Reviewerly/exHarmony huggingface dataset:
MERLIon CCS Challenge: A English-Mandarin code-switching child-directed speech corpus for language identification and diarization

To enhance the reliability and robustness of language identification (LID) and language diarization (LD) systems for heterogeneous populations and scenarios, there is a need for speech processing models to be trained on datasets that feature diverse language registers and speech patterns.We present the MERLIon CCS challenge, featuring a first-of-its-kind Zoom video call dataset of parent-child shared book reading, of over 30 hours with over 300 recordings, annotated by multilingual transcribers using a high-fidelity linguistic transcription protocol.The audio corpus features spontaneous and in-the-wild English-Mandarin code-switching, child-directed speech in non-standard accents with diverse language-mixing patterns recorded in a variety of home environments.This report describes the corpus, as well as LID and LD results for our baseline and several systems submitted to the MERLIon CCS challenge using the corpus.
Investigating model performance in language identification: beyond simple error statistics

Language development experts need tools that can automatically identify languages from fluent, conversational speech and provide reliable estimates of usage rates at the level of an individual recording.However, LID systems are typically evaluated on metrics such as equal error rate and balanced accuracy, applied at the level of an entire speech corpus.These overview metrics do not provide information about model performance at the level of individual speakers, recordings, or units of speech with different linguistic characteristics.Overview statistics may mask systematic errors in model performance for some subsets of the data, and consequently, have worse performance on data derived from some subsets of human speakers, creating a kind of algorithmic bias.Here, we investigate how well a number of LID systems perform on individual recordings and speech units with different linguistic properties in the MERLIon CCS Challenge featuring accented code-switched child-directed speech.
[Simulator of Heart Rate and Respiratory Rate for Monitoring Accuracy of Intelligent Sleep Monitoring Devices].

Invent a simulator which provides a simulation of heart rate and respiratory rate to the intelligent sleep monitoring devices based on precision pressure sensors.The simulator was composed of control part and simulated silicone doll. The simulated silicone doll contains heartbeat simulator and breathing simulation airbag. Heartbeat and breathing combination pressure signal can be produced according to frequency set values. Frequencies of pressure signal of the simulator were compared with the monitoring results of intelligent sleep monitoring devices with known accuracy to verify the frequency accuracy of pressure signal of the simulator. Verified the repeatability and stability of the simulator with a stopwatch.The heart rate of the simulator was with in ±2 beats per minute of the monitoring results of intelligent sleep monitoring devices and the respiratory rate of the simulator was with in ±2 times per minute of the monitoring results. The repeatability and stability of the simulator was better than ±5% according to results with a stopwatch.It's practicable to use the simulator which provides a simulation of heart rate and respiratory rate to the accuracy test of the intelligent sleep monitoring devices based on precision pressure sensor.
Research on low-quality text image detection and recognition based on EDSR and improved ASTER

This paper proposes an improved ASTER-based low-quality text image recognition algorithm to address the issue of blurry, low-resolution, and poor-quality text image recognition.The basic idea is to use super-resolution reconstruction technology EDSR to convert low-resolution text images into high-resolution text images, thereby enriching text feature information. In addition, the DCNV2 module is added to ASTER's feature extraction network to focus on the extraction of text feature information. Compared with previous methods, the method proposed in this paper significantly improves the recognition rate on seven public datasets, especially in the SVT and SVTP text image datasets, which have poor quality.
Decision-Making Support to Auto-scale Smart City Platform Infrastructures

Smart city platforms support application development and deployment and typically rely on a robust, scalable underlying Information Technology infrastructure composed of cloud platforms, containers, virtual machines, storage, and other services. Such a runtime infrastructure must deal with the highly dynamic workload of the different applications, with simultaneous access from multiple users and sometimes working with many interconnected devices and systems. This scenario requires auto-scaling mechanisms that automatically and timely add or remove cloud resources in response to dynamic variations in workload. This paper introduces a decision-making mechanism that analyzes the monitored state of a smart city platform and its underlying infrastructure at runtime to decide whether auto-scaling is needed. The performance of the decision-making mechanism has been evaluated upon the computational environment that supports a platform for developing real-world smart city applications.
Occlusion-Based Approach for Interpretable Semantic Segmentation

In this paper, we investigate the application of an occlusion-based approach for the task of interpreting semantic segmentation results. With an increasing deployment of deep learning systems in critical domains, interpretability plays a key role in providing additional information about the model besides the evaluation metric score. An extended modification of occlusion sensitivity allows the generation of saliency maps based on the effect of occlusions on the evaluation metric. Such a perturbation-based post-hoc interpretability method can be used to visualize those image regions that the selected segmentation class is most sensitive to. We observe that, compared to classification cases, the evaluation metric scores for segmentation remain similar to each other even after occlusions. To generate more color intensities in the saliency map, we use normalization and standardization techniques. We also evaluate the results quantitatively using deletion curves.
A New Multimodal Biomedicine Tomography System: Architecture and Methods

A new multifunction and multimodal biomedical imaging system in which combines five kinds of agent is proposed. It was mainly based on magneto-acoustic tomography with magnetic induction, integrated four other species of imaging methods, for seamlessly fusing of functional with anatomical information to create multimodality images. The image data acquisition and processing was consisted of upper and lower computer which they form a virtual equipment. The lower computer was made up by five kinds of smart imaging agent while all of them were an embedded system severally. Linked with the networks, the upper computer process the image data and reconstruct the multimodal image. The image fusion was achieved by the system automatically itself and without other post image registrations and software. Besides, most prominent feature of the system is the spatial resolution of images was 0.24mm, and the imaging cycle was greatly shorten, got 50 frameworks per second that realized real time imaging. The phantom system promised a new type noninvasive biomedical imaging multimodality and one-size-fits-all system to realize one-stop-shop omni-tomography as well as holographic imaging service. Further, those techniques and methods can promote the construction as well as realization of smart medicine and smart hospitals.
Granular-Ball Clustering Based Neighbourhood Outliers Detection Method

The detection of outliers and boundary points is a popular, valuable and interesting topic under any method, and often more important than normal points in some practical usage scenarios. Hyber Granular-Balls(GBs,This is what we will call it in the following) are a robust, scalable, adaptive class of spheres suitable for many frontier scenarios with applications ranging from astronomy to bioinformatics and pattern recognition. Currently, recognition methods based on correlation information from individual data are a common technique for outlier detection. In granular computing, efficiency and robustness to noise are proportional to the size of the granularity. Inspired by the idea of granularity computing, this paper uses GBs with multiple granularities instead of single data to obtain coarse-grained outlier features. Based on the structure and information of the GBs, we propose an adaptive coarse-grained outlier detection method: by detecting the number of points inside the GBs, the overlap, and the change in the neighbourhood relationship of each GB, we are able to produce effective detection identification of outliers. We have demonstrated the effectiveness of the GB method in several papers, and experimental results on synthetic and real datasets can also show that the method outperforms existing methods.
Gaussian Process Component Mining with the Apriori Algorithm

Gaussian process models are a commonly used tool for model-based analysis of time series data. With growing database size, the difficulty to identify the most interesting insights in order to gain a deeper understanding of the data’s underlying behavior increases. To address this issue, we propose a novel approach for finding frequent kernel components efficiently. In this way, data scientists are empowered to focus their investigations on the most common parts hidden in a set of Gaussian process models. We show how to solve this task by means of frequent item set mining methods, which are capable of analyzing large databases efficiently. We provide evidence of our proposal with a first series of experiments, indicating that our method is capable of detecting frequent kernel components from Gaussian process models. Though this short paper can be thought of as a first preliminary approach towards analyzing Gaussian processes with conventional data mining methods, it simultaneously opens a novel research direction of Gaussian process mining at the intersection between machine learning and database research.
SISTEM MONITORING SERVER BERBASIS ANDROID DI DINAS KOMUNIKASI DAN INFORMATIKA KABUPATEN REMBANG

In this research, identification has been made to make an Android-based server monitoring application using the System Development Life Cycle Technique. This research is based on the work of monitoring servers to find out which servers are down / offline or which servers are up / online.&#x0D; This research uses descriptive research method. Descriptive research is generally carried out with the aim of systematically describing the facts and characteristics of the object and subject being studied appropriately. The stages carried out in the descriptive model include stages such as analysis, design, implementation, evaluation, improvement, and development.&#x0D; Based on testing the quality of the software for functionality aspect, the server monitoring information system has fulfilled the overall function with a success percentage of 100%.&#x0D; Based on the results of software quality testing on the usability aspect, it produces a total score of 893 which is converted into an index scale getting a value of 94%. Then a percentage of 94% is converted into a qualitative scale resulting in a feasible rating scale. So it can be concluded that the server monitoring information system has met the usability aspect.&#x0D; The result of this research is an android based software which can monitor server. Users can find out which servers are up / online or which servers are down / offline. Users can monitor server conditions in realtime
The Influence of Remote Work on Turnover Rates : An action research in an IT micro-company in a Brazilian countryside city

Abstract - Technology is the sector with the highest turnover rates worldwide due to high demand for professionals and wage competitiveness, a number that is also expressive in Brazil. The index volume increased after the rise of remote work in the COVID-19 pandemic, developing a global competition for talent. The turnover was even higher in small companies far from metropolitan regions, such as XYZW (fictitious name), a company based in a Brazilian countryside city, which reached 23.07% in the index in this period. Thus, this study seeks to verify the possible impact of the rise of remote work due to the pandemic on the turnover rates of the company X Y Z W and develop an action plan for reducing this turnover. To this end, a study with two cycles of action research was conducted to identify this influence and propose interventions. The findings point out the influence of remote work on the turnover of professionals at XYZW company and up to 382% difference between her salaries and those of the market, in addition to various legal risks, promoting the immediate adherence of the action plan focused on the valorization of the IT sectors of the company. This study confirmed the influence of remote work on the turnover of the company XYZW and the gap that a small IT company outside large urban centers like it can have for the market, generating low competitiveness in the face of remote work and increasing the risks of voluntary turnover.
Multimodal Batch-Wise Change Detection

We address the problem of detecting distribution changes in a novel batch-wise and multimodal setup.This setup is characterized by a stationary condition where batches are drawn from potentially different modalities among a set of distributions in R d represented in the training set.Existing change detection (CD) algorithms assume that there is a unique-possibly multipeaked-distribution characterizing stationary conditions, and in batch-wise multimodal context exhibit either low detection power or poor control of false positives.We present MultiModal QuantTree (MMQT), a novel CD algorithm that uses a single histogram to model the batch-wise multimodal stationary conditions.During testing, MMQT automatically identifies which modality has generated the incoming batch and detects changes by means of a modality-specific statistic.We leverage the theoretical properties of QuantTree to: 1) automatically estimate the number of modalities in a training set and 2) derive a principled calibration procedure that guarantees false-positive control.Our experiments show that MMQT achieves high detection power and accurate control over false positives in synthetic and real-world multimodal CD problems.Moreover, we show the potential of MMQT in Stream Learning applications, where it proves effective at detecting concept drifts and the emergence of novel classes by solely monitoring the input distribution.
Cross-modality-guided contrast enhancement on liver segmentation

Obtaining rapid and accurate segmentation of organs remains an important and challenging task. Liver segmentation algorithms are slow and inaccurate due to noise and low-quality images from abdominal computed tomography (CT) scans. Chan- Vese is an image segmentation method with great robustness against noise, however it is quite slow due to the computation of complex partial differential equations, especially for large medical data sets. The very low contrast of the liver obtained by CT images reduces the overall quality of liver segmentation. In this work we propose two improvements to obtain optimal liver segmentation: contrast enhancement using a guided crossmodality technique as a preprocessing prior to liver segmentation; as well as the GPU paralysis of the Chan-Vese method. The results present a notable improvement in the sensitivity and precision of liver segmentation, as well as a much faster calculation, desirable to obtain liver segmentation in real-time during the surgery.
Using Machine Learning to Analyze Requirements Quality: A Systematic Literature Review

To conceive quality software, it is necessary that, before the development cycle, requirements do not have writing defects that may impact the final software product. Verifying defects in requirements is a challenging task if done manually. Machine learning algorithms are capable of generating their own decisions and results through pattern recognition and data learning. This article aimed to perform a Systematic Literature Review to search for proposals that use Machine Learning algorithms to identify defects in requirement writing. 703 articles were returned by inserting the search string in online databases, of which 11 final papers were selected. According to the results obtained, it was identified that the Random Forest, Bayes Net, and J48 algorithms obtained some of the best results in their models when classifying requirements. Most of the datasets were manually labeled by professionals, ambiguity was the most investigated defect among the selected articles, only one article used an approach to balance the data, and none of the works investigated defects in writing Use Cases and User Stories using Machine Learning.
Exploring Students’ Utilization of Online Public Access Catalog in the Learning Common

This paper presents the results of a survey conducted to explore the usage of the online public access catalog (OPAC) at a higher educational institution in Pagadian City. The study randomly selected two hundred and forty (240) students from Saint Columban College as respondents from various departments. A qualitative case study design was employed to describe, analyze, and interpret the data collected. The researchers used survey questionnaires as the main instrument and ensured proper organization during their administration, including appropriate briefing and guidance. The findings indicate that the majority of library users rarely utilize OPAC services, attributing it to lack of awareness. Although some patrons have used the OPAC, there is room for improvement in promoting its use and increasing awareness among users. It is evident that students have a consistent need for library resources and are actively engaged in academic research or coursework requiring frequent access to library materials. The study further reveals that student participants are dissatisfied with the OPAC services and lack awareness about its functionalities. In conclusion, this study highlights the low usage of OPAC among library users and emphasizes the need to upgrade the current system to provide better services.
Calibration of Drive Nonlinearity for Arbitrary-Angle Single-Qubit Gates Using Error Amplification

The ability to execute high-fidelity operations is crucial to scaling up quantum devices to large numbers of qubits. However, signal distortions originating from nonlinear components in the control lines can limit the performance of single-qubit gates. In this work, we use a measurement based on error amplification to characterize and correct the small single-qubit rotation errors originating from the nonlinear scaling of the qubit drive rate with the amplitude of the programmed pulse. With our hardware, and for a 15-ns pulse, the rotation angles deviate by up to several degrees from a linear model. Using purity benchmarking, we find that control errors reach $2\ifmmode\times\else\texttimes\fi{}{10}^{\ensuremath{-}4}$, which accounts for half of the total gate error. Using cross-entropy benchmarking, we demonstrate arbitrary-angle single-qubit gates with coherence-limited errors of $2\ifmmode\times\else\texttimes\fi{}{10}^{\ensuremath{-}4}$ and leakage below $6\ifmmode\times\else\texttimes\fi{}{10}^{\ensuremath{-}5}$. While the exact magnitude of these errors is specific to our setup, the presented method is applicable to most sources of nonlinearity. Our work shows that the nonlinearity of qubit drive line components imposes a limit on the fidelity of single-qubit gates, independent of improvements in coherence times, circuit design, or leakage mitigation when not corrected for.
Automated Proctoring System to Detect Anomalous Behavior in E-learning During Times of Crisis

A boom in remote learning has happened recently. However, there has not been a good answer for academic exams. Some institutions use online copy-and-paste assignments, while others use remote proctoring, where a human proctor maintains an eye on students’ activities. There must be a solution as if way we live is to become the new norm. In this essay, we have put out a plan for creating an integrated AI-based system that can aid in discouraging exam cheating. The method recognizes fraud and records the evidence. This technology will be both affordable and secure.
GEODETIC EVEN DECOMPOSITION OF GRAPHS Vol. 10(1) Jan 2022, No. 17, pp. 195-199 E. EBIN RAJA MERLY AND T. IWIN JOEL

Let G = (V (G), E(G)) be the graph.For a non empty set S of V (G) we define I[S] = ∪ I[x, y], for some x, y ∈ S, where I[x, y] is the closed interval consists of x, y and all vertices lying on some x -y geodesic of G.If G is a connected graph, then a set S of vertices is a geodetic set ifThe cardinality of a geodetic set is called the geodetic number and is denoted as g(G).The decomposition of a graph G is a collection of edgedisjoint subgraphs G 1 , G 2 , G 3 , ..., Gn of G such that every edge of G belongs to exactly one G i .A decomposition (G 2 , G 4 , ..., G 2n ) of graph G admits Geodetic even decomposition if g(G 2i ) = 2i + 1, i = 1, 2, ..., n where g(G) is the geodetic number of a graph G.
Modeling Biological Face Recognition with Deep Convolutional Neural Networks

Deep convolutional neural networks (DCNNs) have become the state-of-the-art computational models of biological object recognition. Their remarkable success has helped vision science break new ground, and recent efforts have started to transfer this achievement to research on biological face recognition. In this regard, face detection can be investigated by comparing face-selective biological neurons and brain areas to artificial neurons and model layers. Similarly, face identification can be examined by comparing in vivo and in silico multidimensional "face spaces." In this review, we summarize the first studies that use DCNNs to model biological face recognition. On the basis of a broad spectrum of behavioral and computational evidence, we conclude that DCNNs are useful models that closely resemble the general hierarchical organization of face recognition in the ventral visual pathway and the core face network. In two exemplary spotlights, we emphasize the unique scientific contributions of these models. First, studies on face detection in DCNNs indicate that elementary face selectivity emerges automatically through feedforward processing even in the absence of visual experience. Second, studies on face identification in DCNNs suggest that identity-specific experience and generative mechanisms facilitate this particular challenge. Taken together, as this novel modeling approach enables close control of predisposition (i.e., architecture) and experience (i.e., training data), it may be suited to inform long-standing debates on the substrates of biological face recognition.
Cooling microwave fields into general multimode Gaussian states

Abstract We show that a collection of lossy multichromatic modulated qubits can be used to dissipatively engineer arbitrary Gaussian states of a set of bosonic modes. Our ideas are especially suited to superconducting-circuit architectures, where all the required ingredients are experimentally available. The generation of such multimode Gaussian states is necessary for many applications, most notably measurement-based quantum computation. We build upon some of our previous proposals, where we showed how to generate single-mode and two-mode squeezed states through cooling and lasing. Special care must be taken when extending these proposals to many bosonic modes, and we discuss here how to overcome all the limitations and hurdles that naturally appear. For the sake of illustration, we work out two examples of Gaussian-state families consisting of Greenberger–Horne–Zeilinger and cluster states, which allow us to show that it is possible to use a set of N lossy qubits to cool down a bosonic chain of N modes to any desired Gaussian state.