Reviewerly/exHarmony huggingface dataset:
Cross-Enhancement Transformer for Action Segmentation

Temporal convolutions have been the paradigm of choice in action segmentation, which enhances long-term receptive fields by increasing convolution layers. However, high layers cause the loss of local information necessary for frame recognition. To solve the above problem, a novel encoder-decoder structure is proposed in this paper, called Cross-Enhancement Transformer. Our approach can be effective learning of temporal structure representation with interactive self-attention mechanism. Concatenated each layer convolutional feature maps in encoder with a set of features in decoder produced via self-attention. Therefore, local and global information are used in a series of frame actions simultaneously. In addition, a new loss function is proposed to enhance the training process that penalizes over-segmentation errors. Experiments show that our framework performs state-of-the-art on three challenging datasets: 50Salads, Georgia Tech Egocentric Activities and the Breakfast dataset.
CONTRACT MANAGEMENT WITH BLOCKCHAIN

The new frame of Blockchain and other Distributed Ledger Technologies (DLT), has empowered smart contract applications and has expanded research represented considerable authority in brilliant agreement. Smart Contracts are computerized securities that are dispersed, decentralized and require no outsider. Smart Contracts fill in as an extraordinary option for conventional securities/marks. With the utilization of customary SDLC models and the idea of square permanence these agreements are presented. With these qualities of permanence, decentralized nature, cost-time adequacy, there is no necessity of outsider as we are attempting to make this smart contract as an option in contrast to conventional money contracts. The offering system is by and large utilized by state run administrations and organizations to get labor and products from assembling adventures along with specialist co-ops. In any case, e-offering being the most utilized procurement usual methodology, there are different security suggestions present. Blockchain innovation can be utilized to address these security insinuation as it intensely centers around the decentralization of data and is secured by encryption, unsegregated with undeniable square based design for exchanging the board. Here we have examined on how smart contract gets that dependence on ethereum blockchain and how they can be utilized to plan a circulated e-offering framework. We have isolated the task in four areas which are as per the following (i)Tender creation and distributing process, (ii)Bidding process on the delicate, (iii)Evaluation and arranging the bid, (iv)Selecting the Winning bid. Different calculations are utilized to help through each cycle. The security challenges connected with security and realness are assessed then they are placed into correlation with the ongoing offering process. The chief target of this paper is to incorporate an unprejudiced, straightforward and open offering plan. Keywords: Blockchain, Impartial and Open Tendering Scheme, ethereum, e-tender.
Overcoming Language Disparity in Online Content Classification with Multimodal Learning

Advances in Natural Language Processing (NLP) have revolutionized the way researchers and practitioners address crucial societal problems. Large language models are now the standard to develop state-of-the-art solutions for text detection and classification tasks. However, the development of advanced computational techniques and resources is disproportionately focused on the English language, sidelining a majority of the languages spoken globally. While existing research has developed better multilingual and monolingual language models to bridge this language disparity between English and non-English languages, we explore the promise of incorporating the information contained in images via multimodal machine learning. Our comparative analyses on three detection tasks focusing on crisis information, fake news, and emotion recognition, as well as five high-resource non-English languages, demonstrate that: (a) detection frameworks based on pre-trained large language models like BERT and multilingual-BERT systematically perform better on the English language compared against non-English languages, and (b) including images via multimodal learning bridges this performance gap. We situate our findings with respect to existing work on the pitfalls of large language models, and discuss their theoretical and practical implications. Resources for this paper are available at https://multimodality-language-disparity.github.io/.
A STEAM Learning with Digital Fabrication Laboratory on Cloud Computing Model to Enhance Creative Product

This research developed a model of steam learning with digital fabrication laboratory on cloud computing model to enhance creative product. The objectives of the study were: 1) To create the model, and 2) To evaluate a model. This research method was two parts. The first part about the design&amp;rsquo;s model had four subs: 1) to study and synthesize the relevant documents in this research such as steam, digital fabrication laboratory, cloud and creative product. 2) to develop a process in the model, 3) to present the process model with experts to get it approved to be able to hold in-depth interviews, and 4) to create the tools for assessing the model. The second part is referred to as model evaluation. The sample group has five experts who consist of Information Technology and Instructional Design. Then, this research uses means and standard deviations to analyze data. The process&amp;rsquo;s model has nine procedures in three components. The experts assed of the model overall found were a good level that the model could help learners in building creativity skills.
A Review on The Role of Technology Leadership in Teaching and Learning at Higher Learning Institutions in Post COVID-19

When it comes to technology leadership (TL), this paper reviews and discusses how important it is for the community in higher learning institutions (HLI) to be technology leaders, especially in teaching and learning post-COVID-19. Leadership issues in higher learning institutions are looked at in institutional development, faculty development, and student management. TL person can play a role in these areas. Another topic is how technology changes how HLI is run post-COVID-19. It includes a description of how technology changes how the higher institution is run. Increasing public awareness of the necessity for TL managers and doctorate students should aim at administrative leadership positions for current policy formation, strategic planning, administration, assessment, and institutional development implementation; and a discussion of the necessary skills and tactics for becoming a technology leader. Attributes of TL professionals should do more than just run distribution units or provide essential services. A prerequisite to drive change in institutional growth, employ technology in the learning process, manage resources, and collaborate effectively with academics to plan education. TL should be able to collaborate with institutional leaders on policy and planning for the deployment of instructional technology, faculty development, and institutional development. The positions that allow TL able to do this. The selected TL persons have formal training in technology in education, instructional media, instructional technology, instructional systems, communication in education, and all their related areas. They have also had previous job experience., so they are called Technology Leadership professionals.
Cybernetics Interfaces and Networks: intuitions as a toolbox

Review of concepts and related elaborations based on historical research and experience working with Internet-based services. Independently lead research on different interpretations around the philosophical background for global communication networks and related phenomena such as changes in practices and techniques related to adoption of Artificial Neural Networks at scale in research and industry.&lt;br&gt;In particular, after presenting a minimalist approach to cybernetic systems based on the concept of capacity of network, the text tries to establish continuity between networks represented as graphs, pattern recognition/decision-making, the concept of Interface and its applications in different disciplines. Finally, hypothesis are presented for a descriptive ’economy of information exchange’ between systems (or an ’economy of time’) and its role in navigating fast-growing knowledge-intensive landscapes.&lt;br&gt;
EXACT: How to Train Your Accuracy

Classification tasks are usually evaluated in terms of accuracy. However, accuracy is discontinuous and cannot be directly optimized using gradient ascent. Popular methods minimize cross-entropy, hinge loss, or other surrogate losses, which can lead to suboptimal results. In this paper, we propose a new optimization framework by introducing stochasticity to a model's output and optimizing expected accuracy, i.e. accuracy of the stochastic model. Extensive experiments on linear models and deep image classification show that the proposed optimization method is a powerful alternative to widely used classification losses.
Learning from Bootstrapping and Stepwise Reinforcement Reward: A Semi-Supervised Framework for Text Style Transfer

Text style transfer is an important task in controllable language generation.Supervised approaches have pushed performance improvement on style-oriented rewriting such as formality conversion.However, challenges remain due to the scarcity of large-scale parallel data in many domains.While unsupervised approaches do not rely on annotated sentence pairs for each style, they are often plagued with instability issues such as mode collapse or quality degradation.To take advantage of both supervised and unsupervised paradigms and tackle the challenges, in this work, we propose a semi-supervised framework for text style transfer.First, the learning process is bootstrapped with supervision guided by automatically constructed pseudo-parallel pairs using lexical and semantic-based methods.Then the model learns from unlabeled data via reinforcement rewards.Specifically, we propose to improve the sequence-to-sequence policy gradient via stepwise reward optimization, providing fine-grained learning signals and stabilizing the reinforced learning process.Experimental results show that the proposed approach achieves state-of-the-art performance on multiple datasets, and produces effective generation with as minimal as 10% of training data.
A Novel Skeleton-Based Human Activity Discovery Using Particle Swarm Optimization with Gaussian Mutation

Human activity discovery aims to cluster the activities performed by humans without any prior information on what defines each activity. Most methods presented in human activity recognition are supervised, where there are labeled inputs to train the system. In reality, it is difficult to label activities data because of its huge volume and the variety of human activities. This paper proposes an unsupervised framework to perform human activity discovery in 3D skeleton sequences. First, an approach for data pre-processing is presented. In this stage, important frames are selected based on kinetic energy. Next, the displacement of joints, statistical displacements, angles, and orientation features are extracted to represent the activities information. Since not all extracted features have useful information, the dimension of features is reduced using PCA. Most methods proposed for human activity discovery are not fully unsupervised. They use pre-segmented videos before categorizing activities. To deal with this, we have used a sliding time window to segment the time series of activities with some overlapping. Then, activities are discovered by our proposed Hybrid Particle swarm optimization (PSO) with Gaussian Mutation and K-means (HPGMK) algorithm to provide diverse solutions. PSO is used due to its straightforward idea and powerful global search capability which can identify the ideal solution in a few iterations. Finally, k-means is applied to the outcome centroids from each iteration of the PSO to overcome the slow convergence rate of PSO. The experiment results on five datasets show that the proposed framework has superior performance in discovering activities compared to the other state-of-the-art methods and has increased accuracy of at least 4% on average.
"Study of Educational Effectiveness of Face-to-Face Mode and MOOCs in Blended Mode for Post-graduate students Belonging to Different Socio-economic Status of Teacher Education Program"

Massive Open Online Courses emerged as a very popular mode of learning in 2012 and aims at providing unlimited participation by learners and their open access to educational material with the help of internet.For a developing and very diverse country like India, it is very difficult to provide equal access and equity based higher education to all the learners.Government of India has started its indigenously developed MOOC delivery platform known as SWAYAM for the MOOCs and other digital initiatives in this regard.The present research work was an effort to study the educational effectiveness of Face-to-Face mode of teaching and MOOCs in blended mode for the post graduate students of teacher education program belonging to different Socio-economic Status.Experimental method was used to study and compare educational effectiveness of the MOOCs in blended mode and face to face teaching on the post graduate students of Teacher Education program belonging to different SES.3×2 Factorial design was used as a research design.Two-way ANOVA was used as a statistical technique.The findings revealed that MOOCs in blended mode are proved to be more effective from educational effectiveness in terms of educational achievement in comparison to face to face mode of teaching.Further the post graduate students from different SES achieved significantly different scores on the post test after the experimental treatment of teaching methods.The students taught with MOOCs in Blended mode belonging to High and moderate SES achieved significantly high scores on post test than the students taught with Face-to-Face mode of teaching from respective SES.The students belonging to low SES taught with Face-to-Face mode of teaching scored significantly higher than the students taught with Blended mode of teaching from respective SES.
Augmented reality application in prosthodontics

Augmented reality refers to: “a technology that superimposes a computer-generated image on a user’s view of the real world, thus providing a composite view”. It has become resourceful in many fields including dentistry and medicine. Currently augmented reality is restricted to education, maxillofacial surgeries and implantology but further research and development can expand its uses to many other critical areas. In prosthodontics, visualization of a smile design or a prosthesis outcome becomes difficult to the patient. Augmented reality will make it easier for all the persons involved to establish a proper communication and desired treatment outcome for the patient as it will superimpose the desired outcome on the current patient situation. As augmented reality is becoming more common, its applications will become wider. This article presents a brief history of the development of augmented reality and its current applications in the field of prosthodontics.
Teknik Asosiasi Datamining Dalam Menentukan Pola Penjualan dengan Metode Apriori

Camping or what is commonly called camping is an activity that is carried out outside the room. This activity usually requires some equipment such as a carriel, tent, gas cylinder, stove, mattress, salamon jacket, and several other equipment. AM Camping is a shop that sells equipment camping. AM Camping has quite a lot of sales transaction activities. Sales transaction data can be used and processed into useful information to increase sales of equipment at AM Camping stores. The problem that often occurs in AM Camping stores is that the camping equipment needed by consumers does not exist or is out of stock because the owner does not pay attention to the camping equipment stock. To solve this problem, the authors use association datamining with a priori algorithm. Based on these results, it can be obtained that there are 8 rules that meet a minimum confidence of 50%, but because of the same itemset, only one rule is chosen so that the results of the itemset rules are 5 final rules, namely if you buy a 60 L Carrier, then 94% most likely buy a Gas Cylinder, if you buy a flat stove, you are 89% most likely to buy a gas cylinder, if you buy a frem rope, then you have an 84% chance of buying a gas cylinder, if you buy a cuff, you are 73% most likely to buy a gas cylinder and if you buy a gas cylinder you are 67% likely to buy a rope frem. With this research, it is hoped that it can provide information to AM Camping shop owners in providing stock of camping equipment needed by consumers and to find out the combination of itemset from consumer purchases
Grocery Store in Product Information

The main purpose of this project is to make on, these days we've to avoid crowded places and conjointly watch out that the group does not increase. If we tend to attend a food market, we discover the item we would like and if we tend to don't have the correct merchandise, then we tend to return. This wastes time and it conjointly will increase the group, we tend to failed to take some merchandise however by going there, our time was conjointly wasted and conjointly came connected with alternative client there. That is why we tend to are creating this application to avoid of these things. In this, if we tend to enter in any food market or any sales outlet, there'll be a displayed during which all the main points of the merchandise are there, during which check all the merchandise beforehand and conjointly see the supply and worth. If we've an acceptable product, then we will attend the shop and purchase the merchandise, however if we tend to don't just like the product or supply or worth, then we will return from outside. This may conjointly save our time and cannot are available in contact with someone. There valuable data/information are often hold on for an extended amount with straightforward to accessing and manipulation of a similar. The specified hardware is simply on the market and our software system straightforward to figure with. This show system, as delineate higher than, will cause error free, secure, reliable, and quick management system. It will assist the user to consider their alternative activities rather to consider the record keeping. That one needn't be distracted by info that's not relevant, whereas having the ability to succeed in the data. The aim is to automatic its existing manual system by the assistance of processed equipments and full-fledged pc software system, fulfilling their necessities, in order that their valuable data/information are often hold on for a extended amount with straightforward accessing and manipulation of a similar. Primarily the project is show the shop of all things details or offers and higher services for the client. Key Words: Html, CSS, JavaScript, Bootstrap, E-Grocery Store, Self Service, Technology
Optimization and Simulation of Accounting Information Practice Model Assisted by Discrete Dynamic Events

The performance function expressed by the expected value of the discrete event dynamic system usually does not have a closed analytical expression, so it can only be estimated by the Monte-Carlo method. The inherent limitations of the Monte-Carlo method (low simulation efficiency) and the complexity of the system bring a lot to the simulation optimization problem. A huge challenge has come. A reasonable combination of mathematical methods and computer simulation technology is an important direction to study the optimal design of DESD. On the basis of linear systems on maximal algebras, this article studies a class of multimodel systems on maximal algebras. This article presents a non-Markov DEDS time-synchronized parallel simulation algorithm and proves that the common random number method can maximize the simulation efficiency of an existing event-synchronized algorithm. We take advantage of the inherent parallelism of genetic algorithm, and combine the idea of parallel simulation and DEDS order optimization to give an optimization algorithm for discrete parameter space DEDS. This article discusses the relationship between ultimate control, internal control, and accounting information quality. This article analyzes the relationship between ultimate control right, internal control, and accounting information quality. We use the manipulated accruals calculated by the modified Jones model and the earnings response coefficient of the earnings-return model to measure the quality of accounting information, and analyze the impact of the ultimate control ratio and internal control level on the quality of accounting information. Based on the difference in the nature of the controller and the ratio of ultimate control rights, this article examines the difference in the effect of internal control on the quality of accounting information in companies with high and low ratios of ultimate control rights. The robustness tests of the two accounting information measurement models are carried out, respectively, and the results obtained are basically consistent with the empirical research conclusions. The situation is the most important to its own development. Among them, the asset turnover rate has a greater effect on the early warning of corporate financial crisis, and the status of the current asset turnover rate and the total asset turnover rate in the financial early warning is also crucial. This article studies the correlation between the overall level of internal control and the quality of accounting information, avoiding the one-sidedness caused by using a single index to measure the level of internal control.
SNaC: Coherence Error Detection for Narrative Summarization

Progress in summarizing long texts is inhibited by the lack of appropriate evaluation frameworks. When a long summary must be produced to appropriately cover the facets of that text, that summary needs to present a coherent narrative to be understandable by a reader, but current automatic and human evaluation methods fail to identify gaps in coherence. In this work, we introduce SNaC, a narrative coherence evaluation framework rooted in fine-grained annotations for long summaries. We develop a taxonomy of coherence errors in generated narrative summaries and collect span-level annotations for 6.6k sentences across 150 book and movie screenplay summaries. Our work provides the first characterization of coherence errors generated by state-of-the-art summarization models and a protocol for eliciting coherence judgments from crowd annotators. Furthermore, we show that the collected annotations allow us to train a strong classifier for automatically localizing coherence errors in generated summaries as well as benchmarking past work in coherence modeling. Finally, our SNaC framework can support future work in long document summarization and coherence evaluation, including improved summarization modeling and post-hoc summary correction.