Reviewerly/exHarmony huggingface dataset:
A Novel Sequential Pattern Mining Algorithm for Large Scale Data Sequences

Sequential pattern mining algorithms are unsupervised machine learning algorithms that allow finding sequential patterns on data sequences that have been put together based on a particular order. These algorithms are mostly optimized for finding sequential data sequences containing more than one element. Hence, we argue that there is a need for algorithms that are particularly optimized for data sequences that contain only one element. Within the scope of this research, we study the design and development of a novel algorithm that is optimized for data sets containing data sequences with single elements and that can detect sequential patterns with high performance. The time and memory requirements of the proposed algorithm are examined experimentally. The results show that the proposed algorithm has low running times, while it has the same accuracy results as the algorithms in the similar category in the literature. The obtained results are promising.
Aspect term extraction via information-augmented neural network

Abstract Aspect term extraction (ATE) aims at identifying the aspect terms that are expressed in a sentence. Recently, Seq2Seq learning has been employed in ATE and significantly improved performance. However, it suffers from some weaknesses, such as lacking the ability to encode the more informative information and integrate information of surrounding words in the encoder. The static word embeddings employed in ATE fall short of modeling the dynamic meaning of words. To alleviate the problems mentioned above, this paper proposes the information-augmented neural network (IANN) which is a novel Seq2Seq learning framework. In IANN, a specialized neural network is developed as the key module of the encoder, named multiple convolution with recurrence network (MCRN), to encode the more informative information and integrate information of surrounding words in the encoder. The contextualized embedding layer is designed to capture the dynamic word sense. Besides, the novel AO ({ A spect, O utside}) tags are proposed as the less challenging tagging scheme. A lot of experiments have been performed on three widely used datasets. These experiments demonstrate that the proposed IANN acquires state-of-the-art results and validate that the proposed IANN is a powerful method for the ATE task.
Creating Audio Object-Focused Acoustic Environments for Room-Scale Virtual Reality

Room-scale virtual reality (VR) affordance in movement and interactivity causes new challenges in creating virtual acoustic environments for VR experiences. Such environments are typically constructed from virtual interactive objects that are accompanied by an Ambisonic bed and an off-screen (“invisible”) music soundtrack, with the Ambisonic bed, music, and virtual acoustics describing the aural features of an area. This methodology can become problematic in room-scale VR as the player cannot approach or interact with such background sounds, contradicting the player’s motion aurally and limiting interactivity. Written from a sound designer’s perspective, the paper addresses these issues by proposing a musically inclusive novel methodology that reimagines an acoustic environment predominately using objects that are governed by multimodal rule-based systems and spatialized in six degrees of freedom using 3D binaural audio exclusively while minimizing the use of Ambisonic beds and non-diegetic music. This methodology is implemented using off-the-shelf, creator-oriented tools and methods and is evaluated through the development of a standalone, narrative, prototype room-scale VR experience. The experience’s target platform is a mobile, untethered VR system based on head-mounted displays, inside-out tracking, head-mounted loudspeakers or headphones, and hand-held controllers. The authors apply their methodology to the generation of ambiences based on sound-based music, sound effects, and virtual acoustics. The proposed methodology benefits the interactivity and spatial behavior of virtual acoustic environments but may be constrained by platform and project limitations.
Wrap reduction algorithm for fringe projection profilometry

Carrier removal has been typically associated with wrap reduction, and its application has strongly been recommended as a previous step to phase unwrapping. In general, the carrier signal contributes to an increase of the number of wraps, and its removal leads to a reduction of the number of phase inconsistencies, having a positive impact on the subsequent unwrapping operation. In this paper, we show that phase wrap reduction can be better tackled by other alternative and more effective methods that exploit local information and attempt to also remove the signal component associated with the object’s surface. The results are analyzed on a series of real fringe patterns and confirm the better performance of the proposed method, which leads to signals with a lesser number of wraps than by just removing the carrier.
Automatic question answering for multiple stakeholders, the epidemic question answering dataset

One of the effects of COVID-19 pandemic is a rapidly growing and changing stream of publications to inform clinicians, researchers, policy makers, and patients about the health, socio-economic, and cultural consequences of the pandemic. Managing this information stream manually is not feasible. Automatic Question Answering can quickly bring the most salient points to the user's attention. Leveraging a collection of scientific articles, government websites, relevant news articles, curated social media posts, and questions asked by researchers, clinicians, and the general public, we developed a dataset to explore automatic Question Answering for multiple stakeholders. Analysis of questions asked by various stakeholders shows that while information needs of experts and the public may overlap, satisfactory answers to these questions often originate from different information sources or benefit from different approaches to answer generation. We believe that this dataset has the potential to support the development of question answering systems not only for epidemic questions, but for other domains with varying expertise such as legal or finance.
The Application of Adaptive Tolerance and Serialized Facial Feature Extraction to Automatic Attendance Systems

The aim of this study was to develop a real-time automatic attendance system (AAS) based on Internet of Things (IoT) technology and facial recognition. A Raspberry Pi camera built into a Raspberry Pi 3B is used to transfer facial images to a cloud server. Face detection and recognition libraries are implemented on this cloud server, which thus can handle all the processes involved with the automatic recording of student attendance. In addition, this study proposes the application of data serialization processing and adaptive tolerance vis-à-vis Euclidean distance. The facial features encountered are processed using data serialization before they are saved in the SQLite database; such serialized data can easily be written and then read back from the database. When examining the differences between the facial features already stored in the SQLite databases and any new facial features, the proposed adaptive tolerance system can improve the performance of the facial recognition method applying Euclidean distance. The results of this study show that the proposed AAS can recognize multiple faces and so record attendance automatically. The AAS proposed in this study can assist in the detection of students who attempt to skip classes without the knowledge of their teachers. The problem of students being unintentionally marked present, though absent, and the problem of proxies is also resolved.
Emojis and conditionals: exploring the super linguistic interplay of pictorial modifiers and conditional meaning

Abstract In recent years, formal linguistic analysis has expanded its scope to include objects of study beyond natural language, under the umbrella of Super Linguistics (where the intended meaning of super is its Latinate meaning ‘beyond’); see (Patel-Grosz et al. 2022. Super linguistics: An introduction. Unpublished manuscript, April 2022 version. Available at: https://ling.auf.net/lingbuzz/005242 ). One super linguistic object of study is emojis, which can be analyzed as digital counterparts of gestures and facial expressions, but which also share properties with natural language expressions such as alas and unfortunately (Grosz, Patrick Georg, Gabriel Greenberg, Christian De Leon &amp; Elsi Kaiser. 2021b. A semantics of face emoji in discourse . Manuscript, December 2021 version. https://ling.auf.net/lingbuzz/005981 (Accepted with minor revisions for publication in Linguistics and Philosophy)). In this paper, I use conditionals as a case study to argue that natural language semantics can benefit from investigating the semantics of emojis. I start by arguing that face emojis (disk-shaped pictographs with stylized facial expressions) operate on contextually salient propositions. I show that they can comment on the presuppositions of wh -questions and definite descriptions, but not on conversational implicatures. I then show that face emojis can also comment on the counterfactual inferences of subjunctive conditionals (or, more broadly, subjunctive if -clauses). This suggests that these counterfactual inferences may be presupposition-like and not, as widely assumed, an instance of implicature (see Zakkou, Julia. 2019. Presupposing counterfactuality. Semantics and Pragmatics 12(21). 1–20, for recent discussion). The study of emojis, a nonstandard object for linguistic inquiry, can thus directly inform more traditional linguistic exploration.
Detecting and Locating Aftershocks for the 2020 Mw 6.5 Stanley, Idaho, Earthquake Using Convolutional Neural Networks

Abstract Our study is to build an aftershock catalog with a low magnitude of completeness for the 2020 Mw 6.5 Stanley, Idaho, earthquake. This is challenging because of the low signal-to-noise ratios for recorded seismograms. Therefore, we apply convolutional neural networks (CNNs) and use 2D time–frequency feature maps as inputs for aftershock detection. Another trained CNN is used to automatically pick P-wave arrival times, which are then used in both nonlinear and double-difference earthquake location algorithms. Our new one-month-long catalog has 4644 events and a completeness magnitude (Mc) 1.9, which has over seven times more events and 0.9 lower Mc than the current U.S. Geological Survey National Earthquake Information Center catalog. The distribution and expansion of these aftershocks improve the resolution of two north-northwest-trending faults with different dip angles, providing further support for a central stepover region that changed the earthquake rupture trajectory and induced sustained seismicity.
MCO Plan: Efficient Coverage Mission for Multiple Micro Aerial Vehicles Modeled as Agents

Micro aerial vehicle (MAV) fleets have gained essential recognition in the decision schemes for precision agriculture, disaster management, and other coverage missions. However, they have some challenges in becoming massively deployed. One of them is resource management in restricted workspaces. This paper proposes a plan to balance resources when considering the practical use of MAVs and workspace in daily chores. The coverage mission plan is based on five stages: world abstraction, area partitioning, role allocation, task generation, and task allocation. The tasks are allocated according to agent roles, Master, Coordinator, or Operator (MCO), which describe their flight autonomy, connectivity, and decision skill. These roles are engaged with the partitioning based on the Voronoi-tessellation but extended to heterogeneous polygons. The advantages of the MCO Plan were evident compared with conventional Boustrophedon decomposition and clustering by K-means. The MCO plan achieved a balanced magnitude and trend of heterogeneity between both methods, involving MAVs with few or intermediate resources. The resulting efficiency was tested in the GAMA platform, with gained energy between 2% and 10% in the mission end. In addition, the MCO plan improved mission times while the connectivity was effectively held, even more, if the Firefly algorithm generated coverage paths.
Deep Model-Based Security-Aware Entity Alignment Method for Edge-Specific Knowledge Graphs

This paper proposes a deep model-based entity alignment method for the edge-specific knowledge graphs (KGs) to resolve the semantic heterogeneity between the edge systems’ data. To do so, this paper first analyzes the edge-specific knowledge graphs (KGs) to find unique characteristics. The deep model-based entity alignment method is developed based on their unique characteristics. The proposed method performs the entity alignment using a graph which is not topological but data-centric, to reflect the characteristics of the edge-specific KGs, which are mainly composed of the instance entities rather than the conceptual entities. In addition, two deep models, namely BERT (bidirectional encoder representations from transformers) for the concept entities and GAN (generative adversarial networks) for the instance entities, are applied to model learning. By utilizing the deep models, neural network models that humans cannot interpret, it is possible to secure data on the edge systems. The two learning models trained separately are integrated using a graph-based deep learning model GCN (graph convolution network). Finally, the integrated deep model is utilized to align the entities in the edge-specific KGs. To demonstrate the superiority of the proposed method, we perform the experiment and evaluation compared to the state-of-the-art entity alignment methods with the two experimental datasets from DBpedia, YAGO, and wikidata. In the evaluation metrics of Hits@k, mean rank (MR), and mean reciprocal rank (MRR), the proposed method shows the best predictive and generalization performance for the KG entity alignment.
SCAL-E: An Auto Scaling Agent for Optimum Big Data Load Balancing in Kubernetes Environments

During the past years, the issue of effectively balancing incoming big data streams has been under serious research. It still allows for new solutions, even if load balancing is already being addressed by multiple frameworks. This paper proposes a smart agent, named “SCAL-E“ that achieves balancing of big data loads and lives within the Kubernetes Environment. SCAL-E takes advantage of MongoDB’s scaling, replicating & sharding capabilities and decides when to increase or decrease its repository’s sub-components, based on the incoming load. This way, SCAL-E assures of proper resource allocation and gives efficiency to the jobs of big data storing & forwarding.
Hits and Misses: Newcomers' ability to identify Skills needed for OSS tasks

Participation in Open Source Software (OSS) projects offers real software development experience for students and other newcomers seeking to develop their skills. However, onboarding to an OSS project brings various challenges, including finding a suitable task among various open issues. Selecting an appropriate starter task requires newcomers to identify the skills needed to solve a project issue and avoiding tasks too far from their skill set. However, little is known about how effective newcomers are in identifying the skills needed to resolve an issue. We asked 154 undergrad students to evaluate issues from OSS projects and infer the skills needed to contribute. Students reported a total of 94 skills, which we classified into 10 categories. We compared the students&#x0027; answers to those collected from 6 professional developers. In general, students misidentified and missed several skills (f-measure&#x003D;0.37). Students had results closer to professional developers for skills related to database, operating infrastructure, programming concepts, and programming language, and they had worse results in identifying skills related to debugging and program comprehension. Our results can help educators who seek to use OSS as part of their courses and OSS communities that want to label newcomer-friendly issues to facilitate onboarding of new contributors.
Generating Clarifying Questions for Query Refinement in Source Code Search

In source code search, a common information-seeking strategy involves providing a short initial query with a broad meaning, and then iteratively refining the query using terms gleaned from the results of subsequent searches. This strategy requires programmers to spend time reading search results that are irrelevant to their development needs. In contrast, when programmers seek information from other humans, they typically refine queries by asking and answering clarifying questions. Clarifying questions have been shown to benefit general-purpose search engines, but have not been examined in the context of code search. We present a method for generating natural-sounding clarifying questions using information extracted from function names and comments. Our method outperformed a keyword-based method for single-turn refinement in synthetic studies, and was associated with shorter search durations in human studies.
Fine-grained Co-Attentive Representation Learning for Semantic Code Search

Code search aims to find code snippets from large-scale code repositories based on the developer&#x0027;s query intent. A significant challenge for code search is the semantic gap between programming language and natural language. Recent works have indicated that deep learning (DL) techniques can perform well by automatically learning the relationships between query and code. Among these DL-based approaches, the state-of-the-art model is TabCS, a two-stage attention-based model for code search. However, TabCS still has two limitations: semantic loss and semantic confusion. TabCS breaks the structural information of code into token-level words of abstract syntax tree (AST), which loses the sequential semantics between words in programming statements, and it uses a co-attention mechanism to build the semantic correlation of code-query after fusing all features, which may confuse the correlations between individual code features and query. In this paper, we propose a code search model named FcarCS (Fine-grained Co-Attentive Representation Learning Model for Semantic Code Search). FcarCS extracts code textual features (i.e., method name, API sequence, and tokens) and structural features that introduce a statement-level code structure. Unlike TabCS, FcarCS splits AST into a series of subtrees corresponding to code statements and treats each subtree as a whole to preserve sequential semantics between words in code statements. FcarCS constructs a new fine-grained co-attention mechanism to learn interdependent representations for each code feature and query, respectively, instead of performing one co-attention process for the fused code features like TabCS. Generally, this mechanism leverages row/column-wise CNN to enable our model to focus on the strongly correlated local information between code feature and Query. We train and evaluate FcarCS on an open Java dataset with 475k and 10k code/query pairs, respectively. Experimental results show that FcarCS achieves an MRR of 0.613, outperforming three state-of-the-art models DeepCS, UNIF, and TabCS, by 117.38&#x0025;, 16.76&#x0025;, and 12.68&#x0025;, respectively. We also performed a user study for each model with 50 real-world queries, and the results show that FcarCS returned code snippets that are more relevant than the baseline models.
Blockchain-Oriented Software Variant Forks: A Preliminary Study

In collaborative social development platforms such as GitHub, forking a repository is a common activity. A variant fork wants to split the development from the original repository and grow towards a different direction. In this preliminary exploratory research, we analyze the possible reasons for creating a variant fork in blockchain-oriented software. By collecting repositories in GitHub, we created a dataset with repositories and their variants, from which we manually analyzed 86 variants. Based on the variants we studied, the main reason to create a variant in blockchain-oriented software is to support a different blockchain platform (65&#x0025;).
Information System on Digital Humanities: an experiment of describing the Internet resources of Russian archives

The article contains a description of the results of the inventory of the Internet resources of the Russian archives, carried out in order to create a reference and information system on digital humanities (CISG). The objects that together form the infosphere of this discipline are determined. The problem of detailing the level of resources in their accounting and cataloging is considered. The criteria for selecting specific resources that are interesting to the user are discussed. The structure of the database of the projected system is given. The issues of using languages for indexing information objects are discussed. The quantitative characteristics of the inventory are given. The CG system should ensure increased visibility, citation and reuse of digital humanities objects created in Russia. In this study, the resources of federal archives, state archives of the subjects of the Russian Federation presented on the Internet were studied, as well as departmental archives in a small number, if they have socio-humanitarian content. The management bodies of the archives of the subjects of the Russian Federation were included in the database if they have their own website hosting resources of interest. Archives with dominant technical or natural-scientific content were not included in the study. Also, at this stage, the resources of municipal archives were not considered. The study also identified the main methodological problems of accounting and cataloging of Internet resources that need to be solved.
Fighting Pandemics with Physical Distancing Management Technologies

As COVID-19 continues to wreak havoc in everyday lives, the need to limit the spread of the virus remains a challenge, even with advances in medical knowledge, patient care, and vaccine development and distribution. Furthermore, COVID-19 is one in a recent series of airborne diseases, and probably not the last, given the ongoing encroachment of humans into animal habitat. This paper addresses the challenge of managing physical distancing, a highly effective, yet unnatural and contentious, mitigation strategy against infectious diseases. It presents a Pandemic Tech Stack and proposes that physical distancing management technologies are underutilized to fight pandemics. The latter can help ensure that people remain apart when they need to, support the transfer of activities to an online format, and, ultimately, facilitate the gradual reopening of our economies. The challenges associated with the development and use of these technologies are identified and discussed from both the technical and socio-psychological perspectives.