Reviewerly/exHarmony huggingface dataset:
Abnormal Human Behavior Recognition Based on Image Processing Technology

In recent years, digital image processing technology and computer vision technology have been continuously developed and made considerable progress. Based on the image information, through the detection, extraction and recognition operations, the human posture is understood, and abnormal behavior can be recognized in time. In this paper, related work in the abnormal human behavior analysis is first introduced, including many advanced practical projects. Then three methods of human target detection are listed, with the advantages and disadvantages being compared. The detection process is designed according to the inter-frame difference method. Finally, specific realization is proposed from three aspects: area identification, feature extraction, abnormality determination, and the algorithm steps are clearly summarized. Therefore, this study can provide technical support and decision-making guidance for safety management, with a wide range of applications.
Deep Transfer Learning Based Multiway Feature Pyramid Network for Object Detection in Images

Object detection is being widely used in many fields, and therefore, the demand for more accurate and fast methods for object detection is also increasing. In this paper, we propose a method for object detection in digital images that is more accurate and faster. The proposed model is based on Single-Stage Multibox Detector (SSD) architecture. This method creates many anchor boxes of various aspect ratios based on the backbone network and multiscale feature network and calculates the classes and balances of the anchor boxes to detect objects at various scales. Instead of the VGG16-based deep transfer learning model in SSD, we have used a more efficient base network, i.e., EfficientNet. Detection of objects of different sizes is still an inspiring task. We have used Multiway Feature Pyramid Network (MFPN) to solve this problem. The input to the base network is given to MFPN, and then, the fused features are given to bounding box prediction and class prediction networks. Softer-NMS is applied instead of NMS in SSD to reduce the number of bounding boxes gently. The proposed method is validated on MSCOCO 2017, PASCAL VOC 2007, and PASCAL VOC 2012 datasets and compared to existing state-of-the-art techniques. Our method shows better detection quality in terms of mean Average Precision (mAP).
Prague Dependency Treebank - Consolidated 1.0 (PDT-C 1.0)

A richly annotated and genre-diversified language resource, The Prague Dependency Treebank – Consolidated 1.0 (PDT-C 1.0, or PDT-C in short in the sequel) is a consolidated release of the existing PDT-corpora of Czech data, uniformly annotated using the standard PDT scheme. PDT-corpora included in PDT-C: Prague Dependency Treebank (the original PDT contents, written newspaper and journal texts from three genres); Czech part of Prague Czech-English Dependency Treebank (translated financial texts, from English), Prague Dependency Treebank of Spoken Czech (spoken data, including audio and transcripts and multiple speech reconstruction annotation); PDT-Faust (user-generated texts). The difference from the separately published original treebanks can be briefly described as follows: it is published in one package, to allow easier data handling for all the datasets; the data is enhanced with a manual linguistic annotation at the morphological layer and new version of morphological dictionary is enclosed; a common valency lexicon for all four original parts is enclosed. Documentation provides two browsing and editing desktop tools (TrEd and MEd) and the corpus is also available online for searching using PML-TQ.
Conversational Agent in Romanian for Storing User Information in a Knowledge Graph

Information surrounds us and keeping track of relevant details can be challenging.Although there are multiple applications to take notes, organize ideas, or set reminders, existing solutions are semantic-agnostic and rely on the user to manually search for desired information by keywords.We propose a novel method to help people store and retrieve such details with ease in Romanian language.Our conversational agent built on top of the RASA framework is capable to extract relevant information from the user's utterances, store them in a persistent knowledge graph, and ultimately, access them when requested.A set of specific intents regarding locations, timestamps, and properties were created and learned by the agent using manually built examples.In addition, an interaction logic based on a knowledge graph was added to enable the storage and retrieval of information, based on the identified semantic components from the input sentences.The performed tests showed a good accuracy for intent detection, and promising results for the sentence parser.Our conversational agent is accessible as a web application which can process text or speech inputs, and responds with a textual representation of the user's memorized facts.
TAMIL SIGN LANGUAGE RECOGNITION IN REAL TIME

Communication is interchanging of ideas information or message from one person to the other person, sign language is made with the hand and other movement, including facial expressions and posture of the body used by the people of unable to speak and hearing, there are different type of sign language. Tamil sign language is the regional sign language, the aim of the work is provide the real time recognition of Tamil sign Language (TSL) in to Tamil letter, here we introduced the convolutional neural network (CNN) as a classifier used as training the Tamil sign language and predict the Tamil sign language, the process are divided in two section one is to train by using the keras model and the second section is the skin segmentation of the hand gestures in the region of the interest, there are two phase in training the model, having set of the hand gestures images in the training set and testing set by using the image train the model, the model occurred the 100% accuracy in the white background and good lighting condition, 97.5 % in the low lighting condition.
Metagraph Knowledge Base and Natural Language Processing Pipeline for Event Extraction and Time Concept Analysis

Analysis of sentence meaning is necessary to improve the precision and recall of information retrieval. Event extraction is one of methods used for this purpose. An important step in solving this problem is the time extraction and processing. It is necessary to classify time intervals and determine the relation of succession for them. Intervals can be expressed with specific dates and times, pronouns and tense nouns or they are implied with successive events description. The authors propose to use a metagraph knowledge base and a natural language processing pipeline. The metagraph has the emergence property for a detailed description of a specific concept using a graph fragment. This representation of knowledge allows setting different types of relations between time concepts and states of objects. The study was carried out to extract time intervals from OpenCorpora dataset. The number of time concepts in knowledge base was calculated according to their types.
Audio Forensics on Smartphone with Digital Forensics Research Workshop (DFRWS) Method

Audio is one of the digital items that can reveal a happened case. However, audio evidence can also be manipulated and changed to hide information. Forensics audio is a technique to identify the sound’s owner from the audio using pitch, formant, and spectrogram parameters. The conducted research examines the similarity of the original sound with the manipulated voice to determine the owner of the sound. It analyzes the level of similarity or identical sound using spectrogram analysis with the Digital Forensics Research Workshop (DFRWS) Method. The research objects are original and manipulated files. Both files are in mp3 format, which is encoded to WAV format. Then, the live forensics method is used by picking up the data on a smartphone. Several applications are also used. The results show that the research successfully gets digital evidence on a smartphone with the Oxygen Forensic application. It extracts digital evidence in the form of two audio files and two video files. Then, by the hashing process, the four obtained files are proven to be authentic. Around 90% of the data are identical to the original voice recording. Only 10% of the data are not identical.
From Toxicity in Online Comments to Incivility in American News: Proceed with Caution

The ability to quantify incivility online, in news and in congressional debates, is of great interest to political scientists.Computational tools for detecting online incivility for English are now fairly accessible and potentially could be applied more broadly.We test the Jigsaw Perspective API for its ability to detect the degree of incivility on a corpus that we developed, consisting of manual annotations of civility in American news.We demonstrate that toxicity models, as exemplified by Perspective, are inadequate for the analysis of incivility in news.We carry out error analysis that points to the need to develop methods to remove spurious correlations between words often mentioned in the news, especially identity descriptors and incivility.Without such improvements, applying Perspective or similar models on news is likely to lead to wrong conclusions, that are not aligned with the human perception of incivility.
Enhancement of Non-Linear Generators and Calculate the Randomness Test for Autocorrelation Property

In this paper, three main generators are discussed: Linear generator, Geffe generator and Bruer generator. The Geffe and Bruer generators are improved and then calculate the Autocorrelation postulate of randomness test for each generator and compare the obtained result. These properties can be measured deterministically and then compared to statistical expectations using a chi-square test.
Towards Personalised and Document-level Machine Translation of Dialogue

State-of-the-art (SOTA) neural machine translation (NMT) systems translate texts at sentence level, ignoring context: intra-textual information, like the previous sentence, and extra-textual information, like the gender of the speaker.Because of that, some sentences are translated incorrectly.Personalised NMT (PersNMT) and document-level NMT (DocNMT) incorporate this information into the translation process.Both fields are relatively new and previous work within them is limited.Moreover, there are no readily available robust evaluation metrics for them, which makes it difficult to develop better systems, as well as track global progress and compare different methods.This thesis proposal focuses on PersNMT and DocNMT for the domain of dialogue extracted from TV subtitles in five languages: English, Brazilian Portuguese, German, French and Polish.Three main challenges are addressed: (1) incorporating extra-textual information directly into NMT systems; (2) improving the machine translation of cohesion devices; (3) reliable evaluation for PersNMT and DocNMT.
Octave Mix: Data Augmentation Using Frequency Decomposition for Activity Recognition

Research in human activity recognition (HAR) requires a huge amount of data, but it is not easy to collect such measured sensor data. Besides, there is no much application of data augmentation (DA) in HAR. This study proposes Octave Mix as a novel synthetic-style DA method for sensor-based HAR. The proposed method uses frequency decomposition to intersect low- and high-frequency waveforms. In addition, we propose a DA ensemble method and a training algorithm to ensure robustness to the original sensor data while applicable to various feature representations. To evaluate the Octave Mix method’s effectiveness, we conduct experiments using four different benchmark datasets of sensor-based HAR and achieve high estimation accuracy in our results. Furthermore, we demonstrate that ensembling two DA strategies: Octave Mix with rotation and mixup with rotation, achieves higher accuracy.
Handwritten Text Recognition using Deep Learning and Word Beam Search

This paper offers a solution to traditional handwriting recognition techniques using concepts of Deep learning and Word Beam Search. This paper explains about how an individual handwritten word is classified from the handwritten text by translating into a digital form. The digital form when trained with the Connectionist Temporal Classification (CTC) loss function, the output produced is a RNN. This is a matrix containing character probabilities for each time-step. The final text is mapped using a CTC decoding algorithm by converting the character probabilities. The recognized text is constructed by a list of words from the dictionary by using the token passing algorithm. It is found the running time of token passing depends on the size of dictionary. Also the numbers like arbitrary character strings will not able to decode. In this paper the decoding search algorithm word beam search is proposed, in order to tackle these types of problems. This methodology support to constrain words similar to those contained in a dictionary. It allows the character strings such as arbitrary non-word between the words, and integrates into a word-level language model. It is found the running time is better when compared with the token passing. The proposed algorithm comprises of the decoding algorithm named vanilla beam search and token passing using the IAM dataset and Bentham data set.
CAPTCHA: A TOOL FOR WEB SECURITY

Malicious computer programs today have tried to target websites, which have a significant effect on their availability and security.The CAPTCHA is a tool that is an efficient way of solving this problem.CAPTCHA is a full automated public turing test.Many human activities are performed on the Internet every day, such as schooling, commerce, conversations etc.Some hackers write programs to automatically make false registrations, for example when registering in websites, that waste web resources while this may even stop the whole website.Thus, human users should be differentiated from CAPTCHA software systems.CAPTCHA handwritten picture may be a work around.In this paper literature review of CAPTCHA has been done in order to enhance our knowledge about how CAPTCHA can provide web security focusing in particular on handwritten CAPTCHA and audio, video CAPTCHA in general.
How Good (really) are Grammatical Error Correction Systems?

Standard evaluations of Grammatical Error Correction (GEC) systems make use of a fixed reference text generated relative to the original text; they show, even when using multiple references, that we have a long way to go.This analysis paper studies the performance of GEC systems relative to closest-gold -a gold reference text created relative to the output of a system.Surprisingly, we show that the real performance is 20-40 points better than standard evaluations show.Moreover, the performance remains high even when considering any of the top-10 hypotheses produced by a system.Importantly, the type of mistakes corrected by lower-ranked hypotheses differs in interesting ways from the top one, providing an opportunity to focus on a range of errors -local spelling and grammar edits vs. more complex lexical improvements.Our study shows these results in English and Russian, and thus provides a preliminary proposal for a more realistic evaluation of GEC systems.
Impact of Scene Content on High Resolution Video Quality

This paper deals with the impact of content on the perceived video quality evaluated using the subjective Absolute Category Rating (ACR) method. The assessment was conducted on eight types of video sequences with diverse content obtained from the SJTU dataset. The sequences were encoded at 5 different constant bitrates in two widely video compression standards H.264/AVC and H.265/HEVC at Full HD and Ultra HD resolutions, which means 160 annotated video sequences were created. The length of Group of Pictures (GOP) was set to half the framerate value, as is typical for video intended for transmission over a noisy communication channel. The evaluation was performed in two laboratories: one situated at the University of Zilina, and the second at the VSB—Technical University in Ostrava. The results acquired in both laboratories reached/showed a high correlation. Notwithstanding the fact that the sequences with low Spatial Information (SI) and Temporal Information (TI) values reached better Mean Opinion Score (MOS) score than the sequences with higher SI and TI values, these two parameters are not sufficient for scene description, and this domain should be the subject of further research. The evaluation results led us to the conclusion that it is unnecessary to use the H.265/HEVC codec for compression of Full HD sequences and the compression efficiency of the H.265 codec by the Ultra HD resolution reaches the compression efficiency of both codecs by the Full HD resolution. This paper also includes the recommendations for minimum bitrate thresholds at which the video sequences at both resolutions retain good and fair subjectively perceived quality.
Analyzing the instructions vulnerability of dense convolutional network on GPUS

Recently, deep neural networks (DNNs) have been increasingly deployed in various healthcare applications, which are considered safety-critical applications. Thus, the reliability of these DNN models should be remarkably high, because even a small error in healthcare applications can lead to injury or death. Due to the high computations of the DNN models, DNNs are often executed on the graphics processing units (GPUs). However, the GPUs have been reportedly impacted by soft errors, which are extremely serious issues in the healthcare applications. In this paper, we show how the fault injection can provide a deeper understanding of DenseNet201 model instructions vulnerability on the GPU. Then, we analyze vulnerable instructions of the DenseNet201 on the GPU. Our results show that the most significant vulnerable instructions against soft errors PR, STORE, FADD, FFMA, SETP and LD can be reduced from 4.42% to 0.14% of injected faults, after we applied our mitigation strategy.
Stray Off Topic to Stay On Topic: Preserving Interaction and Team Morale in a Highly Collaborative Course while at a Distance

The Covid-19 pandemic has prompted schools and universities to shift their teaching to virtual classrooms from one day to the other.As a unique example, we had to virtualize the second half of a two-semester course on humancentered innovation, which heavily relies on direct interaction of students in small groups.In going virtual, we have found that adapting assignments is only the tip of the iceberg.Despite being familiar with the students, the real challenges were preserving high levels of creative interaction as well as surveying team morale and status.Reflecting on our experiences, we detail solutions related to the lack of creative interaction by fostering off-topic chit-chat and surveying team morale by introducing more explicit communication and seeking team consent.To help teachers adapt to virtual teaching, we discuss how our mitigation approaches, which we developed in an extreme setting requiring close, creative collaboration, may apply to virtual teaching in general.
Impact of work alienation feelings on turnover intention in the Aqaba Special Economic Zone Authority located in Jordan

The aim of this study was to examine the effect of work alienation on employee turnover intention among the employees in the Aqaba Special Economic Zone Authority. Descriptive, correlational, cross-sectional design were used by distributing of (100) self reporting questionnaire, which was consist of three parts; the first part was for the demographical characteristics of the employees, the second part was for the Work Alienation scale and the third part was Anticipated Turnover Scale (ATS). The results revealed that work alienation levels was in a moderate level {mean= 3.13 (SD=.81)}, and a total of 27 (29.3%) employees show the aim of turnover the jobs. Moreover, the regression results reveals that there was a strong significance effect for work alienation on employee turnover intention (r = .729); and (53.1%) of the employee turnover intention could be explained from work alienation. The negative effect of work alienation in the preservation of the expert employees, need more focus and hard work by the human resources departments to take precautionary measures for this negative effect.
An Optimized Cloud Model Algorithm Adapted to Comprehensive Benefit Evaluation of Water-Saving Irrigation

The comprehensive benefit evaluation of water-saving irrigation in the irrigated areas is a complex and dynamic evaluation system.According to the characteristics of randomness and fuzziness of the system, an optimized cloud model algorithm for the comprehensive benefit evaluation of water-saving irrigation in the irrigated areas was constructed, and the calculation of the weight cloud model was optimized.At the same time, taking Yahekou as the research area, an evaluation system considering the ecological, economic, and social benefits was established, and the comprehensive benefits of watersaving irrigation in this area were evaluated with the optimization algorithm.The results showed that the comprehensive benefit of water-saving irrigation in the Yahekou irrigated area was evaluated as grade II "good" , selecting the Normal-Water-Level-Year as a typical year, following the life cycle of winter wheat-summer maize rotation mode as the time scale.The optimization algorithm not only ensures the accuracy of evaluation results but also takes into account the uncertainty of each index factor.It opens up a new way for the comprehensive benefit evaluation of water-saving irrigation in the
The Development of Fabel <i>E-Comic</i> in Bahasa Indonesia Lesson for Grade II of Elementary School

The Development of Fabel E-Comic in Bahasa Indonesia Lesson for Grade II Of Elementary School.During online learning process in the era of covid-19, there is the lack of students understanding in reading learning process especially in looking for information from the text of fable and the lack of instructional media application from the teacher during learning process in Bahasa Indonesia class.It causes the students are not interested to the materials.Therefore, an instructional media that can make the students are interested in the learning process is needed so that the learning process can be interesting for the students.The aim of this research is to describe the development process and to find out the validation result of Fable E-Comic based on the review of the experts, individual try out, and small group try out.This research used development research which applied ADDIE.The data were collected using questionnaire and were analysed using descriptive quantitative method.The result from the analysis were the result from the subject master got 100 with very good qualification, the result from the instructional design expert got 91.42 with very good qualification, the result from the instructional media expert got 89.41 with good qualification, the result from the individual try out on 3 students got 93.33 with very good qualification, and the result from small group try out on 9 students got 94.66 with very good qualification.Based on the result of the product try out from the experts (subject master, instructional design expert, and instructional media expert) and the result of the try out on the subject of the research (students), it can be concluded that Fable E-comic can be used to teach Bahasa Indonesia for the second grade students of elementary school.