Reviewerly/exHarmony huggingface dataset:
Intent-enhanced attentive Bert capsule network for zero-shot intention detection

Spoken language understanding (SLU) plays an indispensable role in the dialogue system. The traditional intention detection task is regarded as a classification problem where utterances are associated with pre-defined intents. However, the various expressions of user's intents and constantly emerging novel intents make the annotating time-consuming and labor-intensive, building massive obstacles for extending the model to new tasks. Identifying unexpected user intention and achieving the user's desire goal is a challenging task. Therefore, we conduct zero-shot intention detection based on a transformation-based learning manner. In this paper, we propose an intent-enhanced attentive capsule network (IE-BertCapsNet) further guides the aggregation process of the capsule network and generalizable useful features that can be adapted to emerging intentions. Coupling with the large margin cosine loss function, the proposed model can identify discriminative features by forcing the whole network to minimize inter-class distance and minimize intra-class distance. Finally, we leverage the IE-BertCapsNet’s feature extraction ability and knowledge transferring capability to conduct zero-shot intent detection and generalized zero-shot intent detection. Extensive experiments on five benchmark task-oriented datasets in four languages demonstrate that the proposed model can achieve competitive performance that can better discriminate known intents and detect unknown intents.
News Text Categorization using Random Forest and Naïve Bayes

As this whole world is gradually digitized in every aspects, there is an exponential increase of amount of data in every fields. Retrieving valuable information from this unstructured, unorganized raw data is challenging and time consuming. There are so many techniques has been proposed in the field of information retrieval to organize the unorganized data efficiently. Text Categorization is one of the techniques introduced for this purpose of categorization of documents into pre-determined categories depending on their contents. This is one of the sub technique of text classification. Text Categorization is also known as Topic Spotting. The experimental study is done on standard benchmark news data set of Reuter using machine learning techniques like Random Forest and Naïve Bayes. TFIDF Vectorizer and Count Vectorizer is used for extracting features from the data set efficiently. Chi-Square is used for reduction of feature set from the extracted feature set to select the best features to accelerate performance. The result is captured in form of the two metrics accuracy and kappa statistics to analyze the effect of different features extractions and classification technique on news data set. This experimental evaluation will improvise the future research of the use of different machine learning techniques on news data set.
Scheduling for heterogeneous systems in accelerator-rich environments

The world is creating ever more data and the applications are required to deal with ever-increasing datasets. To process such datasets heterogeneous and manycore accelerators are being deployed in various computing systems to improve energy efficiency. In this work, we present a runtime management system designed for such heterogeneous systems with manycore accelerators. More specifically, we design a resource-based runtime management system that considers application characteristics and respective execution properties on the nodes and accelerators. We propose scheduling heuristics and run time environment solutions to achieve better throughput and reduced energy in computing systems with different accelerators. We give implementation details about our framework; show different scheduling algorithms, and present experimental evaluation of our system. We also compare our approaches with an optimal scheme where integer linear programming approach has been implemented for mapping applications on the heterogeneous system. While it is possible to extend the proposed framework to a wide variety of accelerators, our initial focus is on Graphics Processing Units (GPUs). Our experimental evaluations show that including accelerator support in the management framework improves energy consumption and execution time significantly. We believe that this approach has the potential to provide an effective solution for next generation accelerator-based computing systems.
Wireless Bus Identification System for Visually Impaired Person

The goal of this work RF based Wireless communication is to simplify the traveling and movement of visually impaired person. As someone who is totally blind, every day presents its own challenge. But the biggest problem still remains are traveling in public transport. Usually travelling by bus is considered to be safe and comfortable but when it comes to outdoor environments then it very difficult to manage navigation for people who are born completely blind. In the bus transport system, the route numbers are only displayed on the number plate due to which a visually challenged person has to rely on sighted assistance that also makes them dependent on others and lowers their self-esteem. Visually impaired people have adopted various measures to access public transport that make them dependent on others that are sometimes unreliable. So, keeping in mind these problems we have come up with an idea to help exclusively for visually impaired persons who are the daily commuters accessing public transport by the use of recent advancements in the technology. The purpose of this is to design and propose a model that will help the visually disabled person to navigate in outdoor surroundings and fulfill their requirement of identification. The proposed system consists of two detection subsystems: (i) Visually impaired person's personal assistance segment, and, (ii) Bus driver's segment. When the user (blind person) reaches the bus station and switches on his device, the ultrasonic sensor senses the presence of the bus in the nearby area and transmits the bus presence to the Arduino. To this, the buzzer beeps and the visually impaired person get to know about the presence of the bus in the nearby area. The blind person then turns on the toggle switch on the user module, thereby indicating his presence to the bus driver. Henceforth, begins the working of bus driver's module. The information transmitted by the blind person is received by the receiver installed on bus driver's module and thereby gets notified by a buzzer. In order to reply to the blind person, the bus driver takes the help of a Bluetooth application which has a wireless connection with the Bluetooth module installed over the user's module from his phone. The bus driver sends an acknowledgement to the blind person by entering an alphabet `a' from the Bluetooth app. The second buzzer installed in the blind person's device beeps on receiving the acknowledgement, thereby confirming his presence. Thus by this way, wireless communication is carried out between the bus driver and the visually impaired person using RF module. The result shows that the proposed system is exhibiting a better safety, cost and practicality in performance.
No-Reference Image Quality Assessment Of T2-Weighted Magnetic Resonance Images In Prostate Cancer Patients

No-reference image quality assessment in magnetic resonance (MR) imaging is a challenging task due to the variable nature of these images and lack of standard quantification methods, which makes the interpretation to be almost always subjective. In this study, we propose an architecture where we: (i) extended the no-reference image quality assessment problem of MRI into a full-reference image quality assessment using unpaired generative adversarial network (GAN) and (ii) employed a weakly-supervised trained deep classifier to determine the quality of MR images by comparing each image with its synthetic higher quality reference image. Using this approach, we achieved 11.28% improvement in the accuracy of our MR image quality assessment algorithm on an independent data test with FPR in detecting low quality images, reduced from 13% to 9.6%.
Vericom: A Verification and Communication architecture for IoT-based blockchain

Blockchain has received tremendous attention as a secure, distributed, and anonymous framework for the Internet of Things (IoT). As a distributed system, blockchain trades off scalability for distribution, which limits the technology's adaptation for large scale networks such as IoT. All transactions and blocks must be broadcast and verified by all participants which limits scalability and incurs computational and communication overheads. The existing solutions to scale blockchains have so far led to partial recentralization, limiting the technology's original appeal. In this paper, we introduce a distributed yet scalable Verification and Communication architecture for blockchain referred to as Vericom. Vericom concurrently achieves high scalability and distribution using hash function outputs to shift blockchains from broadcast to multicast communication. Unlike conventional blockchains where all nodes must verify new transactions/blocks, Vericom uses the hash of IoT traffic to randomly select a set of nodes to verify transactions/blocks which in turn reduces the processing overhead. Vericom incorporates two layers: (i) transmission layer where a randomized multicasting method is introduced along with a backbone network to route traffic, i.e., transactions and blocks, from the source to the destination, and (ii) verification layer where a set of randomly selected nodes are allocated to verify each transaction or block. The performance evaluation shows that Vericom reduces the packet and processing overhead as compared with conventional blockchains. In the worst case, packet overhead in Vericom scales linearly with the number of nodes while the processing overhead remains scale-independent.
Enhancing 4d Cardiac Mri Registration Network With A Motion Prior Learned From Coronary Cta

Registration between phases in 4D cardiac MRI is essential for reconstructing high-quality anatomy and appreciating the dynamics. Complex sequential compartmental motion and heterogeneous image quality make it challenging to design regularization functionals in classic optimization settings. In this study, we propose to introduce a novel motion representation model (MRM) into an image registration network to impose spatially variant prior for cardiac motion. A set of highly representative deformation vector fields (DVFs) were generated from high-contrast CTA images. In the form of a convolutional auto-encoder, the MRM was trained to capture the spatial variant pattern of the DVF Jacobian. The CT-derived MRM was then incorporated into an unsupervised network to facilitate 4D MRI registration. Our method was evaluated on ten 4D MRI scans with multi-compartment manual segmentations and achieved 2.25 mm target registration errors (TRE) on left ventricle. Compared to networks without MRM, introduction of the MRM reduced TREs on two ventricles and pulmonary artery with statistical significance. Compared to the tuned SimpleElastix, our method achieved comparable results on all compartments without statistical significance, but with a much shorter registration time of 0.02 s.
Reflections on the role of the professor from the TPACK model perspective during covid-19

Education worldwide has changed dramatically because of the pandemic caused by COVID-19; in this regard, it is necessary to reflect on the teaching process in these times and see the knowledge professors require to achieve a learning teaching process that is appropriate to this new context. To this end, the TPACK (technological pedagogical content knowledge) model was used as a reference for reviewing the knowledge and skills that professors must have, emphasizing on: technological knowledge (TK) and technological pedagogical knowledge (TPK) since they are important in tele-education. A survey of 25 questions was applied to 381 Ecuadorian professors to obtain their perception of the mastery of the knowledge proposed by TPACK, and the results indicate a perception of high knowledge in the TK and TPK components, which allows to visualize a positive scenario for the correct development of tele-education in Ecuador.
Sterling: A Web-Based Visualizer for Relational Modeling Languages

We introduce Sterling, a web-based visualization tool that provides interactive views of relational models and allows users to create custom visualizations using modern JavaScript libraries like D3 and Cytoscape. We outline its design goals and architecture, and describe custom visualizations developed with Sterling that enable verification studies of scientific software used in production. While development is driven primarily by the Alloy community, other relational modeling languages are accommodated by Sterling’s data agnostic architecture.
Machine Learning Algorithms for Breast Cancer Detection and Prediction

Breast cancer has been recorded one of the most common disease among the females worldwide. It is the most common type of melanoma and responsible for the increasing mortality rate. The accurate and timely diagnosis of this fatal disease is particularly important to increase the chances of survival of the patients. There have been several implementations in this area, and numerous machine learning and soft computing algorithms have been proposed to analyze and detect the cancer and help the doctors to provide right medications on time. In this paper, a comparative analysis of prominent machine learning techniques is done, and their performance is evaluated. The techniques used for detecting and diagnosing the breast cancer are support vector machines (SVM), random forest, and k-nearest neighbor (k-NN). All experiments have been conducted with R programming software (data mining tool). The outcomes indicated that k-NN has achieved the highest accuracy (97.32%) in comparison with SVM, RF.
Dynamic visual servoing with Kalman filter-based depth and velocity estimator

Camera calibration error, vision latency, nonlinear dynamics, and so on present a major challenge for designing the control scheme for a visual servoing system. Although many approaches on visual servoing have been proposed, surprisingly, only a few of them have taken into account system dynamics in the control design of a visual servoing system. In addition, the depth information of feature points is essential in the image-based visual servoing architecture. As a result, to cope with the aforementioned problems, this article proposes a Kalman filter-based depth and velocity estimator and a modified image-based dynamic visual servoing architecture that takes into consideration the system dynamics in its control design. In particular, the Kalman filter is exploited to deal with the problems caused by vision latency and image noise so as to facilitate the estimation of the joint velocity of the robot using image information only. Moreover, in the modified image-based dynamic visual servoing architecture, the computed torque control scheme is used to compensate for system dynamics and the Kalman filter is used to provide accurate depth information of the feature points. Results of visual servoing experiments conducted on a two-degree of freedom planar robot verify the effectiveness of the proposed approach.
Automated Phrase Mining Using POST: The Best Approach

Phrase mining is the way toward deriving aspects of expressions from the collection of texts. Several uses of phrase mining include information retrieval/extraction, taxonomy construction, and topic modeling. The existing strategy requires a prepared linguistic analyzer and has an unacceptable execution for new areas since it requires human specialists for labeling the phrase. The phrase generated in those systems for a given input text contains only a single word that may be often unambiguous to the user. The aim is to automate the phrase mining process and enhance its performance. The proposed method is a framework that requires minimal human labeling effort and only shallow linguistic analysis. A POS_tagger is used to extract the important words (nouns and noun phrases) from a text after which text ranking is applied. Then cosine similarity is used to identify the quality phrase from the text. Phrase quality can be estimated at two levels, once after POS_guided segmentation and then re-estimate the score at the end. Compared to the existing method, the proposed method has showna significant improvement in effectiveness and efficiency across different domains. This technique can be reached out to support any language up to a normal learning base (e.g. Wikipedia) of comparing vocabulary is accessible.
Representative Region Based Active Learning For Histological Classification Of Colorectal Cancer

The advent of advanced deep learning algorithms has contributed to many successful applications in digital histopathology. Regularly, attributed to the extremely large size, whole slide images (WSIs) have to be decoupled into numerous smaller patches to be independently processed by convolutional neural networks (CNNs) for training and inference. The routine tessellation strategy chops the images by a sliding-window moving across the entire whole slide. The straightforward result of this methodology is a large proportion of non-representative patches, as well as a labor-intensive manual annotation. Although active learning based models to sort out informative data instances are many, the identification of the most informative region of patches to train a patch-wise classifier has not been discussed. In this research, we propose an active learning based model to select patches with optimized offsets in and spatial adaptive manner. With these most representative patches, the patch-level classification models can be more effectively and efficiently trained. To the best of our knowledge, this is the first literature on an adaptive representative patch generation system. The empirical results on large patient cohorts in The Cancer Genome Atlas (TCGA) show a scale reduction in training set of 38.0% can achieve the tumor classification accuracy of 92.70%.
Room Classification in Floor Plan Recognition

Home improvement companies usually build 3D models based on 2D floor plans, and then the computer will automatically simulate the decoration of different rooms, so clarifying the exact type of room becomes a necessary task for the automatic decoration. Although it is possible to recognize text in floor plans, and the technology for text recognition is well developed, the challenge is to get the type of room when the floor plan lacks a textual description of the room. When text is missing from the floor plan, it means that you need to guess the type of room. To solve this problem, this paper attempts to apply the classification algorithm to solve the problem of rooms lacking textual information and to complete the classification of the rooms using the improved C4.5 algorithm. To enable the classification task to be performed smoothly, this paper also proposes a door and window detection method based on the YOLOv3 model. This method can accurately identify doors and windows, so the generated vector graphics can become more accurate, which is beneficial to the classification task. The experiments show that the YOLOv3 model-based door and window detection method achieves good results on the recognition task, and the mAP (mean Average Precision) of this model is 94.013% on the dataset we build. On the other hand, the improved C4.5 algorithm also enables efficient room classification with an accuracy of 78.71% and it is 6.22% more accurate than the traditional C4.5 algorithm.
Cell Abundance Aware Deep Learning For Cell Detection On Highly Imbalanced Pathological Data

Automated analysis of tissue sections allows a better understanding of disease biology, and may reveal biomarkers that could guide prognosis or treatment selection. In digital pathology, less abundant cell types can be of biological significance, but their scarcity can result in biased and sub-optimal cell detection model. To minimize the effect of cell imbalance on cell detection, we proposed a deep learning pipeline that considers the abundance of cell types during model training. Cell weight images were generated, which assign larger weights to less abundant cells and used the weights to regularize Dice overlap loss function. The model was trained and evaluated on myeloma bone marrow trephine samples. Our model obtained cell detection F1-score of 0.78, a 2% increase compared to baseline models, and it outperformed baseline models at detecting rare cell types. We found that scaling deep learning loss function by the abundance of cells improves cell detection performance. Our results demonstrate the importance of incorporating domain knowledge on deep learning methods for pathological data with class imbalance.
A taxonomy, data set, and benchmark for detecting and classifying malevolent dialogue responses

Abstract Conversational interfaces are increasingly popular as a way of connecting people to information. With the increased generative capacity of corpus‐based conversational agents comes the need to classify and filter out malevolent responses that are inappropriate in terms of content and dialogue acts. Previous studies on the topic of detecting and classifying inappropriate content are mostly focused on a specific category of malevolence or on single sentences instead of an entire dialogue. We make three contributions to advance research on the malevolent dialogue response detection and classification (MDRDC) task. First, we define the task and present a hierarchical malevolent dialogue taxonomy. Second, we create a labeled multiturn dialogue data set and formulate the MDRDC task as a hierarchical classification task. Last, we apply state‐of‐the‐art text classification methods to the MDRDC task, and report on experiments aimed at assessing the performance of these approaches.
Multi-layer Access Control Mechanism based on Blockchain for Mobile Edge Computing

Massive data generated by mobile terminals in edge computing brings new data security threats to data access control. However, previous access control strategies have deficiencies, i.e., coarse-graininess and poor flexibility, which cannot meet the requirements of secure data access to support various mobile edge computing applications. To overcome these shortcomings, in this paper, a Blockchain based Multi-layer Access Control mechanism, named BMAC, is proposed. Specifically, each resource data is assigned a security level. Then, an InfoMap based algorithm is developed to achieve credibility based users grouping. Next, a multi-blockchain is designed, based on which a flexible and fine-grained trusted data access control mechanism is established. The experiment results show that the BMAC scheme has better performance in terms of processing efficiency, throughput, CPU utilization, and latency.
Secure Remote E-Voting using Blockchain

Blockchain has opened up research opportunities to develop a proofreading system in many digital services. The research in this area is still in progress to merge the legal aspects with the technical process to create a self-sustained system. This paper has presented the proof of concept for implementing a remote internet-based voting system providing the physical absence of any invigilating authority thereby providing the provision for voters to cast the vote from home location. The blockchain-based system along with the implemented cryptosystem has been designed to provide reliability, security, anonymity of the votes and the voters. The system is also resistant to the prediction of the winning party until the results are announced thereby building the citizen trust as it adds transparency and security to the voting system.