Reviewerly/exHarmony huggingface dataset:
Developing a Community of Practice: Building the Research Data Management Librarian Academy

The Research Data Management Librarian Academy (RDMLA) is a free, online global professional development program designed by librarians for librarians working in research-intensive environments. Developed through a unique partnership that includes a Library and Information Sciences academic program, research and health sciences libraries, and industry, the RDMLA’s inception, development, and launch provide helpful insights into the creation of online professional development courses. The RDMLA team’s experience building the course’s curriculum with an instructional designer (ID) and evaluating the operation and usefulness of the course’s content through usability testing provides valuable lessons learned for librarians constructing an online continuing education (CE) course.
Passenger detection in cars with small form-factor IR sensors (Grid-eye)

In this paper, an experiment of passenger detection in cars is presented. A small and cost-effective, commercial IR sensor was used to investigate the possibility of detecting people in the different seats of the car. The electronics are based on the Arduino platform, which is used to communicate with a PC to collect the data, and an AMG8833 IR sensor board, which enables fast and efficient temperature detection in 8×8 matrix. The Grid-Eye sensor has its limits, so these aspects were also investigated, according to the positioning, the resolution and temperature differences, which parameters point to the maximum number of detectable people. The sensor was validated in room temperature laboratory environment, where it was possible to differentiate a person from the background. Four positions were considered in the car, with front, and back seated people (0-5 persons) for different use cases (Case A-D). It was found, that with the aforementioned limitations, at given positions, it was possible to differentiate seated and non-seated positions in the car, thus with more sensor nodes, the passenger detection is possible with a low-cost solution.
Anonymous communication scheme based on quantum walk on Cayley graph

Information security is the cornerstone and lifeblood of national security in the information society, and anonymous quantum communication is one of the important ways to protect information security. Using quantum walk randomness to effectively solve sensitive problems such as leakage of identity information. In this paper, an anonymous communication scheme based on quantum walks on the Cayley graph is proposed. First, both parties in the communication hide their identity information, and the sender Alice anonymously selects the receiver Bob through logic or operation. Secondly, the trusted third party and the communicating parties use the BB84 protocol to generate and distribute the security key. Alice encrypts the information sequence according to the security key to obtain the blind information; Bob uses the joint Bell state measurement and security key to sign and the trusted third party verifies the signature information. Third, the trusted third party calculates the position probability distribution function of Bob’s quantum walk via the Fourier transform, converts the position information corresponding to the maximum probability into a confirmation frame and sends it to Alice; Alice uses the quantum compression algorithm by decreasing dimensions to reduce the number of transmitted information bits(the length of the information bit can be reduced by up to 37.5%) and uses the security key to complete the information encryption and then transmit the information to the location indicated by the confirmation frame. Bob uses quantum walks to search the location node to obtain the transmission information and complete the anonymous quantum communication. Finally, the security analysis of the scheme is carried out, and the numerical simulation results of the Cayley graph of 200 nodes are given. At the 10-step walk, the maximal probability of the 6th node is 45.31%. According to the simulation results, the probability that Bob is eavesdropped on the specific location at his 10-step walk during the communication of this scheme is approximately 6 × 10&lt;sup&gt;–7&lt;/sup&gt;%, so the receiver can avoid the identity information from the eavesdropping with a high probability, and the quantum network anonymity protocol is not broken.
Exciting experiences in the ‘<i>Rocky road to digital diagnostics</i>’

The review paper by Professor Paul J van Diest and colleagues entitled ‘ Rocky road to digital diagnostics: implementation issues and exhilarating experiences ’ describes and retraces, in a very attracting way, the path they have followed since 2007 in the fully implementation of digital diagnostics in pathology.1 We have to admit that we have followed at the beginning with some scepticism and then with enthusiasm, if not with some envy, the development of digitalisation in their institution that eventually has become so successful.Our scepticism that then became enthusiasm has been linked to the fact that, in one way or another, we witnessed and even participated since the early 1980s in the development and application of what in the past was called ‘morphometry’ or ‘quantitative histopathology’ and what can be considered the background of the modern digital diagnostics. The senior author of our contribution still vividly remembers Professor van Diest entering the field of ‘morphometry’ at the beginning as a medical student and then as a young pathologist, and contributing actively to its development and application in ‘diagnostic pathology’.Several groups were involved in the basic research, developing from scratch theoretical bases, algorithms and applications and even contributing to the development and update of image analysis systems, parallel to the developments in computer science. All this became common knowledge and appreciation among pathologists through publications and meetings. What has currently been achieved by Professor …
Type Theory Unchained : Extending Agda with User-Defined Rewrite Rules

Dependently typed languages such as Coq and Agda can statically guarantee the correctness of our proofs and programs. To provide this guarantee, they restrict users to certain schemes - such as strictly positive datatypes, complete case analysis, and well-founded induction - that are known to be safe. However, these restrictions can be too strict, making programs and proofs harder to write than necessary. On a higher level, they also prevent us from imagining the different ways the language could be extended.In this paper I show how to extend a dependently typed language with user-defined higher-order non-linear rewrite rules. Rewrite rules are a form of equality reflection that is applied automatically by the typechecker. I have implemented rewrite rules as an extension to Agda, and I give six examples how to use them both to make proofs easier and to experiment with extensions of type theory. I also show how to make rewrite rules interact well with other features of Agda such as η-equality, implicit arguments, data and record types, irrelevance, and universe level polymorphism. Thus rewrite rules break the chains on computation and put its power back into the hands of its rightful owner: yours.
Ensembles of Networks Produced from Neural Architecture Search

Neural architecture search (NAS) is a popular topic at the intersection of deep learning and high performance computing. NAS focuses on optimizing the architecture of neural networks along with their hyperparameters in order to produce networks with superior performance. Much of the focus has been on how to produce a single best network to solve a machine learning problem, but as NAS methods produce many networks that work very well, this affords the opportunity to ensemble these networks to produce an improved result. Additionally, the diversity of network structures produced by NAS drives a natural bias towards diversity of predictions produced by the individual networks. This results in an improved ensemble over simply creating an ensemble that contains duplicates of the best network architecture retrained to have unique weights.
Pandemic dynamics prediction in Java using the Moving Average method and the Knowledge Growing System (KGS)

This study aims to analyze the comparative performance of pandemic dynamics prediction methods on the island of Java, based on data from March to May 2020 covering the provinces of DKI Jakarta, West Java, Central Java, DI Yogyakarta, and East Java. The prediction uses Knowledge Growing System (KGS) and time series models, namely Single Moving Average (SMA) and Exponential Moving Average (EMA). Based on the Mean Absolute Percentage Error (MAPE) computational results, the EMA method produces a lower error rate than the SMA method with 47.94 % on average. The KGS prediction with a Degree of Certainty (DoC) produced a trend analysis that the pandemic dynamics in DKI Jakarta province will decrease gradually if the current policy is still implemented. Whereas in the other provinces, the KGS predicted the pandemic dynamics trends will still increase.
Large-Scale Transfer Learning for Low-Resource Spoken Language Understanding

End-to-end Spoken Language Understanding (SLU) models are made increasingly large and complex to achieve the state-ofthe-art accuracy. However, the increased complexity of a model can also introduce high risk of over-fitting, which is a major challenge in SLU tasks due to the limitation of available data. In this paper, we propose an attention-based SLU model together with three encoder enhancement strategies to overcome data sparsity challenge. The first strategy focuses on the transferlearning approach to improve feature extraction capability of the encoder. It is implemented by pre-training the encoder component with a quantity of Automatic Speech Recognition annotated data relying on the standard Transformer architecture and then fine-tuning the SLU model with a small amount of target labelled data. The second strategy adopts multitask learning strategy, the SLU model integrates the speech recognition model by sharing the same underlying encoder, such that improving robustness and generalization ability. The third strategy, learning from Component Fusion (CF) idea, involves a Bidirectional Encoder Representation from Transformer (BERT) model and aims to boost the capability of the decoder with an auxiliary network. It hence reduces the risk of over-fitting and augments the ability of the underlying encoder, indirectly. Experiments on the FluentAI dataset show that cross-language transfer learning and multi-task strategies have been improved by up to 4:52% and 3:89% respectively, compared to the baseline.
A principle-based robustness analysis of admissibility-based argumentation semantics

The principle-based approach is a methodology to classify and analyse argumentation semantics. In this paper we classify seven of the main alternatives for argumentation semantics using a set of new robustness principles. These principles complement Baroni and Giacomin’s original classification and deal with the behaviour of a semantics when the argumentation framework changes due to the addition or removal of an attack between two arguments. We distinguish so-called persistence principles and monotonicity principles, where the former deal with the question of whether a labelling or extension of an argumentation framework under a given semantics persists after a change, and the latter with the question of whether new labellings or extensions are created after a change. We furthermore show in which sense labelling-based and extension-based semantics lead to subtly different principles and results. Our results can be used for choosing a semantics for a particular application, or to guide the search for new argumentation semantics, but they have been used also in the design of algorithms.
KESENJANGAN KUALITAS LAYANAN PENDIDIKAN DI INDONESIA PADA MASA DARURAT COVID-19: TELAAH DEMOGRAFI ATAS IMPLEMENTASI KEBIJAKAN BELAJAR DARI RUMAH

One of the effects of the COVID-19 pandemic on the education sector in Indonesia is the closure of schools. To ensure education services continue, the government issued a policy of learning home (Belajar dari Rumah/BDR) through online/distance learning. This paper aims to describe the implementation of the BDR for learning quality. To that aim, we have conducted telephone interviews with school principals and teachers from frontier, outermost and remote regions (3T) and non-3T regions. By looking at demographics perspective, implementation of BDR has six variations are a) learning by interactive virtual classroom b) learning assignment by social media c) learning in teacher's or student's home d) learning by using TV/radio e) learning by â€˜mouth-to-mouthâ€™ messages and f) learning stopped. The variations generate different quality of learning and will produce an increasing education inequality in Indonesia.
Empirical Oversampling Threshold Strategy for Machine Learning Performance Optimisation in Insurance Fraud Detection

Insurance fraud is one of the most practiced frauds in the sectors of the economy.Faced with increasingly imaginative underwriters to create fraud scenarios and the emergence of organized crime groups, the fraud detection process based on artificial intelligence remains one of the most effective approaches.Real world datasets are usually unbalanced and are mainly composed of "no-fraudulent" class with a very small percentage of "fraudulent" examples to train our model, thus prediction models see their performance severely degraded when the target class appears so poorly represented.Therefore, the present work aims to propose an approach that improves the relevance of the results of the best-known machine learning algorithms and deals with imbalanced classes in classification problems for prediction against insurance fraud.We use one of the most efficient approaches to re-balance training data: SMOTE.We adopted the supervised method applied to automobile claims dataset "carclaims.txt".We compare the results of the different measurements and question the results and relevance of the measurements in the field of study of unbalanced and labeled datasets.This work shows that the SMOTE Method with the KNN Algorithm can achieve better classifier performance in a True Positive Rate than the previous research.The goal of this work is to lead a study of algorithm selections and performance evaluation among different ML classification algorithms, as well as to propose a new approach TH-SMOTE for performance improvement using the SMOTE method by defining the optimum oversampling threshold according to the G-mean measure.
Black-Box Adaptation of ASR for Accented Speech

We introduce the problem of adapting a black-box, cloud-based ASR system to speech from a target accent. While leading online ASR services obtain impressive performance on main-stream accents, they perform poorly on sub-populations - we observed that the word error rate (WER) achieved by Google's ASR API on Indian accents is almost twice the WER on US accents. Existing adaptation methods either require access to model parameters or overlay an error-correcting module on output transcripts. We highlight the need for correlating outputs with the original speech to fix accent errors. Accordingly, we propose a novel coupling of an open-source accent-tuned local model with the black-box service where the output from the service guides frame-level inference in the local model. Our fine-grained merging algorithm is better at fixing accent errors than existing word-level combination strategies. Experiments on Indian and Australian accents with three leading ASR models as service, show that we achieve as much as 28% relative reduction in WER over both the local and service models.
Software test quality evaluation based on fuzzy mathematics

The thesis starts with the connotation and attributes of software testing quality, introduces software testing quality evaluation methods, and analyzes and discusses software testing quality evaluation models based on fuzzy mathematics theory. Focusing on the key technical problems of software testing quality, discuss the key technologies to solve the software testing quality evaluation model establishment. Through the use of fuzzy models, the cost of software testing quality evaluation is effectively reduced, and the reliability of software testing quality evaluation methods is improved. This model can quickly evaluate the quality of software testing, can avoid the occurrence of local maxima, overcome the shortcomings of existing evaluation models and tools, and can correctly reflect the relationship between the internal and external properties of the software. Using the new software testing quality evaluation method, comparing the evaluation models and tools used before, summarizing the methods of software testing quality improvement. The application of these methods effectively improves the software testing quality.
Implementation of Web Log Mining Device Under Apriori Algorithm Improvement and Confidence Formula Optimization

With the increasing number of internet users, a large number of network alarm information increases, resulting in the increasing pressure of SMS gateway and frequent alarm delay. Therefore, in order to effectively improve the above problems, the article is based on the improved Apriori algorithm and confidence formula, points and grabbing module, model training module and the test evaluation module device, three steps to realize the web log mining and mining system design for the data acquisition module, data preprocessing module, mining model building blocks, mining model checking module and mining model analysis and evaluation module five modules. Finally, the Python program was used to verify the test data of about two million pieces of original alarm data in a company's network management database for a consecutive month. The verification results show that the design of this paper has greatly reduced the number of original alarms and completed the merging of related rules.
Some Aspects of Creation of Flexible Modeling Software Package for NPP

An approach is considering, according to which the flexible modeling software package is the core of the digital twin of an NPP power unit, which can apply for a wide range of tasks from application in operator information support systems and diagnostic systems to assessing the cybersecurity of a nuclear power plant and developing cybersecurity measures.
Corporate fraud and relationships: a systematic literature review in the light of research onion

Purpose This paper aims to investigate how the literature has been addressing the relationships between corporate fraud and executive behavior and corporate fraud and information technology (IT) controls. Design/methodology/approach A systematic literature review was performed following the planning phases proposed by Levy and Ellis (2006), illuminated by the research onion, developed by Saunders et al. (2007). Findings The main findings of the studies analyzed refer basically to models to assess the risk of fraud. These risks originate from the market, from the organization itself or from individuals and also from their relationship networks. Subsequently, the main risks identified by the authors were classified according to their origin, the main theories approached and the “solutions” for the risks presented by the authors as the product of their work. Research limitations/implications It should be noted that this study is not free of limitations, of which two stand out: the full body of articles on the subject was certainly not evaluated. Although the search has been systematic and judicious both by the combination of keywords for the searches, as well as by the use of the main databases and also by the rigor in the description of the procedures and the analysis of the articles in the light of Research Onion was based on the authors’ knowledge that may have been limited in some respect. Practical implications As a practical implication, there is the relationship of red flags and their classification by origin, as they can be very useful for planning the work of internal and external auditors. Social implications It is considered that this work can be a starting point for scholars who are interested in the corporate fraud phenomenon, given that the data was collected and organized systematically. Originality/value The analysis of the articles in relation to Research Onion shed light on the main philosophical and methodological characteristics of the studies. Also, regarding the relationships between corporate fraud and IT controls, existing scientific research appears to be limited. Searches for the terms information technology and information systems were extended, as well as search strings tested with the terms data governance and IT governance without results. This fact demonstrates that there may be (as far as the results have reached) a vast area of research on corporate fraud in the field of systems knowledge and information technology.
Task-Completion Dialogue Policy Learning via Monte Carlo Tree Search with Dueling Network

We introduce a framework of Monte Carlo Tree Search with Double-q Dueling network (MCTS-DDU) for task-completion dialogue policy learning. Different from the previous deep model-based reinforcement learning methods, which uses background planning and may suffer from low-quality simulated experiences, MCTS-DDU performs decision-time planning based on dialogue state search trees built by Monte Carlo simulations and is robust to the simulation errors. Such idea arises naturally in human behaviors, e.g. predicting others’ responses and then deciding our own actions. In the simulated movie-ticket booking task, our method outperforms the background planning approaches significantly. We demonstrate the effectiveness of MCTS and the dueling network in detailed ablation studies, and also compare the performance upper bounds of these two planning methods.
Sensing single atoms in a cavity using a broadband squeezed light

We investigate a single atom cavity-QED system directly driven by a broadband squeezed light.We demonstrate how the squeezed radiation can be used to sense the presence of a single atom in a cavity.This happens by transferring one of the photons from the field in a state with even number of photons to the atom and thereby populating odd number Fock states.Specifically, the presence of the atom is sensed by remarkable changing in the presence of one photon and the loss of squeezing of the cavity field.A complete study of quantum fluctuations and the excitation of multiphoton transitions is given.
Neural Networks with Recurrent Generative Feedback

Neural networks are vulnerable to input perturbations such as additive noise and adversarial attacks. In contrast, human perception is much more robust to such perturbations. The Bayesian brain hypothesis states that human brains use an internal generative model to update the posterior beliefs of the sensory input. This mechanism can be interpreted as a form of self-consistency between the maximum a posteriori (MAP) estimation of an internal generative model and the external environment. Inspired by such hypothesis, we enforce self-consistency in neural networks by incorporating generative recurrent feedback. We instantiate this designon convolutional neural networks (CNNs). The proposed framework, termed Convolutional Neural Networks with Feedback (CNN-F), introduces a generative feedback with latent variables to existing CNN architectures, where consistent predictions are made through alternating MAP inference under a Bayesian framework. In the experiments, CNN-F shows considerably improved adversarial robustness over conventional feedforward CNNs on standard benchmarks.