Reviewerly/exHarmony huggingface dataset:
A Serverless Distributed Ledger for Enterprises

Enterprises have been attracted by the capability of blockchains to provide a single source of truth for workloads that span companies, geographies, and clouds while retaining the independence of each party's IT operations.However, so far production applications have remained rare, stymied by technical limitations of existing blockchain technologies and challenges with their integration into enterprises' IT systems.In this paper, we collect enterprises' requirements on distributed ledgers for data sharing and integration from a technical perspective, argue that they are not sufficiently addressed by available blockchain frameworks, and propose a novel distributed ledger design that is "serverless", i.e., built on cloud-native resources.We evaluate its qualitative and quantitative properties and give evidence that enterprises already heavily reliant on cloud service providers would consider such an approach acceptable, particularly if it offers ease of deployment, low transactional cost structure, and a combination of latency and scalability aligned with real-time IT application needs.
Spatial transition management for improving outdoor cinematic augmented reality experience of the TV show

There have been attempts to provide new cinematic experiences by connecting TV or movie content to suitable locations through augmented reality (AR). However, few studies have suggested a method to manage breakdowns in continuity due to spatial transitions. Thus, we propose a method to manage the spatial transition that occurs when we create a TV show trajectory by mapping TV show scenes with spatiotemporal information to the real world. Our approach involved two steps. The first step is to reduce the spatial transition considering the sequence, location, and importance of TV show scenes when creating the TV show trajectory in the authoring tool. The second is to fill the spatial transition with additional TV show scenes considering sequence, importance, and user interest when providing the TV show trajectory in the mobile application. The user study results showed that reducing spatial transition increases narrative engagement by allowing participants to see important content within the trajectory. The additional content in spatial transition decreased the physical demand and effort in terms of the perceived workload, although it increased the task completion time. Integrated spatial transition management improved the overall cinematic augmented reality (CAR) experience of the TV show. Furthermore, we suggest design implications for realizing the CAR of TV shows based on our findings.
FarSpot: Optimizing Monetary Cost for HPC Applications in the Cloud Spot Market

Recently, we have witnessed many HPC applications developed and hosted in the cloud, which can benefit from the elastic and diversified resources on the cloud, while on the other hand confronting high costs for executing the long-running HPC applications. Although public clouds such as Amazon EC2 offer spot instances with dynamic and usually low prices compared to on-demand ones, the spot prices can vary significantly and sometimes can even be more expensive than on-demand prices of the same type. Previous work on reducing the monetary cost for HPC applications using spot instances focused on designing fault tolerance techniques or selecting appropriate instance types/bid prices to make good usage of the low spot prices. However, with the recent update of spot pricing model on Amazon EC2, these work may become either inefficient or invalid. In this paper, we present FarSpot which is an optimization framework for HPC applications in the latest cloud spot market with the goal of minimizing application cost while ensuring performance constraints. FarSpot provides accurate long-term price prediction for a wide range of spot instance types using ensemble-based learning method. It further incorporates a cost-aware deadline assignment algorithm to distribute application deadline to each task according to spot price changes. With the assigned subdeadline of each task, FarSpot dynamically migrates tasks among spot instances to reduce execution cost. Evaluation results using real HPC benchmark show that 1) the prediction error of FarSpot is very low (below 3%), 2) FarSpot reduced the monetary cost by 32% on average compared to state-of-the-art algorithms, and 3) FarSpot satisfies the user-specified deadline constraints at all time.
Greedy Optimization Method for Extractive Summarization of Scientific Articles

This work presents a method for summarizing scientific articles from the arXive and PubMed datasets using a greedy Extractive Summarization algorithm. We used the approach along with Variable Neighborhood Search (VNS) to learn what is the top-line exists in the area of Extractive Text Summarization quality in terms of ROUGE scores. The algorithm is based on first selecting for the summary the sentences from the text containing the maximum number of words with the higher TFIDF values along with minimum document frequency parameter tuning for TFIDF vectorization. As a result, the method achieves 0.43/0.12 and 0.40/0.13 for ROUGE-1/ROUGE-2 scores on arXive and PubMed datasets, respectively. These results are comparable to the state-of-the-art models using complex neural network architectures and serious computational resources together with the large amounts of training data. In contrast, our method uses a straightforward statistical inference methodology.
Challenges (and Opportunities!) of a Remote Agile Software Engineering Project Course During COVID-19

COVID-19 and its immediate impacts on teaching activities have required changes from computer science educators worldwide.We switched our on-site courses to remote setups without detailed knowledge of what tools, techniques, and methods would work in different teaching contexts.A growing amount of experience reports on general best practices for remote teaching in higher education are available.However, university courses featuring practical software development projects present unique challenges regarding remote learning, as effective student collaboration is vital.In these courses, students tackle situations in the project and their team meetings that would also occur in real software projects experienced in industry settings.In this paper, we share our experiences on how we successfully adapted our software engineering project course to a remote setup, which challenges we observed in student teams and how they can be mitigated, and what (surprisingly) worked better than expected.Finally, we propose improvements that we expect will be beneficial not only for future remote-only but also for hybrid or on-site courses.
Deep Embedded Fuzzy Clustering Model for Collaborative Filtering Recommender System

The increasing user of Internet has witnessed a continued exploration in applications and services that can bring more convenience in people's life than ever before.At the same time, with the exploration of online services, the people face unprecedented difficulty in selecting the most relevant service on the fly.In this context, the need for recommendation system is of paramount importance especially in helping the users to improve their experience with best value-added service.But, most of the traditional techniques including collaborative filtering (CF) which is one of the most successful recommendation technique suffer from two inherent issues namely, rating sparsity and cold-start.Inspired by the breakthrough results and great strides of deep learning in a wide variety of applications, this work for the first time proposes a deep clustering model leveraging the strength of deep learning and fuzzy clustering for user-based collaborative filtering (UCF).At first, the proposed model applies deep autoencoder (AE) to learn users' latent feature representation from the original rating matrix and then conducts fuzzy clustering assignments on learnt users' latent representation to form user clusters.More importantly, the proposed model defines a unified objective function combining the reconstruction loss and clustering loss to yield end-toend UCF process that can jointly optimize the process of data representation learning and clustering assignment.The effectiveness of the proposed model is evaluated with two real-world datasets, MoiveLens100K and MovieLens1M using two standard performance metrics.Experimental results of the proposed model demonstrated the prospects for gain in top-K recommendation accuracy against recent related works with resistant to rating sparsity and cold start problems.
Evaluating Research Trends from Journal Paper Metadata, Considering the Research Publication Latency

Investigating the research trends within a scientific domain by analyzing semantic information extracted from scientific journals has been a topic of interest in the natural language processing (NLP) field. A research trend evaluation is generally based on the time evolution of the term occurrence or the term topic, but it neglects an important aspect—research publication latency. The average time lag between the research and its publication may vary from one month to more than one year, and it is a characteristic that may have significant impact when assessing research trends, mainly for rapidly evolving scientific areas. To cope with this problem, the present paper is the first work that explicitly considers research publication latency as a parameter in the trend evaluation process. Consequently, we provide a new trend detection methodology that mixes auto-ARIMA prediction with Mann–Kendall trend evaluations. The experimental results in an electronic design automation case study prove the viability of our approach.
The effects of applying filters on EEG signals for classifying developers’ code comprehension

EEG signals are a relevant indicator for measuring aspects related to human factors in Software Engineering. EEG is used in software engineering to train machine learning techniques for a wide range of applications, including classifying task difficulty, and developers’ level of experience. The EEG signal contains noise such as abnormal readings, electrical interference, and eye movements, which are usually not of interest to the analysis, and therefore contribute to the lack of precision of the machine learning techniques. However, research in software engineering has not evidenced the effectiveness when applying these filters on EEG signals. The objective of this work is to analyze the effectiveness of filters on EEG signals in the software engineering context. As literature did not focus on the classification of developers’ code comprehension, this study focuses on the analysis of the effectiveness of applying EEG filters for training a machine learning technique to classify developers' code comprehension. A Random Forest (RF) machine learning technique was trained with filtered EEG signals to classify the developers' code comprehension. This study also trained another random forest classifier with unfiltered EEG data. Both models were trained using 10-fold cross-validation. This work measures the classifiers' effectiveness using the f-measure metric. This work used the t-test, Wilcoxon, and U Mann Whitney to analyze the difference in the effectiveness measures (f-measure) between the classifier trained with filtered EEG and the classifier trained with unfiltered EEG. The tests pointed out that there is a significant difference after applying EEG filters to classify developers' code comprehension with the random forest classifier. The conclusion is that the use of EEG filters significantly improves the effectivity to classify code comprehension using the random forest technique.
SummerTime: Text Summarization Toolkit for Non-experts

Recent advances in summarization provide models that can generate summaries of higher quality.Such models now exist for a number of summarization tasks, including query-based summarization, dialogue summarization, and multi-document summarization.While such models and tasks are rapidly growing in the research field, it has also become challenging for non-experts to keep track of them.To make summarization methods more accessible to a wider audience, we develop SummerTime by rethinking the summarization task from the perspective of an NLP non-expert.Summer-Time is a complete toolkit for text summarization, including various models, datasets and evaluation metrics, for a full spectrum of summarization-related tasks.SummerTime integrates with libraries designed for NLP researchers, and enables users with easy-touse APIs.With SummerTime, users can locate pipeline solutions and search for the best model with their own data, and visualize the differences, all with a few lines of code.We also provide explanations for models and evaluation metrics to help users understand the model behaviors and select models that best suit their needs.Our library, along with a notebook demo, is available at https://github.com/Yale-LILY/SummerTime.
LMdiff: A Visual Diff Tool to Compare Language Models

While different language models are ubiquitous in NLP, it is hard to contrast their outputs and identify which contexts one can handle better than the other.To address this question, we introduce LMDIFF, a tool that visually compares probability distributions of two models that differ, e.g., through finetuning, distillation, or simply training with different parameter sizes.LMDIFF allows the generation of hypotheses about model behavior by investigating text instances token by token and further assists in choosing these interesting text instances by identifying the most interesting phrases from large corpora.We showcase the applicability of LMDIFF for hypothesis generation across multiple case studies.A demo is available at http://lmdiff.net.
M2ASR-MONGO: A Free Mongolian Speech Database and Accompanied Baselines

Deep learning has significantly improved the performance of automatic speech recognition (ASR), in particular for major languages such as English and Chinese. However, for minor languages such as Mongolian, ASR performance is still limited, mostly due to the restricted amount of speech data. In this paper, we publish a free Mongolian speech database and the associated resources. The entire database involves 170 hours of speech signals produced by 259 native speakers. To our knowledge, this is the largest Mongolian speech database that is publicly available and free so far. We also publish two baseline recipes using the new database. The first one is based on Kaldi and aims to demonstrate the hybrid architecture (DNN-HMM), while the second one is based on Espnet and aims to demonstrate the end-to-end architecture (Transformer). This publication is a part of the M2ASR project, and all the resources are free for the research purpose.
A Blockchain-Based Secure Radio Frequency Identification Ownership Transfer Protocol

Supply chain management (SCM) governance is the streamline of the IoT product life cycle from its production to delivery. Integrating blockchain with supply chain management is essential to ensure end-to-end tracking, trustiness between manufacturers and customers, fraud and counterfeit elimination, and customizing administrative costs and paperwork. This paper proposes an RFID ownership transfer protocol with the help of zk-SNARKs (Zero Knowledge-Succinct Noninteractive Arguments of Knowledge) using Ethereum blockchain. When the owner performs RFID transfer, the transferred information will be recorded on the blockchain using smart contracts. When using a smart contract to transfer ownership on the Ethereum blockchain, because the content on the blockchain will not be tampered with, all accounts in the Ethereum can view the transfer results and verify them. The privacy of the supply chain is attained by generating the proof of product code via zk-SNARKs algorithm. This algorithm also enhances the scalability of the supply chain system by creating a trusted setup in off-chain mode.
Efficient Visualization of Whole Slide Images in Web-based Viewers for Digital Pathology

Wide adoption of digital pathology requires efficient visualization and navigation in Web-based digital slide viewers, which is poorly defined.To define and quantify relevant performance metrics for efficient visualization of cases and slides in digital slide viewers.With a universal slide viewer used in clinical routine diagnostics, we evaluated the impact of slide caching, compression type, tile, and block size of whole slide images generated from Philips, Leica, and 3DHistech scanners on streaming performance on case, slide, and field of view levels.Two hundred thirty-nine pathologists routinely reviewed 60 080 whole slide images over 3 months. The median time to open a case's slides from the laboratory information system was less than 4 seconds, the time to change to a slide within the case was less than 1 second, and the time to render the adjacent field of view when navigating the slide was less than one-quarter of a second. A whole slide image's block size and a viewer tile size of 1024 pixels showed best performance to display a field of view and was preferrable over smaller tiles due to fewer mosaic effects. For Philips, fastest median slide streaming pace was 238 ms per field of view and for 3DHistech, 125 ms. For Leica, the fastest pace of 108 ms per field of view was established with block serving without decompression.This is the first study to systematically assess user-centric slide visualization performance metrics for digital viewers, including time to open a case, time to change a slide, and time to change a field of view. These metrics help to improve the viewer's configuration, leading to an efficient visualization baseline that is widely accepted among pathologists using routine digital pathology.
What’s in Your Head? Emergent Behaviour in Multi-Task Transformer Models

The primary paradigm for multi-task training in natural language processing is to represent the input with a shared pre-trained language model, and add a small, thin network (head) per task.Given an input, a target head is the head that is selected for outputting the final prediction.In this work, we examine the behaviour of non-target heads, that is, the output of heads when given input that belongs to a different task than the one they were trained for.We find that non-target heads exhibit emergent behaviour, which may either explain the target task, or generalize beyond their original task.For example, in a numerical reasoning task, a span extraction head extracts from the input the arguments to a computation that results in a number generated by a target generative head.In addition, a summarization head that is trained with a target question answering head, outputs query-based summaries when given a question and a context from which the answer is to be extracted.This emergent behaviour suggests that multi-task training leads to nontrivial extrapolation of skills, which can be harnessed for interpretability and generalization.
A new hybrid algorithm based on MVO and SA for function optimization

Hybrid algorithms are widely used today to increase the performance of existing algorithms. In this paper, a new hybrid algorithm called IMVOSA that is based on multi-verse optimizer (MVO) and simulated annealing (SA) is used. In this model, a new method called the black hole selection (BHS) is proposed, in which exploration and exploitation can be increased. In the BHS method, the acceptance probability feature of the SA algorithm is used to increase exploitation by searching for the best regions found by the MVO algorithm. The proposed IMVOSA algorithm has been tested on 50 benchmark functions. The performance of IMVOSA has been compared with other latest and well-known metaheuristic algorithms. The consequences show that IMVOSA produces highly successful and competitive results.
Question Text Classification Method of Tourism Based on Deep Learning Model

The Internet of Things applications are diverse in nature, and a key aspect of it is multimedia sensors and devices. These IoT multimedia devices form the Internet of Multimedia Things (IoMT). Compared with the Internet of Things, it generates a large amount of text data with different characteristics and requirements. Aiming at the problems that machine learning and single structure deep learning model cannot effectively grasp the text emotional information in text processing, resulting in poor classification effect, this paper proposes a text classification method of tourism questions based on deep learning model. First, the corpus is trained with word2vec tool based on continuous word bag model to obtain the text word vector representation. Then, the attention mechanism is introduced into the long-short term network (LSTM), and the attention-based LSTM model is constructed for text feature extraction, which highlights the impact of different words in the input text on the text emotion category. Finally, the text features are input into the Softmax classifier to obtain the probability distribution of text categories, and the model is trained combined with the cross entropy loss function. The experimental results show that the average accuracy, recall, and <math xmlns="http://www.w3.org/1998/Math/MathML" id="M1"> <mi>F</mi> </math> value are 0.943, 0.867, and 0.903, respectively, which has better classification effect than other methods.
The Impact of an Intensive English Reading Course Based on the Production-Oriented Approach on the L2 Motivational Self System Among Chinese University English Majors From a Dynamic Systems Theory Perspective

This article reports on a study that took a Dynamic Systems Theory (DST) perspective to second language (L2) motivational self system (L2MSS). More specifically, it investigated the influence of an Intensive English Reading course based on the Production-Oriented Approach (POA) upon the L2MSS of Chinese university English majorsfrom the DST perspective. To this end, two intact classes composed of 50 students were assigned into experimental group (EG) ( N = 23) and control group (CG) ( N = 27), who responded to an L2MSS scale before and after the one-semester intervention. Eight and five students were respectively selected using the purposive sampling method from the experimental and control groups for follow-up semi-structured interviews. The quantitative results revealed that the overall and dimensional (Ideal L2 Self and L2 Learning Experience) levels of L2MSS were significantly strengthened over time in the EG while kept stable in the CG. The qualitative results suggested that the enhanced Ideal L2 Self of the participants stemmed from an attractor basin that was deepened by a number of attractors encompassing Output Tasks and Peer Performance . The interview results also showed that the increased L2 Learning Experience of the participants pertained to an attractor basin that was consolidated by an array of attractors containing Output Tasks , Teacher Guidance , Group Discussion , and Peer Assessment . The findings indicated that the attractors at the subjective and social dimensions in the POA-based course collectively worked together to cause changes in L2MSS among the participants. The implications for intervening L2 motivation from a POA approach in English as a Foreign Language (EFL) classrooms were discussed.