Reviewerly/exHarmony huggingface dataset:
How Do you Migrate your System to the Cloud Environment? An Example of Conversion from Silo Type to Horizontal Type

At Fukuoka University, we are promoting the transition of information systems centered on administrative systems to the cloud environment. Existing systems are composed of on-premises, and migration over 100 servers to the cloud environment including Azure is our task. Our main missions are two. One is to unify the complex system infrastructure by utilizing virtualization technology and the cloud. The second is to reduce the operating costs of those system infrastructures for the future. In this transition, we aimed to standardize the system environment by establishing a new server infrastructure utilizing the cloud, from silo type system construction. As an actual effort, we created standard service specifications to present to each system construction vendor, unified support windows, and routinely apply server patches. As a result of this standardization, centralized management operation of the system infrastructure was achieved. Moreover, we were able to contribute to a reduction in human cost and operation cost associated with system operation. In this report, we will introduce our approach and report the position of each step. In particular, we would like to share our approach and experiences with the vendors and departments that build and operate each system as an information infrastructure department and how we organized the structure and operation of the campus information system.
Design and specification of analog artificial neural network

In this paper, we have implemented, using Matlab Simulink an analog artificial neural network for breast cancer classification. Simulated results with ideal building blocks exhibit a total error of classification of 2.6%. Thanks to this value, we have modified Simulink models of the building blocks (i.e. multiplier, activation function and its derivative) in order to take into account their non-idealities. This study allows to determine their influence on the classification quality and to extract some specifications of these building blocks.
Up Close, but not too Personal: Hypotargeting for Recommender Systems

Hypotargeting for recommender systems (hyporec) is the idea of controlling the number of unique lists of items that a recommender system can recommend to users during a given time period. The main advantage of hyporec is oversight. If a recommender system offers only a finite number of unique lists, then it becomes feasible for a person without technological knowledge to audit the recommender system. Oversight makes it possible to spot filter bubbles or cases in which users are being bombarded with divisive content. We argue that hyporec is actually not so far from many existing recommender system ideas, and that with further research hyporec systems could be capable of making good tradeoffs between the number of unique lists, rate of list renewal (which controls coverage), and conventional evaluation metrics for user satisfaction.
May I Check Again? — A simple but efficient way to generate and use contextual dictionaries for Named Entity Recognition. Application to French Legal Texts.

In this paper we present a new method to learn a model robust to typos for a Named Entity Recognition task. Our improvement over existing methods helps the model to take into account the context of the sentence inside a justice decision in order to recognize an entity with a typo. We used state-of-the-art models and enriched the last layer of the neural network with high-level information linked with the potential of the word to be a certain type of entity. More precisely, we utilized the similarities between the word and the potential entity candidates the tagged sentence context. The experiments on a dataset of french justice decisions show a reduction of the relative F1-score error of 32%, upgrading the score obtained with the most competitive fine-tuned state-of-the-art system from 94.85% to 96.52%.
An Integrated Approach for Making Inference on the Number of Clusters in a Mixture Model

This paper presents an integrated approach for the estimation of the parameters of a mixture model in the context of data clustering. The method is designed to estimate the unknown number of clusters from observed data. For this, we marginalize out the weights for getting allocation probabilities that depend on the number of clusters but not on the number of components of the mixture model. As an alternative to the stochastic expectation maximization (SEM) algorithm, we propose the integrated stochastic expectation maximization (ISEM) algorithm, which in contrast to SEM, does not need the specification, a priori, of the number of components of the mixture. Using this algorithm, one estimates the parameters associated with the clusters, with at least two observations, via local maximization of the likelihood function. In addition, at each iteration of the algorithm, there exists a positive probability of a new cluster being created by a single observation. Using simulated datasets, we compare the performance of the ISEM algorithm against both SEM and reversible jump (RJ) algorithms. The obtained results show that ISEM outperforms SEM and RJ algorithms. We also provide the performance of the three algorithms in two real datasets.
TAB-VCR: Tags and Attributes based VCR Baselines

Reasoning is an important ability that we learn from a very early age. Yet, reasoning is extremely hard for algorithms. Despite impressive recent progress that has been reported on tasks that necessitate reasoning, such as visual question answering and visual dialog, models often exploit biases in datasets. To develop models with better reasoning abilities, recently, the new visual commonsense reasoning(VCR) task has been introduced. Not only do models have to answer questions, but also do they have to provide a reason for the given answer. The proposed baseline achieved compelling results, leveraging a meticulously designed model composed of LSTM modules and attention nets. Here we show that a much simpler model obtained by ablating and pruning the existing intricate baseline can perform better with half the number of trainable parameters. By associating visual features with attribute information and better text to image grounding, we obtain further improvements for our simpler & effective baseline, TAB-VCR. We show that this approach results in a 5.3%, 4.4% and 6.5% absolute improvement over the previous state-of-the-art on question answering, answer justification and holistic VCR. Webpage: https://deanplayerljx.github.io/tabvcr/
PENERAPAN ANIMASI INTERAKTIF BERBASIS 2D SEBAGAI MEDIA PENGENALAN PLANET UNTUK ANAK USIA DINI

The planet is one of the celestial bodies in the solar system that circulates around the Sun in an elliptical shape. Planet learning has entered the curriculum since Kindergarten or Early Childhood Education. While in everyday life explaining the planet is difficult to convey, coupled with the teaching system that is still conventional / classic. Therefore we need media that can help in delivering material. Interactive animation as a medium that can support the learning process. Animation can make the learning process difficult to convey conventionally / classically, but it will be easier to express and be understood by designing this interactive animation using the library method to get clear data. With this interactive animation, it is hoped that it can help the learning process of children, so that they can increase their knowledge and desire to learn.
Automatic summarization of Malayalam documents using clause identification method

&lt;span&gt;Text summarization is an active research area in the field of natural language processing. Huge amount of information in the internet necessitates the development of automatic summarization systems. There are two types of summarization techniques: Extractive and Abstractive. Extractive summarization selects important sentences from the text and produces summary as it is present in the original document. Abstractive summarization systems will provide a summary of the input text as is generated by human beings. Abstractive summary requires semantic analysis of text. Limited works have been carried out in the area of abstractive summarization in Indian languages especially in Malayalam. Only extractive summarization methods are proposed in Malayalam. In this paper, an abstractive summarization system for Malayalam documents using clause identification method is proposed. As part of this research work, a POS tagger and a morphological analyzer for Malayalam words in cricket domain are also developed. The clauses from input sentences are identified using a modified clause identification algorithm. The clauses are then semantically analyzed using an algorithm to identify semantic triples - subject, object and predicate. The score of each clause is then calculated by using feature extraction and the important clauses which are to be included in the summary are selected based on this score. Finally an algorithm is used to generate the sentences from the semantic triples of the selected clauses which is the abstractive summary of input documents.&lt;/span&gt;
The Concept of “Anti-Hoax Intelligence (CI1)” Inside Social Media using Ken Watanabe &amp; Johari Window Methods

The speed of social media has a huge influence on human life. This change requires a simple guide on how to deal with hoaxes and solutions. This research is a development of previously published journals: The Development and Implementation of Wise Netizens (E-Comment) in Indonesia; Developing Framework Intelligence (CI3) Inside Using Social Media Johari Window Methods; Developing “Framework Leadership Intelligence (CI2)” Inside Social Media to Develop an Ethical Leader using the Johari Window Method. The problems that exist in social media are lies which rose directly and indirectly; information contains “half-truth”; “information but not enough knowledge”; and “lie for good”. Furthermore, the methods for the research: Johari window and Ken Watanabe-Problem solving, which are two methods for dealing with hoax problems. The end result of this research is a formula and a guide to dealing with hoaxes. Moreover, this research still has to go through several stages of development and will produce a complete guide to dealing with hoaxes and problems found on social media. Therefore, this research requires more in-depth data and surveys; researchers will continue to carry out observations, surveys, and data collection until the research can create an appropriate formula in overcoming all problems in social media.
An Automata-based Abstract Semantics for String Manipulation Languages.

In recent years, dynamic languages, such as JavaScript or Python, have been increasingly used in a wide range of fields and applications. Their tricky and misunderstood behaviors pose a hard challenge for static analysis of these programming languages. A key aspect of any dynamic language program is the multiple usage of strings, since they can be implicitly converted to another type value, transformed by string-to-code primitives or used to access an object-property. Unfortunately, string analyses for dynamic languages still lack precision and do not take into account some important string features. Moreover, string obfuscation is very popular in the context of dynamic language malicious code, for example, to hide code information inside strings and then to dynamically transform strings into executable code. In this scenario, more precise string analyses become a necessity. This paper is placed in the context of static string analysis by abstract interpretation and proposes a new semantics for string analysis, placing a first step for handling dynamic languages string features.
Conversation Generation with Concept Flow

Human conversations naturally evolve around related concepts and scatter to multi-hop concepts. This paper presents a new conversation generation model, ConceptFlow, which leverages commonsense knowledge graphs to explicitly model conversation flows. By grounding conversations to the concept space, ConceptFlow represents the potential conversation flow as traverses in the concept space along commonsense relations. The traverse is guided by graph attentions in the concept graph, moving towards more meaningful directions in the concept space, in order to generate more semantic and informative responses. Experiments on Reddit conversations demonstrate ConceptFlow's effectiveness over previous knowledge-aware conversation models and GPT-2 based models while using 70% fewer parameters, confirming the advantage of explicit modeling conversation structures. All source codes of this work are available at this https URL.
Service Discovery Method for Agile Mashup Development

With the rapid expansion of services on the Internet, Mashup development has become a trend toward mainstream development. How to efficiently and quickly discover available services in Mashup development and make full use of existing services to meet the changing needs of users has become a new concern. Although there are a lot of work for service discovery, there are still some problems in the existing methods, such as limiting the service description to a single structured document, limiting the service search statement to keywords, and rarely mining the deeper semantics of the service text. information. In view of the above problems, this paper proposes the Service Discovery approach for Agile Mashup Development (SDAMD), which breaks the limitation of the single document and drives the user story in agile development as a service search. The original text, through the natural language processing technology, extracts the three elements of agile requirements, and then extracts the three service attributes of the agile service; then finds and recommends similar services by calculating the similarity between the service description and the search text. This article uses the real data of the services on the Programmable Web to verify the validity of SDAMD.
Improved Algorithms for Zero Shot Image Super-Resolution with Parametric Rectifiers

Recently, a novel Zero-Shot Super-Resolution (ZSSR) method is proposed to generate high-resolution (HR) images from their low-resolution (LR) counterparts. ZSSR employs a convolutional neural network (CNN) to represent transformations from LR images to HR images and is trained on a single image. ZSSR achieves state-of-the-art performance on both real low-resolution images (i.e., historic images, and images taken with a mobile phone) and several benchmark datasets (e.g., Set 5 and Set 14 to name a few). However, the training of the CNN network of ZSSR is not stable since rectifier is used as the activation function and a custom learning rate adjustment policy is proposed in ZSSR. In this paper, we use parametric rectifier as the activation function and present an improved algorithm for the training of ZSSR. Experimental results demonstrate that the proposed method outperforms ZSSR in terms of both reconstruction accuracy and speed on two benchmark datasets: Set 5 and Set 14, respectively.
Correction to: Investigating the parameter space of evolutionary algorithms

Following publication of the original article [1], an error was reported in one of the experiments.
Expert2Vec: Distributed Expert Representation Learning in Question Answering Community

Community question answering (CQA) has attracted increasing attention recently due to its potential as a de facto knowledge base. Expert finding in CQA websites also has considerably board applications. Stack Overflow is one of the most popular question answering platforms, which is often utilized by recent studies on the recommendation of the domain expert. Despite the substantial progress seen recently, it still lacks relevant research on the direct representation of expert users. Hence hereby we propose Expert2Vec, a distributed Expert Representation learning in question answering community to boost the recommendation of the domain expert. Word2Vec is used to preprocess the Stack Overflow dataset, which helps to generate representations of domain topics. Weight rankings are then extracted based on domains and variational autoencoder (VAE) is unitized to generate representations of user-topic information. This finally adopts the reinforcement learning framework with the user-topic matrix to improve it internally. Experiments show the adequate performance of our proposed approaches in the recommendation system.
The Structural Relationship Between Self-Directed Learning Team Efficacy, and Initiative in Problem Solving for General Physical Activity Participants in Based Team Project Lesson

Purpose: The aim of this research is to verify structural relationship among self-directed learning, team efficacy, and initiative in problem solving for general physical activity participants in based team project lesson.BR Method: The study selected 236 people who joined the general physical activities as subjects. For the data processing, it conducted frequency, reliability, confirmatory factor, correlation, and structure equaling modeling analyses by SPSS 18.0 and AMOS 18.0 programs.BR Results: The results of this research are follows. 1) Self-directed learning of general physical activity in based team project lesson had influenced the team efficacy p(.001). 2) Self-directed learning of general physical activity in based team project lesson had influenced the initiative in problem solving (p.001). 3) Team efficacy of general physical activity in based team project lesson had influenced the initiative in problem solving(p.001).BR Conclusion: The fourth industrial revolutionary introduced a variety of educational methods aiming at the ability of problem solving that is different from cramming method of education. In the field of sports, it is necessary to cultivate self-directed learning ability that inspires students to learn by team-based project lesson.
A CSF-Based CNR Approach for Small-Size Image Sequences

For non-rigid structure from motion (NRSFM), the performance of most traditional approaches may decrease significantly when the frame number of the image sequence is relatively small. In this letter, a column space fitting (CSF) based consensus of non-rigid (CNR) reconstruction approach is proposed to deal with the 3D structure estimation problem for small-size image sequences. In the proposed method, a set of trajectory groups are first extracted by utilizing the distance weight of the pairwise points. In order to improve the estimation accuracy, an adaptive rank selection strategy is designed to choose the approximately optimal rank parameter. Corresponding to the trajectory group, the z-coordinates of the observation matrix are estimated by the CSF algorithm due to its good performance. After obtaining the outputs of the CSF-based weak estimators, the final 3D shape is derived by combining the outputs via the alternating directional method of multipliers. Experimental results on several widely used image sequences demonstrate the effectiveness and feasibility of the proposed algorithm.
Goal-Embedded Dual Hierarchical Model for Task-Oriented Dialogue Generation

Hierarchical neural networks are often used to model inherent structures within dialogues.For goal-oriented dialogues, these models miss a mechanism adhering to the goals and neglect the distinct conversational patterns between two interlocutors.In this work, we propose Goal-Embedded Dual Hierarchical Attentional Encoder-Decoder (G-DuHA) able to center around goals and capture interlocutorlevel disparity while modeling goal-oriented dialogues.Experiments on dialogue generation, response generation, and human evaluations demonstrate that the proposed model successfully generates higher-quality, more diverse and goal-centric dialogues.Moreover, we apply data augmentation via goal-oriented dialogue generation for task-oriented dialog systems with better performance achieved.
A Physical Design Strategy on a NoSQL DBMS

Massive amounts of data are being generated by users and electronic devices every day. In an era of big data, the organizations need to collect and efficiently manage such amount of data. In this sense, graphs easily model data connections, and they are widely applicable to a variety of systems. Currently, the graph technology is becoming increasingly important, and graphs are used to model dynamic and complex relationships of data to generate knowledge. Particularly, Neo4j is a database management system that currently leads the NoSQL systems on graph databases. In this chapter, our main objective is to propose physical design guidelines that improves query execution time on graph databases in terms of a specific workload in Neo4j. In this work, indexes, path materialization, and query rewriting are considered as guidelines for the physical design on Neo4j databases. The application of the proposed physical design guideline was empirically studied for the LDBC (Linked Data Benchmark Council) Social Network Benchmark. The experiments were conducted on the basis of cold cache, and two metrics were measured: query execution time and the amount of operation required to retrieve data or database hits. Each of the query was first executed using the physical design guidelines proposed in this work and then on a database without any physical design. The reported results show that our physical design guidelines are better for all queries in terms of at least one of the two metrics measured. Also, we showed in our experimental study that the improvement obtained using our physical design guidelines was statistically significant.
Learning to Generalize via Self-Supervised Prediction

Author(s): Pathak, Deepak | Advisor(s): Darrell, Trevor; Efros, Alexei A. | Abstract: Generalization, i.e., the ability to adapt to novel scenarios, is the hallmark of human intelligence. While we have systems that excel at recognizing objects, cleaning floors, playing complex games and occasionally beating humans, they are incredibly specific in that they only perform the tasks they are trained for and are miserable at generalization. Could optimizing towards fixed external goals be hindering the generalization instead of aiding it? In this thesis, we present our initial efforts toward endowing artificial agents with a human-like ability to generalize in diverse scenarios. The main insight is to first allow the agent to learn general-purpose skills in a completely self-directed manner, without optimizing for any external goal.To be able to learn on its own, the claim is that an artificial agent must be embodied in the world, develop an understanding of its sensory input (e.g., image stream) and simultaneously learn to map this understanding to its motor outputs (e.g., torques) in an unsupervised manner. All these considerations lead to two fundamental questions: how to learn rich representations of the world similar to what humans learn?; and how to re-use such a representation of past knowledge to incrementally adapt and learn more about the world similar to how humans do? We believe prediction is the key to this answer. We propose generic mechanisms that employ prediction as a self-supervisory signal in allowing the agents to learn sensory representations as well as motor control. These two abilities equip an embodied agent with a basic set of general-purpose skills which are then later repurposed to perform complex tasks.We discuss how this framework can be instantiated to develop curiosity-driven agents (virtual as well as real) that can learn to play games, learn to walk, and learn to perform real-world object manipulation without any rewards or supervision. These self-directed robotic agents, after exploring the environment, can generalize to find their way in office environments, tie knots using rope, rearrange object configuration, and compose their skills in a modular fashion.