in investigation procedures.
Improving Recurrent Neural Network Responsiveness to Acute Clinical Events

Predictive models in acute care settings must immediately recognize precipitous changes in a patient's status when presented with data reflecting such changes. Recurrent neural networks (RNN) have become popular for clinical decision support models but exhibit a delayed response to acute events. New information must propagate through the RNN's cell state before the total impact is reflected in the model's predictions. Input data perseveration is a method to train more responsive RNN-based models. Input data is replicated k times during training and deployment. Each replication propagates through the cell state and output of the RNN, but only the output at the final replication is maintained and broadcast as the prediction for evaluation. De-identified Electronic Medical Records (EMR) of 12, 826 patients admitted to a tertiary care pediatric academic center between 01/2009-02/2019 were analyzed. A baseline Long Short-Term Memory (LSTM) model ( k=1), four LSTMs with increasing amounts of input data perseveration ( k=2 to k=5), and an LSTM with an attention mechanism were trained to predict ICU-mortality. Performance of models was compared using Area Under the Receiver Operating Characteristic Curve (AUROC) after increasing periods of observation from one to 12 hours. The average variation of the change in predicted mortality immediately following defined acute events measured responsiveness. The AUROC gains due to input perseveration were larger at the earlier times of prediction ( ≤ 6 hours), increasing at the first hour from 0.77 with no input data perseveration to 0.83 when k=5. An LSTM with k=5 was $2-3$ times more responsive to acute events than a baseline LSTM.
The Impact of Using Different Color Spaces in Histological Image Classification using Convolutional Neural Networks

Classification is an important aspect of medical image analysis. Nowadays, Convolutional Neural Networks (CNNs) are extensively used in the field of medical image classification. There are several kinds of research on medical image classification combining different CNN architectures and data sets. When using color image data sets, most of those research works use RGB as the standard color space to train and test the models. While RGB is a standard color space to represent images on multimedia devices, RGB might not be the best color space to train CNN models for medical image classification applications. We implement an AlexNet CNN to classify colon tissue images to detect tumors. We perform this task using several color spaces, such as RGB, XYZ, CIELAB, HSV, and YCbCr. We analyze the results and indicate which color spaces give the best accuracy in performing this medical image classification task.
Addressing Soft Error and Security Threats in DNNs Using Learning Driven Algorithmic Checks

The reliability of Deep Neural Networks (DNNs) is of great concern due to their widespread use in safety-critical applications. Prior research has focused on adaptation of algorithm based fault tolerance schemes for error detection in the dot product (linear) computations of DNNs. In this research, we show that compact machine-learned algorithmic checks inspired by prior work on linear checksums but adapted to the overall nonlinear nature of DNN computations can be used to detect both soft errors and image-triggered trojans in real-time. Experiments indicate that the method incurs low computation overhead (4%-20%), while achieving high coverage (up to 90% for soft errors and 99% for image-triggered attacks).
The V-Localizer for Stereotactic Guidance

Image-guidance for frame-based stereotaxis is facilitated by incorporating three to four N-localizers or Sturm-Pastyr localizers into a stereotactic frame.An extant frame that incorporates only two N-localizers violates the fundamental principle of the N-localizer, which requires three non-colinear points to define a plane in three-dimensional space.Hence, this two N-localizer configuration is susceptible to error.The present article proposes the V-localizer that comprises multiple diagonal bars to provide four or more noncolinear points to minimize error.
Estimation of Solar Radiation at Farasan Island with Two-Step ANN Concepts

The availability of information about solar radiation characteristics, particularly solar radiation predictions, is important for efficiently designing solar energy systems. Solar radiation information available in Saudi Arabia with this study, a new two-step artificial neural network (ANN), is proposed to estimate both the daily average and hourly solar radiation at Farasan Island, KSA (Kingdom of Saudi Arabia). The input parameters for the daily average solar radiation estimation are the location and time required, along with five selected monthly meteorological parameters that predict for the subsequent month. The selected meteorological parameters are temperatures, relative humidity, and precipitation. These three selected meteorological parameters (temperatures, relative humidity, and precipitation) are used them for measurement. The accuracy of the proposed method is comparable to previous studies with an average R2 of 96.80% for the daily average solar radiation estimate and 94.33% for the hourly solar radiation estimate.
Fast and Unsupervised Neural Architecture Evolution for Visual Representation Learning

Unsupervised visual representation learning is one of the hottest topics in computer vision, yet performance still lags behind compared with the best supervised learning methods. At the same time, neural architecture search (NAS) has produced state-of-the-art results on various visual tasks. It is a natural idea to explore NAS as a way to improve unsupervised representation learning, yet it remains largely unexplored. In this paper, we propose a Fast and Unsupervised Neural Architecture Evolution (FaUNAE) method to evolve an existing architecture, manually constructed or the result of NAS on a small dataset, to a new architecture that can operate on a larger dataset. This partial optimization can utilize prior knowledge to reduce search cost and improve search efficiency. The evolution is self-supervised where the contrast loss is used as the evaluation metric in a student-teacher framework. By eliminating the inferior or least promising operations, the evolutionary process is greatly accelerated. Experimental results show that we achieve state-of-the-art performance for downstream applications, such as object recognition, object detection, and instance segmentation.