Reviewerly/exHarmony huggingface dataset:
Improving Few-shot Text Classification via Pretrained Language Representations.

Text classification tends to be difficult when data are deficient or when it is required to adapt to unseen classes. In such challenging scenarios, recent studies have often used meta-learning to simulate the few-shot task, thus negating implicit common linguistic features across tasks. This paper addresses such problems using meta-learning and unsupervised language models. Our approach is based on the insight that having a good generalization from a few examples relies on both a generic model initialization and an effective strategy for adapting this model to newly arising tasks. We show that our approach is not only simple but also produces a state-of-the-art performance on a well-studied sentiment classification dataset. It can thus be further suggested that pretraining could be a promising solution for few-shot learning of many other NLP tasks. The code and the dataset to replicate the experiments are made available at this https URL.
Decouplings for Real Analytic Surfaces of Revolution

We extend the decoupling results of the first two authors to the case of real analytic surfaces of revolution in $${\mathbb R}^3$$ . New examples of interest include the torus and the perturbed cone.
Enriching Character-Based Neural Machine Translation with Modern Documents for Achieving an Orthography Consistency in Historical Documents

The nature of human language and the lack of a spelling convention make historical documents hard to handle for natural language processing. Spelling normalization tackles this problem by adapting their spelling to modern standards in order to get an orthography consistency. In this work, we compare several character-based machine translation approaches, and propose a method to profit from modern documents to enrich neural machine translation models. We tested our proposal with four different data sets, and observed that the enriched models successfully improved the normalization quality of the neural models. Statistical models, however, yielded a better result.
On-the-Fly Usability Evaluation of Mobile Adaptive UIs Through Instant User Feedback

Adaptive User Interfaces (AUIs) have been promoted as a solution for context variability due to their ability to automatically adapt to the context-of-use at runtime. For the acceptance of AUIs, usability plays a crucial role. Classical usability evaluation methods like usability tests, interviews or cognitive walkthroughs are not sufficient for evaluating end-user satisfaction of AUIs at runtime. The main reason is that the UI and context-of-use are dynamically changing and acceptance of UI adaptation features has to be evaluated in the moment and context-of-use when the adaptations occur. To address this challenge, we present an on-the-fly usability evaluation solution that combines continuous context monitoring together with collection of instant user feedback to assess end-user satisfaction of UI adaptation features. We demonstrate the usefulness and benefit of our solution based on a mobile Android mail app, which served as basis for conducting a usability study.
Panlingua-KMI MT System for Similar Language Translation Task at WMT 2019

The present paper enumerates the development of Panlingua-KMI Machine Translation (MT) systems for Hindi ↔ Nepali language pair, designed as part of the Similar Language Translation Task at the WMT 2019 Shared Task. The Panlingua-KMI team conducted a series of experiments to explore both the phrase-based statistical (PBSMT) and neural methods (NMT). Among the 11 MT systems prepared under this task, 6 PBSMT systems were prepared for Nepali-Hindi, 1 PBSMT for Hindi-Nepali and 2 NMT systems were developed for Nepali↔Hindi. The results show that PBSMT could be an effective method for developing MT systems for closely-related languages. Our Hindi-Nepali PBSMT system was ranked 2nd among the 13 systems submitted for the pair and our Nepali-Hindi PBSMTsystem was ranked 4th among the 12 systems submitted for the task.
TEASPN: Framework and Protocol for Integrated Writing Assistance Environments

Language technologies play a key role in assisting people with their writing.Although there has been steady progress in e.g., grammatical error correction (GEC), human writers are yet to benefit from this progress due to the high development cost of integrating with writing software.We propose TEASPN 1 , a protocol and an open-source framework for achieving integrated writing assistance environments.The protocol standardizes the way writing software communicates with servers that implement such technologies, allowing developers and researchers to integrate the latest developments in natural language processing (NLP) with low cost.As a result, users can enjoy the integrated experience in their favorite writing software.The results from experiments with human participants show that users use a wide range of technologies and rate their writing experience favorably, allowing them to write more fluent text.
Kernel-Based Approaches for Sequence Modeling: Connections to Neural Methods

We investigate time-dependent data analysis from the perspective of recurrent kernel machines, from which models with hidden units and gated memory cells arise naturally. By considering dynamic gating of the memory cell, a model closely related to the long short-term memory (LSTM) recurrent neural network is derived. Extending this setup to $n$-gram filters, the convolutional neural network (CNN), Gated CNN, and recurrent additive network (RAN) are also recovered as special cases. Our analysis provides a new perspective on the LSTM, while also extending it to $n$-gram convolutional filters. Experiments are performed on natural language processing tasks and on analysis of local field potentials (neuroscience). We demonstrate that the variants we derive from kernels perform on par or even better than traditional neural methods. For the neuroscience application, the new models demonstrate significant improvements relative to the prior state of the art.
Private Heavy Hitters and Range Queries in the Shuffled Model

An exciting new development in differential privacy is the shuffled model, in which an anonymous channel enables non-interactive, differentially private protocols with error much smaller than what is possible in the local model, while relying on weaker trust assumptions than in the central model. In this paper, we study basic counting problems in the shuffled model and establish separations between the error that can be achieved in the single-message shuffled model and in the shuffled model with multiple messages per user. For the problem of frequency estimation for $n$ users and a domain of size $B$, we obtain: - A nearly tight lower bound of $\tilde{\Omega}( \min(\sqrt[4]{n}, \sqrt{B}))$ on the error in the single-message shuffled model. This implies that the protocols obtained from the amplification via shuffling work of Erlingsson et al. (SODA 2019) and Balle et al. (Crypto 2019) are essentially optimal for single-message protocols. A key ingredient in the proof is a lower bound on the error of locally-private frequency estimation in the low-privacy (aka high $\epsilon$) regime. - Protocols in the multi-message shuffled model with $poly(\log{B}, \log{n})$ bits of communication per user and $poly\log{B}$ error, which provide an exponential improvement on the error compared to what is possible with single-message algorithms. For the related selection problem on a domain of size $B$, we prove: - A nearly tight lower bound of $\Omega(B)$ on the number of users in the single-message shuffled model. This significantly improves on the $\Omega(B^{1/17})$ lower bound obtained by Cheu et al. (Eurocrypt 2019), and when combined with their $\tilde{O}(\sqrt{B})$-error multi-message protocol, implies the first separation between single-message and multi-message protocols for this problem.
BACRank: Ranking Building Automation and Control System Components by Business Continuity Impact

Organizations increasingly depend on Building Automation and Control Systems (BACSs) to support their daily tasks and to comply with laws and regulations. However, BACSs are prone to disruptions caused by failures or active attacks. Given the role BACSs play in critical locations such as airports and hospitals, a comprehensive impact assessment methodology is required that estimates the effect of unavailable components in the system. In this paper, we present the foundations of the first impact assessment methodology for BACSs focused on business continuity. At the core of our methodology, we introduce a novel graph centrality measure called BACRank. We quantify the contribution of BACS components to different business activities. Moreover, we take functional dependencies among components into account to estimate indirect consequences throughout the infrastructure. We show the practical applicability of our approach on a real BACS deployed at a 5-story building hosting 375 employees on an international university campus. The experimental evaluation confirms that the proposed methodology successfully prioritizes the most relevant components of the system with respect to the business continuity perspective.
On-Bicycle Vehicle Tracking at Traffic Intersections Using Inexpensive Low-Density Lidar

This paper explores the challenges in developing an inexpensive on-bicycle sensing system to track vehicles at a traffic intersection. In particular, opposing traffic with vehicles that can travel straight or turn left are considered. The estimated vehicle trajectories can be used for collision prevention between bicycles and left-turning vehicles. A compact solid-state 2-D low-density Lidar is mounted at the front of a bicycle to obtain distance measurements from vehicles. Vehicle tracking can be achieved by clustering based approaches for assigning measurement points to individual vehicles, introducing a correction term for position measurement refinement, and by exploiting data association and interacting multiple model Kalman filtering approaches for multi-target tracking. The tracking performance of the developed system is evaluated by both simulation and experimental results. Two types of scenarios that involve straight driving and left turning vehicles are considered. Experimental results show that the developed system can successfully track cars in these scenarios accurately in spite of the low measurement density of the sensor.
Identifying Adverse Drug Events Mentions in Tweets Using Attentive, Collocated, and Aggregated Medical Representation

Identifying mentions of medical concepts in social media is challenging because of high variability in free text.In this paper, we propose a novel neural network architecture, the Collocated LSTM with Attentive Pooling and Aggregated representation (CLAPA), that integrates a bidirectional LSTM model with attention and pooling strategy and utilizes the collocation information from training data to improve the representation of medical concepts.The collocation and aggregation layers improve the model performance on the task of identifying mentions of adverse drug events (ADE) in tweets.Using the dataset made available as part of the workshop shared task, we show that careful selection of neighborhood contexts can help uncover useful local information and improve the overall medical concept representation.
Finding New Diamonds: Temporal Minimal-World Query Answering over Sparse ABoxes

Lightweight temporal ontology languages have become a very active field of research in recent years. Many real-world applications, like processing electronic health records (EHRs), inherently contain a temporal dimension, and require efficient reasoning algorithms. Moreover, since medical data is not recorded on a regular basis, reasoners must deal with sparse data with potentially large temporal gaps. In this paper, we introduce a temporal extension of the tractable language , which features a new class of convex diamond operators that can be used to bridge temporal gaps. We develop a completion algorithm for our logic, which shows that entailment remains tractable. Based on this, we develop a minimal-world semantics for answering metric temporal conjunctive queries with negation. We show that query answering is combined first-order rewritable, and hence in polynomial time in data complexity.
Identifying Critical Features for Formative Essay Feedback with Artificial Neural Networks and Backward Elimination

For predicting and improving the quality of essays, text analytic metrics (surface, syntactic, morphological and semantic features) can be used to provide formative feedback to the students. In this study, the intent was to find a small number of features that exhibit a fair proxy of the scores given by the human raters. Using an existing corpus and a text analysis tool for the Dutch language, a large number of features were extracted. Artificial neural networks, Levenberg Marquardt algorithm and backward elimination were used to reduce the number of extracted features automatically. Irrelevant features were eliminated based on the inter-rater agreement between predicted and human scores calculated using Cohen’s Kappa ( $$\kappa $$ ). By using our algorithm, the number of features in this study was reduced from 457 to 23. The selected features were grouped into six different categories. Of these categories, we believe that the features present in the groups “Word Difficulty” and “Lexical Diversity” are most useful for providing automated formative feedback to the students. The approach presented in this research paper is the first step towards our ultimate goal of providing meaningful formative feedback to the students for enhancing their writing skills and capabilities.
Defence Against Adversarial Attacks Using Clustering Algorithm

Deep learning model is vulnerable to adversarial examples in the task of image classification. In this paper, a cluster-based method for defending against adversarial examples is proposed. Each adversarial example before attacking a classifier is reconstructed by a clustering algorithm according to the pixel values. The MNIST database of handwritten digits was used to assess the defence performance of the method under the fast gradient sign method (FGSM) and the DeepFool algorithm. The defence model proposed is simple and the trained classifier does not need to be retrained.
Optimization and Application of Monte Carlo Sampling in Bridge

The design difficulty of the bridge program lies in its huge search space. The traditional sampling has the problems of sample accuracy and low efficiency. According to the characteristics of incomplete information game, the Monte Carlo sampling bridge program is designed and implemented. The experimental results show that the optimized Monte Carlo sampling bridge program improves the ability of playing bridge card effectively and has certain reference value for the game research of incomplete information.
Formation of readiness and motivation of students for independent work in higher technical educational institution

The article is devoted to the problem of organizing independent work of students in a higher technical educational institution, held in Tyumen Industrial University of the Nizhnevartovsk branch. This paper analyzes the requirements for students to master the skills of independent work on the basis of the competency model of a university graduate in accordance with the requirements of the professional standard of higher education. The competence of a successful specialist in any dynamically developing industry is considered. The author made an analysis of the works of scientists, teachers, researchers, dealing with the problem of motivation and improving the efficiency of the organization of work activities of students. The tendency to steady interest of students in additional material related to the profile of the main direction of study in the form of problem situations is revealed. Three forms of organization of independent work are highlighted and described in detail: extracurricular, classroom, and creative in the form of NIRS (students' research work). Express questionnaires of students were conducted to measure the time spent on independent work, to identify students' claims for research skills, for the types of independent work that are most laborious for respondents, diagnosing time spent on extracurricular work per week. According to the results of the study, graphs were made and diagnostic diagrams were constructed. The conclusion is formulated.
Fire Image Generation Based on ACGAN

In order to solve the problem that it is difficult to obtain fire image data in CNN training, this paper discusses the method of generating fire image by means of generative adversarial networks. How to generate the desired fire image according to the known observation variables is discussed. According to the structure of InfoGAN and ACGAN, a GAN structure for generating fire image is proposed. Fire area is selected as a known observation variable to generate the corresponding fire image. Experiments show that the network structure can generate the required images according to the values of a observed variables. And the quality of the generated image is related to the distribution of observed variables in the data set.
Multi-label Logo Classification Using Convolutional Neural Networks

The classification of logos is a particular case within computer vision since they have their own characteristics. Logos can contain only text, iconic images or a combination of both, and they usually include figurative symbols designed by experts that vary substantially besides they may share the same semantics. This work presents a method for multi-label classification and retrieval of logo images. For this, Convolutional Neural Networks (CNN) are trained to classify logos from the European Union TradeMark (EUTM) dataset according to their colors, shapes, sectors and figurative designs. An auto-encoder is also trained to learn representations of the input images. Once trained, the neural codes from the last convolutional layers in the CNN and the central layer of the auto-encoder can be used to perform similarity search through kNN, allowing us to obtain the most similar logos based on their color, shape, sector, figurative elements, overall features, or a weighted combination of them provided by the user. To the best of our knowledge, this is the first multi-label classification method for logos, and the only one that allows retrieving a ranking of images with these criteria provided by the user.
The Formation Control of Mobile Autonomous Multi-Agent Systems Using Deep Reinforcement Learning

The formation control of mobile autonomous multi-agent systems has been an important task in the fields of automatic control and robotics. Many applications, from swarm robots to autonomous cars, need the corresponding agents (i.e., robots or cars) to follow the designed control law for their group behaviors. Finding a feasible and collision free trajectory for each agent in the formation control is critical and challenging for these multi-agent systems. This work presents a formation control algorithm for mobile autonomous multi-agent systems based on the application of deep reinforcement learning. Specifically, the proposed method can lead the agent to develop a policy network to learn not only its own policy but also the policy of its neighbors. Thus, the value network can evaluate the policy that it has learned and find the correct actions after the training process. Also, the proposed method removes the assumption that other agents perform the same policy, which is widely used in some existing collision avoidance algorithms. Finally, the experimental results show the effectiveness of our proposed method.
Perancangan E-Kuisioner menggunakan CodeIgniter dan React-Js sebagai Tools Pendukung Penelitian

The development of questionnaires that can capture usability problems in each case is expected to be more effective and efficient. At present, the use of e-questionnaires has evolved with the use of technologies such as the emergence of monkey surveys, type forms, google forms, Zoho surveys, and gizmo surveys that can be used to facilitate the work of creating, distributing and retrieving survey feedback online. This research tries to build an e-questionnaire model using Codeigniter and react-js which can be used as an alternative while prioritizing the appearance of end-users by adopting the Google form service but having features by being able to add questions in the form of multimedia and also being able to disseminate information on survey results with the concept of sharing datasets to various digital online sources. The methodology used in the research to be carried out is the DSRM (Design Science Research Method) method and at the research, the stage consists of; problem identification, defining objects and solutions, design and development, implementation, evaluation, and conclusion. This research resulted in an e-questionnaire application that can be used as an alternative while prioritizing the appearance of end-users by adopting the Google form service but has features that can add questions in the form of multimedia and can also disseminate survey results with the concept of sharing datasets to various digital sources. online with the use of the Codelnigter and React-Js framework can improve effective appearance while maintaining system security. It is hoped that the e-questionnaire can increase the distribution of research data in the form of electronic datasets and can be fully used as a reference for researchers in Indonesia, especially AMIK Indonesia.
Learning Communicating State Machines

We consider the problems of learning and conformance testing of components in a modular system. We assume that each component can be modelled as a Finite State Machine (FSM), the topology of the system is known, but some (or all) component FSMs are unknown and have to be learned by testing the whole system, as it cannot be disassembled. Thus the classical problem of active inference of an automaton in isolation is now further lifted to a system of communicating FSMs of an arbitrary topology. As opposed to the existing work on automata learning, the proposed approach neither needs a Minimally Adequate Teacher, also called the Oracle, nor uses it a conformance tester to approximate equivalence queries. The approach further enhances a SAT solving method suggested by the authors and allows to adaptively test conformance of a system with unknown components assuming that internal communications are observable. The resulting tests are much smaller than the classical universal conformance tests derived from the composite machine of the system.
Emotion Detection with n-stage Latent Dirichlet Allocation for Turkish Tweets

Understanding the reason behind the emotions placed in the social media plays a key role to learn mood characterization of any written texts that are not seen before. Knowing how to classify the mood characterization leads this technology to be useful in a variety of fields. The Latent Dirichlet Allocation (LDA), a topic modeling algorithm, was used to determine which emotions the tweets on Twitter had in the study. The dataset consists of 4000 tweets that are categorized into 5 different emotions that are anger, fear, happiness, sadness, and surprise. Zemberek, Snowball, and first 5 letters root extraction methods are used to create models. The generated models were tested by using the proposed n-stage LDA method. With the proposed method, we aimed to increase model’s success rate by decreasing the number of words in the dictionary. Using the multi-stage LDA (2-stages:70.5%, 3-stages:76.375%) method, the success rate was increased compared to normal LDA (60.375%) for 5 class.