Reviewerly/exHarmony huggingface dataset:
Deep Learning Based Program Generation From Requirements Text: Are We There Yet?

To release developers from time-consuming software development, many approaches have been proposed to generate source code automatically according to software requirements. With significant advances in deep learning and natural language processing, deep learning-based approaches are proposed to generate source code from natural language descriptions. The key insight is that given a large corpus of software requirements and their corresponding implementations, advanced deep learning techniques may learn how to translate software requirements into source code that fulfill such requirements. Although such approaches are reported to be highly accurate, they are evaluated on datasets that are rather small, lack of diversity, and significantly different from real-world software requirements. To this end, we build a large scale dataset that is composed of longer requirements as well as validated implementations. We evaluate the state-of-the-art approaches on this new dataset, and the results suggest that their performance on our dataset is significantly lower than that on existing datasets concerning the common metrics, i.e., BLEU. Evaluation results also suggest that the generated programs often contain syntactic and semantical errors, and none of them can pass even a single predefined test case. Further analysis reveals that the state-of-the-art approaches learn little from software requirements, and most of the successfully generated statements are popular statements in the training programs. Based on this finding, we propose a popularity-based approach that always generates the most popular statements in training programs regardless of the input (software requirements). Evaluation results suggest that none of the state-of-the-art approaches can outperform this simple statistics-based approach. As a conclusion, deep learning-based program generation requires significant improvement in the future, and our dataset may serve as a basis for future research in this direction.
Predicting bursting in a complete graph of mixed population through reservoir computing

We report our investigation and success story, to an extent, on the prediction of spiking and bursting dynamics in globally coupled networks, using echo state network/reservoir computing-based learning procedure. Two exemplary dynamical models, Josephson junctions and Hindmarsh-Rose neurons, are used to construct two separate networks and thereby illustrate the efficacy of our strategy. In the absence of coupling, the networks consist of mixed populations in which few nodes are oscillatory (self-sustained spiking) and the rest of the nodes maintain a quiescent state. When single-input data from one oscillatory node of a network (under stronger interactions between the nodes) is used for learning, the ESN is able to predict the key dynamical features (spiking and bursting) of the other nodes. In comparison, the machine performs with improved predictions if it is fed with two inputs: one from the oscillatory population and another from an excitable population. The machine's leaking parameter plays a crucial role, which can be tuned appropriately to enhance prediction. Furthermore, a cluster synchronization in the mixed population is confirmed from the machine-generated output signals. Our work is expected to be useful as a burst predictor.Received 14 March 2020Accepted 6 August 2020DOI:https://doi.org/10.1103/PhysRevResearch.2.033338Published by the American Physical Society under the terms of the Creative Commons Attribution 4.0 International license. Further distribution of this work must maintain attribution to the author(s) and the published article's title, journal citation, and DOI.Published by the American Physical SocietyPhysics Subject Headings (PhySH)Research AreasSpiking neuronsSynchronization transitionPhysical SystemsCoupled oscillatorsTechniquesMachine learningNonlinear DynamicsInterdisciplinary Physics
Multi-scale traffic sign detection model with attention

The current traffic sign detection technology is disturbed by factors such as illumination changes, weather, and camera angle, which makes it unsatisfactory for traffic sign detection. The traffic sign data set usually contains a large number of small objects, and the scale variance of the object is a huge challenge for traffic indication detection. In response to the above problems, a multi-scale traffic sign detection algorithm based on attention mechanism is proposed. The attention mechanism is composed of channel attention mechanism and spatial attention mechanism. By filtering the background information on redundant contradictions with channel attention mechanism in the network, the information on the network is more accurate, and the performance of the network to recognize the traffic signs is improved. Using spatial attention mechanism, the proposed method pays more attention to the object area in traffic recognition image and suppresses the non-object area or background areas. The model in this paper is validated on the Tsinghua-Tencent 100K data set, and the accuracy of the experiment reached a higher level compared to state-of-the-art approaches in traffic sign detection.
Attention guided for partial domain adaptation

How to effectively extract feature representations from unlabeled samples from the target domain is critical for unsupervised domain adaptation, specific for partial domain adaptation where source label space is a super-space of target label space, as it helps reduce large performance gap due to domain shift or domain bias. In this paper, a novel partial domain adaptation method named Multiple Self-Attention Networks (MSAN) based on adversarial learning is proposed. Unlike most existing partial domain adaptation methods which only focus on high-level features, MSAN focuses on effective high-level context features and low-level structural features from unlabeled target data with the help of labeled source data. Specifically, we present multiple self-attention network, a general approach to learning more fine-grained and transferable features in a manner of gradual feature enhancement so that domain shift can be relatively decreased to boost the model generalization power. Comprehensive experiments on Office-31 and Office-Home datasets demonstrate that the proposed approach significantly improves upon representation partial domain adaptation methods to yield state-of-the-art results for various partial transfer tasks.
Does the locomotion technique matter in an immersive virtual store environment? – Comparing motion-tracked walking and instant teleportation

Immersive virtual reality shopping environments may use a variety of locomotion techniques. Currently the most prominent are instant teleportation and motion-tracked walking. Motion-tracked walking more closely mimics natural shopping behaviour as it allows a shopper to physically walk through the virtual store; however, it requires a larger space and has design and cost implications. Instant teleportation is likely to be cheaper and to enable larger store formats; yet it is unknown whether the choice of instant teleportation results in different in-store behaviour by shoppers. Our research examines whether instant teleportation provides a feasible design alternative to motion-tracked walking by determining whether it does result in altered shopper behaviour. Using a split sample experiment in a purpose-built immersive VR convenience store, we compare shoppers’ emotional states (engagement, excitement and stress, measured through electroencephalography), purchase behaviour metrics, and store coverage (represented by heat maps) between motion-tracked walking and instant teleportation locomotion techniques. Results demonstrate that the absence of physical walking had no impact on emotional states or the investigated shopping outcomes. While instant teleportation led to some skipped shelf sections, there was no impact observed on the number of unplanned purchases. To the extent that the naturalism of immersive VR relies on proprioceptive engagement or embodied cognition, this appears to be adequately supported by upper body movements that do not require physical walking. These findings provide fresh insights for the design of Virtual Reality shopping environments for market research or future online-retailing platforms.
Robot exploration of indoor environments using incomplete and inaccurate prior knowledge

Exploration is a task in which autonomous mobile robots incrementally discover features of interest in initially unknown environments. We consider the problem of exploration for map building, in which a robot explores an indoor environment in order to build a metric map. Most of the current exploration strategies used to select the next best locations to visit ignore prior knowledge about the environments to explore that, in some practical cases, could be available. In this paper, we present an exploration strategy that evaluates the amount of new areas that can be perceived from a location according to a priori knowledge about the structure of the indoor environment being explored, like the floor plan or the contour of external walls. Although this knowledge can be incomplete and inaccurate (e.g., a floor plan typically does not represent furniture and objects and consequently may not fully mirror the structure of the real environment), we experimentally show, both in simulation and with real robots, that employing prior knowledge improves the exploration performance in a wide range of settings.
Real-time Bhutanese Sign Language digits recognition system using Convolutional Neural Network

The communication gap between the deaf and public is the concern for both parents and the government of Bhutan. The deaf school urges people to learn Bhutanese Sign Language (BSL) but learning Sign Language (SL) is difficult. This paper presents the BSL digits recognition system using the Convolutional Neural Network (CNN) and a first-ever BSL dataset which has 20,000 sign images of 10 static digits collected from different volunteers. Different SL models were evaluated and compared with the proposed CNN model. The proposed system has achieved 97.62% training accuracy. The system was also evaluated with precision, recall, and F1-score.
Learning ladder neural networks for semi-supervised node classification in social network

Graph convolutional networks (GCNs) and network embedding are the two main categories of popular methods for Semi-Supervised Node Classification (SSNC) in social network. However, the former is commonly oriented to attributed networks with efficient auxiliary information in nodes. The latter is usually not geared towards specific graph mining tasks. Therefore, these methods often perform poorly for specific tasks in non-attributed networks. To solve the above problems, in this paper, we propose a novel semi-supervised Node Classification method with Ladder Neural Networks named NCLNN for non-attributed network. We first preprocess the graph for capturing the structural information. Then we present and learn a deep ladder neural network for SSNC. Our trained ladder neural networks could combine supervised learning with unsupervised learning in deep neural networks via simultaneously minimizing the sum of supervised and unsupervised loss functions. Extensive experiments on three real-world network datasets demonstrate that the proposed NCLNN substantially outperforms the state-of-the-art methods on SSNC task.
Gait Parameter Estimation of Elderly People using 3D Human Pose Estimation in Early Detection of Dementia

Early detection of dementia is becoming increasingly important as it plays a crucial role in handling the patients and offering better treatment. Many of the recent studies concluded a tight relationship between dementia and gait disorders. For this purpose, identification of gait abnormalities is key factor. Novel technologies provide many options such as wearable and non-wearable approaches for analysis of gait. As the occurrence of dementia is more prominent in elderly people, wearable technology is considered out of scope for this work. The gait data of several elderly people over 80 years is acquired over certain intervals during the scope of the project. The elderly people are classified into three study groups namely cognitively healthy individuals (CHI), subjectively cognitively impaired persons (SCI) and possible mildly cognitively impaired persons due to inconclusive test results (pMCI) based on their cognitive status. The gait data is acquired using Kinect sensor. The acquired data consists of both RGB image sequences and depth data of the test persons. 3D human pose estimation is performed on this gait data and gait analysis is done. The transformations in the gait cycles are observed and the health condition of the individual is analyzed. From the analysis, the patterns in the gait abnormalities are correlated with the above-mentioned classification and are used in the detection of dementia in advance. The obtained results look promising and further analysis of gait parameters is under progress.
Machine Learning techniques for Prediction from various Breast Cancer Datasets

Cancer has become very common disease among Indians. Breast cancer occurs 14% of all cancers in women. One in every 28 women is getting affected by breast cancer [2]. Breast cancer detection and identification are done from gene expression and large datasets. Due to the increase in the generation of large datasets of medical data we can analyze specialized patterns that are hidden inside. To analyze the pattern from datasets machine learning techniques such as SVM, KNN, Decision tree are applied. In this paper, we tried to apply some Deep learning techniques to identify the patterns resides in a reoccurrence of breast cancer datasets, Mammogram datasets and to identify the patterns.
Prospective Immersive Human-Machine Interface for Future Vehicles: Multiple Zones Turn the Full Windscreen Into a Head-Up Display

The physical bottleneck of optical design and the complexity associated with the human visual system (HVS) limits the true potential of the head-up display (HUD) for use in vehicles. A full windscreen of visual information feedback is required for driving safety, and its implementation to enhance the driving experience is one of the most significant challenges. We present an immersive augmented reality (AR) HUD concept to support future vehicle design following a human-centric design approach. The limited field of view of contemporary optical solutions can be overcome by multiple display elements tiled to utilize images over the entire windscreen and so create an immersive experience. The design takes important human factors into account and improves the operator's driving experience. Furthermore, the images are “distributed,” meaning that the physical interface generates the images via multiple optical apertures/image sources and displays these images according to HVS requirements. These configurations are tested in a laboratory environment with a replica of a real car interior and a prototype vehicle installed with distributed multiple HUD units. The proposed concept of an immersive human-machine interface (HMI) can be further extended in various forms to other parts of the vehicle interior, including surfaces and free space, which we envisage will take place in future car designs.
Weakly-Supervised Self-Training for Breast Cancer Localization

The use of deep learning methods has dramatically increased the state-of-the-art performance in image object localization. However, commonly used supervised learning methods require large training datasets with pixel-level or bounding box annotations. Obtaining such fine-grained annotations is extremely costly, especially in the medical imaging domain. In this work, we propose a novel weakly supervised method for breast cancer localization. The essential advantage of our approach is that the model only requires image-level labels and uses a self-training strategy to refine the predicted localization in a step-wise manner. We evaluated our approach on a large, clinically relevant mammogram dataset. The results show that our model significantly improves performance compared to other methods trained similarly.
High throughput assessment of biomarkers in tissue microarrays using artificial intelligence: PTEN loss as a proof-of-principle in multi-center prostate cancer cohorts

Phosphatase and tensin homolog (PTEN) loss is associated with adverse outcomes in prostate cancer and has clinical potential as a prognostic biomarker. The objective of this work was to develop an artificial intelligence (AI) system for automated detection and localization of PTEN loss on immunohistochemically (IHC) stained sections. PTEN loss was assessed using IHC in two prostate tissue microarrays (TMA) (internal cohort, n = 272 and external cohort, n = 129 patients). TMA cores were visually scored for PTEN loss by pathologists and, if present, spatially annotated. Cores from each patient within the internal TMA cohort were split into 90% cross-validation (N = 2048) and 10% hold-out testing (N = 224) sets. ResNet-101 architecture was used to train core-based classification using a multi-resolution ensemble approach (×5, ×10, and ×20). For spatial annotations, single resolution pixel-based classification was trained from patches extracted at ×20 resolution, interpolated to ×40 resolution, and applied in a sliding-window fashion. A final AI-based prediction model was created from combining multi-resolution and pixel-based models. Performance was evaluated in 428 cores of external cohort. From both cohorts, a total of 2700 cores were studied, with a frequency of PTEN loss of 14.5% in internal (180/1239) and external 13.5% (43/319) cancer cores. The final AI-based prediction of PTEN status demonstrated 98.1% accuracy (95.0% sensitivity, 98.4% specificity; median dice score = 0.811) in internal cohort cross-validation set and 99.1% accuracy (100% sensitivity, 99.0% specificity; median dice score = 0.804) in internal cohort test set. Overall core-based classification in the external cohort was significantly improved in the external cohort (area under the curve = 0.964, 90.6% sensitivity, 95.7% specificity) when further trained (fine-tuned) using 15% of cohort data (19/124 patients). These results demonstrate a robust and fully automated method for detection and localization of PTEN loss in prostate cancer tissue samples. AI-based algorithms have potential to streamline sample assessment in research and clinical laboratories.
Traceability and Detection of Counterfeit Medicines in Pharmaceutical Supply Chain Using Blockchain-Based Architectures

New age advancement seen in the ICT has enthralled human enthusiasm and excitement to the next level of getting informed and educated about the things they interact with. We want to capture the history of the things from their point of origin. But it has been that history can be altered and misinterpreted for self means. Blockchain is one such revolutionary architecture that ensures history is preserved by maintaining concoction of consortium or consensus over the network between all nodes for historical data and ensures immutability. Different blockchain architecture gives different types of flexibility and usability. Traceability is must for accountability for ensuring history is preserved. Supply chain for pharmaceutical drugs is one such problem where customers need to know the exact timeline of the medicine they are about to intake. Counterfeit drugs are big issue in the market and it is bigger problem than we realize. Fake drugs are entering into the mainstream supplies in the country as traceability is never assured from any other technology. This chapter solves this problem by jotting down each transaction into an immutable distributed ledger as blockchain architecture which cannot be altered or changed. Whenever there is a change of hand of medicines, two things are being recorded, first represent the current owner, and latter remain the timestamp when this took place. Ethereum and Hyperledger Fabric along with Hyperledger Composer are the two building blocks of the Blockchain platform used in this chapter. This chapter shows the comparative difference between the two under the use case of pharmaceutical supply chain and defines what the adoptive approach should be while choosing between the two platforms. This chapter shares some pseudo codes for the two platforms showing how to interact with these platforms. At the end, some challenges faced are discussed along with future scope and direction to research areas with some additional resources listed as references.
Image steganography based on a new hybrid chaos map and discrete transforms

With the development of Internet networks, the importance of information security is becoming more and more significant. Therefore, it is considered necessary to study the existing methods in order to develop them for creating more suitable approaches. Due to the fact that, in the most communication networks image is one of the most popular and widely used communication formats, the method presented in this paper is the image steganography method in transform domain. In order to enhance the image transfer, first of all, we have introduced a hybrid chaotic map based on the sine and Logistic-Tent systems by applying radical function and a variety of trigonometric functions. Furthermore, we have represented some new kinds of shift operators for shuffling pixel positions of the cover image in order to improve resistance of the algorithm against different kinds of attacks. In the proposed algorithms, we have also applied discrete framelet and wavelet transforms on secret and cover images, respectively. To show the ability of the proposed algorithm, the results have been evaluated.
CNN with Pose Segmentation for Suspicious Object Detection in MMW Security Images

Millimeter-wave (MMW) imaging scanners can see through clothing to form a three-dimensional holographic image of the human body and suspicious objects, providing a harmless alternative for non-contacting searches in security check. Suspicious object detection in MMW images is challenging, since most of them are small, reflection-weak, shape, and reflection-diverse. Conventional detectors with artificial neural networks, like convolution neural network (CNN), usually take the problem of finding suspicious objects as an object recognition task, yielding difficulties in developing large-amount and complete sample sets of objects. In this paper, a new algorithm is developed using the human pose segmentation followed by the deep CNN detection. The algorithm is emphasized to learn the similarity with humans' body clutter applied to training corresponding CNNs after the image segmentation base of the pose estimation. Moreover, the suspicious object recognition in the MMW image is converted to a binary classification task. Instead of recognizing all sorts of suspicious objects, the CNN detector determines whether the body part images present the abnormal patterns containing suspicious objects. The proposed algorithm that is based on CNN with the pose segmentation has concise configuration, but optimal performance in the suspicious object detection. Extensive experiments confirm the effectiveness and superiority of the proposal.