Reviewerly/exHarmony huggingface dataset:
Emitter Tracking via Direct Target Motion Analysis

This paper considers tracking of a non-cooperative emitter based on a single sensor. To this end, the direct target motion analysis (DTMA) approach, where the target state is straightforwardly achieved from the received signal, is exploited. In order to achieve observability, the sensor has to perform a maneuver relative to the emitter. By suitably building an approximated likelihood function, the unscented Kalman filter (UKF), which is able to work under high nonlinearity of the measurement model, is adopted to recursively estimate the target state. Besides, the posterior Cramér-Rao bound (PCRB) of DTMA, which can be used as performance benchmark, is also achieved. The effectiveness of proposed method is verified via simulation experiments.
Analysis and Evaluation of Two Feature Selection Algorithms in Improving the Performance of the Sentiment Analysis Model of Arabic Tweets

Recently, Sentiment analysis from Twitter is one of the most interesting research disciplines; it combined data mining technologies with natural language processing techniques.The sentiment analysis system aims to evaluate the texts that are posted on social platforms to express positive, negative, or neutral feelings of people regarding a certain domain.The high dimensionality of the feature vector is considered to be one of the most popular problems of Arabic sentiment analysis.The main contribution of this paper is to solve the dimensionality problem by presenting a comparative study between two feature selection algorithms, namely, Information Gain (IG), and Chi-Square to choose the best one which may lead to improve the classification accuracy.In this paper, the Arabic Jordanian sentiment analysis model is proposed through four steps.First, a preprocessing step has been applied to the database and includes (Remove Non-Arabic Symbols, Tokenizing, Arabic Stop Word Removal, and Stemming).In the second step, the TF-IDF algorithm is used as a feature extraction method to represent the text into feature vectors.Then, we utilized IG and Chi-Square as feature selection steps to obtain the best subset of features and decrease the total number of features.Finally, different algorithms have been used in the classification step such as (SVM, DT, and KNN) to classify the views people have shared on Twitter, into two classes (positive, and negative).Several experiments were performed on Jordanian dialectical tweets using the AJGT database.The experimental results show the following: 1) The information acquisition algorithm outperformed the Chi-Square Algorithm in the feature selection step, as it was able to reduce the number of features from 1170 to 713 and increase the accuracy of the classifiers by 10%, 2) SVM classifier shows the greatest classification performance among all the classifiers tested which gives the highest accuracy of 85% with IG algorithm.
Augmented System for Food Crops Production in Agricultural Supply Chain using Blockchain Technology

The elevated version of the Agricultural Traceability System dealing with food production holds utmost significance in not only assuring food insecure, smart contracts and agri-insurance to farmers but also guaranteeing insurance to them during natural disaster. The proposed and improved system for food cultivation traceability deals with agri-crops and farmers that hires the Blockchain technology guarantee at par safety, agreement, distributed ledger, immediate payment, de-centralization and thereby achieving the goal of minimizing the cost incurred in the food processing system and building trust. Smart contracts play a pivot role in the field of agricultural insurance. Agricultural insurance based upon Blockchain comprises of major weather incidents and associated payouts enlisted on a smart contract, connected to the mobile wallets with timely weather updates notified by the field sensors and interrelated with data from proximity weather stations would enable prompt payout during any natural calamity such as flood or drought. A panel of advisers in the decentralized system which is professionally governed and managed by certain retired officers makes the traceability system more trustworthy. These professionals can offer wise suggestions to the planters aiding them to acquire productive outcome.
An Empirical Study on Anomaly Detection Algorithms for Extremely Imbalanced Datasets

Anomaly detection attempts to identify abnormal events that deviate from normality. Since such events are often rare, data related to this domain is usually imbalanced. In this paper, we compare diverse preprocessing and Machine Learning (ML) state-of-the-art algorithms that can be adopted within this anomaly detection context. These include two unsupervised learning algorithms, namely Isolation Forests (IF) and deep dense AutoEncoders (AE), and two supervised learning approaches, namely Random Forest and an Automated ML (AutoML) method. Several empirical experiments were conducted by adopting seven extremely imbalanced public domain datasets. Overall, the IF and AE unsupervised methods obtained competitive anomaly detection results, which also have the advantage of not requiring labeled data.
Towards Stronger Adversarial Baselines Through Human-AI Collaboration

Natural language processing (NLP) systems are often used for adversarial tasks such as detecting spam, abuse, hate speech, and fake news.Properly evaluating such systems requires dynamic evaluation that searches for weaknesses in the model, rather than a static test set.Prior work has evaluated such models on both manually and automatically generated examples, but both approaches have limitations: manually constructed examples are time-consuming to create and are limited by the imagination and intuition of the creators, while automatically constructed examples are often ungrammatical or labeled inconsistently.We propose to combine human and AI expertise in generating adversarial examples, benefiting from humans' expertise in language and automated attacks' ability to probe the target system more quickly and thoroughly.We present a system that facilitates attack construction, combining human judgment with automated attacks to create better attacks more efficiently.Preliminary results from our own experimentation suggest that human-AI hybrid attacks are more effective than either human-only or AI-only attacks.A complete user study to validate these hypotheses is still pending.
Hadoop as a Service: Integration of a Company’s Heterogeneous Data to a Remote Hadoop Infrastructure

Data analysis is very important for the development of any business today.It helps to identify organizational bottlenecks, optimize business processes, foresee customers' demands and behavior, and provides summarized data that could help reducing costs and increase profits.Having this information when designing new products or services highly increases the chances of their success, and thus provides an additional competitive advantage over other businesses.However, having a single data analyst with a computer is far from enough in the era of big data.There are powerful data analytical software tools, but they are either expensive or hard to deploy and require multiple high-performance servers to run.Buying expensive hardware and software, and hiring highqualified IT experts, is not affordable for all companies, especially for smaller ones and start-ups.Therefore, this article proposes an architecture for integration of a company's heterogeneous data (stored within a database of any type, or in the file system) to a remote Hadoop cluster, providing powerful data analytical services on demand.This is an affordable and cost-effective cloud-based solution, suitable for a company of any size.Businesses are not required to by any hardware or software, but use the data analytical services on demand, paying a small processing fee per request or by subscription.
Evaluation of Dipo Car Showroom at Gatot Subroto Street Jakarta

Car Showroom is a facility where customer experience the brand engagement in quality, memorable and seamless.Direct relationship to customer evaluation consists of: facade, environment-road orientation, entrance, circulation, zoning, accessibility, space relationship and organization of showroom.Literature sources from principal / main distributor, architecture design manual and others: journal, newspaper, internet, comparison to other brand competitors.Research applies with inductive, descriptive, qualitative method.Research object is Dipo car showroom located at Gatot Subroto street
NMT-Based Code Generation for Coding Assistance with Natural Language

This paper proposes an attempt to realize coding assistance that generates Python code from natural language descriptions using neural machine translation. Although coding assistance with deep learning has recently become a major concern, few applications have used neural machine translation models. One of the major barriers is the shortage of a parallel corpus of natural language descriptions and source code. To overcome the shortage of parallel corpora, we propose a method for synthesizing parallel corpora that utilizes the formal nature of programming languages. We aim to establish a new method using an abstract syntax tree (AST) and a corpus of code fragments. Using the proposed synthesis method, we successfully constructed tens of thousands of parallel corpora and trained PyNMT models to generate Python code from Japanese input sentences. The trained PyNMT models successfully predicted the Python code from user input sentences with an accuracy of 28%. In this study, we propose a synthetic method for a parallel corpus and summarize the results of the evaluation experiments conducted on PyNMT models.
SRL4E – Semantic Role Labeling for Emotions: A Unified Evaluation Framework

In the field of sentiment analysis, several studies have highlighted that a single sentence may express multiple, sometimes contrasting, sentiments and emotions, each with its own experiencer, target and/or cause.To this end, over the past few years researchers have started to collect and annotate data manually, in order to investigate the capabilities of automatic systems not only to distinguish between emotions, but also to capture their semantic constituents.However, currently available gold datasets are heterogeneous in size, domain, format, splits, emotion categories and role labels, making comparisons across different works difficult and hampering progress in the area.In this paper, we tackle this issue and present a unified evaluation framework focused on Semantic Role Labeling for Emotions (SRL4E), in which we unify several datasets tagged with emotions and semantic roles by using a common labeling scheme.We use SRL4E as a benchmark to evaluate how modern pretrained language models perform and analyze where we currently stand in this task, hoping to provide the tools to facilitate studies in this complex area.
Embodied Navigation at the Art Gallery

Embodied agents, trained to explore and navigate indoor photorealistic environments, have achieved impressive results on standard datasets and benchmarks. So far, experiments and evaluations have involved domestic and working scenes like offices, flats, and houses. In this paper, we build and release a new 3D space with unique characteristics: the one of a complete art museum. We name this environment ArtGallery3D (AG3D). Compared with existing 3D scenes, the collected space is ampler, richer in visual features, and provides very sparse occupancy information. This feature is challenging for occupancy-based agents which are usually trained in crowded domestic environments with plenty of occupancy information. Additionally, we annotate the coordinates of the main points of interest inside the museum, such as paintings, statues, and other items. Thanks to this manual process, we deliver a new benchmark for PointGoal navigation inside this new space. Trajectories in this dataset are far more complex and lengthy than existing ground-truth paths for navigation in Gibson and Matterport3D. We carry on extensive experimental evaluation using our new space for evaluation and prove that existing methods hardly adapt to this scenario. As such, we believe that the availability of this 3D model will foster future research and help improve existing solutions.
When Chosen Wisely, More Data Is What You Need: A Universal Sample-Efficient Strategy For Data Augmentation

Data Augmentation (DA) is known to improve the generalizability of deep neural networks.Most existing DA techniques naively add a certain number of augmented samples without considering the quality and the added computational cost of these samples.To tackle this problem, a common strategy, adopted by several state-of-the-art DA methods, is to adaptively generate or re-weight augmented samples with respect to the task objective during training.However, these adaptive DA methods: (1) are computationally expensive and not sample-efficient, and (2) are designed merely for a specific setting.In this work, we present a universal DA technique, called Glitter, to overcome both issues.Glitter can be plugged into any DA method, making training sample-efficient without sacrificing performance.From a pre-generated pool of augmented samples, Glitter adaptively selects a subset of worst-case samples with maximal loss, analogous to adversarial DA.Without altering the training strategy, the task objective can be optimized on the selected subset.Our thorough experiments on the GLUE benchmark, SQuAD, and HellaSwag in three widely used training setups including consistency training, self-distillation and knowledge distillation reveal that Glitter is substantially faster to train and achieves a competitive performance, compared to strong baselines.
CURAJ_IIITDWD@LT-EDI-ACL 2022: Hope Speech Detection in English YouTube Comments using Deep Learning Techniques

Hope Speech are positive terms that help to promote or criticise a point of view without hurting the user's or community's feelings.Non-Hope Speech, on the other side, includes expressions that are harsh, ridiculing, or demotivating.The goal of this article is to find the hope speech comments in a YouTube dataset.The datasets were created as part of the "LT-EDI-ACL 2022: Hope Speech Detection for Equality, Diversity, and Inclusion" shared task.The shared task dataset was proposed in Malayalam, Tamil, English, Spanish, and Kannada languages.In this paper, we worked at English-language YouTube comments.We employed several deep learning based models such as DNN (dense or fully connected neural network), CNN (Convolutional Neural Network), Bi-LSTM (Bidirectional Long Short Term Memory Network), and GRU(Gated Recurrent Unit) to identify the hopeful comments.We also used Stacked LSTM-CNN and Stacked LSTM-LSTM network to train the model.The best macro avg F1-score 0.67 for development dataset was obtained using the DNN model.The macro avg F1-score of 0.67 was achieved for the classification done on the test data as well.
Research on Expressway Traffic Event Detection at Night Based on Mask-SpyNet

Expressway traffic event detection at night is essential for improving rescue efficiency and avoiding secondary accidents. Most expressways in China have built a complete Expressway video monitoring system. However, at night, the expressway traffic event detection still adopts manual detection, which is inefficient. In this dissertation, the strategy of expressway traffic event detection at night has been analysed first. On this basis, by combining the Mask method and SpyNet deep learning, this study develops a night highway vehicle detection deep learning network with a dense optical flow formed by night vehicle light flow as the detection object. Finally, the Deepsort algorithm is used to track and measure the velocity of the detected target. The measured data are used to compare the background difference method, classical optical flow method, YOLO-v3 and proposed method in this paper. The results show that the proposed method has the advantages of high detection accuracy and fast detection speed.
Importance of Memory Management Layer in Big Data Architecture

The generation of daily massive amounts of heterogeneous data from a variety of sources presents a challenge in terms of storage and analysis capabilities and brings new problems into high-performance computing clusters.To better utilize this huge and heterogeneous data, the continuous development of advanced Big Data platforms and Big Data analytic techniques are required.One of the significant issues with in-memory Big Data processing platforms, such as Apache Spark, is the user's responsibility to decide whether the intermediate data should be cached or not.In addition, the data may be kept in several storage systems and physically scattered over different racks, regions, and clouds.Data need to be close to the computation nodes and hence data locality issue is a challenge.In this paper, using a distinct memory management layer between the data processing layer and the data storage layer, which automatically caches data without the need for any interaction from the applications' developers, is evaluated.Kmeans, PageRank and WordCount workloads from the HiBench benchmark beside a real case to predict the price of Real Estate that is implemented using Gradient Boosting Regression Tree model, are used to evaluate this framework.Experiments show that the memory management layer outperforms the Apache Spark in reducing the execution time.
An efficient post-quantum KEM from CSIDH

Abstract The SIDH and CSIDH are now the two most well-known post-quantum key exchange protocols from the supersingular isogeny-based cryptography, which have attracted much attention in recent years and served as the building blocks of other supersingular isogeny-based cryptographic schemes. The famous SIKE is a post-quantum key encapsulation mechanism (KEM) constructed on the SIDH, motivated by which, this article presents a new post-quantum KEM-based on the CSIDH, which is thereby named as CSIKE. The presented CSIKE has much higher computation efficiency in the decapsulation part by involving an additional tag in the encapsulation results. The new CSIKE is formally proved to be IND-CCA secure under the standard isogeny-based quantum resistant security assumption. Moreover, by comparing the new CSIKE with the only two existing CSIDH-based KEM schemes, i.e., CSIDH-PSEC-KEM and CSIDH-ECIES-KEM, it can be easily found that the new CSIKE has a slightly longer encapsulation size than CSIDH-PSEC-KEM and CSIDH-ECIES-KEM, but (i) it beats the CSIDH-PSEC-KEM by the improvement of approximately 50% in decapsulation speed, and (ii) it has a certain advantage over the CSIDH-ECIES-KEM in security since in the random oracle model, the security proof for CSIDH-ECIES-KEM needs to rely on the stronger CSI-GDH assumption, while the new CSIKE just needs to rely on the basic CSI-CDH assumption.
Construction of Hierarchical Structured Knowledge-based Recommendation Dialogue Dataset and Dialogue System

We work on a recommendation dialogue system to help a user understand the appealing points of some target (e.g., a movie).In such dialogues, the recommendation system needs to utilize structured external knowledge to make informative and detailed recommendations.However, there is no dialogue dataset with structured external knowledge designed to make detailed recommendations for the target.Therefore, we construct a dialogue dataset, Japanese Movie Recommendation Dialogue (JMRD), in which the recommender recommends one movie in a long dialogue (23 turns on average).The external knowledge used in this dataset is hierarchically structured, including title, casts, reviews, and plots.Every recommender's utterance is associated with the external knowledge related to the utterance.We then create a movie recommendation dialogue system that considers the structure of the external knowledge and the history of the knowledge used.Experimental results show that the proposed model is superior in knowledge selection to the baseline models.
Effects of Image Presentation Using a Head-Mounted Display on Muscle and Brain Activity during Swallowing

Introduction: It is well known that humans have an increased sense of taste and consume more food when eating with others.This effect is called social facilitation of eating.We previously reported that self-images also have the effect of social facilitation of eating.In the present study, we investigated the effect of a head-mounted display (HMD) as a means of presenting self-images.Methods: Fourteen healthy adults (7 females and 7 males) were included in the study.The mean age of the participants was 20.4 years.The experimental conditions were as follows: the presentation conditions include the SG condition, in which an image was projected on the smart glasses, and the MT condition, in which an image was projected on the monitor.The stimulation conditions include the self-image condition (Self condition), in which the participant's own image was projected, and the background image condition (Background condition), in which the background image was projected.Participants were asked to consume green tea jelly three times under the four conditions, which consisted of combinations of the presentation and stimulation conditions.Electromyography (EMG) and electroencephalography (EEG) were used to measure the muscle activity of the suprahyoid muscle group and brain activity, respectively.Results: The EMG of the "SG-Self" condition was significantly higher than that of "SG-Background" and "MT-Background" conditions.The EMG of the "MT-Self" condition was significantly higher than that of the "MT-Background" condition.In the EEG before the start of swallowing, "SG-Self" was significantly lower in the alpha band and higher in the beta band than "MT-Background".The beta-alpha ratio was significantly higher for "SG-Self" than for "SG-Background" and "MT-Background" and higher for "MT-Self" than for "MT-Background".Conclusion: These results suggest that self-images influence muscle and brain activities during swallowing, regardless of the presentation conditions, such as HMD or monitor.
DU-VLG: Unifying Vision-and-Language Generation via Dual Sequence-to-Sequence Pre-training

Due to the limitations of the model structure and pre-training objectives, existing vision-andlanguage generation models cannot utilize pairwise images and text through bi-directional generation.In this paper, we propose DU-VLG, a framework which unifies vision-and-language generation as sequence generation problems.DU-VLG is trained with novel dual pre-training tasks: multi-modal denoising autoencoder tasks and modality translation tasks.To bridge the gap between image understanding and generation, we further design a novel commitment loss.We compare pre-training objectives on image captioning and text-to-image generation datasets.Results show that DU-VLG yields better performance than variants trained with uni-directional generation objectives or the variant without the commitment loss.On the image captioning task, our model reaches better performance than other pre-trained systems.On text-to-image generation datasets, our model achieves better or comparable results than previous state-of-the-art models.In addition, human judges further confirm that our model generates real and relevant images as well as faithful and informative captions. Transformer EncoderTransformer Decoder Transformer Encoder Transformer DecoderRows of unripe bananas… Caption Rows of unripe bananas on a display shelf. <BOS> Rows of unripe …Rows of unripe bananas …