Reviewerly/exHarmony huggingface dataset:
study on organizational climate metropolitan transport corporation (Chennai) limited

The term organizational climate “it is the distinctive work environment that influences the behaviour of the individuals employed in an organization”. It is a set of measurable properties of the work environment perceived directly or indirectly by the people who live and work in the environment and assumed to influence their motivation and behaviour. Organizational climate within an organization refers to how organizational environments are perceived and interpreted by its employees. It is to survive and continue to play a vital role in the promotion of cognitive, affective and practical competence in individuals and ensure the preservation of our cultural values. It is important that the teachers who are responsible for helping the students to acquire the knowledge, skills and practical orientations essential for self as well as for national development, be effectively motivated. This study discusses and analyses various components of organization climate of employees working in MTC- Chennai.
English Podcast as Media for English Language Learning: A Case of Higher Education in the Pandemic Situation

The aim of the study was the implementation of English Podcast as the media for English language learning in higher education in the pandemic situation. The study used Research and Development Approach with the students of English department of UNNES as the subject of the study in three courses – Statistics, Intensive Listening and Functional Syntax. The procedure of doing the study consisted of introduction covering literature study and need survey; and development phases. The results produced a concept and a product of podcast based on literature study of books and national as well as international journal articles. Also, it shows that Podcast is very possible to be applied in higher education for its affordable infrastructures. The podcast is an Education Podcast using free-software application – Audacity with some supports such as Television, Portable Podcast Console, Desktop Podcasting Microphone Kit, Studio Headphone, dan Portable Computer [laptop]. The audio products were uploaded in the Podcast Website - https://anchor.fm/arif-suryopriyatmojo/episodes/. Suggestions for the study are the need of support from all stakeholders for podcast development that can reach broader community and 2) active participation of lecturers to integrate technology in the learning process.
Reliable Visual Question Answering: Abstain Rather Than Answer Incorrectly

Machine learning has advanced dramatically, narrowing the accuracy gap to humans in multimodal tasks like visual question answering (VQA). However, while humans can say "I don't know" when they are uncertain (i.e., abstain from answering a question), such ability has been largely neglected in multimodal research, despite the importance of this problem to the usage of VQA in real settings. In this work, we promote a problem formulation for reliable VQA, where we prefer abstention over providing an incorrect answer. We first enable abstention capabilities for several VQA models, and analyze both their coverage, the portion of questions answered, and risk, the error on that portion. For that, we explore several abstention approaches. We find that although the best performing models achieve over 70% accuracy on the VQA v2 dataset, introducing the option to abstain by directly using a model's softmax scores limits them to answering less than 7.5% of the questions to achieve a low risk of error (i.e., 1%). This motivates us to utilize a multimodal selection function to directly estimate the correctness of the predicted answers, which we show can increase the coverage by, for example, 2.3x from 6.8% to 15.6% at 1% risk. While it is important to analyze both coverage and risk, these metrics have a trade-off which makes comparing VQA models challenging. To address this, we also propose an Effective Reliability metric for VQA that places a larger cost on incorrect answers compared to abstentions. This new problem formulation, metric, and analysis for VQA provide the groundwork for building effective and reliable VQA models that have the self-awareness to abstain if and only if they don't know the answer.
Body as Starting Point 5: Exploring the Inbodied Interaction Design Framework - New Methodologies in Interactive Health Design

We invite you to celebrate the fifth inbodied interaction workshop at CHI by exploring the Inbodied Interaction Framework to align your designs with the internal complexity of the human body's interconnected, physical, and biological networks first with the goal to "#makeNormalBetter" for all at scale. This year we are introducing the new Inbodied Interaction Design Framework with a set of guiding questions and provocations to lean on and re-invent working practices. In this virtual workshop, we welcome participants with no prior experience with inbodied interaction and familiar practitioners who want to gain an alternative perspective for technology design that takes the body as a starting point.
An Adversarial Attack Analysis on Malicious Advertisement URL Detection Framework

Malicious advertisement URLs pose a security risk since they are the source of cyber-attacks, and the need to address this issue is growing in both industry and academia. Generally, the attacker delivers an attack vector to the user by means of an email, an advertisement link or any other means of communication and directs them to a malicious website to steal sensitive information and to defraud them. Existing malicious URL detection techniques are limited and to handle unseen features as well as generalize to test data. In this study, we extract a novel set of lexical and web-scrapped features and employ machine learning technique to set up system for fraudulent advertisement URLs detection. The combination set of six different kinds of features precisely overcome the obfuscation in fraudulent URL classification. Based on different statistical properties, we use twelve different formatted datasets for detection, prediction and classification task. We extend our prediction analysis for mismatched and unlabelled datasets. For this framework, we analyze the performance of four machine learning techniques: Random Forest, Gradient Boost, XGBoost and AdaBoost in the detection part. With our proposed method, we can achieve a false negative rate as low as 0.0037 while maintaining high accuracy of 99.63%. Moreover, we devise a novel unsupervised technique for data clustering using K- Means algorithm for the visual analysis. This paper analyses the vulnerability of decision tree-based models using the limited knowledge attack scenario. We considered the exploratory attack and implemented Zeroth Order Optimization adversarial attack on the detection models.
Adaptation of ITE activities during COVID-19

This chapter narrates innovative variation of ITE activities to make it relevant and meaningful in the COVID-19 lockdown period (CLP) context. Besides innovations, the chapter cites research on ITE activities in the CLP indicating ITE engagement in the pre-CLP-facilitated teachers and students to adapt distance technologies comfortably in teaching and learning in the CLP.
NMTScore: A Multilingual Analysis of Translation-based Text Similarity Measures

Being able to rank the similarity of short text segments is an interesting bonus feature of neural machine translation. Translation-based similarity measures include direct and pivot translation probability, as well as translation cross-likelihood, which has not been studied so far. We analyze these measures in the common framework of multilingual NMT, releasing the NMTScore library (available at https://github.com/ZurichNLP/nmtscore). Compared to baselines such as sentence embeddings, translation-based measures prove competitive in paraphrase identification and are more robust against adversarial or multilingual input, especially if proper normalization is applied. When used for reference-based evaluation of data-to-text generation in 2 tasks and 17 languages, translation-based measures show a relatively high correlation to human judgments.
Recommender System for Software Engineering using SQL Semantic Search

Recommender Systems are software tools that can assist developers with a wide range of activities, from reusing codes to suggest developers what to do during development of these systems. This paper outlines an approach to generating recommendation using SQL Semantic Search. Performance measurement of this recommender system is conducted by calculating precision, recall and F1-measure. Subjective evaluations consisted of 10 experienced developers for validating the recommendation. A statistical test t-Test is used to compare the means of two approaches of evaluations.
Derivation of Learning Rules for Coupled Principal Component Analysis in a Lagrange-Newton Framework

We describe a Lagrange-Newton framework for the derivation of learning rules with desirable convergence properties and apply it to the case of principal component analysis (PCA). In this framework, a Newton descent is applied to an extended variable vector which also includes Lagrange multipliers introduced with constraints. The Newton descent guarantees equal convergence speed from all directions, but is also required to produce stable fixed points in the system with the extended state vector. The framework produces "coupled" PCA learning rules which simultaneously estimate an eigenvector and the corresponding eigenvalue in cross-coupled differential equations. We demonstrate the feasibility of this approach for two PCA learning rules, one for the estimation of the principal, the other for the estimate of an arbitrary eigenvector-eigenvalue pair (eigenpair).
Complex and Simple Sentences: Issues Translation Correspondence in Russian and Chinese

The question of correspondences in the translation of complex sentences from Russian into Chinese is considered. It is noted that at present, translation correspondences in Russian and Chinese, including in the field of syntax, have not been studied enough. It is indicated that the difficulties of translation may be related to the discrepancy between the number of types of complex sentences in Russian, which are more than in Chinese. It is shown that some complex structures do not have an equivalent in Chinese and are translated into simple Chinese sentences. The author shows that complex sentences with explanatory clauses in Russian correspond not only to sentences with an included part in Chinese, but also to two-part and one-part sentences. The correspondence of complex sentences with attributive clauses, with relative clauses and degrees to different members of simple sentences of the Chinese language, as well as to chain sentences is substantiated. It is stated that some clauses of the tense (for example, with the conjunction NOT YET) correspond to circumstances, post-positional definitions of predicates, or one of the verbal constructions of a sentence with a sequential verbal predicate (连动句) in Chinese.
RobBERTje: a Distilled Dutch BERT Model

Pre-trained large-scale language models such as BERT have gained a lot of attention thanks to their outstanding performance on a wide range of natural language tasks. However, due to their large number of parameters, they are resource-intensive both to deploy and to fine-tune. Researchers have created several methods for distilling language models into smaller ones to increase efficiency, with a small performance trade-off. In this paper, we create several different distilled versions of the state-of-the-art Dutch RobBERT model and call them RobBERTje. The distillations differ in their distillation corpus, namely whether or not they are shuffled and whether they are merged with subsequent sentences. We found that the performance of the models using the shuffled versus non-shuffled datasets is similar for most tasks and that randomly merging subsequent sentences in a corpus creates models that train faster and perform better on tasks with long sequences. Upon comparing distillation architectures, we found that the larger DistilBERT architecture worked significantly better than the Bort hyperparametrization. Interestingly, we also found that the distilled models exhibit less gender-stereotypical bias than its teacher model. Since smaller architectures decrease the time to fine-tune, these models allow for more efficient training and more lightweight deployment of many Dutch downstream language tasks.
Bisimulation Makes Analogies in Goal-Conditioned Reinforcement Learning

Building generalizable goal-conditioned agents from rich observations is a key to reinforcement learning (RL) solving real world problems. Traditionally in goal-conditioned RL, an agent is provided with the exact goal they intend to reach. However, it is often not realistic to know the configuration of the goal before performing a task. A more scalable framework would allow us to provide the agent with an example of an analogous task, and have the agent then infer what the goal should be for its current state. We propose a new form of state abstraction called goal-conditioned bisimulation that captures functional equivariance, allowing for the reuse of skills to achieve new goals. We learn this representation using a metric form of this abstraction, and show its ability to generalize to new goals in simulation manipulation tasks. Further, we prove that this learned representation is sufficient not only for goal conditioned tasks, but is amenable to any downstream task described by a state-only reward function. Videos can be found at https://sites.google.com/view/gc-bisimulation.
The Effect of using Zoom Application on Students Speaking Skills

This study intended to see whether there is an effect of using the zoom application on student’s speaking skills or not. This study was conducted at SMP Negeri 11 Tanjungpinang in the academic year of 2021/2022. The authors selected class VIII.4 which the number of students was 31 as the object of this study. The authors used quantitative methodology and the design was a pre-experimental design in which the authors used one-group pre-test/post-test with treatment. In this study, the authors used an oral test as the instrument of this study. The alternative hypothesis (Ha) was accepted and the null hypothesis (H0) was rejected. It could be seen from data calculated using paired sample t-test formula. Based on the data calculated, it could be seen that the significance was 0.133. It was normal because 0.133 &gt; 0.005. While the Sig. 2 tailed was 0.000. It means that there is a significant effect of using zoom application on student’s speaking skills at 8th grade of SMP Negeri 11 Tanjungpinang because the sig.2 tailed 0.000&lt; 0.005. Based on the findings, the authors suggest the teacher use zoom application for teaching speaking, because this application gave a significant effect on student’s speaking skills.
Analysis of the Implementation of Project-Based Learning Method During the Pandemic Covid-19

This study was conducted to describe the teacher’s process of teaching English by implementing project-based learning method during the pandemic covid-19 by using Whatsapp. This study use qualitative design and the data collection techniques used observation sheet, and interview. The data analysis uses Miles Huberman and Saldana concept and the participant of this study only one teacher who teaches in the 7th grade at SMPS Kartika XX-6 Kendari. The results showed that based on the project-based learning steps in the 2013 curriculum, the teacher only applies 4 steps such as: 1) creating a set schedule means the teacher determines the time when student projects must be collected. 2) monitoring student projects because learning is done online, so teachers involve parents in monitoring students during the learning process and completing projects, 3) assessing student's outcomes, the teacher assigns a value to the student project according to the scoring rubric created, 4) evaluating students' experiences, the teacher provides feedback to students so they can correct the mistakes they made. The teacher gives a project in the form of a video regarding conversations about greetings, self-introductions and talking about daily activities with the aim that students get used to speaking in English. Project work is carried out individually by students due to the online learning process as a result of the covid-19 pandemic which was submitted by using WhatsApp.
To Fold or Not to Fold: a Necessary and Sufficient Condition on Batch-Normalization Layers Folding

Batch-Normalization (BN) layers have become fundamental components in the evermore complex deep neural network architectures. Such models require acceleration processes for deployment on edge devices. However, BN layers add computation bottlenecks due to the sequential operation processing: thus, a key, yet often overlooked component of the acceleration process is BN layers folding. In this paper, we demonstrate that the current BN folding approaches are suboptimal in terms of how many layers can be removed. We therefore provide a necessary and sufficient condition for BN folding and a corresponding optimal algorithm. The proposed approach systematically outperforms existing baselines and allows to dramatically reduce the inference time of deep neural networks.
Decision Models for Selecting Federated Learning Architecture Patterns

Federated machine learning is growing fast in academia and industries as a solution to solve data hungriness and privacy issues in machine learning. Being a widely distributed system, federated machine learning requires various system design thinking. To better design a federated machine learning system, researchers have introduced multiple patterns and tactics that cover various system design aspects. However, the multitude of patterns leaves the designers confused about when and which pattern to adopt. In this paper, we present a set of decision models for the selection of patterns for federated machine learning architecture design based on a systematic literature review on federated machine learning, to assist designers and architects who have limited knowledge of federated machine learning. Each decision model maps functional and non-functional requirements of federated machine learning systems to a set of patterns. We also clarify the drawbacks of the patterns. We evaluated the decision models by mapping the decision patterns to concrete federated machine learning architectures by big tech firms to assess the models' correctness and usefulness. The evaluation results indicate that the proposed decision models are able to bring structure to the federated machine learning architecture design process and help explicitly articulate the design rationale.
Visualization of face-centered cubic energy band using spreadsheet and javascript as innovative learning

Constructing mathematical equations in physics often creates difficulties in students' learning process. Therefore, it is necessary to have technology-based simulations to understand physical phenomena. One technology that is easy to use for simulations in physics learning is the spreadsheet program. This study aims to use spreadsheet media to visualize the face-centered cubic (FCC) energy band using the tight-binding method and to compare the results with the JavaScript programming language. This paper succeeded in making visualization of face-centered cubic (FCC) energy band using a spreadsheet as an alternative to distance learning. The spreadsheet is easier to use because they do not use complicated programming languages like JavaScript. This paper shows the use of innovative learning media, spreadsheets, in materials courses.
SwiftAgg+: Achieving Asymptotically Optimal Communication Loads in Secure Aggregation for Federated Learning

We propose SwiftAgg+, a novel secure aggregation protocol for federated learning systems, where a central server aggregates local models of $N \in \mathbb{N}$ distributed users, each of size $L \in \mathbb{N}$, trained on their local data, in a privacy-preserving manner. SwiftAgg+ can significantly reduce the communication overheads without any compromise on security, and achieve optimal communication loads within diminishing gaps. Specifically, in presence of at most $D=o(N)$ dropout users, SwiftAgg+ achieves a per-user communication load of $(1+\mathcal{O}(\frac{1}{N}))L$ symbols and a server communication load of $(1+\mathcal{O}(\frac{1}{N}))L$ symbols, with a worst-case information-theoretic security guarantee, against any subset of up to $T=o(N)$ semi-honest users who may also collude with the curious server. Moreover, the proposed SwiftAgg+ allows for a flexible trade-off between communication loads and the number of active communication links. In particular, for $T<N-D$ and for any $K\in\mathbb{N}$, SwiftAgg+ can achieve the server communication load of $(1+\frac{T}{K})L$ symbols, and per-user communication load of up to $(1+\frac{T+D}{K})L$ symbols, where the number of pair-wise active connections in the network is $\frac{N}{2}(K+T+D+1)$.
Presence of a New Space-Time in Which the Path S (Along the Motion Spiral) is a Constant: A Descriptive Study Approach

The article describes open accelerating longitudinal vortices in 3D. It proves existence of such a special space-time in which the path along the motion spiral is a constant (Sconst). According to the Classic Field Theory (or theory of close vortices), every close vortex moves and spreads only evenly [1]. Every open vortex in 2D and 3D is either accelerating or decelerating, according to the Theory of New Axioms and Laws (or the theory of open vortices). The decelerating open vortex emits decelerating vortices into the surroundings, which convert into free vortices (according to Law5), and the accelerating open vortex sucks in these free vortices and accelerates faster and more in each subsequent step by adding them to itself (according Law6) [2,3]. When two accelerating open vortices are located close enough, then naturally they suck in each other. The more accelerated vortex sucks the less accelerated vortex. What is special about this action is that the faster vortex has a smaller cross section, perpendicular to the direction of moving. And therefore the faster vortex sucks in the slower one as the faster inserts or poke in inside the slower. So these two open accelerating vortices form something like a funnel. In case of more number open accelerating vortices, it turns out that in the center of the funnel is disposed the fastest vortex. The fastest vortex has a maximum vortex acceleration, minimum cross-sectional radius, minimum number of sucked vortices and minimum number of loops with a minimum diameter. It turns out that the fastest vortex will be extended in case that the path in the direction of movement of vortex curve is a constant (Sconst). That is way the height of fastest vortex will be maximum and it will appear first in time T 1. The slowest vortex is located at the funnel's edge. The slowest vortex has the smallest vortex acceleration, the largest cross-sectional radius, the most sucked vortices, and the most loops. If the path in the direction of movement is constant, the height of the vortex is minimum or the slowest vortex will be maximally decreased (Sconst). Because the vortex is maximally shrunk the slowest vortex will appear latest in time Tn. Because the accelerating funnel is packed on the principle of the constant path in the direction of movement (Sconst), the open accelerating vortices will appear successively from center in time T1 to periphery in time Tn respectively. They will form the typically blade from center to periphery of accelerating funnel. The accelerating funnel demonstrate an sucking Gravity Funnel. The accelerating Gravity Funnel sucks in both directions - from bottom to up along direction of vortices movement and from outside to inside in the perpendicular to movement section.