Reviewerly/exHarmony huggingface dataset:
Rancang Bangun Fitur Chatbot Customer Service Menggunakan Dialogflow

The quality of providing customer service channels is very influential on customer satisfaction. To keep customer satisfaction, it is necessary to have a good interaction from customer service to the customer. In this case, PT Dian Prima Jayaraya is one of the companies that utilize Solo's application product as a medium for customer service. The development of the Solo application makes the application's features more complex and makes it difficult for users to do activities in the application. Customer service has a role in accommodating questions and answering difficulties experienced by users. The number of incoming questions is quite large, so that the existing customer service team cannot answer. Ignored or unanswered questions can lower the level of customer satisfaction. Therefore, we need a system that can help optimize customer service in a Chatbot to minimize unanswered customer questions. A chatbot is a service powered by rules and artificialintelligence, which the user interacts with through a chat interface. The Chatbot will be built on an iOS prototype application and use the NLP (Natural Language Processing) platform, namely Dialogflow. This platform will process and scan every question given by the customer.
THE INFLUENCE OF ANDROID-BASED EDUCATIONAL GAME MEDIA ON COGNITIVE AND PSYCHOMOTORIC STUDENTS AGED 4-5 YEARS OF KINDERGARTEN PERTIWI MAYANG JEMBER

This research is causal quantitative research that wants to determine the effect of android-based educational games on cognitive and psychomotor for students aged 4-5 years at TK Pertiwi Mayang even semester of the 2019-2020 academic year. The problems studied in this problem are? Is there and to what extent is the influence of Android-based educational game learning media on the cognitive enhancement of students aged 4-5 years at Kindergarten Pertiwi Mayang?&#x0D; The research design used by the researcher is quantitative with the causal method. Analysis of the data used is multiple correlation. From the results of multiple correlation test analysis using the SPSS v.22 for Windows application. Obtained the value of Sig. (2-tailed) &lt;0.05 that is 0.00 &lt;0.05. It can be concluded that Ho is rejected and Ha is accepted. So that there is an effect of Android-based educational games on cognitive and psychomotor for children aged 4-5 years at Pertiwi Mayang Kindergarten.&#x0D; Suggestions addressed to several parties in order to increase student interest in learning for the future are as follows: 1) In particular, the headmaster of Kindergarten Pertiwi Mayang can take advantage of the available time to provide competency development to teachers or educators to carry out their roles and duties as educators by even better for the achievement of educational goals as expected. In addition, the principal must also take the time to just look, moreover, it can provide motivation for his students. 2) The teacher councils so as not to get bored guiding, teaching, and achieving creative, innovative, and fun learning breakthroughs in order to carry out a good and optimal learning process so as to generate a better sense of student confidence. 3) Parents should give more space or time to their children to express themselves in order to support children's character development so that it can be even better. Furthermore, parents must be more active in monitoring and motivating their children to explore better potential than before
Undergraduate Students’ Uses of Code Switching (CS) in Pakistani Universities: A Reflective Study

Pakistan is a multicultural, multilingual and multiethnic society, therefore, code switching (CS) is a common feature of the Pakistani society.The study has deployed the qualitative and quantitative methods to answer the research questions.The data was collected from the undergraduate students and their teachers of two public sector universities located in the province of Punjab, Pakistan.The findings reveal that both undergraduate students and university teachers are using CS as a tool beneficially for multiple reasons in universities such as, teaching, translation, classroom discussion, comprehension, and explanation, conversation, asking for clarification and responding to teachers' questions.The code switching alleviates students' language anxiety.It is recommended that CS as an effective bilingual strategy of teaching should be included in teacher education programs to train the university faculty members for implementing it successfully in their classrooms for achieving the objectives of their academic courses.
APPLICATION OF MULTIMEDIA LEARNING MEDIA TO INCREASE STUDENT MOTIVATION AND LEARNING OUTCOMES IN PKN SUBJECTS AT SMP 01 ISLAM JEMBER

the teacher's learning process has an important role in making teaching more meaningful and memorable so that students are motivated to learn. To improve learning outcomes, an effective teaching media is needed, so that students are motivated to learn by using learning media,This study uses a descriptive qualitative research approach. Qualitative research is research that intends to understand the phenomena experienced by research subjects, such as behavior, perception, motivation, action and others. the use of multimedia learning media by teachers can explain the material without having to rewrite the material described. In addition, the benefits obtained by students are students can more easily understand the material / teaching materials. This is felt by students because the material is presented with videos which allow students to know the material is not abstract / just wishful thinking, so that their learning motivation will increase. the evaluation of the application of multimedia learning media this time has shown a high level of student learning enthusiasm. In addition, some of the benefits of using this learning media have been obtained by both teachers and students
조직 내 학습네트워크 연구동향 : 2002~2021년 국내외 학술지 비교를 중심으로

The purpose of this study is to identify and compare the trends of research related to learning networks within organizations targeting Korean and foreign journal articles in HRD related field from 2002 to 2021. Based on the framework of research analysis, we applied content analysis. The study results are as follows. First, an study of the learning network within the organization was requested to expand the interaction and solidarity of members of the organization. Second, research was mainly conducted at companies, but their research activities were needed due to the insufficient participation of their researchers. Third, research subjects should be expanded to search for physical resources and cyber networks to respond to changing times. Fourth, in the research method, it was revealed that domestic and foreign patterns were different, and each implication was derived. Fifth, in the 2010s, it was defined as a learning organization/community, and the change into a learning organization that learns and develops on its own was discovered. Sixth, domestic and foreign key words were analyzed to suggest implications necessary for domestic research and development.
IMPROVING STUDENT'S LEARNING OUTCOMES USING THE CONTEXTUAL LEARNING MODEL ON LISTENING COMPETENCY IN INDONESIAN LESSONS

&#x0D; This study applies a contextual learning model in order to improve learning outcomes in listening to learning so that students have perfect Indonesian language skills, learning learning concepts that help teachers relate the Indonesian language material they teach to real environmental situations and encourage students to make connections between language knowledge that they teach. possessed by the application of language in everyday life&#x0D; Classroom action research activities were carried out for three cycles. The learning outcomes obtained from the researchers in the first cycle were with an average of 58% (less) or only 24 students who completed or 59% while 41% or as many as 15 students were still incomplete and scored below the KKM (75). Then in the second cycle the average increased by obtaining 73% or students completed as many as 30 students or 73%. Then in the third cycle there was a significant increase, namely the average obtained was 94% or as many as 39 students who scored above the KKM (75). In addition, the activity of class XI students of the Department of Administration 2 during learning activities by applying contextual learning experienced an increase in listening material, in the first cycle it reached 57.5%, the second cycle increased to 72.86% and in the third cycle it increased again to 93.62 % with very good category. This indicates that the student's response to contextual learning is included in the interested category
Assessment of some physical and chemical properties of the Tigris River water passing through Al-Dour District

The current study was conducted on the course of the Tigris River passing in the district of Al-Dur / Salah Al-Din Governorate, with three stations distributed along the distance of the study area, for the period from the beginning of November 2021 until April 2022. The first station ST1 is located near the water purification project of Al-Bu Ajil area and the station The second station, ST2, is located at the crossing, and the third and last station, ST3, is located near the Al-Dour silo. The study included the measurement of some physical factors (air temperature, water temperature, electrical conductivity) and some chemical factors (pH, total hardness, calcium ion, magnesium ion). The results showed that the air temperature ranged between 12-22°C, while the water temperature ranged between 8-20.5°C. As for the electrical conductivity, it recorded values ​​between 499,54-679.45µs/cm, while the pH values ​​were neutral. Its levels ranged between 7.34-8.04, and the total hardness may have been affected by the geological characteristic of the land on which the water flows in the stations, and it was found that its water is hard, which may be mostly caused by the bicarbonate ion.
WEB-BASED OFFICE EQUIPMENT AND INVENTORY PROCUREMENT INFORMATION SYSTEM AT BANK MANDIRI, CENTRAL JAKARTA

The manual procurement system for office stationery and inventory that has been felt so far has many shortcomings and is less effective and more time consuming, so it is necessary to develop effective and efficient goods management activities by building a procurement management information system through a web-based application, the use of which can be automatically and more familiar or very easy for the user. So the purpose of this system is to make it easier for Bank Mandiri staff to systematically monitor the procurement of equipment and investments. Interview is a process of communication or interaction to collect as much information as possible by means of question and answer. The Information System for the Procurement of Office Stationery (ATK) and Inventory has a system that automates the internet network so that it is safer and creates efficiency. Controlled the stock of incoming and outgoing goods. Facilitate monitoring of procurement and inventory data for Office Stationery (ATK) and inventory for decision making in management.
Tiled Squeeze-and-Excite: Channel Attention With Local Spatial Context

In this paper we investigate the amount of spatial context required for channel attention. To this end we study the popular squeeze-and-excite (SE) block which is a simple and lightweight channel attention mechanism. SE blocks and its numerous variants commonly use global average pooling (GAP) to create a single descriptor for each channel. Here, we empirically analyze the amount of spatial context needed for effective channel attention and find that limited localcontext on the order of seven rows or columns of the original image is sufficient to match the performance of global context. We propose tiled squeeze-and-excite (TSE), which is a framework for building SE-like blocks that employ several descriptors per channel, with each descriptor based on local context only. We further show that TSE is a drop-in replacement for the SE block and can be used in existing SE networks without re-training. This implies that local context descriptors are similar both to each other and to the global context descriptor. Finally, we show that TSE has important practical implications for deployment of SE-networks to dataflow AI accelerators due to their reduced pipeline buffering requirements. For example, using TSE reduces the amount of activation pipeline buffering in EfficientDetD2 by 90% compared to SE (from 50M to 4.77M) without loss of accuracy. Our code and pre-trained models will be publicly available.
TransformerFusion: Monocular RGB Scene Reconstruction using Transformers

We introduce TransformerFusion, a transformer-based 3D scene reconstruction approach. From an input monocular RGB video, the video frames are processed by a transformer network that fuses the observations into a volumetric feature grid representing the scene; this feature grid is then decoded into an implicit 3D scene representation. Key to our approach is the transformer architecture that enables the network to learn to attend to the most relevant image frames for each 3D location in the scene, supervised only by the scene reconstruction task. Features are fused in a coarse-to-fine fashion, storing fine-level features only where needed, requiring lower memory storage and enabling fusion at interactive rates. The feature grid is then decoded to a higher-resolution scene reconstruction, using an MLP-based surface occupancy prediction from interpolated coarse-to-fine 3D features. Our approach results in an accurate surface reconstruction, outperforming state-of-the-art multi-view stereo depth estimation methods, fully-convolutional 3D reconstruction approaches, and approaches using LSTM- or GRU-based recurrent networks for video sequence fusion.
Quantum key distribution over noisy channels by the testing state method

Quantum key distribution(QKD) might be the most famous application of quantum information theory. The idea of QKD is not difficult to understand but in practical implementations, many problems are needed to be solved, for example, the noise of the channels. Previous works usually discuss the estimate of the channels and employ error-correcting procedures, whose feasibility and efficiency depend on the strength of the noise, or assist with entanglement distillation procedures, which often result in a large consumption of states while not all states can be distilled. This paper aims to study QKD over noisy channels including Pauli noises, amplitude damping noises, phase damping noises, collective noises as well as mixtures of them, in any strength without distillations. We provide a method, called the testing state method, to implement QKD protocols without errors over arbitrarily strength noisy channels. The method can be viewed as an error-correcting procedure, and can also be employed for other tasks.
Contradiction Detection in Persian Text

Detection of semantic contradictory sentences is one of the most challenging and fundamental issues for NLP applications such as recognition of textual entailments. Contradiction in this study includes different types of semantic confrontation, such as conflict and antonymy. Due to lack of sufficient data to apply precise machine learning and specifically deep learning methods to Persian and other low resource languages, rule-based approaches that can function similarly to these systems will be of a great interest. Also recently, emergence of new methods such as transfer learning, has opened up the possibility of deep learning for low-resource languages. Considering two above points, in this study, along with a simple rule-base baseline, a novel rule-base system for identifying semantic contradiction along with a Bert base deep contradiction detection system for Persian texts have been introduced. The rule base system has used frequent rule mining method to extract appropriate contradiction rules using a development set. Extracted rules are tested for different categories of contradictory sentences. In this system the maximum f-measure among contradiction categories is obtained for negation about 90% and the average F-measure of system for all classes is about 76% which outperforms other algorithms on Persian texts. On the other hand, because of medium performance of rule base system for some categories of contradiction, we use a Bert base deep learning system using our translated dataset; with average F-measure of 73. Our hybrid system has f-measure of about 80.
Label noise in segmentation networks : mitigation must deal with bias

Imperfect labels limit the quality of predictions learned by deep neural networks. This is particularly relevant in medical image segmentation, where reference annotations are difficult to collect and vary significantly even across expert annotators. Prior work on mitigating label noise focused on simple models of mostly uniform noise. In this work, we explore biased and unbiased errors artificially introduced to brain tumour annotations on MRI data. We found that supervised and semi-supervised segmentation methods are robust or fairly robust to unbiased errors but sensitive to biased errors. It is therefore important to identify the sorts of errors expected in medical image labels and especially mitigate the biased errors.
Quantum key distribution based on orthogonal states

Quantum key distribution(QKD) is one of the most significant areas in quantum information theory. For nearly four decades, substantial QKD schemes are developed. In early years, the security of QKD protocols is depend on switching different bases, namely based on non-orthogonal states. The most famous example is the BB84 protocol. Later, other techniques were developed for orthogonal state cryptography. Examples of such protocols include the GV protocol and order-rearrangement protocols. In this paper, we present two QKD protocols based on orthogonal states. One of them does not need to employ order-rearrangement techniques while the other needs to. We provide analyses of them, demonstrating that they are highly efficient when considering consumptions of both qubits and classical bits. Furthermore, the employment of maximally entangled states could be less than previous ones and so the measurement efficiency could be increased. We also modify the protocols for implementing in noisy channels by applying the testing state method.
Distance-based Hyperspherical Classification for Multi-source Open-Set Domain Adaptation

Vision systems trained in closed-world scenarios fail when presented with new environmental conditions, new data distributions, and novel classes at deployment time. How to move towards open-world learning is a long-standing research question. The existing solutions mainly focus on specific aspects of the problem (single domain Open-Set, multi-domain Closed-Set), or propose complex strategies which combine several losses and manually tuned hyperparameters. In this work, we tackle multi-source Open-Set domain adaptation by introducing HyMOS: a straightforward model that exploits the power of contrastive learning and the properties of its hyperspherical feature space to correctly predict known labels on the target, while rejecting samples belonging to any unknown class. HyMOS includes style transfer among the instance transformations of contrastive learning to get domain invariance while avoiding the risk of negative-transfer. A self-paced threshold is defined on the basis of the observed data distribution and updates online during training, allowing to handle the known-unknown separation. We validate our method over three challenging datasets. The obtained results show that HyMOS outperforms several competitors, defining the new state-of-the-art. Our code is available at https://github.com/silvia1993/HyMOS.
Mind Your Outliers! Investigating the Negative Impact of Outliers on Active Learning for Visual Question Answering

Active learning promises to alleviate the massive data needs of supervised machine learning: it has successfully improved sample efficiency by an order of magnitude on traditional tasks like topic classification and object recognition. However, we uncover a striking contrast to this promise: across 5 models and 4 datasets on the task of visual question answering, a wide variety of active learning approaches fail to outperform random selection. To understand this discrepancy, we profile 8 active learning methods on a per-example basis, and identify the problem as collective outliers -- groups of examples that active learning methods prefer to acquire but models fail to learn (e.g., questions that ask about text in images or require external knowledge). Through systematic ablation experiments and qualitative visualizations, we verify that collective outliers are a general phenomenon responsible for degrading pool-based active learning. Notably, we show that active learning sample efficiency increases significantly as the number of collective outliers in the active learning pool decreases. We conclude with a discussion and prescriptive recommendations for mitigating the effects of these outliers in future work.
Characterization of Multiframelet Set on Local Fields of Positive Characteristic

This paper presents a discussion on multiframelet set, multiwavelet set and set correspond to super wavelet on local fields of positive characteristic. We characterize Parseval multiframelet set and give equivalent conditions multiwavelet set holds. Furthurmore, we characterize MRA multiwavelet set with the help of dimension function.
Uso de GSO cooperativos com decaimentos de pesos para otimizacao de redes neurais

Training of Artificial Neural Networks is a complex task of great importance in supervised learning problems. Evolutionary Algorithms are widely used as global optimization techniques and these approaches have been used for Artificial Neural Networks to perform various tasks. An optimization algorithm, called Group Search Optimizer (GSO), was proposed and inspired by the search behaviour of animals. In this article we present two new hybrid approaches: CGSO-Hk-WD and CGSO-Sk-WD. Cooperative GSOs are based on the divide-and-conquer paradigm, employing cooperative behaviour between GSO groups to improve the performance of the standard GSO. We also apply the weight decay strategy (WD, acronym for Weight Decay) to increase the generalizability of the networks. The results show that cooperative GSOs are able to achieve better performance than traditional GSO for classification problems in benchmark datasets such as Cancer, Diabetes, Ecoli and Glass datasets.
Graphical State Space Model

In this paper, a new framework, named as graphical state space model, is proposed for the real time optimal estimation of a class of nonlinear state space model. By discretizing this kind of system model as an equation which can not be solved by Extended Kalman filter, factor graph optimization can outperform Extended Kalman filter in some cases. A simple nonlinear example is given to demonstrate the efficiency of this framework.