Reviewerly/exHarmony huggingface dataset:
A DESIGN OF SPEAKING ASSESSMENT RUBRIC FOR ENGLISH IMMERSION CAMP

The aim of this mix-method study is to design a speaking assessment rubric for an English Immersion Camp (EIC) program in Muhammadiyah University of North Maluku called UMMU English Immersion Camp (UEIC). In UEIC, there were seven activities applied for triggering the participants to continuously speak English during the program (see Syahidah, Umasugi, Buamona, 2019). However, the program did not have a proper assessment form to measure the UEIC participants’ speaking development. Therefore, this study was administered to design the assessment in the form of a speaking rubric. Three instruments were used to gather the data for designing the rubric. Interviews were adminstered to two experts of English language assessment who have been having years of experince in teaching English at the university. Document analyses were done to analyze the UEIC syllabus from previous studies and speaking rubrics from various sources to make a match between the design of the assessment instrument with the UEIC achievement targets, objectives, materials, and learning techniques. Observations were conducted by four observers who were fasilitators at the UEIC to measure the participants’ speaking progress. Data from obervations were used to test the validity and reliability of the rubric. The speaking assessment rubric was designed based on the steps of designing speaking assessment proposed by O’Malley and Pierce (1996) which are identifying the purposes of speaking assessment, planning speaking assessment, developing speaking test rubric and setting standards. The rubric contained ten items divided into six aspects of speaking assessments namely (1) grammar, (2) vocabulary, (3) pronunciation, (4) fluency, and (5) comprehenesibility respectively represented into two items, and (6) accuracy measured by three items. Moreover, the validity and reliability analyses showed that the rubric containing 10 items were valid and reliable because all values needed have reached the theoretical standard value for validity and reliability of a speaking test instrument.
Multi Output Learning using Task Wise Attention for Predicting Binary Properties of Tweets : Shared-Task-On-Fighting the COVID-19 Infodemic

In this paper, we describe our system for the shared task on Fighting the COVID-19 Infodemic in the English Language.Our proposed architecture consists of a multi-output classification model for the seven tasks, with a taskwise multi-head attention layer for inter-task information aggregation.This was built on top of the Bidirectional Encoder Representations obtained from the RoBERTa Transformer.Our team, dunder_mifflin, was able to achieve a mean F1 score of 0.891 on the test data, leading us to the second position on the test-set leaderboard.* * Denotes Equal Contribution soft-parameter-sharing.They also highlight ways for loss weighting based on task-dependent uncertainty and learning-speed.
ANALISIS PEMBELAJARAN JARAK JAUH BERBASIS INTERNET PADA MASA PANDEMI COVID-19 DI SD NEGERI WIDORO YOGYAKARTA

The purpose of this study was to determine the process, supporting factors, inhibiting factors, and efforts to overcome obstacles to the implementation of distance learning. The data in this article were collected and processed using qualitative methods. The data collection techniques used observation, interview, and documentation techniques. Data analysis techniques used the analysis of the Miles and Huberman model which consists of data collection, data reduction, data display, and verification. The results show that (1) the internet-based distance learning process is carried out using the WhatsApp application, google meets, and Google Forms. (2) Supporting factors for implementing distance learning namely students are encouraged to be more independent in learning, teachers, school principals, and parents to collaborate, and use internet networks. (3) The inhibiting factor is that there are still some students who still do not understand the material, do not have complete facilities and lack guidance from parents. (4) Efforts to overcome obstacles The principal is holding meetings with the teacher twice a week, the teacher making creative learning videos, working with parents in supervision, and the teacher explaining again when meeting in person at school.
Reversible Data Hiding using Block-wise Histogram Shifting and Run-length Encoding

Histogram shifting-based Reversible Data Hiding (RDH) is a well-explored information security domain for secure message transmission.In this paper, we propose a novel RDH scheme that considers the block-wise histograms of the image.Most of the existing histogram shifting techniques will have additional overhead information to recover the overflow and/or the underflow pixels.In the new scheme, the meta-data that is required for a block is embedded within the same block in such a way that the receiver can perform image recovery and data extraction.As per the proposed data hiding process, all the blocks need not be used for data hiding, so we have used marker information to distinguish between the blocks which are used to hide data and the blocks which are not used for data hiding.Since the marker information needs to be embedded within the image, we have compressed the marker information using runlength encoding.The run-length encoded sequence is represented by an Elias gamma encoding procedure.The compression on the marker information ensures a better Embedding Rate (ER) for the proposed scheme.The proposed RDH scheme will be useful for secure message transmission also where we are also concerned about the restoration of the cover image.The proposed scheme's experimental analysis is conducted on the USC-SIPI image dataset maintained by the University of Southern California, and the results show that the proposed scheme performs better than the existing schemes.
Organizational Factors That Affect Service Delivery in the Hospitality Sector

The paper examines the set of organizational factors that affect service delivery (job performances) in the hospitality industry. Descriptive research design was adopted for the study. A sample of 150 frontline hotel employees in Surat served as the study setting. Data was gathered through a self-administered questionnaire. Some of the tools used for analysis were Cronbach alpha, Mann-Whitney test, cross tabulation, and factor analysis. By reviewing literature, 22 constructs that affect service delivery in the organization were identified. Through factor analysis, the authors arrived at seven dimensions that affect service delivery, namely, organization personality, internal marketing activities, role demands and organizational leadership, employee empowerment, hygiene factors, organizational conflict and organization resources. Gender differences were found in the opinion related to dimensions such as job autonomy, empowerment, rewards, perceived organizational support, role conflict, role overload, and servant leadership.
Image registration comparative analysis: normalized correlation versus SIFT-based registration

The paper compares the image registration algorithms: the classical normalized correlation (as a representative of intensity-based algorithms) and the SIFT-based algorithm (feature-based registration). A gradient subpixel correction algorithm was also used for normalized correlation. We compared the effectiveness of their work on real images (including a terrain map) when modeling artificial distortions. The accuracy of determining the position (shift) of one image relative to another in the presence of rotation and scale changes was studied. The experiment was carried out using a simulation model created in the Python programming language using the OpenCV computer vision library.&#x0D; The results of the experiments show that in the absence of rotation and scale changes between the registered images the normalized correlation provides a slightly smaller root-mean-square error. At the same time, if there are even small such distortions, for example, a rotation of more than 2 degrees and a scale change of more than 2 percent, the probability of correct registration for the normalized correlation drops sharply. It was also noted that the advantages of normalized correlation are almost 5 times higher speed and the possibility of using it for small fragments (50x50 or less), where it is problematic for the SIFT algorithm to allocate a sufficient number of keypoints.&#x0D; It was also shown that the use of a two-stage algorithm (SIFT-based registration at the first stage, and optimization with normalized correlation as a criterion at the second) allows you to get both high accuracy and stability to rotation and scale change, but this will be accompanied by high computational costs.
Description-based person search with multi-grained matching networks

Description-based person search aims to retrieve a person in the image database based on a description about that person. It is a challenging task since the visual image and the textual description belong to different modalities. To fully capture the relevance between person images and textual descriptions, we propose a multi-grained framework with three branches for visual-textual matching. Specifically, in the global-grained branch, we extract global contexts from the entire images and descriptions. In the fine-grained branch, we adopt visual human parsing and linguistic parsing to split images and descriptions into semantic components related to different body parts. We design two attention mechanisms including segmentation-based and linguistics-based attention to align visual and textual semantic components for fine-grained matching. To further exploit the spatial relations between fine-grained semantic components, we construct a body graph in the coarse-grained branch and exploit graph convolutional neural networks to aggregate fine-grained components into coarse-grained representations. The visual and textual representations learned by three branches are complementary to each other which enhance the visual-textual matching performance. Experimental results on the CUHK-PEDES dataset show that our approach performs favorably against state-of-the-art description-based person search methods.
Correlation Clustering in Constant Many Parallel Rounds

Correlation clustering is a central topic in unsupervised learning, with many applications in ML and data mining. In correlation clustering, one receives as input a signed graph and the goal is to partition it to minimize the number of disagreements. In this work we propose a massively parallel computation (MPC) algorithm for this problem that is considerably faster than prior work. In particular, our algorithm uses machines with memory sublinear in the number of nodes in the graph and returns a constant approximation while running only for a constant number of rounds. To the best of our knowledge, our algorithm is the first that can provably approximate a clustering problem on graphs using only a constant number of MPC rounds in the sublinear memory regime. We complement our analysis with an experimental analysis of our techniques.
KD3A: Unsupervised Multi-Source Decentralized Domain Adaptation via Knowledge Distillation

Conventional unsupervised multi-source domain adaptation (UMDA) methods assume all source domains can be accessed directly. This neglects the privacy-preserving policy, that is, all the data and computations must be kept decentralized. There exists three problems in this scenario: (1) Minimizing the domain distance requires the pairwise calculation of the data from source and target domains, which is not accessible. (2) The communication cost and privacy security limit the application of UMDA methods (e.g., the domain adversarial training). (3) Since users have no authority to check the data quality, the irrelevant or malicious source domains are more likely to appear, which causes negative transfer. In this study, we propose a privacy-preserving UMDA paradigm named Knowledge Distillation based Decentralized Domain Adaptation (KD3A), which performs domain adaptation through the knowledge distillation on models from different source domains. KD3A solves the above problems with three components: (1) A multi-source knowledge distillation method named Knowledge Vote to learn high-quality domain consensus knowledge. (2) A dynamic weighting strategy named Consensus Focus to identify both the malicious and irrelevant domains. (3) A decentralized optimization strategy for domain distance named BatchNorm MMD. The extensive experiments on DomainNet demonstrate that KD3A is robust to the negative transfer and brings a 100x reduction of communication cost compared with other decentralized UMDA methods. Moreover, our KD3A significantly outperforms state-of-the-art UMDA approaches.
Findings of the Second Workshop on Automatic Simultaneous Translation

This paper presents the results of the shared task of the 2nd Workshop on Automatic Simultaneous Translation (AutoSimTrans).The task includes two tracks, one for text-to-text translation and one for speech-to-text, requiring participants to build systems to translate from either the source text or speech into the target text.Different from traditional machine translation, the AutoSimTrans shared task evaluates not only translation quality but also latency.We propose a metric "Monotonic Optimal Sequence" (MOS) considering both quality and latency to rank the submissions.We also discuss some important open issues in simultaneous translation.
How Do Adam and Training Strategies Help BNNs Optimization

The best performing Binary Neural Networks (BNNs) are usually attained using Adam optimization and its multi-step training variants. However, to the best of our knowledge, few studies explore the fundamental reasons why Adam is superior to other optimizers like SGD for BNN optimization or provide analytical explanations that support specific training strategies. To address this, in this paper we first investigate the trajectories of gradients and weights in BNNs during the training process. We show the regularization effect of second-order momentum in Adam is crucial to revitalize the weights that are dead due to the activation saturation in BNNs. We find that Adam, through its adaptive learning rate strategy, is better equipped to handle the rugged loss surface of BNNs and reaches a better optimum with higher generalization ability. Furthermore, we inspect the intriguing role of the real-valued weights in binary networks, and reveal the effect of weight decay on the stability and sluggishness of BNN optimization. Through extensive experiments and analysis, we derive a simple training scheme, building on existing Adam-based optimization, which achieves 70.5% top-1 accuracy on the ImageNet dataset using the same architecture as the state-of-the-art ReActNet while achieving 1.1% higher accuracy. Code and models are available at this https URL.
A Novel Method to Optimize the Computation Overhead in Cloud Computing by Using Linear Programming

In the current days cloud computing plays a very important role in almost all aspects of real time environments like MNC companies, colleges, hospitals, industries and IT companies. As we all know that almost all the cloud servers are storing and accessing the data in a plain text rather than in an encrypted manner, so there is no form of security in the primitive cloud servers. If we assume that some cloud servers are using cryptography techniques in order to give security for the data, but there is a lot of time complexity for processing number of iterations and cycles to convert into cipher text. In order to reduce the time complexity for data storage in the cloud, in this proposed thesis we mainly try to implement the problem of linear programming (LP) for both public or private cloud service providers, which is owned by the customer to provide security for their out sourced data. Specifically, by formulating private/Public LP problem as a set of matrices/vectors, we develop efficient privacy preserving problem transformation techniques, which allow customers to transform the original LP into some random one while protecting sensitive input/output information.
Measuring the ‘I don’t know’ Problem through the Lens of Gricean Quantity

We consider the intrinsic evaluation of neural generative dialog models through the lens of Grice's Maxims of Conversation (1975).Based on the maxim of Quantity (be informative), we propose Relative Utterance Quantity (RUQ) to diagnose the 'I don't know' problem, in which a dialog system produces generic responses.The linguistically motivated RUQ diagnostic compares the model score of a generic response to that of the reference response.We find that for reasonable baseline models, 'I don't know' is preferred over the reference the majority of the time, but this can be reduced to less than 5% with hyperparameter tuning.RUQ allows for the direct analysis of the 'I don't know' problem, which has been addressed but not analyzed by prior work.
Linguistic Complexity Loss in Text-Based Therapy

The complexity loss paradox, which posits that individuals suffering from disease exhibit surprisingly predictable behavioral dynamics, has been observed in a variety of both human and animal physiological systems.The recent advent of online text-based therapy presents a new opportunity to analyze the complexity loss paradox in a novel operationalization: linguistic complexity loss in text-based therapy conversations.In this paper, we analyze linguistic complexity correlates of mental health in the online therapy messages sent between therapists and 7,170 clients who provided 30,437 corresponding survey responses on their anxiety.We found that when clients reported more anxiety, they showed reduced lexical diversity as estimated by the moving average type-token ratio.Therapists, on the other hand, used language of higher reading difficulty, syntactic complexity, and age of acquisition when clients were more anxious.Finally, we found that clients, and to an even greater extent, therapists, exhibited consistent levels of many linguistic complexity measures.These results demonstrate how linguistic analysis of text-based communication can be leveraged as a marker for anxiety, an exciting prospect in a time of both increased online communication and increased mental health issues.
Understanding the role of agricultural data on market power in the emerging Digital Agriculture sector: a critical analysis of the BayerMonsanto decision

With the advent of so-called 'Digital Agriculture' or 'Smart Farming', digitally-enabled data-driven agronomic solutions generate significant benefits for farmers. However, this data-driven transformation has also brought about competition law concerns. For instance, the European Commission reacted to inter alia the Digital Agriculture aspects of the Bayer/Monsanto merger by adopting remedies. This study aims to explore the fundamental drivers of data-driven market power in this nascent sector and discusses to what extent the Commission performed its analysis compatible with the sector realities. In particular, this chapter argues that switching costs deriving from legal and technical problems are the core reasons for the entry barriers. Additionally, lack of data substitutability due to the distinctive cross-dependency between farmers and companies exacerbates data-driven lock-ins. However, the Commission neglected them to a large extent. Building on this discussion, this piece points at possible required elements for an optimal market power test for this emerging sector.
The introduction of care robots as a leadership challenge in home care facilities in Finland

This paper analyses the factors that influence home care employees' intention to introduce robots.The introduction of different kinds of care robots is a topical issue in elderly care now and in the near future.Cross-sectional research conducted through a questionnaire. The survey data (N = 162) were collected in five locations around Finland in 2019. The analysis was carried out by regression analysis, Sobel test and by Hayes' bootstrapping method.The results show that self-efficacy is pivotal in the willingness to introduce care robots. Employees' age increases the enthusiasm to introduce robots but reduces self-efficacy. Work engagement does not correlate with self-efficacy or behavioural intention related to the introduction of care robots.The present paper reveals the significance of attitudes, cognitive factors and age in the adoption of care robots in home care facilities.It is important to pay attention to supporting the employees' sense of technology management and the construction of a robot-positive atmosphere when introducing care robots, and the development of skills of older employees and employees with a lower educational level should be supported.
The Community Workshop Series: A Case Study for Community-Engaged Learning in LIS

For over a decade, the University of North Carolina at Chapel Hill (UNC) has offered some variation of the Community Workshop Series (CWS), a partnership to provide digital literacy and computer technology classes to community members at local public libraries. Both authors have served as coordinators of the program as library science graduate students at the UNC School of Information and Library Science. We situate this program within existing literature on digital and information literacy, community engagement, and the graduate student experience to show the utility of this program and similar programs for training graduate students, enhancing the graduate student experience, supporting the needs of community members, and bolstering the capacities of public libraries. The authors provide an overview of the program and encourage others to start similar programs. To this end, the authors present a case study of the CWS, including discussion of creating the program and keeping things going, and provide a how-to guide for creating your own. The authors identify four recommendations for creating a similar program to clearly delineate takeaways that might inform readers’ attempts to create similar programs, and they provide additional materials and documentation in appendices to support the creation of new community-engaged programs in LIS.
Performance Prediction Based on Random Forest for the Stream Processing Checkpoint

Since real-time processing scenarios for ever-increasing amount and type of streaming data caused by the development of the Internet of Things (IoT) keep increasing, and strategies based on empirical knowledge for checkpoint configuration are deficiencies, the strategy faces huge challenges, such as time-consuming, labor-intensive, causing system anomalies, etc. To address these challenges, regression algorithm-based prediction is proposed for checkpoint performance. Firstly, six kinds of features, which have a huge influence on the performance, are analyzed, and then feature vectors of the training set are input into the regression algorithms for training, finally, test sets are used for the checkpoint performance prediction. Compared with other machine learning algorithms, the experimental results illustrat that the Random Forest (RF) has lower errors, higher accuracy and faster execution on CPU intensive benchmark, memory intensive benchmark and network intensive benchmark.