Reviewerly/exHarmony huggingface dataset:
H3CSA: A makespan aware task scheduling technique for cloud environments

Abstract Task scheduling is essential to enhance the performance of large scale collaborative and distributed e‐business and e‐science applications. A typical such application comprises multiple communicating tasks to be executed on virtualized resources also called as virtual machines (VMs). However, scheduling multiple tasks to VMs is non‐trivial and is proven to be NP‐Complete. The primary agenda of any scheduling technique is to reduce the makespan , which reflects the completion time of the exit task. Focusing on makespan reduction for scheduling multiple tasks across heterogeneous VMs, in this paper, we propose a model called H3CSA based on a meta‐heuristic crow search algorithm (CSA). In order to assess the performance of H3CSA , its performance is compared with baselines such as HEFT‐B, HEFT‐T, HEFT‐L, a genetic algorithm based technique (N‐GA) and particle swarm optimization based technique (PPSO). Simulation results show improved performance of H3CSA with respect to N‐GA and PPSO for smaller applications. However, considering sizeable applications H3CSA shows comparable performance with respect to N‐GA and improved results for PPSO.
A Grammar of Digital Fabrication Machines

Digital fabrication tools for makers have increased access to manufacturing processes such as 3D printing and computer-controlled laser cutting or milling. Despite research advances in novel hardware and software tools for fabrication tasks, there is no formal way to reason about the fabrication machine itself. There is no standard format for representing the high-level features of machines and trade-offs between them; instead, this important information is relegated to folk knowledge. To make machine information explicit, we present Taxon, a machine specification language broad enough to represent many machines, while also allowing for enough expressivity to meaningfully compare and infer performance. We describe and detail the motivation behind the design of Taxon, as well as how Taxon programs compile to a simulation of physical machines. We discuss opportunities for future work in digital fabrication that requires a standard, formalized representation of machines.
Compositionally-warped additive mixed modeling for a wide variety of non-Gaussian spatial data

As with the advancement of geographical information systems, non-Gaussian spatial data sets are getting larger and more diverse. This study develops a general framework for fast and flexible non-Gaussian regression, especially for spatial/spatiotemporal modeling. The developed model, termed the compositionally-warped additive mixed model (CAMM), combines an additive mixed model (AMM) and the compositionally-warped Gaussian process to model a wide variety of non-Gaussian continuous data including spatial and other effects. A specific advantage of the proposed CAMM is that it requires no explicit assumption of data distribution unlike existing AMMs. Monte Carlo experiments show the estimation accuracy and computational efficiency of CAMM for modeling non-Gaussian data including fat-tailed and/or skewed distributions. Finally, the model is applied to crime data to examine the empirical performance of the regression analysis and prediction. The result shows that CAMM provides intuitively reasonable coefficient estimates and outperforms AMM in terms of prediction accuracy. CAMM is verified to be a fast and flexible model that potentially covers a wide variety of non-Gaussian data modeling.
Advance Prediction of Maryland Elective Admission Fatalities Using Machine Learning

Elective hospital admissions are those admissions that are planned in advance by the physician in consultation with the patient to address a non-urgent health condition. As such, there would not be an expectation of patient death. However, data demonstrates that there is a possibility of death, which can be in the order of 1% of elective admissions as in the case of the current study's dataset. Due to the discretionary nature of the admission, the ability to predict mortality in advance, for example at the time of scheduling the admission, would provide a critical life-saving tool. In this study we utilized machine learning techniques to predict elective admission mortality from a large dataset of approximately 600,000 Maryland hospital inpatient admissions from 2018. The results demonstrate high predictive performance, with the best performing model achieving an AUC of 0.95 and demonstrating strong performance maintenance into the future. This is indicative of the high practical value of the utilized technique, noting that in the order of 1% of elective admissions represents tens of thousands of elective patient in-hospital fatalities in the US per year.
Technical Leverage: Dependencies Are a Mixed Blessing

One year ago, Mack and Schroer captured in this column1 the current crisis faced by software security quality control:
Pseudorandom sequences derived from automatic sequences

Many automatic sequences, such as the Thue-Morse sequence or the Rudin-Shapiro sequence, have some desirable features of pseudorandomness such as a large linear complexity and a small well-distribution measure. However, they also have some undesirable properties in view of certain applications. For example, the majority of possible binary patterns never appears in automatic sequences and their correlation measure of order 2 is extremely large. Certain subsequences, such as automatic sequences along squares, may keep the good properties of the original sequence but avoid the bad ones. In this survey we investigate properties of pseudorandomness and non-randomness of automatic sequences and their subsequences and present results on their behaviour under several measures of pseudorandomness including linear complexity, correlation measure of order k, expansion complexity and normality. We also mention some analogs for finite fields.
Semi-Supervised Multimodal Image Translation for Missing Modality Imputation

Missing data is a common problem in multimodal and multi-view learning. It raises a critical challenge for most multimodal algorithms, which are unable to deal with incomplete datasets. Rather than discarding entries with missing modalities, this paper aims to reconstruct the complete image-based multimodal data by imputing missing modalities. We solve the imputation problem as an image translation task, which transforms images in one domain to other domains. Existing image translation techniques either can not fully utilize the information contained in partially complete entries or are limited to the bimodal situation. We propose a semi-supervised algorithm for multimodal learning with missing data, namely Cyclic Autoencoder (CycAE). Specifically, a novel cyclical structure, as well as the correlation among modalities, is integrated to leverage infoπnation from complete entries to incomplete ones. Experiments on two multimodal datasets show that our model outperforms state-of-the-art models. Downstream tasks can also benefit from the completed datasets.
Multisensory Teleportation in Virtual Reality Applications

This position paper aims to briefly summarise existing research in vection, teleportation and multisensory stimuli, present our cross-disciplinary research setup and argue towards the importance of discussing haptic feedback design in Virtual Reality (VR) locomotion techniques. In particular, haptic feedback stimulation has been shown to enhance the perception of-self motion when applied to various parts of the body. The recent developments of haptic devices opens the possibilities to explore "whole-body" haptics in virtual environments for locomotion techniques. We argue that crossmodal stimulation frameworks that have been already applied to study self-motion in VR could potentially provide benefits to locomotion studies.
XSnare: Application-specific client-side cross-site scripting protection

We present XSnare, a client-side Cross-Site Scripting (XSS) solution implemented as a Firefox extension. The client-side design of XSnare can protect users before application developers release patches and before server operators apply them.XSnare blocks XSS attacks by using previous knowledge of a web application&#x2019;s HTML template content and the rich DOM context. XSnare uses a database of exploit descriptions, which are written with the help of previously recorded CVEs. It singles out injection points for exploits in the HTML and dynamically sanitizes content to prevent malicious payloads from appearing in the DOM. XSnare displays a secured version of the site, even if is exploited.We evaluated XSnare on 81 recent CVEs related to XSS attacks, and found that it defends against 93.8% of these exploits. To the best of our knowledge, XSnare is the first protection mechanism for XSS that is application-specific, and based on publicly available CVE information. We show that XSnare&#x2019;s specificity protects users against exploits which evade other, more generic, XSS defenses.Our performance evaluation shows that our extension&#x2019;s overhead on web page loading time is less than 10% for 72.6% of the sites in the Moz Top 500 list.
Selection Based on Statistical Characteristics for Object Detection

In the domain of object detection, automatically selecting positive and negative samples methods have become a hot research topic in recent years. However, most of them focus on improving the sampling process but ignore the relationship between object size and feature map, in which the shallow and deep feature layers can capture small and large size objects well respectively. In this paper, we propose a multi-scale sample selection based on statistical characteristics for object detection. To improve the robustness of the Intersection over Union (IoU) threshold, we design a multi-scale sample selection module (MSSM), which takes full advantage of different feature layers. Besides, we introduce a multi-scale attention module (MSAM) by embedding in the feature pyramid networks (FPN) to improve the efficiency of feature fusion. Experiments on MS COCO dataset demonstrate that our method achieves significant improvement over the state-of-the-art methods.
Lattice-based weak-key analysis on single-server outsourcing protocols of modular exponentiations and basic countermeasures

We investigate the problem of securely outsourcing the modular exponentiations in cryptography to an untrusted server, and analyze the security and the efficiency of three privacy-preserving outsourcing protocols for exponentiations proposed in Ding et al. (2017) [18]. Based on Coppersmith's lattice-based method, we present heuristic polynomial-time and ciphertext-only weak-key attacks on these protocols, which shows that the recommended size of the secret keys in their protocols can not assure the input privacy of the exponents. Correspondingly, we explicitly estimate the size of the secure secret keys to circumvent our attacks, and analyze the efficiency of the revised protocols with security settings. Our theoretical analysis and experimental results demonstrate that the protocol of single modular exponentiation is unavailable, the protocol of simultaneous modular exponentiations is not so efficient as claimed but still available, and the protocol of multiple modular exponentiations becomes more efficient as the number of exponentiations increases.
A Finetuned Language Model for Recommending cQA-QAs for Enriching Textbooks

Textbooks play a vital role in any educational system, despite their clarity and information, students tend to use community question answers (cQA) forums to acquire more knowledge. Due to the high data volume, the quality of Question-Answers (QA) of cQA forums can differ greatly, so it takes additional effort to go through all possible QA pairs for a better insight. This paper proposes an “sentence-level text enrichment system” where the fine-tuned BERT (Bidirectional Encoder Representations from Transformers) summarizer understands the given text, picks out the important sentence, and then rearranged them to give the overall summary of the text document. For each important sentence, we recommend the relevant QA pairs from cQA to make the learning more effective. In this work, we fine-tuned the pre-trained BERT model to extract the relevant QA sets that are most relevant for enriching important sentences of the textbook. We notice that fine-tuning the BERT model significantly improves the performance for QA selection and find that it outperforms existing RNN-based models for such tasks. We also investigate the effectiveness of our fine-tuned BERT\(_\mathrm{Large}\) model on three cQA datasets for the QA selection task and observed a maximum improvement of 19.72% compared to the previous models. Experiments have been carried out on NCERT (Grade IX and X) Textbooks from India and “Pattern Recognition and Machine Learning” Textbook. The extensive evaluation methods demonstrate that the proposed model offers more precise and relevant recommendations in comparison to the state-of-the-art methods.
Simpleflat: A Simple Whole-Network Pre-Training Approach for RNN Transducer-Based End-to-End Speech Recognition

Recurrent neural network-transducer (RNN-T) is promising for building time-synchronous end-to-end automatic speech recognition (ASR) systems, in part because it does not need frame-wise alignment between input features and target labels in the training step. Although training without alignment is beneficial, it makes it difficult to discern the relation between input features and output token sequences. This, in effect, degrades RNN-T performance. Our solution is SimpleFlat (SF), a novel and simple whole-network pretraining approach for RNN-T. SF extracts frame-wise alignments on-the-fly from the training dataset, and does not require any external resources. We distribute equal numbers of target tokens to each frame following RNN-T encoder output lengths by repeating each token. The frame-wise tokens so created are shifted, and also used as the prediction network inputs. Therefore, SF can be implemented by cross entropy loss computation as in autoregressive model training. Experiments on Japanese and English ASR tasks demonstrate that SF can effectively improve various RNN-T architectures.
A Meta-Analysis of Effect Sizes of CHI Typing Experiments

While designing an HCI experiment, planning the sample size with a priori power analysis is often skipped due to the lack of reference effect sizes. On the one hand, it can lead to a false-negative result, missing the effect that is present in the population. On the other hand, it poses a risk of spending more resources if the number of participants is too high. In this work, I present the reference for small, medium, and large effect sizes for typing experiments based on a meta-analysis of well-cited papers from CHI conference. This effect size ruler can be used to conduct a priori power analysis or assess the magnitude of the found effect. This work also includes comparisons to other fields and conclude with a discussion of the existing issues with reporting practices and data availability. This paper and all data and materials are freely available at https://osf.io/nqzpr.
Automatized Educational Chatbot using Deep Neural Network

Most of the time, students must visit universities or web pages to collect various information e.g. semester schedule, grade, curriculum, advisor info, etc. This process is time consuming and requires manpower providing such required information. Nowadays, social media and online services such as email, direct message, or live chat are becoming popular. Many students use live chat service because it is easy, fast and comfortable. However, there are some issues occurred an extensive amount of valuable time to answer by admin are wasted. Hence, in order to solve the problem properly, this article proposes an approach for building a question-answering system via chatbot technology. Because chatbots is an enable human-machine interactions with the system by the human language, which is very intuitive and user-friendly. This research objective was to develop a question-answer system in Thai using chatbot technology, which was integrated with the LINE mobile application. The experiment added a keyword in the educational domain to the dictionary and comparing word segmentation for 3 methods with text classification, combination of natural language processing (NLP) and Deep Neural Network (DNN) are used in the form of intent for text classification in this article.
Home Studio: A Mixed Reality Staging Tool for Interior Design

Home design services employ virtual real estate staging to visually convey proposals to prospective customers, instilling confidence in property owners on how spaces will look once they are furnished. However, creating room-scale visualizations requires days of expert labor to composite products on images provided by the customer. Advances in 3D capture now let users scan their spaces with a smartphone in a matter of minutes, enabling scale-accurate mixed reality experiences that can be leveraged to lower the skill bar and time required to produce visualizations of furniture in the user's context. We present Home Studio, a web-based tool that lets designers stage any Matterport scan and generate photorealistic renders of products in the user's context. Our tool enables a drag and drop experience that reconstructs the perspective view of the Matterport scene in a remote service, employing a rendering engine to produce a photorealistic composite.
G-Repo: a Tool to Support MSR Studies on GitHub

GitHub currently hosts more than 100 million public repositories. This has made it very popular to conduct Mining Software Repositories (MSR) studies. Researchers have been exploiting the information stored in GitHub (e.g., commits, pull requests, or issues) to investigate both developer- and project-related aspects. GitHub provides the REST API to make queries without cloning repositories. In this tool-demo paper, we highlight some issues we noticed when conducting an MSR study on GitHub by using the REST API and present G-Repo: a tool developed to support researchers when tackling these issues able to ease the creation of datasets for MSR studies. Also, we provide a manually-annotated dataset with information about the kind and the (spoken) languages of 1,500 repositories hosted on GitHub. A video showing the functioning of G-Repo is available at: https://youtu.be/mb9CIALBFZk.
High Precision Multiplier for RNS {2n−1,2n,2n+1}

The Residue Number System (RNS) is a non-weighted number system. Benefiting from its inherent parallelism, RNS has been widely studied and used in Digital Signal Processing (DSP) systems and cryptography. However, since the dynamic range in RNS has been fixed by its moduli set, it is hard to solve the overflow problem, which can be easily solved in Two’s Complement System (TCS) by expanding the bit-width of it. For the multiplication in RNS, the traditional way to deal with overflow is to scale down the inputs so that the result can fall in its dynamic range. However, it leads to a loss of precision. In this paper, we propose a high-precision RNS multiplier for three-moduli set 2n−1,2n,2n+1, which is the most used moduli set. The proposed multiplier effectively improves the calculation precision by adding several compensatory items to the result. The compensatory items can be obtained directly from preceding scalers with little extra effort. To the best of our knowledge, we are the first one to propose a high-precision RNS multiplier for the moduli set 2n−1,2n,2n+1. Simulation results show that the proposed RNS multiplier can get almost the same calculation precision as the TCS multiplier with respect to Mean Square Error (MSE) and Signal-to-Noise Ratio(SNR), which outperforms the basic scaling RNS multiplier about 2.6–3 times with respect to SNR.
Learned Transferable Architectures Can Surpass Hand-Designed Architectures for Large Scale Speech Recognition

In this paper, we explore the neural architecture search (NAS) for automatic speech recognition (ASR) systems. We conduct the architecture search on the small proxy dataset, and then evaluate the network, constructed from the searched architecture, on the large dataset. Specially, we propose a revised search space that theoretically facilitates the search algorithm to explore the architectures with low complexity. Extensive experiments show that: (i) the architecture learned in the revised search space can greatly reduce the computational overhead and GPU memory usage with mild performance degradation. (ii) the searched architecture can achieve more than 15% (average on the four test sets) relative improvements on the large dataset, compared with our best hand-designed DFSMN-SAN architecture. To the best of our knowledge, this is the first report of NAS results with a large scale dataset (up to 10K hours), indicating the promising application of NAS to industrial ASR systems.
Performance Comparison of Naïve Bayes and Neural Network in Predicting Student Violation

Education is a process of building human character to become more civilized and knowledgeable. The purpose of education is to enable students to develop and control themselves. However, in the process of implementing it, many students committed some violations. Therefore, this study aims to enable school authorities to easily predict students who tend to break the rules. In a previous study, the Naïve Bayes algorithm was widely used to make this kind of prediction. Therefore, this study will compare Naive Bayes with the Neural Network algorithm to determine which algorithm is the most optimal for predicting student tendency in committing a violation. The experiment's accuracy of the Neural Network algorithm is slightly higher than the Naïve Bayes algorithm, which is 98.76% versus 97.84%.
Entangled Materialities

This paper discusses craft and design practices through their impact on the environment. We consider how to act concerning the consequences of the craft and design industry. Also, we reflect on the agency of our field of practice in changing how we perceive the environment. We present three case studies of the European glass industry sites in Sweden, Italy and Finland, where we study contamination of the soil with participatory, speculative and craft methods. Through these cases, we reflect on our role in soil communities and ask how we may act in them with responsibility, hope and care. We conclude by proposing to act locally, to share our practices and make them visible, expanding our situated, personal skills and knowledge towards the political.
A Deep Knowledge Tracking Model Integrating Difficulty Factors

With the rapid development of artificial intelligence education, it is of great significance to establish a scientific and effective model of learners' knowledge mastery state. Knowledge tracking (KT) as a modeling method for learners to infer knowledge level based on their learning achievements, plays a key role in intelligent education system. There are some problems in the existing knowledge tracking models, such as incomplete input features and unstable model. In order to improve the accuracy of the prediction of learners' learning effect, this paper proposes a deep knowledge tracking model (DDKT) based on the comprehensive difficulty of the topic. The model mainly includes two parts: on the one hand, the construction of the comprehensive difficulty model, on the other hand, the improvement of the model. The model combines topic difficulty with deep knowledge tracking model, enriches learners' learning process dimensions and improves the generalization ability of the model. Through comparative experiments, it is proved that the DDKT model has achieved better results.