Reviewerly/exHarmony huggingface dataset:
Combining Knowledge with Attention Neural Networks for Short Text Classification

Text classification has emerged as an important research area over the last few years in natural language processing (NLP). Different from formal documents and paragraphs, short texts are more ambiguous, due to the lack of contextual information and the data sparsity problem, which poses a great challenge to traditional classification methods. In order to solve this problem, conceptual knowledge is introduced to enrich the information of short texts. However, this method assumes that all knowledge is equally important which is not conducive to distinguishing short texts classification. In addition, it also brings knowledge noise to the text, and causes the degradation of classification performance. To measure the importance of concepts to short texts, the paper introduces the attention mechanism. Text-Relevant-Concept (T-RC) is utilized to resolve the ambiguity of concepts and choose the most appropriate meaning to align short text. We employ Concept-Relevant-Concept (C-RC) to handle conceptual hierarchy and the relative importance of the concept. We investigate a model combining Knowledge with Attention Neural Networks (CK-ANN). Experiments show that CK-ANN outperforms state-of-the-art methods on text classification benchmarks, which proves the effectiveness of our method.
Implementation of A Smart Helmet with Alcohol and Fall Detection and Navigation System

Since the invention of the first motor vehicle, the automobile industry has been advancing by leaps and bounds. Two-wheelers are no exception, but the safety of the driver is still a precarious situation. Due to the very built and design of the two-wheeler, the rider is more vulnerable to accidents as compared to the traditional four-wheeler vehicle. Therefore, in this paper we propose a holistic solution for the safety of the driver of a two-wheeler. The proposed solution aims to perform three main tasks. Firstly, alcohol detection which would be required to detect whether the rider is under the influence of alcohol. Secondly, a fall detection system which would be required to send notifications to the emergency contacts in case of an accident or a serious fall from the two-wheeler. Lastly, a navigation system to enhance the user experience. Our solution consists of a synchronous working of both hardware and software. For both the alcohol and fall detection we would use sensors which would feed the data to the microcontroller, which in turn would compare the data with the threshold values in real time. By pushing notifications to the relevant emergency contacts, we have managed to make use of the Internet of Things which always helps the system to remain connected to the Internet. The details of the implementation have been elaborated in this paper. In addition, the implementation and observations have also been highlighted to solidify our proposed solution. We believe such a system can not only help secure the life of the rider of the two-wheeler but also improve the experience one has while driving.
Possibility of Getting On/Off Public Vehicle by Manual Wheelchair with 4 Degrees of Freedom Contact Arm Mechanism

This paper proposes an elevating mechanism and its control rules for wheelchair users to get on and off public transportation vehicles by themselves. It is shown that it is possible to move up and down with a wheelchair by applying the grounding function with a 4-degree-of-freedom arm to a vehicle environment with two stages at the entrance. We apply an automatic arm grounding judgment method that applies a disturbance observer as a method for detecting whether the arm touches the floor. Its control availability is shown in getting off/on experiment for a railcar.
COVID-19 Fatality Rate Classification Using Synthetic Minority Oversampling Technique (SMOTE) for Imbalanced Class

SARS-Cov-2 is not to be introduced anymore. The global pandemic that originated more than a year ago in Wuhan, China has claimed thousands of lives. Since the arrival of this plague, face mask has become part of our dressing code. The focus of this study is to design, develop and evaluate a COVID-19 fatality rate classifier at the county level. The proposed model predicts fatality rate as low, moderate, or high. This will help government and decision makers to improve mitigation strategy and provide measures to reduce the spread of the disease. Tourists and travelers will also find the work useful in planning of trips. Dataset for the experiment contained imbalanced fatality levels. Therefore, class imbalance was offset using SMOTE. Evaluation of the proposed model was based on precision, F1 score, accuracy, and ROC curve. Five learning algorithms were trained and evaluated. Experimental results showed the Bagging model has the best performance.
Revisiting “Recurrent World Models Facilitate Policy Evolution”

This paper contributes a detailed analysis of the architecture of Ha and Schmidhuber []. The original paper proposes an architecture comprising 3 main components: a “visual” module, a “memory” module; and a controller. As a whole, such architecture performed well in challenging domains. We investigate how each of the aforementioned components contributes individually to the final performance of the system. Our results shed additional light on the role of the different components in the overall behavior of the agent, and illustrate how the different design options affect the behavior of the resulting agent.
Using Presentation Slides and Adjacent Utterances for Post-editing of Speech Recognition Results for Meeting Recordings

In recent years, the use of automatic speech recognition (ASR) systems in meetings has been increasing, such as for minutes generation and speaker diarization. The problem is that ASR systems often misrecognize words because there is domain-specific content in meetings. In this paper, we propose a novel method for automatically post-editing ASR results by using presentation slides that meeting participants use and utterances adjacent to a target utterance. We focus on automatic post-editing rather than domain adaptation because of the ease of incorporating external information, and the method can be used for arbitrary speech recognition engines. In experiments, we found that our method can significantly improve the recognition accuracy of domain-specific words (proper nouns). We also found an improvement in the word error rate (WER).
The Effect of Teaching Strategies and StudentsMotivation on Reading Comprehension Achievement

Teaching strategies especially Think pair Shareand Directed Reading Thinking Activity (TPS) and (DRTA)model used to teach students motivation on readingcomprehension achievement. The purpose of this study to findout the effect of Teaching Strategies on students’ readingcomprehension achievement. This study is experimental researchdesign using the experiment and control groups. This researchwas experimental design, the instruments were questionnairesheet and reading test. The result showed that from mean scoreof experimental class 87.8, and mean score of control class 84.67.The data were analyzed by using SPSS program for windowsversion 20.Based on the data analysis of reading score showedthat
Adaptive Control of Differentially Private Linear Quadratic Systems

In this paper we study the problem of regret minimization in reinforcement learning (RL) under differential privacy constraints. This work is motivated by the wide range of RL applications for providing personalized service, where privacy concerns are becoming paramount. In contrast to previous works, we take the first step towards non-tabular RL settings, while providing a rigorous privacy guarantee. In particular, we consider the adaptive control of differentially private linear quadratic (LQ) systems. We develop the first private RL algorithm, Private-OFU-RL which is able to attain a sub-linear regret while guaranteeing privacy protection. More importantly, the additional cost due to privacy is only on the order of <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\frac{\ln(1/\delta)^{1/4}}{\varepsilon^{1/2}}$</tex> given privacy parameters <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\varepsilon, \delta &gt; 0$</tex> . Through this process, we also provide a general procedure for adaptive control of LQ systems under changing regularizers, which not only generalizes previous non-private controls, but also serves as the basis for general private controls.
Aspect and Opinion Terms Co-extraction Using Position-Aware Attention and Auxiliary Labels

Aspect and opinion terms co-extraction is to extract what target is being discussed from the sentences and know what kind of emotion this target has. There are many studies about this task. To obtain the important context in the input sentences and improve the model’s performance, recent studies use the attention mechanism. But the attention mechanism used in them does not take the distance into account when calculating the weight of words and ignores the important influence of their neighboring words. To solve this problem, we introduce an Aspect and Opinion Terms Co-Extraction model, AOExtractor, that utilizes position-aware attention to extract the local context features for each word from their neighboring words and utilizes BERT to extract the global context features. At the same time, auxiliary labels are introduced to solve the problem of subword tokenization imposed by BERT. Finally, we concatenate the local and global context features to extract aspect and opinion terms simultaneously. The experimental results on four SemEval datasets prove the effectiveness of AOExtractor. Compared with the baseline, our model improves F1 by about 2–5%.
Data-efficient Deep Reinforcement Learning Method Toward Scaling Continuous Robotic Task with Sparse Rewards

Dealing with the robotic continuous control problem with sparse rewards is a longstanding challenge in deep reinforcement learning (RL). While existing DRL algorithms have demonstrated great progress in learning policies from visual observations, learning effective policies still requires an impractical number of real-world data samples. Moreover, some robotic tasks are naturally specified with sparse rewards, which makes the precious data inefficient and slows down the learning process, making DRL infeasible. In addition, manually shaping reward functions is a complex work because it needs specific domain knowledge and human intervention. To alleviate the issue, this paper proposes a model-free, off-policy RL approach named TD3MHER, to learn the manipulating policy for continuous robotic tasks with sparse rewards. To be specific, TD3MHER utilizes Twin Delayed Deep Deterministic policy gradient algorithm (TD3) and Model-driven Hindsight Experience Replay (MHER) to achieve highly sample-efficient training property. Because while the agent is learning the policy, TD3MHER could also help it to learn the potation physical model of the robot which is helpful to solve the task, and it does not necessitate any novel robot-environment interactions. The performance of TD3MHER is assessed on a simulated robotic task using a 7-DOF manipulator to compare the proposed technique to a previous DRL algorithm and to verify the usefulness of our method. Results of the experiments on simulated robotic task show that the proposed approach is capable of successfully utilizing previously store samples with sparse rewards, and obtain a faster learning speed.
Competition and Collaboration in Document Analysis and Recognition

Over the last twenty years, competitions aimed at showcasing research on various aspects of document analysis have become a significant part of our communal activities. After a quick look at competition in general and organized competitions in other domains, we focus on the organizers' reports of the 18 competitions completed in conjunction with the 14th Conference on Document Analysis and Research, ICDAR 2017. We provide descriptive statistics on the 130 organizers of these contests, their affiliations, the 450 participants, the platforms that underlie the evaluations, and the spectrum of specified tasks. We comment on the ~ 100 citations garnered by these contests over the intervening 3.5 years. Finally, in what we consider a logical sequel, we speculate on the possibility of an alternative model of small-scale, short-range communal research based on collaboration that seems to offer benefits competitions cannot capture.
A Novel Method to Estimate Parents and Children for Local Bayesian Network Learning

The Markov Blanket of a random variable is the minimum conditioning set of variables that makes the variable independent of all other variables. A core step to estimate the Markov Blanket is the identification of the Parents and Children (PC) variable set. This paper propose a novel Parents and Children discovery algorithm, called Max-Min Random Walk Parents and Children (MMRWPC), which improves the computational burden of the classical Max-Min Parents and Children method (MMPC). The improvement was achieved with a series of modifications, including the introduction of a random walk process to better identifying conditioning sets in the conditional independence (CI) tests, implying in a significantly reduction of expensive high-order CI tests. In a series of experiments with data sampled from benchmark Bayesian networks we show the suitability of the proposed method.
Exploration of university education reform based on mobile learning environment

This study analyzes the advantages and functions of mobile learning in college teaching under the hybrid teaching mode of adding mobile learning to conventional teaching. Examples show that mobile learning can make students more enthusiastic, more active and better learning effect. At the same time, it lists the shortcomings of mobile learning and gives the corresponding countermeasures.
Interpolation sub-pixel mapping based on multiple subpixel shifted images based on a consideration of the point spread function effect

In this paper, the interpolation sub-pixel mapping (ISM) based on multiple sub-pixel shifted images (MSSIs) based on a consideration of the point spread function (PSF) effect, named as MSSI-PSF, is proposed. In the proposed MSSI-PSF, MSSIs are first unmixed to produce the coarse fractional images. Second, under considering the PSF effect, the improved coarse fractional images are obtained by applying the area-to-point kriging (ATPK) and then the ideal square-wave filter to the coarse fractional images. Third, the improved coarse fractional images are upsampled by interpolation to obtain the upsampled fractional images, and then the upsampled fractional images are integrated to obtain a fine fractional image. Finally, according to the fine fractional images from all MSSIs, the class allocation method is utilized to assign class labels to sub-pixels to obtain the final mapping result. The experimental results show that the proposed MSSI-PSF model produces better mapping result than the existing ISM models.
Web-Based E-Catalog Application for Kenongo Batik SMEs Tulangan Sidoarjo

Sidoarjo is one area that deserves to be put forward. The culture of batik and textile creation is interesting to explore further. With current technological developments, making data, information and matters related to information systems is very fast to be obtained and distributed to various layers of society, various circles, and various social layers. The purpose of this study was to find out how to design and build a website-based e-catalog as a medium of information for Batik Sari Kenongo. The research method used by the author in designing a website-based e-catalog as a collection of information media at Batik Sari Kenongo includes two methods, namely data collection methods and data collection methods. system development method. The result of this research with the existence of an e-catalog information system at Batik Sari Kenongo is that it is easy and fast for visitors to get information on Batik Sari Kenongo and with the implementation of the e-catalog, it is expected to be able to attract visitors to come to Batik Sari Kenongo. The conclusion of this study is that it is easy and fast for visitors to get information on Batik Sari Kenongo. With the implementation of the e-catalog, it is hoped that it will attract visitors to come to Batik Sari Kenongo.
A High-Speed Architecture for the Reduction in VDF Based on a Class Group

Due to the enormous energy consuming involved in the proof of work (POW) process, the resource-efficient blockchain system is urged to be released. The verifiable delay function (VDF), being slow to compute and easy to verify, is believed to be the kernel function of the next-generation blockchain system. In general, the reduction over a class group, involving many complex operations, such as the large-number division and multiplication operations, takes a large portion in the VDF. In this paper, for the first time, we propose a highspeed architecture for the reduction by incorporating algorithmic transformations and architectural optimizations. Firstly, based on the fastest reduction algorithm, we present a modified version to make it more hardware-friendly by introducing a novel transformation method that can efficiently remove the large-number divisions. Secondly, highly parallelized and pipelined architectures are devised respectively for the large-number multiplication and addition operations to reduce the latency and the critical path. Thirdly, a compact state machine is developed to enable maximum overlapping in time for computations. The experiment results show that when computing 209715 reduction steps with the input width of 2048 bits, the proposed design only takes 137.652ms running on an Altera Stratix-10 FPGA at 100MHz frequency, while the original algorithm needs 3278ms when operating over an i7-6850K CPU at 3.6GHz frequency. Thus we have obtained a drastic speedup of nearly 24x over an advanced CPU.
Annoyed Turnout Ability Transmittal Using Heap-structure

Using today's more advanced technologies, this application seeks to leverage machine learning skills to use all of the vast volumes of data available on patients to enhance cancer detection and more customized care choices. DCOT-LS-svms. This article introduces the ls-svms command. Its aim is to save time by avoiding the time-consuming dynamic optimization process that many neural network kernels go through to improve least-squares support. Modules that make up the higher layer have additional input features that take into consideration the predictions of previous modules and the cross- used to leverage knowledge from predictions of previous modules to improve the learning process in the Consignable information. The next section is production. The proposed treatment is available on the UCI dataset, with five distinct approaches and a description of prostate cancer diagnosis.
Building the Bridge Between Automotive SPICE® and Agile Development

The V-model approach to development, well established in the automotive industry, is subject to high regulations imposed by the required compliance to standards, however, the increasing demand for innovation induces an increasing interest for agile methodologies both on OEM’s and supplier’s side. Compared to traditional product development methods, agile work approaches lead to a different understanding of cooperation, responsibilities and intermediary goals and approval. Automotive SPICE®, originally created and used in a traditional development environment, can be applied and interpreted in its current form for any type of development organizations. Nevertheless, industries struggle to interpret and implement Automotive SPICE in an agile environment and many Automotive SPICE assessors have difficulty mapping agile development appropriately to the Automotive SPICE model. The Working Group Agile SPICE at intacs™ is currently developing the add-on Agile SPICE™ as a new Process Assessment Model to reduce misunderstandings and to give a clear interpretation of the terminology for both the agile and the V-Model driven traditional world. In this paper, we outline the motivations of our work and the main concepts underlying the model, complemented by the experience gathered during the first pilot utilization.
Efficient Codebook Constructions of AIVF Codes

The almost instantaneous variable-to-fixed (AIVF) code is a class of non-prefix codes that parses data sequences into some fixed-length codewords. Recently, the dictionary construction of optimal AIVF code is proposed via a dynamic programming algorithm. However, the algorithm has time complexity <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$O(AM^{2})$</tex> in both single- and multiple-tree modes, where <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$A$</tex> denotes the alphabet size, and <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$M$</tex> denotes the dictionary size. In this paper, we present a greedy-like algorithm to construct the codebook. In particular, the proposed algorithm has time complexity <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$O(M)$</tex> in single-tree mode and <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$O(AM)$</tex> in multiple-tree mode.
Variational Bayesian representation learning for grocery recommendation

Abstract Representation learning has been widely applied in real-world recommendation systems to capture the features of both users and items. Existing grocery recommendation methods only represent each user and item by single deterministic points in a low-dimensional continuous space, which limit the expressive ability of their embeddings, resulting in recommendation performance bottlenecks. In addition, existing representation learning methods for grocery recommendation only consider the items (products) as independent entities, neglecting their other valuable side information, such as the textual descriptions and the categorical data of items. In this paper, we propose the Variational Bayesian Context-Aware Representation (VBCAR) model for grocery recommendation. VBCAR is a novel variational Bayesian model that learns distributional representations of users and items by leveraging basket context information from historical interactions. Our VBCAR model is also extendable to leverage side information by encoding contextual features into representations based on the inference encoder. We conduct extensive experiments on three real-world grocery datasets to assess the effectiveness of our model as well as the impact of different construction strategies for item side information. Our results show that our VBCAR model outperforms the current state-of-the-art grocery recommendation models while integrating item side information (especially the categorical features with the textual information of items) results in further significant performance gains. Furthermore, we demonstrate through analysis that our model is able to effectively encode similarities between product types, which we argue is the primary reason for the observed effectiveness gains.