Reviewerly/exHarmony huggingface dataset:
Aspect Level Sentiment Classification Based on Double Attention Mechanism

Aspect sentiment classification is a fine-grained sentiment classification method, which is used to identify the sentimental polarity of a given aspect word in one sentence. Among the existing aspect-level sentiment classification methods, the deep learning model with the attention mechanism solves the problem of key word recognition in sentiment analysis and achieves good results. However, the effect of sentiment classification is not good in complex sentence structure and informal expression. In the deep learning model of aspect-level sentiment classification, this paper combines internal attention with external attention, and constructs an aspect-level sentiment classification model based on double attention, which consider the internal structure of the text as well as the external attention concerns of people. Experiments were conducted on SemEval2014 and Twitter datasets. Compared with the classical classification methods, the recognition accuracy of the proposed algorithm in this paper was improved by about 1%, and better results were achieved.
Dynamics of entropic uncertainty for three types of three-level atomic systems under the random telegraph noise*

We study the dynamics of the entropic uncertainty for three types of three-level atomic systems coupled to an environment modeled by random matrices. The results show that the entropic uncertainty in the Ξ-type atomic system is lower than that in the V-type atomic system which is exactly the same as that in the Λ-type atomic system. In addition, the effect of relative coupling strength on entropic uncertainty is opposite in Markov region and non-Markov region, and the influence of a common environment and independent environments in Markov region and non-Markov region is also opposite. One can reduce the entropic uncertainty by decreasing relative coupling strength or placing the system in two separate environments in the Markov case. In the non-Markov case, the entropic uncertainty can be reduced by increasing the relative coupling strength or by placing the system in a common environment.
Augmented Reality and Mobile Devices as Tools to Enhance Spatial Vision in Graphic Representations

The application of new technologies in the teaching field provides a series of advantages in teaching-learning processes. This paper presents a tool based on augmented reality that enhances and develops the capacity of spatial vision in engineering students, and more specifically in subjects of the Graphic Expression area. To do this, the student is provided with a series of QR codes that accompany the drawings of parts or mechanical assemblies usually used in these subjects. The tool, called ARTID (Augmented Reality for Teaching, Innovation and Design), consists of a free mobile application that can be used on any mobile device with the Android operating system. In this way, students can scan the QR code that accompanies each drawing and obtain a virtual 3D model of the represented object. The operation of the application has been verified by several tests on different devices, obtaining satisfactory results in all cases and demonstrating its ease of use and great utility.
A Comparative Sentiment Analysis On Bengali Facebook Posts

The proliferation of social network is generating a huge amount of texts and drawing attentions. Sentiment Analysis (SA) extracts useful information from such data. Maximum researches on SA have been done on the English language, but others main languages such as Bengali requests obligation too. It is pivotal to work on Bengali social posts because it is sixth most spoken language by native speakers and heavily used in social media. Although such a crowd, modest work has been done on Bengali SA. This paper proposes to automatically classify the overall polarity of sentiments expressed in Bengali Facebook posts by Facebook users in five classes: Positive, Strong Positive, Negative, Strong Negative and Neutral, and determine a fruitful approach to solve this problem. Samples are collected through Facebook Graph API and later divided into two different types of corpuses. Each sample in both corpuses is annotated with a polarity label (positive, strong positive, negative, strong negative or neutral) by a body of ten independent annotators. We take supervised machine learning and supervised deep learning approach to this classification problem for each corpus. In machine learning approach, comparison of three established classification algorithms namely Naive Bayes, Support Vector Machine and Logistic Regression for each corpus is shown. For each classifier, an input feature (a combination of unigram and bigram to achieve the best result) is experimented with. In deep learning approach, a popular Recurrent Neural Network namely LSTM is tested. SVM performs best for this classification task in the pre-processed corpus; producing a maximum accuracy of 86.7%. Deep learning approach achieves lower result than machine learning approach for the pre-processed corpus but performs better for unpre-processed corpus producing an accuracy of 72.86%.
The Development and Evaluation of A Progress Monitoring Prototype Tool for Software Project Management

Progress monitoring is an essential task of the Software Project Management (SPM), which is executed during the development process of software projects. In the market today, several tools are available for monitoring the progress of a project. However, most of these tools lack sufficient progress monitoring mechanisms, making them unattractive for many organizations. Moreover, some of the tools do not report and/or visualize useful information regarding the project status. To address these limitations, this study investigates some of the common tools used by Software Development Organizations (SDOs), which leverage on project management approaches and the concept of Agile methods. The SPM tools' pros and cons were identified through a literature survey. The key limitations were highlighted to show the gap in this area that led to the inception of this study. Resultantly, a Progress Monitoring Prototype Tool (PM-PT) was developed to overcome the limitations of the existing tools used in SPM. Subsequently, PM-PT was tested and evaluated by interviewing seven software practitioners. The initial results indicate that PM-PT is a useful and effective tool for software project monitoring task. Future work will focus on improving PM-PT based on the constructive suggestions and recommendations provided by the software practitioners.
LFSR-based Bit-Serial $GF(2^m)$ Multipliers using Irreducible Trinomials

In this article, a new architecture of bit-serial polynomial basis (PB) multipliers over the binary extension field GF(2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">m</sup> ) generated by irreducible trinomials is presented. Bit-serial GF(2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">m</sup> ) PB multiplication offers a performance/ area trade-off that is very useful in resource constrained applications. The architecture here proposed is based on LFSR (Linear-Feedback Shift Register) and can perform a multiplication in m clock cycles with a constant propagation delay of TA þ TX. These values match the best time results found in the literature for bit-serial PB multipliers with a slight reduction of the space complexity. Furthermore, the proposed architecture can perform the multiplication of two operands fort different finite fields GF(2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">m</sup> ) generated by t irreducible trinomials simultaneously in m clock cycles with the inclusion of t(m - 1Þ flipflops and tm XOR gates.
Chinese Spelling Check based on Sequence Labeling

Chinese has been widely used by people from all over the world. Various Chinese spelling errors may occur inevitably from Chinese as Foreign Language (CFL) beginners. This paper presents a method for Chinese spelling check to detect and correct spelling errors in a sentence. Our approach is based on the sequence labeling model using the bi-direction LSTM network. We also utilize the Pinyin feature and Chinese strokes feature to improve performance. The evaluation on the SIGHAN-8 shows that our approach gets good performance on both detection and correction tasks.
Efficient cuDNN-Compatible Convolution-Pooling on the GPU

The main contribution of this paper is to show efficient implementations of the convolution-pooling in the GPU, in which the pooling follows the multiple convolution. Since the multiple convolution and the pooling operations are performed alternately in earlier stages of many Convolutional Neural Networks (CNNs), it is very important to accelerate the convolution-pooling. Our new GPU implementation uses two techniques, (1) convolution interchange with direct sum, and (2) conversion to matrix multiplication. By these techniques, the computational and memory access cost are reduced. Further the convolution interchange is converted to matrix multiplication, which can be computed by cuBLAS very efficiently. Experimental results using Telsa V100 GPU show that our new GPU implementation compatible with cuDNN for the convolution-pooling is at least 1.34 times faster than the multiple convolution and then the pooling by cuDNN, the most popular library of primitives to implement the CNNs in the GPU.
<i>UACFinder</i>

During the system development process, domain experts and developers often make assumptions about specifications and implementations. However, most of the assumptions being taken for granted by domain experts and developers are too tedious to be documented by them. When these unspecified assumptions are violated in an environment in which the system operates, failures can occur. According to the U.S. Food and Drug Administration (FDA) medical device recall database, medical device recalls caused by software failures are at an all-time high. One major cause of these recalls is violations of unspecified assumptions made in medical systems. Therefore, it is crucial to have tools to automatically identify such unspecified assumptions at an early stage of the systems development process to avoid fatal failures. In this article, we present a tool called Unspecified Assumption Carrier Finder ( UACFinder ) that uses data mining techniques to automatically identify potential syntactic carriers of unspecified assumptions in system design models. The main idea of this tool is based on the observation we obtained from our earlier analysis of software failures in medical device recalls caused by unspecified assumptions. We observed that unspecified assumptions often exist in medical systems through syntactic carriers , such as constant variables , frequently read/updated variables , and frequently executed action sequences . Therefore, we develop the UACFinder to automatically find these potential unspecified assumption syntactic carriers rather than unspecified assumptions themselves. Once the UACFinder identifies the potential unspecified assumption syntactic carriers , domain experts and developers can validate whether these syntactic carriers indeed carry unspecified assumptions. We use a simplified cardiac arrest treatment scenario as a case study to evaluate the UACFinder in mining potential syntactic carriers of unspecified assumptions. In addition, we invite a medical doctor to validate unspecified assumptions carried by the mined syntactic carriers . The case study demonstrates that the UACFinder is effective in helping to identify potential unspecified assumptions from system design models.
End-to-End Discrete Motion Planner based on Deep Neural Network for Autonomous Mobile Robots

For autonomous navigation of mobile robots, collision avoidance with obstacles is an essential capability. Previously, we found that operators controlled a mobile robot with discrete motion commands, such as straight, right, and left, through a joystick. In this paper, therefore, we propose an end-to-end discrete motion planner. This motion planner is based on a deep neural network. For network training, we adopt a learning-from-demonstration approach. Through the developed teaching system, an operator controls the robot so as to move forward while avoiding collisions with obstacles. In doing so, the robot is allowed to record sensor data and discrete motion commands given by operators as inputs and outputs. In addition to the sensor data, we use a direction angle between the robot and goal destination as the input. All of these input and output data are the training data sets for the deep neural network. In the navigation experiments, we show that the robot based on the end-to-end discrete motion planner is able to move toward the goal destination while avoiding collisions with obstacles.
Plant Genetics-Inspired Evolutionary Optimization: A Descriptive Tutorial

This chapter illustrates the characteristics of plant genetics-inspired evolutionary optimization (PGEO). The computation strategy of PGEO is inspired by the theory of Mendelian evolution. Presented PGEO optimizer is a binary-coded algorithm based on mainly three concepts from plant genetics: (i) the “denaturation” of DNA of two different species to produce the hybrid “offspring DNA,” (ii) the Mendelian evolutionary theory of genetic inheritance, which explains how the dominant and recessive traits appear in two successive generations, (iii) the epimutation, through which organism resists for natural mutation. The above concepts are reconfigured in order to design the binary meta-heuristic evolutionary training technique. Based on this framework, four evolutionary operators—(1) flipper, (2) pollination, (3) breeding, and (4) epimutation—are created in the binary domain. The chapter gives characteristics and a detailed tutorial to PGEO theory.
Deep Learning-Based Quantization of L-Values for Gray-Coded Modulation

In this work, a deep learning-based quantization scheme for log-likelihood ratio (L-value) storage in fading channels affected by interference is introduced. We derive the number of sufficient statistics required to exactly reconstruct the set of L-values corresponding to a channel use as 3+2xNI, where NI is the number of interferers. We analyze the dependency between the average magnitudes of different L-values and show they follow a consistent ordering, regardless of the channel coefficient or interference distribution. Based on this we design a deep autoencoder that jointly compresses and separately reconstructs each L-value, allowing the use of a weighted loss function that promotes more accurate reconstruction of low magnitude inputs. Our method is shown to be competitive with state-of- the-art maximum mutual information quantization schemes, reducing the required memory footprint by a ratio of up to two and achieving a loss of performance lower than 0.1 dB with less than two effective bits per L-value and lower than 0.04 dB with 2.25 effective bits. We demonstrate that the same network can be reused without further training on various channel models and error- correcting codes while preserving the same performance benefits.
Offline optical character recognition (OCR) method: An effective method for scanned documents

Optical Character Recognition (OCR) is a major computer vision task by which characters of image are detected and recognized by comparing to training set images. Process of detecting character is one of the perplexing tasks in computer vision. This is because of input image often not correctly aligned or because of noise. This paper presents a complete Optical Character Recognition (OCR) system which is worked for English character mostly for Calibri font. This system first corrects skew of image if input image is not correctly aligned followed by noise reduction from input image. This process is passed through line and character segmentation that are passed into the recognition module and recognize characters. By experimenting with a set of 50 images, average achievement is 92%, 98% is for Calibri font. Moreover, the developed technique is computationally efficient and requires less time than other Optical character recognition system.
Towards Single-Input Single-Output Nonlinear System Identification and Signal Processing on Near-Term Quantum Computers

Based on a recently developed theory, we propose a realization of a single-input single-output (SISO) quantum reservoir computer on a near-term quantum computer for approximating SISO fading memory maps. Such a quantum reservoir computer can be of interest for applications such as nonlinear system identification and nonlinear signal processing (e.g., speech processing), opening an avenue for early control-oriented applications of near-term quantum computers. We detail an implementation of the quantum reservoir computer on a cloud-based 20 qubit IBM quantum computer and report on simulation results for the proposed scheme on an IBM Qiskit quantum computer simulator for this machine.
CaliForest

Real-world predictive models in healthcare should be evaluated in terms of discrimination, the ability to differentiate between high and low risk events, and calibration, or the accuracy of the risk estimates. Unfortunately, calibration is often neglected and only discrimination is analyzed. Calibration is crucial for personalized medicine as they play an increasing role in the decision making process. Since random forest is a popular model for many healthcare applications, we propose CaliForest, a new calibrated random forest. Unlike existing calibration methodologies, CaliForest utilizes the out-of-bag samples to avoid the explicit construction of a calibration set. We evaluated CaliForest on two risk prediction tasks obtained from the publicly-available MIMIC-III database. Evaluation on these binary prediction tasks demonstrates that CaliForest can achieve the same discriminative power as random forest while obtaining a better-calibrated model evaluated across six different metrics. CaliForest will be published on the standard Python software repository and the code will be openly available on Github.
BERT with Enhanced Layer for Assistant Diagnosis Based on Chinese Obstetric EMRs

This paper proposes a novel method based on the language representation model called BERT (Bidirectional Encoder Representations from Transformers) for Obstetric assistant diagnosis on Chinese obstetric EMRs (Electronic Medical Records). To aggregate more information for final output, an enhanced layer is augmented to the BERT model. In particular, the enhanced layer in this paper is constructed based on strategy 1(A strategy) and/or strategy 2(A-AP strategy). The proposed method is evaluated on two datasets including Chinese Obstetric EMRs dataset and Arxiv Academic Paper Dataset (AAPD). The experimental results show that the proposed method based on BERT improves the F1 value by 19.58% and 2.71% over the state-of-the-art methods, and the proposed method based on BERT and the enhanced layer by strategy 2 improves the F1 value by 0.7% and 0.3% (strategy 1 improves the F1 value by 0.68% and 0.1%) over the method without adding enhanced layer respectively on Obstetric EMRs dataset and AAPD dataset.
Method of Thematic Immersion in the Information Educational Environment as a Tool for the Formation and Assessment of Professional Competence of Future Engineering Teachers

The paper describes the technique of thematic immersion in the information educational environment for the formation and assessment of professional competence of future engineering teachers. The theme of immersion is the development of electronic educational resources by future engineering teachers. The development of electronic educational resources is carried out in an environment as close as possible to the professional environment of the engineering teacher. In this environment, future engineering teachers are included in the pedagogical activity related to the themes of immersion. At the same time, learning acquires an activity character, that is, the formation and assessment of competence are carried out in the course of students’ practical activity: in the process of creating electronic educational resources. In order to take into account the various options for the manifestation of competence under the thematic immersion, a system of particular and synthesis indicators has been formed and a model for the integrated assessment of competence has been developed. The results of the technique experimental verification have been considered and directions for its further improvement have been outlined.
Batch repair actions for automated troubleshooting

Repairing a set of components as a batch is often cheaper than repairing each of them separately. A primary reason for this is that initiating a repair action and testing the system after performing a repair action often incurs non-negligible overhead. However, most troubleshooting algorithms proposed to date do not consider the option of performing batch repair actions. In this work we close this gap, and address the combinatorial problem of choosing which batch repair action to perform so as to minimize the overall repair costs. We call this problem the Batch Repair Problem (BRP) and formalize it. Then, we propose several approaches for solving it. The first seeks to choose to repair the set of components that are most likely to be faulty. The second estimates the cost wasted by repairing a given set of components, and tried to find the set of components that minimizes these costs. The third approach models BRP as a Stochastic Shortest Path Problem (SSP-MDP) [1], and solves the resulting problem with a dedicated solver. Experimentally, we compare the pros and cons of the proposed BRP algorithms on a standard Boolean circuit benchmark and a novel benchmark from the Physiotherapy domain. Results show the clear benefit of performing batch repair actions with our BRP algorithms compared to repairing components one at a time.
Squeeze-and-Excitation SqueezeNext: An Efficient DNN for Hardware Deployment

Convolution neural network is being used in field of autonomous driving vehicles or driver assistance systems (ADAS), and has achieved great success. Before the convolution neural network, traditional machine learning algorithms helped the driver assistance systems. Currently, there is a great exploration being done in architectures like MobileNet, SqueezeNext & SqueezeNet. It improved the CNN architectures and made it more suitable to implement on real-time embedded systems. This paper proposes an efficient and a compact CNN to ameliorate the performance of existing CNN architectures. The intuition behind this proposed architecture is to supplant convolution layers with a more sophisticated block module and to develop a compact architecture with a competitive accuracy. Further, explores the bottleneck module and squeezenext basic block structure. The state-of-the-art squeezenext baseline architecture is used as a foundation to recreate and propose a high performance squeezenext architecture. The proposed architecture is further trained on the CIFAR-10 dataset from scratch. All the training and testing results are visualized with live loss and accuracy graphs. Focus of this paper is to make an adaptable and a flexible model for efficient CNN performance which can perform better with the minimum tradeoff between model accuracy, size, and speed. Having a model size of 0.595MB along with accuracy of 92.60% and with a satisfactory training and validating speed of 9 seconds, this model can be deployed on real-time autonomous system platform such as Bluebox 2.0 by NXP.