Reviewerly/exHarmony huggingface dataset:
Reward Prediction Error as an Exploration Objective in Deep RL

A major challenge in reinforcement learning is exploration, when local dithering methods such as epsilon-greedy sampling are insufficient to solve a given task. Many recent methods have proposed to intrinsically motivate an agent to seek novel states, driving the agent to discover improved reward. However, while state-novelty exploration methods are suitable for tasks where novel observations correlate well with improved reward, they may not explore more efficiently than epsilon-greedy approaches in environments where the two are not well-correlated. In this paper, we distinguish between exploration tasks in which seeking novel states aids in finding new reward, and those where it does not, such as goal-conditioned tasks and escaping local reward maxima. We propose a new exploration objective, maximizing the reward prediction error (RPE) of a value function trained to predict extrinsic reward. We then propose a deep reinforcement learning method, QXplore, which exploits the temporal difference error of a Q-function to solve hard exploration tasks in high-dimensional MDPs. We demonstrate the exploration behavior of QXplore on several OpenAI Gym MuJoCo tasks and Atari games and observe that QXplore is comparable to or better than a baseline state-novelty method in all cases, outperforming the baseline on tasks where state novelty is not well-correlated with improved reward.
JAPANESE NON-VERBAL EDUCATION COMMUNICATION BY THE MAIN CHARACTERS IN THE NOVEL OF TOTTO-CHAN AND THE LIGHT ON CURTAIN OF SAKURA: AN ANALYSISOFKINESICS

Purpose of the studyThis research discussed about Japanese Non-verbal Communication by the Main Characters in the Novel of Totto-chan and the Light on Curtain of Sakura (An analysis of Kinesics). This research is aimed to describe the types and the functions of kinesics that used by the main characters in novels of Totto-chan and the Light on Curtain of Sakura. To get this goal, the researcher proposed a research questions: What are the types of kinesics that used by the main characters in the novels of Totto-chan and the light on Curtain of Sakura and what are the functions of kinesics expressed by the main characters in the novels of Totto-chan and the light on Curtain of Sakura.MethodologyThis research focused on the types and functions of kinesics as the aspects of non-verbal communication that were used by the main characters in the novels of Totto-chan and the light on Curtain of Sakura used the kinesics theory by Hans and Hans (2015) and Knapp (2012) Then, the data were analyzed using descriptive qualitative method. The researcher acted as the main instrument and used note taking to find out the valid dataMain Findingsis the researcher classified the data why the data were categorized as gesture, posture eye contact and facial expression and why the types of the kinesics functions as complementing, regulating, contradicting and substituting. The findings show that the main characters in the novels used all of the types of kinesics. Applications of this studyis the researcher analyzed the types of kinesics that used by the main characters in the novel they are gesture, posture eye contact and facial expression and the functions of kinesics which can be function as complementing, regulating, contradicting and substituting.Novelty/Originality of this studyMost of the functions used of originality of this study by the main character they are substituting and complementing while just one type that functions as regulating. Then, no one the types of kinesics function as contradicting.
Understanding Advertisements with BERT

We consider a task based on CVPR 2018 challenge dataset on advertisement (Ad) understanding.The task involves detecting the viewer's interpretation of an Ad image captured as text.Recent results have shown that the embedded scene-text in the image holds a vital cue for this task.Motivated by this, we fine-tune the base BERT model for a sentencepair classification task.Despite utilizing the scene-text as the only source of visual information, we could achieve a hit-or-miss accuracy of 84.95% on the challenge test data.To enable BERT to process other visual information, we append image captions to the scene-text.This achieves an accuracy of 89.69%, which is an improvement of 4.7%.This is the best reported result for this task.
PORTFOLIO ASSESMENT AS A CATALYST OF PROCESS-ORIENTED SKILL TO PROMOTE STUDENTS IN WRITING BUSINESS LETTERS

The purpose of this study was to improve the writing skill of the Management students at Faculty of Economics and Business Universitas Muhammadiyah Pontianak. A classroom action research was employed to conduct a portfolio assessment as a process-oriented skill. Students are expected to be able to write various kinds of business letters to fulfill the needs of their future career. The ability of how to write well cannot be obtained through a product-oriented approach particularly to students with low level English skill. Therefore, this study aimed at improving students’ writing skill in terms of writing business letters namely letter of offer. The samples consisted of 37 students of Business English subject in the academic year 2018/2019. The findings of the study proved significant improvement on students’ writing with portfolio assessment in terms of sub-skills in writing namely text harmony, vocabulary selection, grammar option, vocabulary writing, and writing tidiness from the first cycle to the second cycle. Subsequently, the students can orderly produce piece of writing since the underlying purpose of this study was to encourage writing as process-oriented skill.
A compressed secret image sharing method with shadow image verification capability

Secret image sharing (SIS) is an important research direction in information hiding and data security transmission. Since the generated shadow images (shares) are always noise-like, it is difficult to distinguish the fake share from the unauthorized participant before recovery. Even more serious is that an attacker with a fake share can easily collect shares of other honest participants. As a result, it is significant to verify the shares, before being taken out for recovery. Based on two mainstream methods of SIS, such as polynomial-based SIS and visual secret sharing(VSS), this paper proposed a novel compressed SIS with the ability of shadow image verification. Considering that the randomness of the sharing phase of polynomial-based SIS can be utilized, one out of shares of (2, 2)-threshold random-grid VSS is embedded into all shares of polynomial-based SIS by a XOR operation as the verification information, while the other binary share is private for verification. Before recovery, each participant must extract the binary share from the grayscale share to perform XOR operation with the private share, and the original binary image can be recovered only with the true share. The proposed scheme also has the characteristics of shadow image verification, pixel compression, loss tolerance and lossless recovery. Through experiments and comparative analysis of related research results, the effectiveness and advantages of the method are verified.
Open Source Multipurpose Multimedia Annotation Tool

Efficient tools and frameworks for image and video annotation become more necessary for pattern recognition and computer vision research as datasets for training and testing of algorithms get increasingly larger. Different software packages have been developed to deal with these tasks, but they are usually designed for specific demands, problems or are not open to the public. This paper presents an open source multipurpose tool for annotation on multimedia datasets with extended flexibility through customizable labels, option of working on a shared database for collaborative annotation and with special care given on usability and efficiency for the best user experience. The Annotation Tool is available in the following link: www.thi.de/go/thi-labeling-tool .
Deep Learning Approach for the Development of a Novel Predictive Model for Prostate Cancer.

We developed a deep learning approach for accurate prediction of PCA patients one year earlier with minimal features from electronic health records. The area under the receiver operating curve for prediction of PCA was 0.94. Moreover, the sensitivity and specificity of CNN were 0.87 and 0.88, respectively.
Combining Mixture Models and Spectral Clustering for Data Partitioning

Gaussian Mixture Models are widely used nowadays, thanks to the simplicity and efficiency of the Expectation-Maximization algorithm. However, determining the optimal number of components is tricky and, in the context of data partitioning, may differ from the actual number of clusters. We propose to apply a post-processing step by means of Spectral Clustering: it allows a clever merging of similar Gaussians thanks to the Bhattacharyya distance so that clusters of any shape are automatically discovered. The proposed method shows a significant improvement compared to the classical Gaussian Mixture clustering approach and promising results against well-known partitioning algorithms with respect to the number of parameters.
Conditional Updates of Answer Set Programming and Its Application in Explainable Planning

In explainable planning, the planning agent needs to explain its plan to a human user, especially when the plan appears infeasible or suboptimal for the user. A popular approach is called model reconciliation, where the agent reconciles the differences between its model and the model of the user such that its plan is also feasible and optimal to the user. This problem can be viewed as a more general problem as follows: Given two knowledge bases πa and πh and a query q such that πa entails q and πh does not entail q, where the notion of entailment is dependent on the logical theories underlying πa and πh, how to change πh - given πa and the support for q in πa  so that πh does entail q. In this paper, we study this problem under the context of answer set programming. To achieve this goal, we (1) define the notion of a conditional update between two logic programs πa and πh with respect to a query q; (2) define the notion of an explanation for a query q from a program πa to a program πh using conditional updates; (3) develop algorithms for computing explanations; and (4) show how the notion of explanation based on conditional updates can be used in explainable planning.
Safe Policy Improvement with an Estimated Baseline Policy

Previous work has shown the unreliability of existing algorithms in the batch Reinforcement Learning setting, and proposed the theoretically-grounded Safe Policy Improvement with Baseline Bootstrapping (SPIBB) fix: reproduce the baseline policy in the uncertain state-action pairs, in order to control the variance on the trained policy performance. However, in many real-world applications such as dialogue systems, pharmaceutical tests or crop management, data is collected under human supervision and the baseline remains unknown. In this paper, we apply SPIBB algorithms with a baseline estimate built from the data. We formally show safe policy improvement guarantees over the true baseline even without direct access to it. Our empirical experiments on finite and continuous states tasks support the theoretical findings. It shows little loss of performance in comparison with SPIBB when the baseline policy is given, and more importantly, drastically and significantly outperforms competing algorithms both in safe policy improvement, and in average performance.
Safety Management Method of Coastal Buildings against Typhoon Based on BIM Technology

Xiao, M. and Zhou, X., 2020. Safety management method of coastal buildings against typhoon based on BIM technology. In: Yang, Y.; Mi, C.; Zhao, L., and Lam, S. (eds.), Global Topics and New Trends in Coastal Research: Port, Coastal and Ocean Engineering. Journal of Coastal Research, Special Issue No. 103, pp. 539–542. Coconut Creek (Florida), ISSN 0749-0208.The traditional safety management method of building anti wind has great error in calculating the parameters of building anti wind, so a safety management method of coastal building anti typhoon based on BIM Technology is designed. Firstly, the structural column family, structural beam family, floor family, and envelope family are created respectively by using Revit platform. According to the construction drawing, the BIM model of the measured building is established, and the typhoon data is analyzed. Based on the data, the building parameters such as the stable weight of the protective cover block, the weight and thickness of the cushion block stone are calculated to ensure that the coastal building has a good anti typhoon effect. In order to verify the effectiveness of the safety management method, the typhoon simulation experiment was designed, and the traditional method was used as the control group. The experimental results show that three parameters of the traditional method are not up to the minimum standard, and the design methods are all qualified.
Mining Public Opinion on Plastic Ban in India

Every product available in our environment has a shelf life, but plastic is the only material that is non-degradable. The complex polymer present in the plastic makes it durable and non-degradable. As a result, it is found in different forms on the earth for a long time. People have become used to plastic-made product in day-to-day life like carrying bags, disposable cutlery, food packaging and many more. Extensive quantities of plastic waste have accumulated in the nature and landfills and have posed an alarming hazard to the environment, and now, it reached a crisis point. Currently, India is ranked as the top four producers of plastic waste in the world. Though there is a law against the use of plastic in India but the usage of plastic-made products is still high as the ban is not implemented completely and effectively. In this paper, we propose a framework for analyzing the opinion of Indian population on the plastic ban with the help of sentiment analysis technique on Twitter textual data. We train and test a machine learning classifier on different combination of datasets achieving 77.94% classification accuracy. The result obtained will help to understand how effective and successful polybags ban scheme will be when entirely implemented in India.
Controlling the Specificity of Clarification Question Generation.

Unlike comprehension-style questions, clarification questions look for some missing information in a given context. However, without guidance, neural models for question generation, similar to dialog generation models, lead to generic and bland questions that cannot elicit useful information. We argue that controlling the level of specificity of the generated questions can have useful applications and propose a neural clarification question generation model for the same. We first train a classifier that annotates a clarification question with its level of specificity (generic or specific) to the given context. Our results on the Amazon questions dataset demonstrate that training a clarification question generation model on specificity annotated data can generate questions with varied levels of specificity to the given context.
Iterative Online Fault Identification Scheme for High-Voltage Circuit Breaker Utilizing a Lost Data Repair Technique

Most of the prior-art electrical noninvasive monitoring systems adopt Zigbee, Bluetooth, or other wireless communication infrastructure. These low-cost channels are often interrupted by strong electromagnetic interference and result in monitoring anomalies, particularly packet loss, which severely affects the precision of equipment fault identification. In this paper, an iterative online fault identification framework for a high-voltage circuit breaker utilizing a novel lost data repair technique is developed to adapt to low-data quality conditions. Specifically, the improved efficient k-nearest neighbor (kNN) algorithm enabled by a k-dimensional (K-D) tree is utilized to select the reference templates for the unintegrated samples. An extreme learning machine (ELM) is utilized to estimate the missing data based on the selected nearest neighbors. The Softmax classifier is exploited to calculate the probability of the repaired sample being classified to each of the preset status classes. Loop iterations are implemented where the nearest neighbors are updated until their labels are consistent with the estimated labels of the repaired sample based on them. Numerical results obtained from a realistic high-voltage circuit breaker (HVCB) condition monitoring dataset illustrate that the proposed scheme can efficiently identify the operation status of HVCBs by considering measurement anomalies.
A Deep Learning Approach to Language-independent Gender Prediction on Twitter

This work presents a set of experiments conducted to predict the gender of Twitter users based on language-independent features extracted from the text of the users’ tweets. The experiments were performed on a version of TwiSty dataset including tweets written by the users of six different languages: Portuguese, French, Dutch, English, German, and Italian. Logistic regression (LR), and feed-forward neural networks (FFNN) with back-propagation were used to build models in two different settings: Inter-Lingual (IL) and Cross-Lingual (CL). In the IL setting, the training and testing were performed on the same language whereas in the CL, Italian and German datasets were set aside and only used as test sets and the rest were combined to compose training and development sets. In the IL, the highest accuracy score belongs to LR whereas, in the CL, FFNN with three hidden layers yields the highest score. The results show that neural network based models underperform traditional models when the size of the training set is small; however, they beat traditional models by a non-trivial margin, when they are fed with large enough data. Finally, the feature analysis confirms that men and women have different writing styles independent of their language.
Taxonomy of Dual Block-Coordinate Ascent Methods for Discrete Energy Minimization.

We consider the maximum-a-posteriori inference problem in discrete graphical models and study solvers based on the dual block-coordinate ascent rule. We map all existing solvers in a single framework, allowing for a better understanding of their design principles. We theoretically show that some block-optimizing updates are sub-optimal and how to strictly improve them. On a wide range of problem instances of varying graph connectivity, we study the performance of existing solvers as well as new variants that can be obtained within the framework. As a result of this exploration we build a new state-of-the art solver, performing uniformly better on the whole range of test instances.
Can Blockchain Technology Enhance Security and Privacy in the Internet of Things?

The Internet of Things (IoT) has changed the traditional computing models. While it has enabled multiple new computing applications, it has also raised significant issues regarding security and privacy. We are gradually shifting to using extended computing architectures, the nodes of which may be lightweight devices limited in hardware resources, scattered in terms of network topology and too diverse in terms of hardware and software to be efficiently administered and managed. Additionally, such nodes usually store, process and transmit sensitive private data of their users; thus, the risk of a security breach is significantly high. Blockchain technology, introduced through Bitcoin, enables the development of secure decentralized systems. It offers guarantees regarding data integrity, application logic integrity and service availability, while it lags behind in terms of privacy and efficiency. Because of the decentralized architecture of blockchain systems, there seems to be a good fit between blockchain and the IoT. Blockchain systems can be employed to develop solutions to some of the main security and privacy issues encountered in the IoT domain. In this chapter we discuss the convergence of the two technologies, we analyze possible use cases, where blockchain technology can enhance internet of things security and privacy, and we propose enhancements of blockchain technology to make it appropriate for application in the IoT domain.
Dependability Assessment for SCADA System Considering Usage of Cloud Resources

Supervisory Control and Data Acquisition (SCADA) system is one the most significant cyber-oriented components of management circuit for any complex hierarchical infrastructure. The effectiveness and performance of the SCADA system depends on a number of relevant factors including a possibility to utilize additional cloud resources. Of particular note, the cloud services can apply in order to create concurrent redundant system for implementation of disaster recovery feature of SCADA information resources during different deliberate malicious impacts and intrusions. However, in spite of the fact that usage of the cloud services allow to enhance functionality of SCADA system, there are dependability and cybersecurity aspects for their joint utilization hat keep to remain a serious challenge for service personnel and users. In this paper, authors propose to employ analytical and stochastic modelling in order to get a numerical dependability assessment for SCADA system with realization of the cloud resourcing support considering influence of diverse negative factors. Furthermore, developers can utilize these modelling results in order to perform justification of overall functional requirements of the SCADA system.
Towards an Academic Abstract Sentence Classification System

This research in progress paper introduces a novel academic abstract sentence classification system intended to improve researcher literature discovery efficiency. The system provides three key functions: 1) displays abstracts with visual identification of each sentence’s indicated literature characteristic class, 2) conversion of unstructured abstracts into structured variants and 3) categorised class sentence extraction available for export to CSV alongside literature metadata. This functionality is made possible by a web application connected to a Python instance via PHP, integration with an open access literature index via an API and a deployed academic abstract sentence classification model. The contribution of the proposed system is its ability to enhance researcher literature discovery. This paper provides context and motivation behind the development of the system, outlines its functionality and provides an outlook for future research.