Reviewerly/exHarmony huggingface dataset:
GAHNE: Graph-Aggregated Heterogeneous Network Embedding

The real-world networks often compose of different types of nodes and edges with rich semantics, widely known as heterogeneous information network (HIN). Heterogeneous network embedding aims to embed nodes into low-dimensional vectors which capture rich intrinsic information of heterogeneous networks. However, existing models either depend on manually designing meta-paths, ignore mutual effects between different semantics, or omit some aspects of information from global networks. To address these limitations, we propose a novel Graph-Aggregated Heterogeneous Network Embedding (GAHNE), which is designed to extract the semantics of HINs as comprehensively as possible to improve the results of downstream tasks based on graph convolutional neural networks. In GAHNE model, we develop several mechanisms that can aggregate semantic representations from different single-type sub-networks as well as fuse the global information into final embeddings. Extensive experiments on three real-world HIN datasets show that our proposed model consistently outperforms the existing state-of-the-art methods.
Detecting Changes and Avoiding Catastrophic Forgetting in Dynamic Partially Observable Environments

The ability of an agent to detect changes in an environment is key to successful adaptation. This ability involves at least two phases: learning a model of an environment, and detecting that a change is likely to have occurred when this model is no longer accurate. This task is particularly challenging in partially observable environments, such as those modeled with partially observable Markov decision processes (POMDPs). Some predictive learners are able to infer the state from observations and thus perform better with partial observability. Predictive state representations (PSRs) and neural networks are two such tools that can be trained to predict the probabilities of future observations. However, most such existing methods focus primarily on static problems in which only one environment is learned. In this paper, we propose an algorithm that uses statistical tests to estimate the probability of different predictive models to fit the current environment. We exploit the underlying probability distributions of predictive models to provide a fast and explainable method to assess and justify the model's beliefs about the current environment. Crucially, by doing so, the method can label incoming data as fitting different models, and thus can continuously train separate models in different environments. This new method is shown to prevent catastrophic forgetting when new environments, or tasks, are encountered. The method can also be of use when AI-informed decisions require justifications because its beliefs are based on statistical evidence from observations. We empirically demonstrate the benefit of the novel method with simulations in a set of POMDP environments.
Securing Robot-assisted Minimally Invasive Surgery through Perception Complementarities

Laparoscopic surgery presents practical benefits over traditional open surgery, including reduced risk of infection, discomfort and recovery time for patients. Introducing robot systems into surgical tasks provides additional enhancements, including improved precision, remote operation, and an intelligent software layer capable of filtering aberrant motion and scaling surgical maneuvers. However, the software interface in telesurgery also lends itself to potential adversarial cyber attacks. Such attacks can negatively effect both surgeon motion commands and sensory information relayed to the operator. To combat cyber attacks on the latter, one method to enhance surgeon feedback through multiple sensory pathways is to incorporate reliable, complementary forms of information across different sensory modes. Built-in partial redundancies or inferences between perceptual channels, or perception complementarities, can be used both to detect and recover from compromised operator feedback. In surgery, haptic sensations are extremely useful for surgeons to prevent undue and unwanted tissue damage from excessive tool-tissue force. Direct force sensing is not yet deployable due to sterilization requirements of the operating room. Instead, combinations of other sensing methods may be relied upon, such as noncontact model-based force estimation. This paper presents the design of a surgical simulator software that can be used for vision-based non-contact force sensing to inform the perception complementarity of vision and force feedback for telesurgery. A brief user study is conducted to verify the efficacy of graphical force feedback from vision-based force estimation, and suggests that vision may effectively complement direct force sensing.
Boundary Enhanced Network for Improved Semantic Segmentation

Deep convolutional neural networks (DCNNs) have become the state-of-art methods for semantic segmentation of very high-resolution remote sensing imagery nowadays. However, the downsampling and upsampling stage in encoder and decoder components have caused inaccurate localization of objects, especially at boundaries. To mitigate this spatial inaccuracy, we propose boundary enhanced network for improved semantic segmentation based on the state-of-art DeepLabV3+ network to reinforce the object class boundary information by jointly learning semantic segmentation and edge detection. Preliminary results demonstrate that our method improves the state-of-the-art network of DeepLabV3+ in overall accuracy, mean F1 score, and IoU score at a high level on the two famous ISPRS 2D Semantic Labeling datasets, indicating it is an effective method for semantic segmentation of very high-resolution remote sensing imagery.
Semantic Segmentation of UAV Videos based on Temporal Smoothness in Conditional Random Fields

Video semantic segmentation is increasingly becoming a vital factor in many Unmanned Aerial Vehicle (UAV) drone-based applications such as surveillance, scene understanding etc. However, the accuracy of video semantic segmentation systems are greatly dependent on temporal consistent labelling. In this regard, a new approach for semantic segmentation of UAV videos is proposed by utilizing U-Net and Conditional Random Field. This algorithm incorporates temporal information to ensure temporal consistency in labelling. This work shows that Conditional Random Field algorithm along with temporal cues reduces the false positives and increases the accuracy of semantic segmentation. Moreover, the proposed method is quantitatively evaluated on ManipalUAVid dataset and achieved a mIoU of 0.88 which is significantly greater than traditional image based segmentation method such as U-Net.
ML-based Telegram bot for real estate price prediction

Abstract Telegram chatbots are one of the main sections in Telegram applications that get more attention and provide huge business opportunities for companies nowadays. Also, Machine learning algorithms have been used in numerous fields and industries and one of the main growing markets is real estate. It is already helping real estate agents to respond more quickly to clients’ questions and develop landing pages that fit customers’ needs. However, one of the main time-consuming stages for a real estate agent is the process of answering questions about price per location and knowing which locations clients are interested in. In order to solve these problems, we have built a telegram chatbot to answer customers of any real estate agent with the price of a real estate property based on their current Geolocation. This bot can predict the price of a real estate property using room numbers, Geolocation, and surface area in square meters. These are the inputs for machine learning algorithms to give an approximate price. The bot can predict based on the source of the data-set, for example, we have collected our data from classified ads website for real estate in Amman, Jordan. We have used python and web scraping library for data extraction, cleaning, and transformation. As a result, by using Geolocation we have increased the model accuracy to 1.3X and our bot can be replicated in any markets based on the dataset. We believe our work can be taken to different markets and make real estate agent’s job easy and more profitable by changing leads to potential customers thought answering their questions.
A MAC Protocol Identification Approach Based on Convolutional Neural Network

The medium access control (MAC) protocol identification is of great application value in cognitive radio. In order to realize the MAC protocol identification with high accuracy and avoid manual feature extraction, we convert the sampled data into the form of spectrogram. Then, a graphical scheme which combines the convolutional neural network (CNN) and the spectrogram is proposed. The simulation includes CNN method and support vector machine (SVM) method. It is suggested that the CNN method has better identification performance.
Security augmentation grounded on Fresnel and Arnold transforms using hybrid chaotic structured phase mask

A novel asymmetric double image encryption technique is introduced to protect the algorithm from the interlopers by using hybrid chaotic structured phase masks in Arnold and Fresnel transforms. Mostly random phase masks are used routinely in many algorithms which are not strong against many attacks. So, taking in consideration replacing of random phase masks with the new hybrid chaotic structured phase masks takes place in the algorithm. With the assistance of Fresnel zone plates, radial Hilbert mask and logistic map functions, the chaotic structured phase mask is accumulated and hybrid chaotic structured phase mask provides extra security to system. Arnold transform helps in pixel shuffling, and with the pixel reorganization, it also helps in image reaping and bordering. The propagation distance of the Fresnel transform and the constraints of the Arnold transform serve as keys for encryption and decryption. To check the sturdiness of hybrid chaotic structured phase mask, simulations have been done which are carried out by the support of MATLAB R2018a (9.4.0.813654).
Simple Approximations for Fast and Secure Deep Learning on Genomic Data

State-of-the-art frameworks for privacy-preserving artificial neural networks often rely on secret sharing to protect sensitive data. Unfortunately, operating on secret shared data complicates a number of non-linear functions that are central to deep learning, such as batch normalization and rectified linear units (ReLUs). We offer simple procedures for approximating these non-linear operations. The approximations we propose significantly reduce the training runtime of a privacy-preserving convolutional neural network (CNN) that we designed to diagnose breast cancer from secret shared gene expression profiles. In just over five minutes of training, our approximation-based privacy-preserving CNN achieves an average test accuracy of 96%. When we apply an exact garbled circuit solution for the ReLU function, we find that the privacy-preserving model requires days of computation to achieve the same level of accuracy. The dramatic improvement in training runtime yielded by our ReLU approximation may prove useful for other medical applications of privacy-preserving neural networks.
Wheelchair Accessibility Evaluation with Deep Learning using a Sensing Acceleration Data-Based Spectrogram

In this study, we generate spectrogram color images from wheelchair acceleration data. Using the spectrogram images, we propose evaluation models for wheelchair vibration classification using deep learning. To evaluate the stress level for the wheelchair on roads, we administered a questionnaire to wheelchair users and classified road accessibility. Using the obtained classification model, we performed experiments with a wheelchair and evaluated accessibility at points on experiment roads.
A Co-Attention Model with Sequential Behaviors and Side Information for Session- based Recommendation

Session-based recommendation aims to recommend the next item a user might be interested in given limited session information, e.g., only clicks are available. Existing researches usually use the RNN-based method or combine user long-term and current session interests to learn certain user preferences. However, due to the uncertainty of user behaviors and the limited behavior information, these methods may lose information of the relevant behavioral features and introduce some noise of unrelated behaviors. For online platforms, such as knowledge base platforms, these items are not isolated but connected with each other. We can use the correlation between items to capture the potential long-distance interests of the user. Therefore, in this paper, we propose a co-attention model with sequential behaviors and side information to obtain a complete representation of user's preferences. For obtaining relevant side information outside the session, we aggregate the corresponding entities and relations of each item to get the representation of the neighborhood information. Finally, extensive experiments are carried out on two real datasets, and the experimental results demonstrate the validity of our model. In particular, the proposed model performs well in cold start scenarios and is well interpreted for the recommended results.
WITHDRAWN: Enhanced age prediction and gender classification (EAP-GC) framework using regression and SVM techniques

In present scenario, biometric based applications are developing rapidly and plays vital role in various fields like military, crime investigations, VISA providing systems, etc. Based on that, the design of intelligent systems for human age and gender classification using face images has gained the interest of researchers and several studies has been done towards that. With that concern, this paper concentrates on developing a combined framework for age and gender classification in accurate and effective manner. Hence, an Enhanced Age Prediction and Gender Classification (EAP-GC) framework has been proposed by incorporating the Supervised Learning Model (SLM) that considers both the shape and texture changes over the learning process. Moreover, Partial Least-Square (PLS) technique is used for dimensionality reduction, thereby, increasing the prediction accuracy. For age determination, poly regression model is incorporated and the gender classification is accomplished using multi- Support Vector Machine (SVM) classification technique. The experimentation has been made with the widely available benchmark datasets, FG-NET and HQFaces, in MATLAB tool. The obtained results are then compared with some traditional classification models for providing evidence for the efficiency of the proposed mechanism. It is shown from the results that the EAP-GC model surpasses the other compared techniques in accurate age prediction and gender classification.
Improving resolution of images using Generative Adversarial Networks

Even with all the achievements in precision and speed of various image super-resolution models, such as better and more accurate Convolutional Neural Networks (CNN), the results have not been satisfactory. The high-resolution images produced are generally missing the finer and frequent texture details. The majority of the models in this area focus on such objective functions which minimize the Mean Square Error (MSE). Although, this produces images with better Peak Signal to Noise Ratio (PSNR) such images are perceptually unsatisfying and lack the fidelity and high-frequency details when seen at a high-resolution. Generative Adversarial Networks (GAN), a deep learningmodel, can be usedfor such problems. In this article, the working of the GAN is shown and described about the production satisfying images with decent PSNR score as well as good Perceptual Index (P1) when compared to other models. In contrast to the existing Super Resolution GAN model, various modifications have been introduced to improve the quality of images, like replacing batch normalization layer with weight normalization layer, modified the dense residual block, taking features for comparison before they are fed in activation layer, using the concept of a relativistic discriminator instead of a normal discriminator that is used in vanilla GAN and finally, using Mean Absolute Error in the model.
Cloud Resource Provisioning and Bottleneck Eliminating for Meshed Web Systems

Most of existing resource provisioning methods are designed for traditional Web applications with linear structures. However, Web systems with the meshed topology are becoming widespread. Meshed connections among different tiers make Virtual Machine (VM) provisioning and bottleneck elimination complex. In this paper, a Jackson network based Proactive and Reactive VM auto-scaling Method (JPRM) is proposed. In JPRM, request transition behaviors among tiers are modeled as a finite-state Markov stochastic process. A transition probability matrix is studied on-line to predict resource requirements based on M/M/N queuing models as proactive control. For reactive provisioning, the final increased request rate of each tier is determined based on stable state checking and Jackson equilibrium equation solving to eliminate bottleneck tiers and avoid bottleneck shifting. The JPRM is evaluated in a simulation environment established using CloudSim. Experimental results show that JPRM avoids bottleneck shifting with reasonable additional VM rental costs compared with existing methods.
Video Anomaly Detection Using Open Data Filter and Domain Adaptation

Video anomaly detection is a very challenging task because of the rarity, openness, and the definition of the anomalies. Researchers pay more attention to the characteristics of anomalies and have proposed a variety of anomaly detection models. However, most existing methods only use normal events to construct anomaly detection models and ignore the diversity and openness of normal events. Actually, because real-world video data often have an open-ended distribution, some normal patterns hardly ever appeared in the training data. In addition, analogous to human experience in identifying anomalies, rare abnormal events can play a certain role in the detection of similar abnormal events in the dataset. Therefore, assuming that a small number of abnormal events are known, we propose a novel supervised anomaly detection model which explicitly detects open normal events and open abnormal events in the dataset and treats open data and seen data with different classifiers. First, we use the training video to train an imbalanced classifier as the seen data classifier. Then, during the testing phase, an open data filter module isused to divide the test data into seen data and open data. Finally, we directly use the seen data classifier to generate anomaly scores for the seen test data. For the open test data, we adopt a domain adaptation method to reduce the distribution difference between it and the training data and train a new classifier to score for it. Extensive experimental results prove the effectiveness of our model.
Visibility Level Estimation in Winter CCTV Images Based on Decision Level Fusion Using Logistic Regression

This paper proposes a method for estimating the degree of visibility (visibility level) in winter closed-circuit television (CCTV) images by decision level fusion based on logistic regression (LR). The proposed method classifies on the basis of two support vector machines (SVMs) classifiers and fuses the classification results by utilizing LRbased late fusion. In the proposed method, the SVMs which tentatively estimate visibility are constructed based on each CCTV image feature that represents the contrast of images and neuron values of the neural network. Also, the proposed method evaluates the visibility based on LR model trained by using SVM outputs. The effectiveness of our method is verified from experiments by utilizing actual CCTV images.
Text classification based on graph convolutional network with attention

Abstract The paper proposes a text classification method based on the graph convolutional network, the traditional text classification problem can be transformed into the node classification problem in the graph, and then proposes a attention structure to increase the weight of certain words and phrases. This paper verifies that the above structure has a positive effect on text classification accuracy. In particular, the structure can handle irregular data, such as citation network. We obtain very competitive results on 5 commonly used text classification datasets and achieve state-of-the-art results on 4 datasets, which include one dataset of citation network. Experiments show that combination of both structures can significantly reduce the classification error.
German Sign Language Translation using 3D Hand Pose Estimation and Deep Learning

Sign language is the primary medium of communication for the majority of the world's population suffering from disabling hearing loss that creates a barrier between the hearing and the hearing-impaired people. In this paper, sign language translation is undertaken for German Sign Language (GSL) characters from a single image by leveraging the technique of 3D object detection. We make use of a three-network architecture that performs segmentation, keypoint localization, and elevation from a two-dimensional plane to the three-dimensional space, from a single RGB image containing the signed gesture. Thirty gestures have been used and the best results were obtained using a combination of pose representation coordinates, joint angles, and pool layer features of AlexNet for classification. The system gives a character error rate of 0.29, a reduction of error rate by 12.12% when compared to the state-of-the-art approach.
Usage of a BART algorithm and cognitive services to research collaboration platforms

Abstract This article examines the possibility of extending the collaborative filtering method in shared information systems. The need to build recommendations dictates the volume and the intensity of the emergence of more and more new information. In the scientific world, the possibilities of building recommendations are widely explored, and this work presents the authors’ vision of a possible direction of development, which is based on a preliminary iteration with the highlighting of keywords.
Efficient list-decoding with constant alphabet and list sizes

We present an explicit and efficient algebraic construction of capacity-achieving list decodable codes with both constant alphabet and constant list sizes. More specifically, for any R ∈ (0,1) and є>0, we give an algebraic construction of an infinite family of error-correcting codes of rate R, over an alphabet of size (1/є)O(1/є2), that can be list decoded from a (1−R−є)-fraction of errors with list size at most exp(poly(1/є)). Moreover, the codes can be encoded in time poly(1/є, n), the output list is contained in a linear subspace of dimension at most poly(1/є), and a basis for this subspace can be found in time poly(1/є, n). Thus, both encoding and list decoding can be performed in fully polynomial-time poly(1/є, n), except for pruning the subspace and outputting the final list which takes time exp(poly(1/є)) · poly(n). In contrast, prior explicit and efficient constructions of capacity-achieving list decodable codes either required a much higher complexity in terms of 1/є (and were additionally much less structured), or had super-constant alphabet or list sizes.