Reviewerly/exHarmony huggingface dataset:
It Is Not As Good As You Think! Evaluating Simultaneous Machine Translation on Interpretation Data

Most existing simultaneous machine translation (SiMT) systems are trained and evaluated on offline translation corpora.We argue that SiMT systems should be trained and tested on real interpretation data.To illustrate this argument, we propose an interpretation test set and conduct a realistic evaluation of SiMT trained on offline translations.Our results, on our test set along with 3 existing smaller scale language pairs, highlight the difference of up-to 13.83 BLEU score when SiMT models are evaluated on translation vs interpretation data.In the absence of interpretation training data, we propose a translationto-interpretation (T2I) style transfer method which allows converting existing offline translations into interpretation-style data, leading to up-to 2.8 BLEU improvement.However, the evaluation gap remains notable, calling for constructing large-scale interpretation corpora better suited for evaluating and developing SiMT systems. 1
Android And Web-Based Islamic Boarding School Academic Information System

Pesantren is an Islamic religious education institution founded by the Islamic community. The goal is to increase faith, piety and have noble character. According to the Law of the Republic of Indonesia Number 18 of 2019 Article 51, the community can participate in the development of Islamic boarding schools to improve the quality and standards of pesantren. In managing academic data, Islamic Boarding Schools have difficulty in managing data on students, alumni, teachers and schedules because it is still done conventionally. Therefore, the development of an academic information system is carried out to improve service quality and fulfill community participation. The methodology used is the FAST (Framework for The Applications of System Technology) methodology which contains the stages of problem identification and evaluation, requirements determination, system design and development, and system implementation and testing. The results of this research are flowchart design, system modeling with UML, database design, interface design, system creation with CodeIgniter framework, implementation and system testing with black box testing. Academic Information System (SIAKAD) is an information system that can facilitate the boarding school in managing academic data in the form of student data, alumni, teachers and schedules where these data are integrated into the required reports. So that this system can help improve the quality of services as well as the quality and standards of Islamic boarding schools in accordance with the Law of the Republic of Indonesia Number 18 of 2019 Article 51 concerning Islamic Boarding Schools.
The IDIA PROCESSMEERKAT pipeline: Fast CASA Processing on a Cloud-Based HPC Cluster

The IDIA PROCESSMEERKAT pipeline is an efficient, user-friendly pipeline that has been widely tested and documented. It has been used to successfully calibrate and image MeerKAT data down to an RMS of several µJy / beam, and supports continuum, polarisation and spectral-line use cases. It runs on the ilifu cluster, making dynamic use of resources and containers, and presents a good framework for implementing pipelines in a HPC environment. The current version performs full-polarisation a priori calibration and produces quick-look images. The next version, which introduces self-calibration, is currently being tested.
Research on Application of Quality Development Training in Higher Vocational Physical Education

In the physical education teaching of higher vocational colleges, the training of the students in terms of quality development can not only effectively improve the physical quality of the students, but also can fully increase the students’ interest in sports, which would achieve more comprehensive growth in higher vocational colleges. This article focuses on the discussion and analysis of quality development training and its application in higher vocational physical education. It describes in detail some influencing factors and application strategies.
How do job characteristics influence the motivation of millennial hospitality employees?

Orientation: Employee retention remains an ongoing challenge for the hospitality industry, especially given the nature of the industry (an often-hectic work environment, long-working hours and perceived poor compensation). The industry also fails to retain millennial employees, which is a grave concern. Research purpose: The investigation studied the impact of job characteristics on the motivation of millennial hospitality employees. The job characteristics model of Hackman and Oldham served as the theoretical guide. Motivation for the study: In addressing the retention issues that the hospitality industry faces, the study provides theoretical and empirical insight on the impact that job characteristics have on the motivation of millennial hospitality employees. Research approach/design: The study employed a quantitative research approach. A structured questionnaire was administered to 96 millennial hospitality employees using QuestionPro. Targeted snowball sampling was used to identify respondents. Partial least squares (PLS) structured equation modelling (SEM) was applied to determine the relationship between the variables. Main findings: The empirical findings revealed a positive relationship amongst task variety, task significance and feedback towards motivation, but a negative relationship between autonomy and task identity towards motivation. Practical/managerial implications: Prospective hospitality employees need to be well informed about the challenges of working in the hospitality industry. Contribution/value added: Autonomy and task identity emerged as factors that do not contribute to the motivation of hospitality employees. Millennial hospitality employees were, however, motivated by task significance, task variety and feedback.
Diverse Cultural Thought In The European Context Through Music Collaboration Networks

Networking in current music education and models, projects and platforms are a means for recovering the importance of music education as a part of artistic education. Music education has value in itself, both in the international and European context, not only from an instrumental or interdisciplinary perspective, but also as a critical reflection on reality, forming an integral part of society which cannot be removed. Art, due to its non-instrumental nature, constitutes a source of living standards and allows the development of the human sensibility which contributes to the acquisition of skills related with perception, and which make up valuable tools for the cognitive process of the science. Networking projects through music education contribute to diverse cultural thought, which places value on European cultural heterogeneity through music, promotes cultural integration and diversity of tastes beyond prevailing and homogenising musical trends.
Novel asynchronous activation of the bio-inspired adaptive tuning in the speed controller: Study case in DC motors

The fusion of bio-inspired algorithms into online controller tuning (adaptive controller tuning) is one of the main topics in Intelligent Control. One crucial issue is to reduce the times that the tuning process performs over time. In this work, a novel Asynchronous Adaptive Controller Tuning (AACT) approach is proposed to reduce the number of tuning process activations, and hence, this promotes resource savings in the overall computational cost for the tuning process. In this approach, an event function is designed to determine the control parameter update where the use of identification and predictive stages set the current control parameters. Furthermore, an elitist initialization in the differential evolution algorithm is also incorporated for solving the optimization problems at each stage. The speed regulation of the DC motor under disturbances is the study case in the ACCT approach. Comparative results with state-of-the-art bio-inspired algorithms in control tuning reveal in the AACT approach that the elitist initialization in differential evolution notably benefits the controller performance. Moreover, the comparative results with the Synchronous Adaptive Controller Tuning (SACT) approach show that the proposal reduces 61% the tuning process computation frequency with a similar speed regulation performance when disturbances appear.
Guided Policy Search using Sequential Convex Programming for Initialization of Trajectory Optimization Algorithms

Nonlinear trajectory optimization algorithms have been developed to handle optimal control problems with nonlinear dynamics and nonconvex constraints in trajectory planning. The performance and computational efficiency of many trajectory optimization methods are sensitive to the initial guess, i.e., the trajectory guess needed by the recursive trajectory optimization algorithm. Motivated by this observation, we tackle the initialization problem for trajectory optimization via policy optimization. To optimize a policy, we propose a guided policy search method that has two key components: i) Trajectory update; ii) Policy update. The trajectory update involves offline solutions of a large number of trajectory optimization problems from different initial states via Sequential Convex Programming (SCP). Here we take a single SCP step to generate the trajectory iterate for each problem. In conjunction with these iterates, we also generate additional trajectories around each iterate via a feedback control law. Then all these trajectories are used by a stochastic gradient descent algorithm to update the neural network policy, i.e., the policy update step. As a result, the trained policy makes it possible to generate trajectory candidates that are close to the optimality and feasibility and that provide excellent initial guesses for the trajectory optimization methods. We validate the proposed method via a real-world 6-degree-of-freedom powered descent guidance problem for a reusable rocket.
GenRadar: Self-Supervised Probabilistic Camera Synthesis Based on Radar Frequencies

Autonomous systems require a continuous and dependable environment perception for navigation and decision-making, which is best achieved by combining different sensor types. Radar continues to function robustly in compromised circumstances in which cameras become impaired, guaranteeing a steady inflow of information. Yet, camera images provide a more intuitive and readily applicable impression of the world. This work combines the complementary strengths of both sensor types in a unique self-learning fusion approach for a probabilistic scene reconstruction in adverse surrounding conditions. After reducing the memory requirements of both high-dimensional measurements through a decoupled stochastic self-supervised compression technique, the proposed algorithm exploits similarities and establishes correspondences between both domains at different feature levels during training. Then, at inference time, relying exclusively on radio frequencies, the model successively predicts camera constituents in an autoregressive and self-contained process. These discrete tokens are finally transformed back into an instructive view of the respective surrounding, allowing to visually perceive potential dangers for important tasks downstream.
Algorithmic Thresholds for Refuting Random Polynomial Systems

Previous chapter Next chapter Full AccessProceedings Proceedings of the 2022 Annual ACM-SIAM Symposium on Discrete Algorithms (SODA)Algorithmic Thresholds for Refuting Random Polynomial SystemsJun-Ting Hsieh and Pravesh K. KothariJun-Ting Hsieh and Pravesh K. Kotharipp.1154 - 1203Chapter DOI:https://doi.org/10.1137/1.9781611977073.49PDFBibTexSections ToolsAdd to favoritesExport CitationTrack CitationsEmail SectionsAboutAbstract Consider a system of m polynomial equations {pi(x) = bi}i≤m of degree D ≥ 2 in n-dimensional variable x ∊ ℝn such that each coefficient of every pi and bis are chosen at random and independently from some continuous distribution. We study the basic question of determining the smallest m–the algorithmic threshold–for which efficient algorithms can find refutations (i.e. certificates of unsatisfiability) for such systems. This setting generalizes problems such as refuting random SAT instances, low-rank matrix sensing and certifying pseudo-randomness of Goldreich's candidate generators and generalizations. We show that for every d ∊ ℕ, the (n + m)O(d)-time canonical sum-of-squares (SoS) relaxation refutes such a system with high probability whenever . We prove a lower bound in the restricted low-degree polynomial model of computation which suggests that this trade-off between SoS degree and the number of equations is nearly tight for all d. We also confirm the predictions of this lower bound in a limited setting by showing a lower bound on the canonical degree-4 sum-of-squares relaxation for refuting random quadratic polynomials. Together, our results provide evidence for an algorithmic threshold for the problem at -time algorithms for all δ. Our upper-bound relies on establishing a sharp bound on the smallest integer d such that degree d–D polynomial combinations of the input pis generate all degree-d polynomials in the ideal generated by the pis. Our lower bound actually holds for the easier problem of distinguishing random polynomial systems as above from a distribution on polynomial systems with a “planted” solution. Our choice of planted distribution is slightly (and necessarily) subtle: it turns out that the natural and well-studied planted distribution for quadratic systems (studied as the matrix sensing problem in machine learning) is easily distinguishable whenever m ≥ Õ(n)–a factor n smaller than the threshold in our upper bound above. Thus, our setting provides an example where refutation is harder than search in the natural planted model. Previous chapter Next chapter RelatedDetails Published:2022eISBN:978-1-61197-707-3 https://doi.org/10.1137/1.9781611977073Book Series Name:ProceedingsBook Code:PRDA22Book Pages:xvii + 3771
Android-Based Application of City Public Transport Tracks in Sumedang

Visiting to the new place in the local or not is very reasonable. The problem is, sometimes it is so hard for some people to remember the path. Especially, the migrants don’t know the route and the bill of the public transport on that’s place. So, along increasing community mobility is not directly proportional to the increasing needs for information in the form of digital maps. At this reseach integrated on google maps is expected to fulfillment needs of public to see the bill and the path with Global Positioning System (GPS) that function as location pinter. The final result of this research is an application who makes it easy for users to find destination route and the bill that are in accordance with regulations. This application has several main features such as data input, displaying a list of stored data, displaying the routes on a map, and displaying the bill.
Testing Autonomous Systems with Believed Equivalence Refinement

Continuous engineering of autonomous driving functions commonly requires deploying vehicles in road testing to obtain inputs that cause problematic decisions. Although the discovery leads to producing an improved system, it also challenges the foundation of testing using equivalence classes and the associated relative test coverage criterion. In this paper, we propose believed equivalence, where the establishment of an equivalence class is initially based on expert belief and is subject to a set of available test cases having a consistent valuation. Upon a newly encountered test case that breaks the consistency, one may need to refine the established categorization in order to split the originally believed equivalence into two. Finally, we focus on modules implemented using deep neural networks where every category partitions an input over the real domain. We present both analytical and lazy methods to suggest the refinement. The concept is demonstrated in analyzing multiple autonomous driving modules, indicating the potential of our proposed approach.
Technology-Mediated Musical Connections: The Ecology of a Screen-Score Performance

This paper presents Puffin, an interactive screen-score system, and the development of a piece for the system with two instrumentalists. Puffin is designed to expose the interconnections between two musicians in a performance ecology and the role of the interactive artifact and musical score in that ecology. We conducted daily interviews with the instrumentalists during the study to understand the different interconnections that emerged among the instruments, musicians, and the score. We visually represent the resulting ecology by using ARCAA, a recently developed framework designed to study performance ecologies. In light of existing literature, this study offers new perspectives on some elements of performance ecologies: how different actors play different roles and perform different actions in different contexts; how a screen-score can act as a mediator; how a piece and a system are interconnected but distinct; and how the piece has some constraints in itself. Finally, we observed how autobiographical and idiographic design can coexist.
Pet Care and Shop Website using Rapid Application Development Method

At present, the rapid information technology has made progress in the business sector, one of which is in the business of selling pet products and services. Ello petshop Sumedang is one of the businesses that sells pet products, and pet care services, especially for cats. Ello the Sumedang pet shop still has a problem that currently, pet owners do not have time to go directly to the pet shop to purchase pet products. The purpose of this research is to create a web-based pet shop information system using the PHP programming language codeigniter framework and the MySQL database. The method used in this development system is the Rapid Application Development (RAD) method which consists of requirements planning, system design, and implementation. The pet shop website can make it easier for owners to purchase pet products, or pet care services without having to come directly to the pet shop, can make purchases through the website, and can see categories, prices of pet products available on the site. the web. Besides that, it can assist in processing reports at Ello Petshop Sumedang
Distilling Knowledge in Federated Learning

Nowadays, Federated Learning has emerged as the prominent collaborative learning approach among multiple machine learning techniques. This framework enables communication-efficient and privacy-preserving solution that a group of users interacts with a server to collaboratively train a powerful global model without exchanging users' raw data. However, federated learning might face the significant challenge with high communication cost when exchanging the huge model parameters. Moreover, training such a large model on devices is an obstacle under the battery limitation of mobile devices. To address this hindrance, we propose the federated learning with bi-level distillation, namely FedBD. The key idea of this proposal is to exchange the soft targets instead of transferring the model parameters between server and clients. The exchange knowledge was constructed based on the prediction outcomes for the shared reference dataset. By interchanging the knowledge of the learning models, our algorithm obtains the benefits of reducing both communication and computation costs. The proposed mechanism allows the different model architectures between server and learning agents. The experiments show that our proposed method can achieve comparable or even slightly higher accuracy than FedAvg algorithm on the image classification task while using fewer communication resources and power.
Underwater Image Enhancement using Improved Bat Algorithm

From the past decades, improving the superiority of an Underwater Image (UI) has received well recognition in terms of the deprived image visibility that is occurred due to the water medium physical property.The light absorption of wavelength-reliant, as well as scattering in an underwater view, degrades the image visibility which causes lesser contrast as well as distorted color casts.To overcome the issues, a highly developed technique is adopted for the advancement and it improves the UI classification.Specifically, by exploiting the Improved Bat Algorithm (IBA), the underwater input image RGB is enhanced.The next stage is feature extraction.To the enhanced Principle Component Analysis (PCA) technique, the extracted attributes are given as input; here the dimensions of the features are minimized.Subsequently, the classification operation is carried out by exploiting the ANFIS classifier.Finally, the classified improved deeper water images besides the improved shallow water images which are seen are available in the testing stage.Finally, the simulation outcomes for the proposed and conventional methods are analyzed.The developed UIE system shows superior accuracy while compared with the conventional techniques.
Analysis And Design Of A Food And Beverage Ordering System Using A Website-Based Qr Code

Currently, the operational processes that occur at the Sumedang area shop are starting from the transaction process to the report generation processSales are still carried out in the conventional way, namely recording in a notebook. Based on that condition, the writer has the idea to implement a food and beverage ordering system that can be done by scanning the QR Code. The method used in this research is the prototype method with the stages of conducting a needs analysis, designing and testing the system. The result of testing this system is that ordering food and drinks can be done by scanning the QR Code, after the scanning process is successful, a link will appear as access to the ordering website.
User Interface Design Research for Modeling Tools

Modeling tools constitute a class of software assisting modelers in creating, documenting, and maintaining conceptual models, for example, via graphical editors, diagram layouting, and syntax checking. Research on modeling tools has been a recurring theme in conceptual modeling research for more than 30 years. An evident focus in modeling tool research is on user interface design. In this literature study, we systematically identify and analyze 72 contributions to user interface design research for modeling tools published between 1980 and 2017 to develop a structuring overview of the current state of research. Building on this overview, we assess and discuss promising paths for future research, and compile recommendations informing user interface design of modeling tools.
A PVT Tolerant True Random Number Generator Based on Oscillator Phase Under Sub-Harmonic Injection Locking

A digital true random number generator based on the oscillator phase generated by the second-order sub-harmonic injection locking phenomenon is proposed in this paper. Owing to the thermal jitter, the phase difference between a free-running ( i.e., unsynchronized) oscillator and an injected external signal collapses to one of two stable solutions in a truly random manner. The injected external signal is generated from another oscillator operating at close to twice the frequency of the free-running oscillator. The random sequence extracted from the resulting stable solutions is bias- and correlation-free, nullifying the need for a bias compensation circuitry. The proposed design is demonstrated with extensive simulations to be robust under process, voltage, and temperature variations. The generated random patterns pass the National Institute of Standards and Technology test suite.