Reviewerly/exHarmony huggingface dataset:
RAFFMAN: Measuring and Analyzing Sentiment in Online Political Forum Discussions with an Application to the Trump Impeachment

Given an online forum, how can we quantify changes in user affect towards a person or an idea over time? We argue that online political forums constitute an untapped opportunity for understanding sentiment toward aspects under discussion. However, the analysis of such forums has received little attention from the research community. In this paper, we develop RAFFMAN, a systematic approach to quantify the impact of external events on the affect of forum users towards a concept, such as a person or an entity. First, we develop an approach to capture and quantify the observed activity: we identify related keywords, filter threads, and establish correlations between events and spikes in the activity. Second, we modify and evaluate state-of-the-art NLP techniques to achieve high accuracy (74%) in a three-class sentiment classification problem. As a case study, we deploy our method to quantify the effect of President Trump’s impeachment on several concepts including: President Trump, Speaker Pelosi, and QAnon. Our data consists of 32M posts from Reddit and 4chan over a span of 6 months from September 2019 to February 2020. This initial analysis hints at an increase in political polarization, especially for people’s affect towards the President. Overall, our work is a building block towards mining the affect of online forum user towards a concept, which constitutes a untapped, massive, and publicly-available source of information.
Distance learning: Artistic disciplines in expectations and results

The long period of distance learning in higher education has set difficult tasks for teachers. The article analyzes the course and results of an experiment that was conducted at the Department of Folk Art Crafts of the Moscow State Regional University in 2020. The experiment involved three groups of students in two fields of study: 54.03.01 Design, specialty Costume Design (CD) and 54.03.02 Decorative and Applied Arts and Crafts (DAAC). Students independently identified problems that could arise during distance learning. These are objective factors, such as: insufficient mastering of skills of working with materials and tools, the inability to correct errors and shortcomings immediately, etc.; and subjective factors: psychological problems, deterioration of health indicators, etc. The authors of the article followed the changes in the problems of teaching and made analytical conclusions about how the students’ attitude to the process of remote learning changed. Despite the positive dynamics in students’ perception of online communication with teachers, some of the problems remain unsolvable. For example, the main problem in teaching creative disciplines, in our opinion, was the lack of direct contact between the master and the student, when the skill of artistic work is transferred “from hand to hand”. The authors conclude that the teaching of creative disciplines cannot be completely taken online.
A Hidden DCT-Based Invisible Watermarking Method for Low-Cost Hardware Implementations

This paper presents an invisible and robust watermarking method and its hardware implementation. The proposed architecture is based on the discrete cosine transform (DCT) algorithm. Novel techniques are applied as well to reduce the computational cost of DCT and color space conversion to achieve low-cost and high-speed performance. Besides, a watermark embedder and a blind extractor are implemented in the same circuit using a resource-sharing method. Our approach is compatible with various watermarking embedding ratios, such as 1/16 and 1/64, with a PSNR of over 45 and the NC value of 1. After Joint Photographic Experts Group (JPEG) compression with a quality factor (QF) of 50, our method can achieve an NC value of 0.99. Results from a design compiler (DC) with TSMC-90 nm CMOS technology show that our design can achieve the frequency of 2.32 GHz with the area consumption of 304,980.08 μm2 and power consumption of 508.1835 mW. For the FPGA implementation, our method achieved a frequency of 421.94 MHz. Compared with the state-of-the-art works, our design improved the frequency by 4.26 times, saved 90.2% on area and increased the power efficiency by more than 1000 fold.
MixMyVisit – Enhancing the Visitor Experience Through Automatic Generated Videos

Cultural places like museums have been looking for means to enrich the visitor experience during and after the visit. This paper reports on a proposal to contribute to this field presenting a system that automatically creates personalized memory videos of the visit, by identifying the paths of visitors in cultural spaces. The MixMyVisit project combines low-cost devices with NFC technology, allowing a simple way for identifying visitors’ paths, a bot for interaction with the visitor through a textual chat implemented in a social network, the ability for the visitor to share its own captured contents (photos or videos), a server-side video engine supported by ffmpeg components and an online responsive video editor. The features and main technical developments are presented on the paper. The results of an evaluation carried by two experts provide positive insights towards such a system and the team expects to get a final validation in a field trial to be carried out.
Incremental Embedding Learning via Zero-Shot Translation

Modern deep learning methods have achieved great success in machine learning and computer vision fields by learning a set of pre-defined datasets. Howerver, these methods perform unsatisfactorily when applied into real-world situations. The reason of this phenomenon is that learning new tasks leads the trained model quickly forget the knowledge of old tasks, which is referred to as catastrophic forgetting. Current state-of-the-art incremental learning methods tackle catastrophic forgetting problem in traditional classification networks and ignore the problem existing in embedding networks, which are the basic networks for image retrieval, face recognition, zero-shot learning, etc. Different from traditional incremental classification networks, the semantic gap between the embedding spaces of two adjacent tasks is the main challenge for embedding networks under incremental learning setting. Thus, we propose a novel class-incremental method for embedding network, named as zero-shot translation class-incremental method (ZSTCI), which leverages zero-shot translation to estimate and compensate the semantic gap without any exemplars. Then, we try to learn a unified representation for two adjacent tasks in sequential learning process, which captures the relationships of previous classes and current classes precisely. In addition, ZSTCI can easily be combined with existing regularization-based incremental learning methods to further improve performance of embedding networks. We conduct extensive experiments on CUB-200-2011 and CIFAR100, and the experiment results prove the effectiveness of our method. The code of our method has been released in https://github.com/Drkun/ZSTCI.
15-Puzzle Problem Solving with the Artificial Bee Colony Algorithm Based on Pattern Database

The N-puzzle problem is one of the most classical problems in mathematics. Since the number of states in the N-puzzle is equal to the factorial of the number of tiles, traditional algorithms can only provide solutions for small-scale ones, such as 8-puzzle. Various uninformed and informed search algorithms have been applied to solve the N-puzzle, and their performances have been evaluated. Apart from traditional methods, artificial intelligence algorithms are also used for solutions. This paper introduces a new approach based on a meta-heuristic algorithm with a solving of the 15-puzzle problem. Generally, only Manhattan distance is used as the heuristic function, while in this study, a linear conflict function is used to increase the effectiveness of the heuristic function. Besides, the puzzle was divided into subsets named pattern database, and solutions were obtained for the subsets separately with the artificial bee colony (ABC) algorithm. The proposed approach reveals that the ABC algorithm is very successful in solving the 15-puzzle problem.
Design and development of arduino based contactless thermometer

In present situations, social distancing is the most important fact. Furthermore, the fact is COVID-19 patient's first symptom is body temperature is high. The reason why, measuring body temperature is most important, but needs to maintain social distancing. While traditional thermometers can't make sure of social distancing, where our developed contactless thermometer can achieve temperature on display by using Arduino unoR3 as the main control device as well as MLX90614 as the infrared (IR) thermometer sensor. As a result, compared with the traditional thermometer, it shows strong points such as convenient reading, wide range of temperature measurement, and accuracy where temperature output is displayed digitally. Besides, it would be used everywhere because of its easy-handling.
Comparative Study of Human Activity Recognition on Sensory Data Using Machine Learning and Deep Learning

There are many technologies which are being used in recognizing people activities like running, jogging, sitting, etc. Nowadays machine learning and deep learning are being used widely by researchers, academicians and educationalists. This paper explains comprehensive overview of traditional machine learning techniques and further explains how deep learning approach like convolution neural network has improved the result and accuracy despite many challenges of activities recognition. We have also investigated future challenges and their solutions.
Out of Context: A New Clue for Context Modeling of Aspect-based Sentiment Analysis

Aspect-based sentiment analysis (ABSA) aims to predict the sentiment expressed in a review with respect to a given aspect. The core of ABSA is to model the interaction between the context and given aspect to extract aspect-related information. In prior work, attention mechanisms and dependency graph networks are commonly adopted to capture the relations between the context and given aspect. And the weighted sum of context hidden states is used as the final representation fed to the classifier. However, the information related to the given aspect may be already discarded and adverse information may be retained in the context modeling processes of existing models. Such a problem cannot be solved by subsequent modules due to two reasons. First, their operations are conducted on the encoder-generated context hidden states, whose value cannot be changed after the encoder. Second, existing encoders only consider the context while not the given aspect. To address this problem, we argue the given aspect should be considered as a new clue out of context in the context modeling process. As for solutions, we design three streams of aspect-aware context encoders: an aspect-aware LSTM, an aspect-aware GCN, and three aspect-aware BERTs. They are dedicated to generating aspect-aware hidden states which are tailored for the ABSA task. In these aspect-aware context encoders, the semantics of the given aspect is used to regulate the information flow. Consequently, the aspect-related information can be retained and aspect-irrelevant information can be excluded in the generated hidden states. We conduct extensive experiments on several benchmark datasets with empirical analysis, demonstrating the efficacies and advantages of our proposed aspect-aware context encoders.
LARGER RECEPTIVE FIELD BASED RGB VISUAL RELOCALIZATION METHOD USING CONVOLUTIONAL NETWORK

Abstract. Visual Relocalization is a key technology in many computer vision applications. Traditional visual relocalization is mainly achieved through geometric methods, while PoseNet introduces convolutional neural network in visual relocalization for the first time to realize real-time camera pose estimation based on a single image. Aiming at the problem of accuracy and robustness of the current PoseNet algorithm in complex environment, this paper proposes and implements a new high-precision robust camera pose calculation method (LRF-PoseNet). This method directly adjusts the size of the input image without cropping, so as to increase the receptive field of the training image. Then, the image and the corresponding pose tags are input into the improved LSTM-based PoseNet network for training, and the Adam optimizer is used to optimize the network. Finally, the trained network is used to estimate the camera pose. Experimental results on open RGB dataset show that the proposed method in this paper can obtain more accurate camera pose compared with the existing CNN-based methods.
Training Adaptive Computation for Open-Domain Question Answering with Computational Constraints

Adaptive Computation (AC) has been shown to be effective in improving the efficiency of Open-Domain Question Answering (ODQA) systems.However, current AC approaches require tuning of all model parameters, and training state-of-the-art ODQA models requires significant computational resources that may not be available for most researchers.We propose Adaptive Passage Encoder, an AC method that can be applied to an existing ODQA model and can be trained efficiently on a single GPU.It keeps the parameters of the base ODQA model fixed, but it overrides the default layer-by-layer computation of the encoder with an AC policy that is trained to optimise the computational efficiency of the model.Our experimental results show that our method improves upon a state-of-theart model on two datasets, and is also more accurate than previous AC methods due to the stronger base ODQA model.
Interpretable Embedding Procedure Knowledge Transfer via Stacked Principal Component Analysis and Graph Neural Network

Knowledge distillation (KD) is one of the most useful techniques for light-weight neural networks. Although neural networks have a clear purpose of embedding datasets into the low-dimensional space, the existing knowledge was quite far from this purpose and provided only limited information. We argue that good knowledge should be able to interpret the embedding procedure. This paper proposes a method of generating interpretable embedding procedure (IEP) knowledge based on principal component analysis, and distilling it based on a message passing neural network. Experimental results show that the student network trained by the proposed KD method improves 2.28% in the CIFAR100 dataset, which is a higher performance than the state-of-the-art (SOTA) method. We also demonstrate that the embedding procedure knowledge is interpretable via visualization of the proposed KD process. The implemented code is available at https://github.com/sseung0703/IEPKT.
Comparative Study on Telugu text Classification using Machine Learning and Deep Learning models

Nowadays, many Telugu Language documents have become available in digital form in this information era. These documents should be grouped into a class based on their content for easy retrieval of these electronic data records. Text categorization is perhaps the crucial issue in information systems concerned with text records, owing to the increasing volume of information contained in digital form. Text categorization methods have been applied to Telugu text in order to derive valuable information and insights from unstructured Telugu text. Text categorization is the method of identifying a category or several categories from a set of predefined choices for a document. Indian languages are difficult to categories because they have a lot of morphology, a lot of different word forms, and a lot of different feature spaces. Since Telugu is morphologically rich and requires special algorithms to perform morphological analysis, there hasn't been much research done on it. To construct an organized and reduced-feature lexicon, the preprocessing methods which are designed specifically for Telugu language are applied to raw data. Significant pre-processing is required to construct accurate classification model Telugu text documents. In this paper, we compare the different machine learning and deep learning classifiers performance on the Telugu text such as Naïve Bayes, Support Vector Machine (SVM), and neural network classifier.
The Possible, the Plausible, and the Desirable: Event-Based Modality Detection for Language Processing

Modality is the linguistic ability to describe events with added information such as how desirable, plausible, or feasible they are.Modality is important for many NLP downstream tasks such as the detection of hedging, uncertainty, speculation, and more.Previous studies that address modality detection in NLP often restrict modal expressions to a closed syntactic class, and the modal sense labels are vastly different across different studies, lacking an accepted standard.Furthermore, these senses are often analyzed independently of the events that they modify.This work builds on the theoretical foundations of the Georgetown Gradable Modal Expressions (GME) work by Rubinstein et al. (2013) to propose an event-based modality detection task where modal expressions can be words of any syntactic class and sense labels are drawn from a comprehensive taxonomy which harmonizes the modal concepts contributed by the different studies.We present experiments on the GME corpus aiming to detect and classify fine-grained modal concepts and associate them with their modified events.We show that detecting and classifying modal expressions is not only feasible, but also improves the detection of modal events in their own right.
Interpretable and Low-Resource Entity Matching via Decoupling Feature Learning from Decision Making

Entity Matching (EM) aims at recognizing entity records that denote the same real-world object.Neural EM models learn vector representation of entity descriptions and match entities end-to-end.Though robust, these methods require many annotated resources for training, and lack of interpretability.In this paper, we propose a novel EM framework that consists of Heterogeneous Information Fusion (HIF) and Key Attribute Tree (KAT) Induction to decouple feature representation from matching decision.Using self-supervised learning and mask mechanism in pre-trained language modeling, HIF learns the embeddings of noisy attribute values by inter-attribute attention with unlabeled data.Using a set of comparison features and a limited amount of annotated data, KAT Induction learns an efficient decision tree that can be interpreted by generating entity matching rules whose structure is advocated by domain experts.Experiments on 6 public datasets and 3 industrial datasets show that our method is highly efficient and outperforms SOTA EM models in most cases.
Coreference Reasoning in Machine Reading Comprehension

Coreference resolution is essential for natural language understanding and has been long studied in NLP.In recent years, as the format of Question Answering (QA) became a standard for machine reading comprehension (MRC), there have been data collection efforts, e.g., Dasigi et al. (2019), that attempt to evaluate the ability of MRC models to reason about coreference.However, as we show, coreference reasoning in MRC is a greater challenge than earlier thought; MRC datasets do not reflect the natural distribution and, consequently, the challenges of coreference reasoning.Specifically, success on these datasets does not reflect a model's proficiency in coreference reasoning.We propose a methodology for creating MRC datasets that better reflect the challenges of coreference reasoning and use it to create a sample evaluation set.The results on our dataset show that state-ofthe-art models still struggle with these phenomena.Furthermore, we develop an effective way to use naturally occurring coreference phenomena from existing coreference resolution datasets when training MRC models.This allows us to show an improvement in the coreference reasoning abilities of state-of-theart models. 1
Towards Kinematics From Motion: Unknown Input Observer and Dynamic Extension Approach

This letter addresses an unknown input observer design to estimate simultaneously the 3D depth of a tracked image feature and the camera linear velocity using a low cost monocular camera and inertial sensor. The camera kinematic model is at first, augmented via the dynamic extension approach then described as a quasi-Linear Parameter Varying (qLPV) model. Further, the qLPV system is transformed into Takagi-Sugeno (T-S) form with unmeasured premise variables. The error convergence analysis is performed based on Lyapunov theory and Input to State Stability (ISS) property to ensure the boundedness of the state estimation error. Gains that guarantee the asymptotic stability of the estimation error can be properly computed by means of Linear Matrix Inequalities (LMIs). Finally the proposed approach is validated using synthetic data.
Learning to Rationalize for Nonmonotonic Reasoning with Distant Supervision

The black-box nature of neural models has motivated a line of research that aims to generate natural language rationales to explain why a model made certain predictions. Such rationale generation models, to date, have been trained on dataset-specific crowdsourced rationales, but this approach is costly and is not generalizable to new tasks and domains. In this paper, we investigate the extent to which neural models can reason about natural language rationales that explain model predictions, relying only on distant supervision with no additional annotation cost for human-written rationales. We investigate multiple ways to automatically generate rationales using pre-trained language models, neural knowledge models, and distant supervision from related tasks, and train generative models capable of composing explanatory rationales for unseen instances. We demonstrate our approach on the defeasible inference task, a nonmonotonic reasoning task in which an inference may be strengthened or weakened when new information (an update) is introduced. Our model shows promises at generating post-hoc rationales explaining why an inference is more or less likely given the additional information, however, it mostly generates trivial rationales reflecting the fundamental limitations of neural language models. Conversely, the more realistic setup of jointly predicting the update or its type and generating rationale is more challenging, suggesting an important future direction.
Readability of Turkish Websites Containing Information about COVID-19

The Anatolian Journal of Family Medicine is an international periodical on family medicine and primary health care, published three times a year on independent, unbiased, double-blinded and peer-review principles.
Lifting the Convex Conjugate in Lagrangian Relaxations: A Tractable Approach for Continuous Markov Random Fields

Dual decomposition approaches in nonconvex optimization may suffer from a duality gap. This poses a challenge when applying them directly to nonconvex problems such as MAP-inference in a Markov random field with continuous state spaces. To eliminate such gaps, this paper considers a reformulation of the original nonconvex task in the space of measures. This infinite-dimensional reformulation is then approximated by a semi-infinite one, which is obtained via a piecewise polynomial discretization in the dual. We provide a geometric intuition behind the primal problem induced by the dual discretization and draw connections to optimization over moment spaces. In contrast to existing discretizations which suffer from a grid bias, we show that a piecewise polynomial discretization better preserves the continuous nature of our problem. Invoking results from optimal transport theory and convex algebraic geometry we reduce the semi-infinite program to a finite one and provide a practical implementation based on semidefinite programming. We show, experimentally and in theory, that the approach successfully reduces the duality gap. To showcase the scalability of our approach, we apply it to the stereo matching problem between two images.