Reviewerly/exHarmony huggingface dataset:
Entropy Based Machine Learning Models for Software Bug Severity Assessment in Cross Project Context

There can be noise and uncertainty in the bug reports data as the bugs are reported by a heterogeneous group of users working across different countries. Bug description is an essential attribute that helps to predict other bug attributes, such as severity, priority, and time fixes. We need to consider the noise and confusion present in the text of the bug report, as it can impact the output of different machine learning techniques. Shannon entropy has been used in this paper to calculate summary uncertainty about the bug. Bug severity attribute tells about the type of impact the bug has on the functionality of the software. Correct bug severity estimation allows scheduling and repair bugs and hence help in resource and effort utilization. To predict the severity of the bug we need software project historical data to train the classifier. These training data are not always available in particular for new software projects. The solution which is called cross project prediction is to use the training data from other projects. Using bug priority, summary weight and summary entropy, we have proposed cross project bug severity assessment models. Results for proposed summary entropy based approach for bug severity prediction in cross project context show improved performance of the Accuracy and F-measure up to 70.23% and 93.72% respectively across all the machine learning techniques over existing work.
Centering a beam of light to the axis of rotation of a planar object

We detail an experimentally simple approach for centering a beam of light to the axis of a rotating surface. This technique can be understood as a rotating analog to knife-edge profilometry, a common experimental technique wherein the intensity (or power) of various masked portions of a beam is used to ascertain the transverse intensity profile of the beam. Instead of collecting the light transmitted through a mask, we give the surface a variable reflectivity (such as with a strip of retro-reflective tape) and sample the light scattered from the surface as it rotates. We co-align the transverse position (not the tilt) of the axis of rotation and the beam centroid by minimizing the modulation amplitude of this scattered light. In a controlled experiment, we compare the centroid found using this approach to the centroid found using the canonical knife-edge approach in two directions. We find our results to be accurate to within the uncertainty of the benchmark measurement, ±0.03 mm (±2.9% of the beam waist). Using simulations that mimic the experiments, we estimate that the uncertainty of the technique is much smaller than that of the benchmark measurement, ±0.01 mm (±1% of the beam waist), limited here by the size of the components used in these experiments. We expect this centering technique to find applications in experimental and industrial fabrication and processing settings where alignment involving rotating surfaces is critical.
Supervisory control of building heating system with insulation changes using three architectures of neural networks

This paper presents a new strategy of supervisory control applied to heating room system that is able to adapt to different operating conditions, in order to reduce the energy consumption and guaranteed a good thermal comfort for the occupants. The aim is to design a multi-model and multi-controller supervisor by using neural networks with a view to overcome the limitations of using a single controller and to deal with nonlinearities, parameters uncertainties and the time constant of the heating system. The proposed methodology is based on three model/controller pairs, a monitoring signal generator and a switching logic. Three architectures of neural network architectures, namely multilayer perceptron neural networks, radial basis function and memory neuron networks are used to evaluate this control technique and to design the different controller/model pairs which correspond to the same work office and a specific isolation state. A comparative study is associated to this work in order to recognize the best neural network in terms of performance and simulation time. The results obtained for three scenarios addressed herein show the importance and the effectiveness of this method.
Interacting with Medical Volume Data in Projective Augmented Reality

Medical volume data is usually explored on monoscopic monitors. Displaying this data in three-dimensional space facilitates the development of mental maps and the identification of anatomical structures and their spatial relations. Using augmented reality (AR) may further enhance these effects by spatially aligning the volume data with the patient. However, conventional interaction methods, e.g. mouse and keyboard, may not be applicable in this environment. Appropriate interaction techniques are needed to naturally and intuitively manipulate the image data. To this end, a user study comparing four gestural interaction techniques with respect to both clipping and windowing tasks was conducted. Image data was directly displayed on a phantom using stereoscopic projective AR and direct volume visualization. Participants were able to complete both tasks with all interaction techniques with respectively similar clipping accuracy and windowing efficiency. However, results suggest advantages of gestures based on motion-sensitive devices in terms of reduced task completion time and less subjective workload. This work presents an important first step towards a surgical AR visualization system enabling intuitive exploration of volume data. Yet, more research is required to assess the interaction techniques’ applicability for intraoperative use.
Transfer Learning with Joint Optimization for Label-Efficient Medical Image Anomaly Detection

Many medical imaging applications require robust capabilities for automated image anomaly detection. Supervised deep learning approaches can be employed for such tasks, but poses large data collection and annotation burdens. To address this challenge, recent works have proposed advanced unsupervised, semi-supervised or transfer learning based deep learning methods for label-efficient image anomaly detection. However, these methods often require extensive hyperparameter tuning to achieve good performance, and have yet to be demonstrated in data-scarce domain centric applications with nuanced normal-vs-anomaly distinctions. Here, we propose a practical label-efficient anomaly detection method that employs fine-tuning of pre-trained model based on a small target domain dataset. Our approach employs a joint optimization framework to enhance discriminative power for anomaly detection performance. In evaluations on two benchmark medical image datasets, we demonstrate (a) strong performance gains over state-of-the-art baselines and (b) increased label efficiency over standard fine-tuning approaches. Importantly, our approach reduces the need for large annotated datasets, requires minimal hyperparameter tuning, and shows stronger performance boost for more challenging anomalies (Supplement: http://s000.tinyupload.com/?file_id=24916959421870989415).
Multi-Scale Explainable Feature Learning for Pathological Image Analysis Using Convolutional Neural Networks

The use of computer-assisted diagnosis (CAD) systems for pathological image analysis constitutes an important research topic. Such systems should be accurate, and their decisions should be explainable to ensure reliability. In this paper, we present an explainable diagnosis method based on convolutional neural networks (CNNs). This method allows us to interpret the basis of the decisions made by the CNN from two perspectives, namely statistics and visualization. For the statistical explanation, the method constructs dictionaries of representative pathological features over multiple scales in training data. It performs diagnoses based on the occurrence and importance of items in the dictionaries to rationalize its decisions. We introduce a vector quantization scheme to the CNN to enable it to construct the feature dictionary. For the visual interpretation, the method provides images of learned features in the dictionary by decoding them from a high-dimensional feature space to a pathological image space. The experimental results showed that the proposed network learned pathological features, which contributed to the diagnosis, and the method yielded approximately an area under the receiver operating curve (AUC) of 0.89 for detecting atypical tissues in pathological images of a uterine cervix by using these features.
Task Scheduling in Cloud Infrastructure using Optimization Technique Genetic Algorithm

In cloud infrastructure, job scheduling is the main critical issue to be solved. Effective and efficient task scheduling algorithm needed to met users requirement and resolve the complexity in cloud environment, Data of resources are shared among several cloud users. Task scheduling comes under NP-complete problem to apply appropriate scheduling policy is major concern. Many researchers consider this problem with fixed length of time which cannot be applicable in dynamic world computing. Our research aim is to consider the number of instruction execution of processor and Grain_size of work of piece. Through the simulation experiment the proposed algorithm helps to improve the efficiency & effectiveness of scheduling as well as to minimize the finished time in (ms).
Semi-Supervised Multimodality Learning With Graph Convolutional Neural Networks For Disease Diagnosis

There is a trend that digitalized clinical data increases dramatically every year.Part of data is multi-modal with imaging and non-imaging data such as phenotypic and genetic information.Though the success of CNNs has empowered a wide range of applications in learning from the imaging data, incorporating both the imaging and non-imaging data complementarily to improve the diagnostic quality is still challenging.To tackle this challenge, we propose a novel graph-convolutional model which is based on the proposed concept of edge adapter for learning an adaptive population graph from a multi-modal database.The edge adapter can be jointly optimized with the proposed graph convolutional neural network for semisupervised node classification.Experimental results on two challenging multimodal medical databases demonstrate the potential of our method in learning from multi-modal data for disease diagnosis.
Hierarchical Embedding for Code Search in Software Q&amp;A Sites

In recent years, code search techniques on software Q&A sites have become increasingly attractive due to the need for software development. Most of the existing work treats code snippets as text fragments, ignoring the effect of the structured information (i.e. sequential information) of the code. Meanwhile, much of the existing work does not take into account the interactive between code snippets and queries. In this paper, we propose a novel deep neural network named HECS <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> (Hierarchical embedding for code search) to solve the problems mentioned above. Our method divides the embedding process of code and query into two hierarchies, that is, the potential information is captured by two modules (the Intra-language encoding module and the Cross-language encoding module). In particular, our approach uses special LSTM (Long Short-Term Memory) variants, which is ON-LSTM (ordered neurons LSTM) to capture the keyword order structure of the code. The Intra-language encoding module is implemented by the LSTM variant and the Cross-language encoding module is an interactive information calculation module implemented by the attention mechanism. In this way, the similarity between the query and the corresponding code snippets in the vector space could be better captured. HECS can understand the difference between positive and negative samples more accurately. We empirically evaluate HECS, using a large scale codebase collected from StackOverflow. The experimental results show that our approach achieves state-of-the-art performance.
Geographical information system of fire incidents data monitoring

Abstract This geographic information system is built as a tool to monitor fire incidents data, taken from the reports of the fire department. The Purpose of this study is to monitoring fire incidents data, so that it can facilitate firefighters to monitor areas that have high occurrence of fires. Therefore, they can prevent fires from occurring. The information system is expected to have benefit in decreasing the rate of fire incidents, mostly by socializing to the publics the prevention of fires in areas where it often occurs. This research was conducted by using the Unified Software Development Process methodology; starting from the analysis model stage, design model, deployment model, implementation model, and testing model. The design model used in this research is Unified Modelling Language which is tested by using the black box method. The result of this study is the implementation of geographic information system that can monitor fire incidents data through the location of the incident. Moreover, this system can improve the performance of firefighters in anticipating and preventing the incidents.
Readiness Model of e-Learning Implementation from Teacher and Student Side at School in The Pramuka Island of Seribu Islands – DKI Jakarta

The relationship between the world of education and the industrial revolution 4.0 is closely related, where the world of education is required to follow the development of information systems and technology and utilize information and communication technology as a facility in the learning process so that it becomes more effective and efficient. For the Thousand Islands area, educational facilities are quite adequate, but the problem is that if you want to continue your education at the upper secondary level, students must stay on Pramuka Island, and incur additional costs. With the development of information systems and technology, making the industrial era 4.0 grow rapidly, so that and bring changes to the education sector with e-learning terminology that can provide solutions for regions in Indonesia that are still constrained in problems of access and distance so it is necessary to know the level of teacher readiness and students in schools in the Pramuka Island environment in the Thousand Islands in utilizing electronic learning. Quantitative research approaches are used and data collection uses a questionnaire with a likert scale with a population of all teachers and all school students who are on Pramuka Island, Thousand Islands. The research method uses the factor analysis method and the regression analysis method is used to build the model. The results showed the existence of ease of use e-learning application systems, rapid response from information technology support, and e-learning application systems that are easy to do if there is a change in learning material.
Enhancing Question Answering over Knowledge Base Using Dynamical Relation Reasoning

The task of Knowledge Base Question Answering (KBQA) is to provide a convenient way for the human to more efficiently and easily answer natural language questions using the substantial and valuable knowledge in the KB. Predicate generation is the most important sub-task of KBQA, which aims to generate the predicate paths from head entity to tail entity. Existing models mostly employ a seq2seq method to handle this task. However, the seq2seq method essentially is a classification model, which needs to know the number of the categories in advance. Meanwhile, KBs often are incompleteness and questions are always unbounded, which would cause the problem that the predicates of the questions would be beyond predefined categories. Obviously, the seq2seq model cannot handle this problem. In this paper, to solve the problem above, we explore to build an scoring module with strong genelization to score the predicates not in predefined categories, and also to improve the performance of the seq2seq method. To bridge the gap, we carefully design a reasoning module to score the predicates through reasonably employing the attention mechanism with memory ability. Simultaneously, in order to improve the generalization of the reasoning module, we try to use multi-task learning to enrich the represent of the reasoning module based on the idea of transfer learning. Massive experiments are conducted on two popular benchmark datasets - SimpleQuestion(SimQ) and WebQuestion(WebQ). The experimental results demonstrate that the proposed relation reasoning framework outperforms the state-of-the-art methods.
An AutoTriage B-CoC model in digital forensic investigation

With the high technologies are wildly adopted in illicit activities, the high volume and complexity of digital evidence make the collection task at the crime scene a great challenge. Triage is a well-known solution to give a quick review and prioritize the data regarding the admissibility of digital evidence. However, conducting triage at the crime scene may lead to evidence contamination due to limited time, space and human resources. And these common vital mistakes are fatal to the prosecution. In order to facilitate the effectiveness of the on-scene criminal investigation, we propose an AutoTriage B-CoC model to support automatic triage collecting and blockchain uploading. The superior accuracy and completeness of the digital evidence can be achieved without human interfering. The experimental results show that the on-scene examiner can manage the preservation and collection of digital evidence by typing two key values, CaseID and EvidenceID. We expect the detailed design of operations regarding to four phases, i.e. Triage, Documentation, Blockchain and Report, can provide important guidance for further practical applications.
Generative Image Inpainting Based on Wavelet Transform Attention Model

Image inpainting is a challenging task in image processing and widely applied in many areas such as photo editing. Traditional patch-based methods are not effective to deal with complex or non-repetitive structures. Recently, deep learning-based approaches have shown promising results for image inpainting. However, they usually generate contents with artificial boundaries, distorted structures or blurry textures. To handle this problem, we propose a novel image inpainting method based on wavelet transform attention model (WTAM). The wavelet transform decomposes features into multi-frequency sub-bands for extracting and transmitting deep information, and the attention mechanism enhances the ability of wavelet transform to capture significant detailed information in each level's subband images. Extensive experimental results on multiple datasets (Paris StreetView, CelebA and CelebAMask-HQ) demonstrate that our method can not only synthesize sharp image structures but also generate fine-detailed textures in missing regions, significantly outperforming the state-of-the-art methods.
Emotion Detection using Periocular Region: A Cross-Dataset Study

Many computer vision methods have been proposed for the affective assessment using facial expressions from full-face images. In many use cases, however, only the ocular region may be available due to the application of masks, clothing items, or privacy issues. In this paper, we show the utility of a robust and yet light deep learning model for ocular affect assessment using cross-dataset evaluation, where we train the model on one dataset and perform testing on another dataset with different demographics. We compare a MobileNet-V2 deep learning model, using transfer learning, with a more traditional method using histogram of oriented gradients (HOG) features with support vector machine (SVM) classifier. Experiments were conducted on the FACES dataset for training with six facial expressions and tested on the more diverse Chicago faces dataset(CFD) to show how evaluated models generalize not only in cross-dataset evaluation but also in the presence of new ethnicities not present during training. The experimental results show that the deep learning model can provide an average accuracy of 76.77% overall facial expressions when compared to HOG and SVM's 62.47% for this challenging cross-dataset emotion assessment using only eye regions.
Single Architecture and Multiple task deep Neural Network for Altered Fingerprint Analysis

Fingerprints are one of the most copious evidence in a crime scene and, for this reason, they are frequently used by law enforcement for identification of individuals. But fingerprints can be altered. “Altered fingerprints” refers to intentionally damage of the friction ridge pattern and they are often used by smart criminals in hope to evade law enforcement. We use a deep neural network approach training an Inception-v3 architecture. This paper proposes a method for detection of altered fingerprints, identification of types of alterations and recognition of gender, hand and fingers. We also produce activation maps that show which part of a fingerprint the neural network has focused on, in order to detect where alterations are positioned. The proposed approach achieves an accuracy of 98.21%, 98.46%, 92.52%, 97.53% and 92,18% for the classification of fakeness, alterations, gender, hand and fingers, respectively on the SO.CO.FING. dataset.
Human Action Recognition Using Deep Neural Networks

Human activities such as body gestures are the most difficult and challenging things in deep neural networks. Human action recognition is nothing but the human gesture recognition. Gesture shows a movement of body parts that convey some meaningful message. Gestures are greater most suitable and natural to have interaction with systems(computer) for humans thus it builds a platform between humans and machines. Human activity recognition provides the platform to interact with the deaf and dumb person. In this research work, we introduce to develop a platform for hand movement recognition, which recognizes hand movement(gestures), by using the CNN, we can identify human gestures in the image. As we are aware there was a quick increment in the amount of deaf and dumb victims because of a few conditions. Since deaf and dumb people can't communicate with a typical person so they want to depend upon a sort of visual correspondence Sign language. The sign language gives the fine verbal exchange platform for listening to impaired men or women to bring their minds and to have interaction with a normal character. The purpose of this research is a gadget that broadens the popularity of gestures, which can recognize gestures and then convert gesture images into text accordingly. The system pays special attention to the CNN training component using the CNN algorithm. The concept includes designing a gadget that uses in-depth mastery standards to treat input as a gesture and then provide recognizable output as text.
Automated Compilation of a Corpus-Based Dictionary and Computing Concreteness Ratings of Russian

The article presents new method implemented by the authors to generate dictionaries of concrete/abstract words for Russian. The method based on pretrained word embeddings computes concreteness ranking defined as a function of similarity between word vectors and the distance between a word in question and the ‘seed’ of concrete/abstract words. Implementation of the method resulted in generating the Russian Dictionary of Concreteness/Abstractness with concreteness ratings. Context information encoded by word embeddings useful to rank words by their concreteness score. The resulting C/A rankings strongly correlate with human experts’ assessments.