Reviewerly/exHarmony huggingface dataset:
Drawing Order Recovery based on deep learning

Humans have the ability to recover the order from static handwritten images, after a large amount of data training, the machine may learn some patterns in the training data to imitate or learn a certain skill similar to humans. To overcome the problem of sequence recovery of static image strokes, this paper proposes a stroke recovery method based on deep convolutional neural network model. In the model training phase, by using the two-dimensional static handwritten image, the process of writing a font is convert into three channels includes strokes that have been written, possible positions of next strokes, and the completed font, and state of the input sample are quantified. In the recovery phase, the restored font is preprocessed to obtain the stroke segments of the font, and the trained model is used to evaluate the sequential combination of different stroke segments, so as to obtain the correct stroke order. With no more than one hundred of characters' writing experiences, the proposed method performs robustly and competitively among multi-writer handwriting DOR tasks.
Model Learning for Look-Ahead Exploration in Continuous Control

We propose an exploration method that incorporates lookahead search over basic learnt skills and their dynamics, and use it for reinforcement learning (RL) of manipulation policies. Our skills are multi-goal policies learned in isolation in simpler environments using existing multigoal RL formulations, analogous to options or macroactions. Coarse skill dynamics, i.e., the state transition caused by a (complete) skill execution, are learnt and are unrolled forward during lookahead search. Policy search benefits from temporal abstraction during exploration, though itself operates over low-level primitive actions, and thus the resulting policies does not suffer from suboptimality and inflexibility caused by coarse skill chaining. We show that the proposed exploration strategy results in effective learning of complex manipulation policies faster than current state-of-the-art RL methods, and converges to better policies than methods that use options or parametrized skills as building blocks of the policy itself, as opposed to guiding exploration. We show that the proposed exploration strategy results in effective learning of complex manipulation policies faster than current state-of-the-art RL methods, and converges to better policies than methods that use options or parameterized skills as building blocks of the policy itself, as opposed to guiding exploration.
On the Convergence of (Stochastic) Gradient Descent with Extrapolation for Non-Convex Minimization

Extrapolation is a well-known technique for solving convex optimization and variational inequalities and recently attracts some attention for non-convex optimization. Several recent works have empirically shown its success in some machine learning tasks. However, it has not been analyzed for non-convex minimization and there still remains a gap between the theory and the practice. In this paper, we analyze gradient descent and stochastic gradient descent with extrapolation for finding an approximate first-order stationary point in smooth non-convex optimization problems. Our convergence upper bounds show that the algorithms with extrapolation can be accelerated than without extrapolation.
Comparative judgments are more consistent than binary classification for labelling word complexity

Lexical simplification systems replace complex words with simple ones based on a model of which words are complex in context.We explore how users can help train complex word identification models through labelling more efficiently and reliably.We show that using an interface where annotators make comparative rather than binary judgments leads to more reliable and consistent labels, and explore whether comparative judgments may provide a faster way for collecting labels.
Privacy-aware Synthesizing for Crowdsourced Data

Although releasing crowdsourced data brings many benefits to the data analyzers to conduct statistical analysis, it may violate crowd users' data privacy. A potential way to address this problem is to employ traditional differential privacy (DP) mechanisms and perturb the data with some noise before releasing them. However, considering that there usually exist conflicts among the crowdsourced data and these data are usually large in volume, directly using these mechanisms can not guarantee good utility in the setting of releasing crowdsourced data. To address this challenge, in this paper, we propose a novel privacy-aware synthesizing method (i.e., PrisCrowd) for crowdsourced data, based on which the data collector can release users' data with strong privacy protection for their private information, while at the same time, the data analyzer can achieve good utility from the released data. Both theoretical analysis and extensive experiments on real-world datasets demonstrate the desired performance of the proposed method.
Towards Stable Symbol Grounding with Zero-Suppressed State AutoEncoder

While classical planning has been an active branch of AI, its applicability is limited to the tasks precisely modeled by humans. Fully automated high-level agents should be instead able to find a symbolic representation of an unknown environment without supervision, otherwise it exhibits the knowledge acquisition bottleneck. Meanwhile, Latplan (Asai and Fukunaga 2018) partially resolves the bottleneck with a neural network called State AutoEncoder (SAE). SAE obtains the propositional representation of the image-based puzzle domains with unsupervised learning, generates a state space and performs classical planning. In this paper, we identify the problematic, stochastic behavior of the SAE-produced propositions as a new sub-problem of symbol grounding problem, the symbol stability problem. Informally, symbols are stable when their referents (e.g. propositional values) do not change against small perturbation of the observation, and unstable symbols are harmful for symbolic reasoning. We analyze the problem in Latplan both formally and empirically, and propose “Zero-Suppressed SAE”, an enhancement that stabilizes the propositions using the idea of closed-world assumption as a prior for NN optimization. We show that it finds the more stable propositions and the more compact representations, resulting in an improved success rate of Latplan. It is robust against various hyperparameters and eases the tuning effort, and also provides a weight pruning capability as a side effect.
Multi-Agent Path Finding with Continuous Time and Geometric Agents Viewed through Satisfiability Modulo Theories (SMT)

This paper addresses a variant of multi-agent path finding (MAPF) in continuous space and time. We present a new solving approach based on satisfiability modulo theories (SMT) to obtain makespan optimal solutions. The standard MAPF is a task of navigating agents in an undirected graph from given starting vertices to given goal vertices so that agents do not collide with each other in vertices of the graph. In the continuous version (MAPF-R) agents move in an n-dimensional Euclidean space along straight lines that interconnect predefined positions. For simplicity, we work with circular omni-directional agents having constant velocities in the 2D plane. As agents can have different sizes and move smoothly along lines, a non-colliding movement along certain lines with small agents can result in a collision if the same movement is performed with larger agents. Our SMT-based approach for MAPF-R called SMT-CBS-R reformulates the Conflict-based Search (CBS) algorithm in terms of SMT concepts. We suggest lazy generation of decision variables and constraints. Each time a new conflict is discovered, the underlying encoding is extended with new variables and constraints to eliminate the conflict. We compared SMT-CBS-R and adaptations of CBS for the continuous variant of MAPF experimentally.
Tsallis entropy, <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>q</mml:mi></mml:math> -expectation value, and constraints on three-party quantum correlations

We show that the mutually exclusive nature of classical and quantum correlations distributed in multi-party quantum systems can be characterized in terms of $q$-expectation. Using Tsallis-$q$ entropy and $q$-expectation, we first provide generalized definitions of classical and quantum correlations, and establish their trade-off relations in three-party quantum systems of arbitrary dimension with respect to $q$-expectation for $q\geq 1$. We also provide equivalence conditions for monogamy and polygamy inequalities of quantum entanglement and quantum discord distributed in three-party quantum systems of arbitrary dimension with respect to $q$-expectation for $q\geq 1$.
Harnessing international remittances for financial development: the role of monetery policy

This study investigates how remittances and monetary policy independently and interactively shape the financial system of developing countries.It employs single equation instrumental variable based estimation procedures to test the hypothesis that, to boost financial development, remittances require a complementary domestic monetary policy framework which ensures price stability while limiting price distortions.The results show that remittances stimulate financial development only in countries with a favourable monetary environment.Building on these results and employing various indicators of financial development, the results suggest that remittances rise to cushion migrant households from the repercussions of poor financial intermediation, weak institutions and unfavourable business environment in the home country.By extension, the findings are germane to monetary and financial policy in developing countries.
A General Planning-Based Framework for Goal-Driven Conversation Assistant

We propose a general framework for goal-driven conversation assistant based on Planning methods. It aims to rapidly build a dialogue agent with less handcrafting and make the more interpretable and efficient dialogue management in various scenarios. By employing the Planning method, dialogue actions can be efficiently defined and reusable, and the transition of the dialogue are managed by a Planner. The proposed framework consists of a pipeline of Natural Language Understanding (intent labeler), Planning of Actions (with a World Model), and Natural Language Generation (learned by an attention-based neural network). We demonstrate our approach by creating conversational agents for several independent domains.
Binarized Neural Networks for Resource-Efficient Hashing with Minimizing Quantization Loss

In order to solve the problem of memory consumption and computational requirements, this paper proposes a novel learning binary neural network framework to achieve a resource-efficient deep hashing. In contrast to floating-point (32-bit) full-precision networks, the proposed method achieves a 32x model compression rate. At the same time, computational burden in convolution is greatly reduced due to efficient Boolean operations. To this end, in our framework, a new quantization loss defined between the binary weights and the learned real values is minimized to reduce the model distortion, while, by minimizing a binary entropy function, the discrete optimization is successfully avoided and the stochastic gradient descend method can be used smoothly. More importantly, we provide two theories to demonstrate the necessity and effectiveness of minimizing the quantization losses for both weights and activations. Numerous experiments show that the proposed method can achieve fast code generation without sacrificing accuracy.
A fast algorithm for automatic phase picker and event location: Application to the 2018 Hualien earthquake sequences

With the vast amounts of data recorded by the CWB seismic networks, a fast and precise algorithm for near real-time earthquake locations as well as fundamentally urgent source and hazard studies of large damaging earthquakes in Taiwan becomes increasingly important.Here we present a novel method for automatic phase identification and event detection, modified from a Python-based PhasePApy package to be more computationally efficient and suitable for the high-rate seismicity and large-scale dense network in Taiwan.The performance efficiency is enhanced by substituting the Python code with a Fortran subroutine to calculate the characteristic function for phase picker.It is further improved by amalgamating the picks close enough in time but on different components, associating pairs of the amalgamated picks with the origin times of candidate events through an empirical linear relation between the P and S-P times reported in the CWB phase catalog, and isolating clustered candidate events and corresponding phase picks to determine the locations of real earthquakes.The algorithm is applied to detecting in almost real time over two thousands of events that occurred within four days accompanying the 4 February 2018 M L 5.89 foreshock and 6 February 2018 M L 6.25 Hualien mainshock, which far surpasses the capability of experienced human analysts.The temporal and spatial distributions of the detected aftershocks provide the timely and first-hand information to estimate the aftershock decay rate and verify the orientation of the fault plane and rupture extent of the foreshock and disastrous mainshock as a guide for fast risk assessment and source characteristics analysis.
Convolutional neural network-based model for web-based text classification

&lt;table width="593" border="1" cellspacing="0" cellpadding="0"&gt;&lt;tbody&gt;&lt;tr&gt;&lt;td valign="top" width="387"&gt;&lt;p&gt;There is an increasing amount of text data available on the web with multiple topical granularities; this necessitates proper categorization/classification of text to facilitate obtaining useful information as per the needs of users. Some traditional approaches such as bag-of-words and bag-of-ngrams models provide good results for text classification. However, texts available on the web in the current state contain high event-related granularity on different topics at different levels, which may adversely affect the accuracy of traditional approaches. With the invention of deep learning models, which already have the capability of providing good accuracy in the field of image processing and speech recognition, the problems inherent in the traditional text classification model can be overcome. Currently, there are several deep learning models such as a convolutional neural networks (CNNs), recurrent neural networks (RNNs), and long-short term memory that are widely used for various text-related tasks; however, among them, the CNN model is popular because it is simple to use and has high accuracy for text classification. In this study, classification of random texts on the web into categories is attempted using a CNN-based model by changing the hyperparameters and sequence of text vectors. We attempt to tune every hyperparameter that is unique for the classification task along with the sequences of word vectors to obtain the desired accuracy; the accuracy is found to be in the range of 85–92%. This model can be considered as a reliable model and applied to solve real-world problem or extract useful information for various text mining applications.&lt;/p&gt;&lt;/td&gt;&lt;/tr&gt;&lt;/tbody&gt;&lt;/table&gt;
Embodied Conversational AI Agents in a Multi-modal Multi-agent Competitive Dialogue

In a setting where two AI agents embodied as animated humanoid avatars are engaged in a conversation with one human and each other, we see two challenges. One, determination by the AI agents about which one of them is being addressed. Two, determination by the AI agents if they may/could/should speak at the end of a turn. In this work we bring these two challenges together and explore the participation of AI agents in multi-party conversations. Particularly, we show two embodied AI shopkeeper agents who sell similar items aiming to get the business of a user by competing with each other on the price. In this scenario, we solve the first challenge by using headpose (estimated by deep learning techniques) to determine who the user is talking to. For the second challenge we use deontic logic to model rules of a negotiation conversation.
Research and Application of APT Attack Defense and Detection Technology Based on Big Data Technology

In order to excavate security threats in power grid by making full use of heterogeneous data sources in power information system, this paper proposes APT (Advanced Persistent Threat) attack detection sandbox technology and active defense system based on big data analysis technology. First, the file is restored from the mirror traffic and executed statically. Then, sandbox execution was carried out to introduce analysis samples into controllable virtual environment, and dynamic analysis and operation samples were conducted. Through analyzing the dynamic processing process of samples, various known and unknown malicious code, APT attacks, high-risk Trojan horses and other network security risks were comprehensively detected. Finally, the threat assessment of malicious samples is carried out and visualized through the big data platform. The results show that the method proposed in this paper can effectively warn of unknown threats, improve the security level of system data, have a certain active defense ability. And it can effectively improve the speed and accuracy of power information system security situation prediction.
Annotating and analyzing the interactions between meaning relations

Pairs of sentences, phrases, or other text pieces can hold semantic relations such as paraphrasing, textual entailment, contradiction, specificity, and semantic similarity.These relations are usually studied in isolation and no dataset exists where they can be compared empirically.Here we present a corpus annotated with these relations and the analysis of these results.The corpus contains 520 sentence pairs, annotated with these relations.We measure the annotation reliability of each individual relation and we examine their interactions and correlations.Among the unexpected results revealed by our analysis is that the traditionally considered direct relationship between paraphrasing and bi-directional entailment does not hold in our data.
Digital Literacy: A Survey Level Digital Literacy Competence among University Students in Jakarta

Digital Literacy Competence is not yet mastered by the people of Indonesia.Understanding of reading material and the ability to produce positive reading has not been successfully implemented.The purpose of the study was to analyze the influence of parental roles on digital literac y competencies of the students of the Faculty of Communication Sciences, Universitas Mercu Buana.The concept of Interpersonal Communication by DeVito (2013) was used in this study.The role of the family can be classified into four, namely: (1) modeling; (2) mentoring; (3) organizing, and ( 4) teaching.Through survey methods and data analysis techniques with correlation and impact testing, the results of the study show that the role of parents has an influence on digital literacy competencies.The role o f parents as models, mentors, organizers and instructors in educating and raising their children is highly recognized by their children.Meanwhile, the role of parents has not had a maximum effect on improving student literacy digital competencies.
Multi-source fault detection and diagnosis based on multi-level Knowledge Graph and Bayesian theory reasoning (S)

In complex industrial processes, complex associations are often involved.This complex relationship makes traditional fault detection and diagnosis methods difficult to achieve satisfactory results in multi-source fault detection and diagnosis.Therefore, this paper proposes a new multi-source fault detection and diagnosis framework.The method can successfully detect the state of the system, and at the same time, can locate the fault source simply and quickly.This method firstly constructs the multi-level knowledge graph in the complex industrial process, and then use the discriminant coefficient R to detect whether the system has failed.If the system fails into the fault diagnosis stage, the probability of the fault is derived based on Bayesian theory.This article describes the framework in detail.The TE process is taken as an example to prove the effectiveness of the method.
HYBRID DECISION SUPPORT SYSTEM FRAMEWORK FOR CROP YIELD PREDICTION AND RECOMMENDATION

In this paper, a hybrid decision support system is presented which uses both quantitative and qualitative data to provide effective and efficient decision making for crop yield prediction and suggestion. Our framework integrates KD-DSS and DD-DSS for solving complex problems by complementing the existing gap of individual decision support system in agriculture domain. For analyzing collected quantitative data of agriculture research center, our framework uses artificial neural network as a data mining technique. So, we use ANN for uncovering hidden knowledge in stored dataset. And this knowledge is further integrated with the knowledge base developed by acquiring qualitative data from expertise and represented using an IF-THEN production rule. The integration of knowledge collected from both qualitative and quantitative source of data provides a potential advantage for solving complex problems for decision makers. Finally, we will have the opportunity to enhance the framework coupling the features which can provide a group knowledge sharing among decision makers. So, this feature can present the opportunities to fill the disparity of decisions made by different decision makers.
Real-Time Control for Power Cost Efficient Deep Learning Processing With Renewable Generation

The explosive increase in deep learning (DL) deployment has led GPU power usage to become a major factor in operational cost of modern HPC clusters. The complex mixture of DL processing, fluctuated renewable generation, and dynamic electricity price impedes the elaborate GPU power control, so as to lead an undesirable cost. However, most previous studies have been concerned only with the design of power management method using DL, and have not care about the cost caused by GPU power consumption for DL processing itself. This paper, as the opposite direction of these trends, proposes a real-time power controller called DeepPow-CTR for cost efficient DL processing in GPU based clusters. We design the GPU frequency scaling algorithm based on model predictive control (MPC), to delicately tune the DL power consumption in response to dynamic renewable generation and electricity price. At the same time, we avoid the unacceptable DL performance degradation by regulating memory-access / feed-forward / back-propagation (MFB) time per each minibatch data in deep neural network (DNN) model training. To solve the designed nonlinear MPC problem rapidly and accurately, we apply the damped Broyden-Fletcher-Goldfarb-Shanno (BFGS) based sequential quadratic programming (SQP) method to our DeepPow-CTR. Our experimental results on lab-scale testbed using real trace data of renewable generation and electricity price, demonstrate that the proposed DeepPow-CTR has superiority and practicality in terms of DL processing power cost and performance, compared to existing methods.