Reviewerly/exHarmony huggingface dataset:
Security and Privacy Concerns in Cloud-based Scientific and Business Workflows: A Systematic Review

Today, the number of data-intensive and compute-intensive applications like business and scientific workflows has dramatically increased, which made cloud computing more popular in the matter of delivering a large amount of computing resources on demand. On the other hand, security is a critical issue affecting the wide adoption of cloud technologies, especially for workflows that are mostly dealing with sensitive data and tasks. In this paper, we carry out a review of the state-of-the-art on how security and privacy concerns in scientific and business workflows in cloud environments are being addressed and identify the limitations and gaps in the current body of knowledge in this area. In this extensive literature review, we first present a classification of the state-of-the-art security solutions organized according to the phases of the workflow life cycle they target. Based on our findings, we provide a detailed review and classification of the most relevant available literature focusing on the execution, monitoring, and adaptation phases of workflows. Finally, we present a list of open research issues related to the security of cloud-based workflows and discuss them.
Data structures for computing unique palindromes in static and non-static strings

Abstract A palindromic substring T[i.. j] of a string T is said to be a shortest unique palindromic substring (SUPS) in T for an interval [p, q] if T [i.. j] is a shortest one such that T [i.. j] occurs only once in T, and [i, j] contains [p, q]. The SUPS problem is, given a string T of length n, to construct a data structure that can compute all the SUPSs for any given query interval. It is known that any SUPS query can be answered in O(α) time after O(n)-time preprocessing, where α is the number of SUPSs to output [Inoue et al., 2018]. In this paper, we first show that α is at most 4, and the upper bound is tight. We also show that the total sum of lengths of minimal unique substrings of string T, which is strongly related to SUPSs, is O(n). Then, we present the first O(n)-bits data structures that can answer any SUPS query in constant time. Also, we present an algorithm to solve the SUPS problem for a sliding window that can answer any query in O(log log W) time and update data structures in amortized O(log σ) time, where W is the size of the window, and σ is the alphabet size. Furthermore, we consider the SUPS problem in the after-edit model and present an efficient algorithm. Namely, we present an algorithm that uses O(n) time for preprocessing and answers any k SUPS queries in O(log n log log n + k log log n) time after single character substitution. Finally, as a by-product, we propose a fully-dynamic data structure for range minimum queries (RmQs) with a constraint where the width of each query range is limited to poly-logarithmic. The constrained RmQ data structure can answer such a query in constant time and support a single-element edit operation in amortized constant time.
DETECTION OF BREAST CARCINOMA USING PYTHON

Breast carcinoma is a disorder in which the breast cells proliferate fast and uncontrollably.Breast carcinoma develops once a malignant (cancerous) tumor forms from the unit cell of the breast.The second most common disease in the world, breast carcinoma primarily affects women.Breast carcinoma affects about 2 million women each year, accounting for the great majority of newly diagnosed cancers and fatalities.Correct categorization helps keep patients out of unnecessary operations.We propose a strategy for assessing the stages of breast carcinoma in this study.We first do K cross-validation on the dataset to decide which approach is best suited for it.The best performance has been SVM.Machine learning approaches for diagnostics can substantially cut costs and speed up processing on a big scale.
FS-NCSR: Increasing Diversity of the Super-Resolution Space via Frequency Separation and Noise-Conditioned Normalizing Flow

Super-resolution suffers from an innate ill-posed problem that a single low-resolution (LR) image can be from multiple high-resolution (HR) images. Recent studies on the flow-based algorithm solve this ill-posedness by learning the super-resolution space and predicting diverse HR outputs. Unfortunately, the diversity of the super-resolution outputs is still unsatisfactory, and the outputs from the flow-based model usually suffer from undesired artifacts which causes low-quality outputs. In this paper, we propose FS-NCSR which produces diverse and high-quality super-resolution outputs using frequency separation and noise conditioning compared to the existing flow-based approaches. As the sharpness and high-quality detail of the image rely on its high-frequency information, FS-NCSR only estimates the high-frequency information of the high-resolution outputs without redundant low-frequency components. Through this, FS-NCSR significantly improves the diversity score without significant image quality degradation compared to the NCSR, the winner of the previous NTIRE 2021 challenge.
Efficient Argument Structure Extraction with Transfer Learning and Active Learning

The automation of extracting argument structures faces a pair of challenges on (1) encoding long-term contexts to facilitate comprehensive understanding, and (2) improving data efficiency since constructing high-quality argument structures is time-consuming. In this work, we propose a novel context-aware Transformer-based argument structure prediction model which, on five different domains, significantly outperforms models that rely on features or only encode limited contexts. To tackle the difficulty of data annotation, we examine two complementary methods: (i) transfer learning to leverage existing annotated data to boost model performance in a new target domain, and (ii) active learning to strategically identify a small amount of samples for annotation. We further propose model-independent sample acquisition strategies, which can be generalized to diverse domains. With extensive experiments, we show that our simple-yet-effective acquisition strategies yield competitive results against three strong comparisons. Combined with transfer learning, substantial F1 score boost (5-25) can be further achieved during the early iterations of active learning across domains.
Is BERT Robust to Label Noise? A Study on Learning with Noisy Labels in Text Classification

Incorrect labels in training data occur when human annotators make mistakes or when the data is generated via weak or distant supervision. It has been shown that complex noise-handling techniques - by modeling, cleaning or filtering the noisy instances - are required to prevent models from fitting this label noise. However, we show in this work that, for text classification tasks with modern NLP models like BERT, over a variety of noise types, existing noisehandling methods do not always improve its performance, and may even deteriorate it, suggesting the need for further investigation. We also back our observations with a comprehensive analysis.
DecBERT: Enhancing the Language Understanding of BERT with Causal Attention Masks

Since 2017, the Transformer-based models play critical roles in various downstream Natural Language Processing tasks. However, a common limitation of the attention mechanism utilized in Transformer Encoder is that it cannot automatically capture the information of word order, so explicit position embeddings are generally required to be fed into the target model. In contrast, Transformer Decoder with the causal attention masks is naturally sensitive to the word order. In this work, we focus on improving the position encoding ability of BERT with the causal attention masks. Furthermore, we propose a new pre-trained language model DecBERT and evaluate it on the GLUE benchmark. Experimental results show that (1) the causal attention mask is effective for BERT on the language understanding tasks; (2) our DecBERT model without position embeddings achieve comparable performance on the GLUE benchmark; and (3) our modification accelerates the pre-training process and DecBERT w/ PE achieves better overall performance than the baseline systems when pre-training with the same amount of computational resources.
Energy Consumption Analysis of Door Opening with a Mobile Manipulator in the Nuclear Power Plants

The problem of rescue is necessary for the mobile manipulator in Nuclear power plants (NPPs), however, the research on energy consumption is of great importance to the mobile manipulator in the process of rescue. In this paper, we focus on the energy consumption of nuclear power plant rescue robot about the task of door-opening, and present a method of path planning of the door opening. The energy consumption model is derived to evaluate the performances of modular manipulators. Extensive door-opening capture experiments have been carried out. The compares of the manipulator summation energy consumption of the different holding positions of knob radius rk, and the energy consumption of each joint in the same path planning are studied. Experimental results show that the minimum summation of manipulator energy consumption is the end-effector grasps the position of door knob, which is the value of rk (40mm), and the third joint of the average energy consumption is the maximal in the same path planning.
Iteratively Selecting an Easy Reference Frame Makes Unsupervised Video Object Segmentation Easier

Unsupervised video object segmentation (UVOS) is a per-pixel binary labeling problem which aims at separating the foreground object from the background in the video without using the ground truth (GT) mask of the foreground object. Most of the previous UVOS models use the first frame or the entire video as a reference frame to specify the mask of the foreground object. Our question is why the first frame should be selected as a reference frame or why the entire video should be used to specify the mask. We believe that we can select a better reference frame to achieve the better UVOS performance than using only the first frame or the entire video as a reference frame. In our paper, we propose Easy Frame Selector (EFS). The EFS enables us to select an 'easy' reference frame that makes the subsequent VOS become easy, thereby improving the VOS performance. Furthermore, we propose a new framework named as Iterative Mask Prediction (IMP). In the framework, we repeat applying EFS to the given video and selecting an 'easier' reference frame from the video than the previous iteration, increasing the VOS performance incrementally. The IMP consists of EFS, Bi-directional Mask Prediction (BMP), and Temporal Information Updating (TIU). From the proposed framework, we achieve state-of-the-art performance in three UVOS benchmark sets: DAVIS16, FBMS, and SegTrack-V2.
Symmetry operators of the asymmetric two-photon quantum Rabi model

The true level crossings in a subspace of the asymmetric two-photon quantum Rabi model (tpQRM) have been observed when the bias parameter of qubit is an even multiple of the renormalized cavity frequency. Generally, such level crossings imply the existence of the hidden symmetry because the bias term breaks the obvious symmetry exactly. In this work, we propose a Bogoliubov operator approach (BOA) for the asymmetric tpQRM to derive the symmetry operators associated with the hidden symmetry hierarchically. The explicit symmetry operators consisting of Lie algebra at low biases can be easily obtained in our general scheme. We believe the present approach can be extended for other asymmetric Rabi models to find the relevant hidden symmetry.
Data Privacy Protection and Utility Preservation through Bayesian Data Synthesis: A Case Study on Airbnb Listings

When releasing record-level data containing sensitive information to the public, the data disseminator is responsible for protecting the privacy of every record in the dataset, simultaneously preserving important features of the data for users' analyses. These goals can be achieved by data synthesis, where confidential data are replaced with synthetic data that are simulated based on statistical models estimated on the confidential data. In this paper, we present a data synthesis case study, where synthetic values of price and the number of available days in a sample of the New York Airbnb Open Data are created for privacy protection. One sensitive variable, the number of available days of an Airbnb listing, has a large amount of zero-valued records and also truncated at the two ends. We propose a zero-inflated truncated Poisson regression model for its synthesis. We utilize a sequential synthesis approach to further synthesize the sensitive price variable. The resulting synthetic data are evaluated for its utility preservation and privacy protection, the latter in the form of disclosure risks. Furthermore, we propose methods to investigate how uncertainties in intruder's knowledge would influence the identification disclosure risks of the synthetic data. In particular, we explore several realistic scenarios of uncertainties in intruder's knowledge of available information and evaluate their impacts on the resulting identification disclosure risks.
Mobile-Assisted Language Learning and Its Effects on Learners’ Speaking Development

This comparative study was to determine the impact of mobile learning classes on English Foreign Language (EFL) learners’ speaking skill development and their motivation to participate in class discussions. The level of students’ satisfaction with this approach was also investigated. 60 students from Kish institute in Ilam, Iran, were assigned through purposive sampling to comparable groups. One group participated in face-to-face classes, and the other took part in mobile learning classes through WhatsApp. Parallel forms of a test were conducted as pretest and post-test for both groups to examine the impact of mobile learning, and a researcher-made questionnaire was distributed through WhatsApp. The obtained data of both groups were analyzed through SPSS 20. The findings revealed that the majority of students were satisfied with mobile learning classes, students in mobile learning classes outperformed the students in face-to-face classes, and that mobile learning classes had a significant role in increasing students’ motivation to participate in class discussions.
Flow direction for analysis of the concentration of metals in the groundwater of the Western Amazon region (Ji-Paraná/RO), Brazil

Abstract Metals refer to one of the main and most dangerous groups of water contaminants, so this research aimed to investigate the groundwater of Dois de Abril micro basin creek regarding the presence of metals and the spatial distribution of the elements regarding the underground flow direction. To that end,15 collections were performed in wells in February, March (High Waters – AA) and September 2019 (Low Waters – AB). The samples were analyzed for 14 inorganic chemicals by means of Inductively Coupled Plasma Optical Emission spectroscopy (ICP-OES). And through the Surfer 8.0 software, the flow direction was obtained using the potentiometric surface of the tubular wells registered in the Groundwater Information System - SIAGAS. Wilcoxon and Spearman tests were applied for the discussion of the results. The averages obtained are within the Maximum Allowed Values – MAV by the national human consumption legislation, but some wells presented high values for aluminum, iron, lead and barium, due to geochemistry and geology of the region, low pH values and the use and occupation of the soil. The trend of the underground flow direction of the municipality contributes to the main river that crosses the city. The flow direction and statistical tests applied were sufficient to discuss the results.
Classical-to-Quantum Transfer Learning for Spoken Command Recognition Based on Quantum Neural Networks

This work investigates an extension of transfer learning applied in machine learning algorithms to the emerging hybrid end-to-end quantum neural network (QNN) for spoken command recognition (SCR). Our QNN-based SCR system is composed of classical and quantum components: (1) the classical part mainly relies on a 1D convolutional neural network (CNN) to extract speech features; (2) the quantum part is built upon the variational quantum circuit with a few learnable parameters. Since it is inefficient to train the hybrid end-to-end QNN from scratch on a noisy intermediate-scale quantum (NISQ) device, we put forth a hybrid transfer learning algorithm that allows a pre-trained classical network to be transferred to the classical part of the hybrid QNN model. The pre-trained classical network is further modified and augmented through jointly fine-tuning with a variational quantum circuit (VQC). The hybrid transfer learning methodology is particularly attractive for the task of QNN-based SCR because low-dimensional classical features are expected to be encoded into quantum states. We assess the hybrid transfer learning algorithm applied to the hybrid classical-quantum QNN for SCR on the Google speech command dataset, and our classical simulation results suggest that the hybrid transfer learning can boost our baseline performance on the SCR task.
S3PRL-VC: Open-source Voice Conversion Framework with Self-supervised Speech Representations

This paper introduces S3PRL-VC, an open-source voice conversion (VC) framework based on the S3PRL toolkit. In the context of recognition-synthesis VC, self-supervised speech representation (S3R) is valuable in its potential to replace the expensive supervised representation adopted by state-of-the-art VC systems. Moreover, we claim that VC is a good probing task for S3R analysis. In this work, we provide a series of in-depth analyses by benchmarking on the two tasks in VCC2020, namely intra-/cross-lingual any-to-one (A2O) VC, as well as an any-to-any (A2A) setting. We also provide comparisons between not only different S3Rs but also top systems in VCC2020 with supervised representations. Systematic objective and subjective evaluation were conducted, and we show that S3R is comparable with VCC2020 top systems in the A2O setting in terms of similarity, and achieves state-of-the-art in S3R-based A2A VC. We believe the extensive analysis, as well as the toolkit itself, contribute to not only the S3R community but also the VC community. The codebase is now open-sourced.
Genuine entanglement, distillability and quantum information masking under noise

Genuineness and distillability of entanglement play a key role in quantum information tasks, and they are easily disturbed by the noise. We construct a family of multipartite states without genuine entanglement and distillability sudden death across every bipartition, respectively. They are realized by establishing the noise as the multipartite high dimensional Pauli channels. Further, we construct a locally unitary channel as another noise such that the multipartite Greenberger-Horne-Zeilinger state becomes the D{\"u}r's multipartite state. We also show that the quantum information masking still works under the noise we constructed, and thus show a novel quantum secret sharing scheme under noise. The evolution of a family of three-qutrit genuinely entangled states distillable across every bipartition under noise is also investigated.
Multilingual Syntax-aware Language Modeling through Dependency Tree Conversion

Incorporating stronger syntactic biases into neural language models (LMs) is a long-standing goal, but research in this area often focuses on modeling English text, where constituent treebanks are readily available. Extending constituent tree-based LMs to the multilingual setting, where dependency treebanks are more common, is possible via dependency-to-constituency conversion methods. However, this raises the question of which tree formats are best for learning the model, and for which languages. We investigate this question by training recurrent neural network grammars (RNNGs) using various conversion methods, and evaluating them empirically in a multilingual setting. We examine the effect on LM performance across nine conversion methods and five languages through seven types of syntactic tests. On average, the performance of our best model represents a 19 \% increase in accuracy over the worst choice across all languages. Our best model shows the advantage over sequential/overparameterized LMs, suggesting the positive effect of syntax injection in a multilingual setting. Our experiments highlight the importance of choosing the right tree formalism, and provide insights into making an informed decision.
TRAINING ON PROPOSAL MAKING OF TRADITIONAL RANGE FESTIVAL SPONSORS IN DUKUH DEMPING, ANGGRASMANIS, JENAWI, KARANGANYAR

Advances in science, technology and science are developing so rapidly. But unfortunately technological progress is not followed by moral, ethical and spiritual progress. Based on the information presented, it can be concluded that the basic problem in the "Community of Tinthir Nusantara" group is the lack of knowledge and understanding of the participants and managers of the traditional snacks festival in making an attractive proposal that will be submitted to the sponsor. Therefore, a program namely the introduction and training of the importance of recognizing Virtual Reality in everyday life as well as in marketing strategies to improve the welfare of the surrounding Community is needed.&#x0D; The training can be used to manage the implementing management as well as promote the activity in order to attract sponsors, so people from other region will interest to join the festival. With this technology, management is expected to be able to improve managerial skills and carry out promotions outside the region. In addition, this training serves as a means of information for the Demping Tourism Village and serves as material for evaluating activities by the manager of the Demping Tourism Village for the next implementation.
EVALUATION OF TRAINING FOR CONTRACEPTION SERVICES TRAINER FOR DOCTORS AND MIDWIFES IN HEALTH FACILITIESThe training for trainers on contraceptive services for doctors and midwives at Health Facilities was carried out for 14 days, using the blended learnin

The training for trainers on contraceptive services for doctors and midwives at Health Facilities was carried out for 14 days, using the blended learning method. Starting with the online method for 9 days, using zoom. Starting from July 4 to July 14, 2022. Participants complete their assignments and then upload them on Google Drive. Then the offline meeting which took place at the Sardjito Hospital Yogyakarta. It was held for 5 days on August 1-5 2022. The training participants consisted of 17 people, 5 people from West Java, 5 people from Papua, 5 people from West Papua and 2 people from Sardjito Hospital. Educational background of the participants who specialize in midwifery from the Indonesian Association of Genecology Organizations (POGI) and midwives from the Indonesian Midwives Association (IBI). Organization The learning method uses question and answer lectures, discussions, and simulations carried out in class, where participants are divided into 3 groups. The first group was West Java plus 1 person from Sardjito, group 2 from Papua 5 people plus 1 from Sardjito Hospital and group 3 consisted of 5 people from West Papua. Participants put the contraceptive IUD, implant, new method of condom on the mannequin. Furthermore, the practice of installing contraceptives at the Sardjito Hospital.