Reviewerly/exHarmony huggingface dataset:
A Simpler Solution to Two Problems Revealed about the Composite-Operation Agree

In this chapter, we first point out two problems confronting Chomsky’s (2000, 2001) formulation of Agree as a composite-operation involving both probe-goal search and feature-valuation. We then seek to eliminate Agree by (i) replacing probe-goal search with Minimal Search (as defined in Chomsky (2013, 2015)) and (ii) removing feature-valuation from the narrow syntax and reassigning it to the morpho-phonological component. We demonstrate that X bearing uF and Y bearing vF will appear convergently only where Minimal Search finds X and Y simultaneously, and after transfer, based on the information provided by these two heads, the relevant morpho-phonological features are assigned to uF in the morpho-phonological component.
The History of Speech Recognition to the Year 2030

The decade from 2010 to 2020 saw remarkable improvements in automatic speech recognition. Many people now use speech recognition on a daily basis, for example to perform voice search queries, send text messages, and interact with voice assistants like Amazon Alexa and Siri by Apple. Before 2010 most people rarely used speech recognition. Given the remarkable changes in the state of speech recognition over the previous decade, what can we expect over the coming decade? I attempt to forecast the state of speech recognition research and applications by the year 2030. While the changes to general speech recognition accuracy will not be as dramatic as in the previous decade, I suggest we have an exciting decade of progress in speech technology ahead of us.
IMPLEMENTASI DISCOVERY LEARNING UNTUK MENGAJARKAN MAHIR MENULIS TEKS DESKRIPSI

The purpose of this research is to teach students proficient in writing description text. Students can better understand andunderstand more quickly in the process of imprisonment, and be more courageous to give their own opinions. This isdirected at the responses and responses of students in writing a description text based on their own experience anddeveloping it into a description paper. With this research will help teachers in the use of discovery learning in learning to writedescription text. The use of discovery learning methods is expected to achieve the learning objectives of the material writingthe description text.
Privacy-Preserving Analytics for Social Network Data

The widespread use and adoption of social media have enabled several improvements in human lives. We now can build and nurture relationships with more people than ever before at a fraction of the cost of previous years. However, with these advancements, social media has also paved the way for privacy concerns like never before; with the growing number of detailed personal profiles online, the need for protecting users' privacy cannot be understated. This chapter aims to provide an accurate landscape of the field of privacy-preserving analytics of social network data. First, the need for privacy-preserving techniques is considered by exploring real-world examples. Next, a popular method of modeling privacy threats for analysis is described, followed by some widely used techniques and tools. Finally, the techniques and tools' relative strengths and capabilities are summarized to paint a complete landscape, and the most pressing directions for future work are discussed. The review of the tools will help to understand the capabilities of the state-of-the-art privacy analysis techniques and their applications.
On minimal representations of shallow ReLU networks

The realization function of a shallow ReLU network is a continuous and piecewise affine function $f:\mathbb R^d\to \mathbb R$, where the domain $\mathbb R^{d}$ is partitioned by a set of $n$ hyperplanes into cells on which $f$ is affine. We show that the minimal representation for $f$ uses either $n$, $n+1$ or $n+2$ neurons and we characterize each of the three cases. In the particular case, where the input layer is one-dimensional, minimal representations always use at most $n+1$ neurons but in all higher dimensional settings there are functions for which $n+2$ neurons are needed. Then we show that the set of minimal networks representing $f$ forms a $C^\infty$-submanifold $M$ and we derive the dimension and the number of connected components of $M$. Additionally, we give a criterion for the hyperplanes that guarantees that all continuous, piecewise affine functions are realization functions of appropriate ReLU networks.
Smart Grass Cutter / Nur Asma Nadiah Mohamad Arshad

In this modern time, it is normal to see the house with the home yard. Most of people like to build their house in lot area or buying a house that has a yard so that they can do gardening and another activity outside their house. Hence, DD Technology Company introduces a new product called Smart Grass Cutter. As we known, the grass cutting process took a long time to finish the work. So, it is designed to make cutting and cleaning work easier. Besides, it will save a lot of time cleaning the home yard. The purpose of this product is to facilitate the work such as cutting, collecting, and cleaning the grass at the same time. Then, it is also designed as a user friendly since it can be used by teenagers until senior citizens. The Smart Grass Cutter user no need to carry the load of the machine motor on the shoulder and swing the cutting blade during the cutting process. First of all, the company has set the target market for intensive selling. In other words, which types of customer groups that would buy the product. The company’s target customer is for customers that have a home yard and the contractor that serves customers in lawn mowing. This Smart Grass Cutter has functions and advantages in today’s grass cutter machine. It is because it can do cutting and cleaning at the same time which is multipurpose. Thus, it is providing a competitive advantage over more established competitors. Lastly, the management team of the company is a successful person and have their own experience in handling the work. So, it can be seen that the outcome of the product will be as great as the team members.
“THE MORE THINGS STAY THE SAME: AN EDUCATORS VIEW ON THE QUESTION OF DISTANCE LEARNING’S IMPACT ON EDUCATION”

A teacher’s look back at the last year as the world struggled through the global pandemic that changed so many of the things we took for granted. Special attention is given to the effects that distance learning has had on the educational process for the worse and for the better.
Learning Proxemic Behavior Using Reinforcement Learning with Cognitive Agents

Proxemics is a branch of non-verbal communication concerned with studying the spatial behavior of people and animals. This behavior is an essential part of the communication process due to delimit the acceptable distance to interact with another being. With increasing research on human-agent interaction, new alternatives are needed that allow optimal communication, avoiding agents feeling uncomfortable. Several works consider proxemic behavior with cognitive agents, where human-robot interaction techniques and machine learning are implemented. However, environments consider fixed personal space and that the agent previously knows it. In this work, we aim to study how agents behave in environments based on proxemic behavior, and propose a modified gridworld to that aim. This environment considers an issuer with proxemic behavior that provides a disagreement signal to the agent. Our results show that the learning agent can identify the proxemic space when the issuer gives feedback about agent performance.
PERAN LINGUIS DALAM WACANA PENGADILAN: MENGURAI KARAKTERISTIK DAN MAKNA ALTERNATIF UJARAN KEBENCIAN

This study aims to discuss the role of pragmatics, which is one of the focuses of forensic linguistic studies, in cases of hate speech in Indonesia. One of the focuses of this study is to explain why speech is categorized as hate speech. Another focus is to provide an understanding that a speech can contain more than one meaning which may not be intended to spread hatred. This study refers to the concept of illocutionary power and perlocutionary action as well as the concept of face threatening action to analyze the characteristics of speech that has the potential to contain hatred or other meanings. The results of this study show that there are characteristics that can be found in hate speech, such as choice of words with negative meanings, dehumanization, association with institutions that have a bad image, and the use of the noun us vs them.
Design And Development of Human Following Robot (Cart)

For a robot that performs autonomously, the communication between the person and the robot is the most important factor. A significant awareness has been observed regarding the usage of such a technology. This research has a trivial involvement in the development of such robots. Robots that function fully autonomously should not only complete the jobs that are desired of them but also somehow establish a connection between themselves and the person operating them. A lot of research has been done of these kinds of robot and a lot of work still needs to be done. In order for a robot to communicate and interact with the person, it should also be capable of following that particular person. Keeping this in mind, there should be a capacity in the robot to get information from the surroundings while perusing the required object. The primary goal of our work was to design and fabricate a robot that not only tracks the target but also moves towards it while doing the tracking. In order to make things simpler, a unique handmade tag was placed on the person that the robot needs to follow.
Penerapan Metode Fuzzy Tsukamoto untuk Mengidentifikasi Banjir Berdasarkan Curah Hujan dan Suhu di Kota Balikpapan pada Tahun 2015 sampai 2019

Flooding occurred in several areas in Balikpapan is still a big issue. Floods that occurred in several places in Balikpapan were basically caused by the unavailability of trenches and places to drain rainwater. This causes rainwater going straight down the road and creating a puddle. One way to anticipate the occurrence of floods is to know the conditions that trigger floods, which are rainfall and air temperature. For this reason, it is a necessary to create a classification system for the level of rainfall and air temperature that affect flooding. The method that can be used for this problem is fuzzy logic, specifically the Tsukamoto method. Tsukamoto method of fuzzy logic is one of the most frequently used methods for prediction. Therefore, method can be used to identify floods. Input variables used in this study are rainfall and temperature, while the output variable is flood or not flood.
Performance Tuning Of Apache Hadoop Framework In Big Data Processing With Respect To Block Size, Operating System Clusters And Map Reduce Techniques

The term Big data is applied to data sets which cannot be processed by traditional relational database applications due to varying data size in terms of TB, PB, EB, ZB and varying data types such as structured, unstructured and semi-structured. In order to store and process big data Apache Hadoop open source framework is available. Apache Hadoop provides storage component known as HDFS .Apache Hadoop provides MapReduce processing component which allows users to write the program in the form of map and reduce functions to implement their big data processing logic. Apache Hadoop offers various configuration files holding configuration settings which can be modified. While deployment of Apache Hadoop , it comes with by default configuration settings. In this research work we are focusing the operating system on which Apache Hadoop can be installed on EC2 instances on AWS and get better performance and along with that Data block size parameter configuration setting customization in order to reduce the execution time of mapreduce jobs in turn tuning the performance.
Blind Constellation Identification for MQAM in Hybrid Satellite-Terrestrial Networks

In hybrid satellite-terrestrial networks (HSTNs), frequency-selective fading can severely deteriorate the quality of transmitted signal by generating undesired and disordered constellation diagrams due to scatters in the mutipath channels. In this paper, we propose a low-overhead blind constellation identification method to combat frequency-selective fading in HSTNs. Specifically, a pre-equalization method is proposed based on a constant modulus algorithm to restore the contour of the constellation diagram. Moreover, the similarity measure function and the difference measure function are derived using template matching to identify the constellation of M-ary quadrature amplitude modulation (MQAM). The proposed method requires less information and no training sequences and pilots. Simulation results show that the proposed method is able to accurately identify the constellation type of MQAM signal, and these features are extremely useful in HSTNs.
Limited Animation as A Means of Spreading Individual Thought Through Social Media

The social system is part of the fabric of modern or the so - called society media and during a short period of time it has become central to the free dissemination and self - expression of the user, prompting governments to allocate services within ministries to follow modern mass media. Like traditional media, this social communication has had its share of guidance and the formation of attitudes and trends, but the difference in that the source of intellectual guidance here comes not from a State, organization or policy of a channel, as in traditional media, but from the different individual platforms in thought, orientation and direction that seek to attract more followers, and the power of attraction and follow-up depends on the ability of the individual to communicate his or her own intellectual content attractively. The expression process consisted of multiple forms, including limited animation.
Automated Classification of Depressive Tweets Detection Using Machine Learning and Deep Learning Techniques

The most occurring emotional mental disorder now a days is Anxiety and depression. Where anxiety anddepression are related and directly proportional to each other. Anxiety and Depression are two very dangerous and harmful diseases or disorders which occurs in children, undergraduates, teenagers, employees and old people. More anxiety and depression have suicidal tendency. Depression occurs due to many parameters like tensions, lack of sleep, lack of confidence and etc. The person who suffers from depression have the higher probability to suffer with anxiety. The main problem with these disorders is that detecting anxiety and depression is a very difficult task, therefore one of the way to detect the people who are suffering from the anxiety and depression is with the help of social support by using the twitter data and extracting the data from the tweets which they post and analysing the data and detecting whether the tweets posted by the people on twitter are depressive or not by using best machine learning techniques like naive Bayes, logistic regression and also the deep learning techniques like CNN and LSTM. The naive Bayes classifier is primarily used in sentiment classification, and it is based on the Bayesian network. The Naive Bayes method predicts text based on the frequency of words in the text. In contrast, logistic regression analysis is a statistical probabilistic classifier that is used in deep learning to estimate the probability of a binary response based on one or more predictors. With the aid of social media (twitter) data, the final result of this research is to classify whether a given tweet from the dataset is depressive or not, and applying feature extraction methods to this classifier helps to improve the accuracy results of the classifier. The TF-IDF feature extraction method was used in this study (Term frequency and inverse document frequency). And whereas in deep learning, LSTM and CNN are used to predict the depressive tweets with best accuracy.
Cybersecurity Incident Response in Organisations: A Meta-level Framework for Scenario-based Training

Cybersecurity incident response teams mitigate the impact of adverse cyber-related events in organisations. Field studies of IR teams suggest that at present the process of IR is under-developed with a focus on the technological dimension with little consideration of practice capability. To address this gap, we develop a scenario-based training approach to assist organisations to overcome socio-technical barriers to incident response. The training approach is informed by a comprehensive list of socio-technical barriers compiled from a comprehensive review of the literature. Our primary contribution is a novel meta-level framework to generate scenarios specifically targeting socio-technical issues. To demonstrate the utility of the framework, a proof-of-concept scenario is presented.
Daft-Exprt: Robust Prosody Transfer Across Speakers for Expressive Speech Synthesis.

This paper presents Daft-Exprt, a multi-speaker acoustic model advancing the state-of-the-art on inter-speaker and inter-text prosody transfer. This improvement is achieved using FiLM conditioning layers, alongside adversarial training that encourages disentanglement between prosodic information and speaker identity. The acoustic model inherits attractive qualities from FastSpeech 2, such as fast inference and local prosody attributes prediction for finer grained control over generation. Experimental results show that Daft-Exprt significantly outperforms strong baselines on prosody transfer tasks, while yielding naturalness comparable to state-of-the-art expressive models. Moreover, results indicate that adversarial training effectively discards speaker identity information from the prosody representation, which ensures Daft-Exprt will consistently generate speech with the desired voice. We publicly release our code and provide speech samples from our experiments.
마루야마 마사오와 2·26사건 - 2·26사건에 대한 이해와 오해, 그리고 동경대 -

I think that we never can separate Maruyama Masao’s thoughts formation and Japanese Militarism. But the relation between Maruyama Masao and February 26th Incident, so to speak especially the part of the military had given all Japanese shocks, had not been paid attention until now. In this paper I want to complement the research situation critically. I watch matters as belows. 1. Maruyama Masao’s academic advisors 2. Youth officers of Imperial Way Faction had the theory of the emperor''s organ 3. Both was influenced by Yoshino Sakuzo 4. February 26th Incident’s main actors may be more progressive about the Emperor system, and so on
Meta Label Correction for Noisy Label Learning

Leveraging weak or noisy supervision for building effective machine learning models has long been an important research problem. Its importance has further increased recently due to the growing need for large-scale datasets to train deep learning models. Weak or noisy supervision could originate from multiple sources including non-expert annotators or automatic labeling based on heuristics or user interaction signals. There is an extensive amount of previous work focusing on leveraging noisy labels. Most notably, recent work has shown impressive gains by using a meta-learned instance re-weighting approach where a meta-learning framework is used to assign instance weights to noisy labels. In this paper, we extend this approach via posing the problem as label correction problem within a meta-learning framework. We view the label correction procedure as a meta-process and propose a new meta-learning based framework termed MLC (Meta Label Correction) for learning with noisy labels. Specifically, a label correction network is adopted as a meta-model to produce corrected labels for noisy labels while the main model is trained to leverage the corrected labeled. Both models are jointly trained by solving a bi-level optimization problem. We run extensive experiments with different label noise levels and types on both image recognition and text classification tasks. We compare the reweighing and correction approaches showing that the correction framing addresses some of the limitation of reweighting. We also show that the proposed MLC approach achieves large improvements over previous methods in many settings.
Small and Medium Manufacturing Enterprises (SMEs) and Knowledge-Base: South Asian Experience

The paper studied small and medium manufacturing enterprises in 12 South Asian developing and neighboring countries: Bangladesh, Cambodia, Lao, Myanmar, Nepal, Pakistan, Sri Lanka, India, Indonesia, the Philippines, Thailand, and Vietnam. It has seen the intensity of manufacturing enterprises and their production and export performance. The paper found that Bangladesh, Nepal, Myanmar, Pakistan, Lao, and Cambodia are poor performers; Thailand, India, Vietnam, the Philippines, and Indonesia are the high performers; and Sri Lanka stands in between. The paper used the data for 2018 in most of the cases. The data were available from national and international organizations directly relevant to SMEs. The main contribution of the paper is the explaining of the role of knowledge-base such as belief and knowledge, faith and reason, and art and aesthetics on SME and entrepreneurship development. R&D, creativity rank, human development rank, entrepreneurship development rank, and government expenditure on religious affairs gave some evidence in support of the claim.
The Fewer the Merrier: Pruning Preferred Operators with Novelty

Heuristic search is among the best performing approaches to classical satisficing planning, with its performance heavily relying on informative and fast heuristics, as well as search-boosting and pruning techniques. While both heuristics and pruning techniques have gained much attention recently, search-boosting techniques in general, and preferred operators in particular have received less attention in the last decade. Our work aims at bringing the light back to preferred operators research, with the introduction of preferred operators pruning technique, based on the concept of novelty. Continuing the research on novelty with respect to an underlying heuristic, we present the definition of preferred operators for such novelty heuristics. For that, we extend the previously defined concepts to operators, allowing us to reason about the novelty of the preferred operators. Our experimental evaluation shows the practical benefit of our suggested approach, compared to the currently used methods.
RadGraph: Extracting Clinical Entities and Relations from Radiology Reports

Extracting structured clinical information from free-text radiology reports can enable the use of radiology report information for a variety of critical healthcare applications. In our work, we present RadGraph, a dataset of entities and relations in full-text chest X-ray radiology reports based on a novel information extraction schema we designed to structure radiology reports. We release a development dataset, which contains board-certified radiologist annotations for 500 radiology reports from the MIMIC-CXR dataset (14,579 entities and 10,889 relations), and a test dataset, which contains two independent sets of board-certified radiologist annotations for 100 radiology reports split equally across the MIMIC-CXR and CheXpert datasets. Using these datasets, we train and test a deep learning model, RadGraph Benchmark, that achieves a micro F1 of 0.82 and 0.73 on relation extraction on the MIMIC-CXR and CheXpert test sets respectively. Additionally, we release an inference dataset, which contains annotations automatically generated by RadGraph Benchmark across 220,763 MIMIC-CXR reports (around 6 million entities and 4 million relations) and 500 CheXpert reports (13,783 entities and 9,908 relations) with mappings to associated chest radiographs. Our freely available dataset can facilitate a wide range of research in medical natural language processing, as well as computer vision and multi-modal learning when linked to chest radiographs.