Reviewerly/exHarmony huggingface dataset:
Validation and Persistence

Two very important processes are needed in any web site: data validation and data persistence. Both of these can be automated with a package named Hibernate. Required validation will be introduced in this chapter. String validation is so important that a standard Java package can simplify it: regular expressions. Spring MVC can use Bean Validation 2.0 [JSR-380] to implement validation. The default implementation uses the Hibernate package. Hibernate will use regular expressions to perform sophisticated required validation on string data. Hibernate can validate strings, numeric data and collections. Spring 3 supports validation annotations on request handler inputs. Custom validation can be added on top of the normal validation through the use of the Validator interface. Custom validation can surpass the limitations of regular expressions. Data persistence is implemented through a Hibernate repository. Adding additional search methods is straight forward. Hibernate makes data persistence a simple task by letting the developer work only with the bean and not with statements from a database access language. When data is retrieved from the database, it will be in the form of a collection of beans.
VOLDOR: Visual Odometry from Log-logistic Dense Optical flow Residuals

We propose a dense indirect visual odometry method taking as input externally estimated optical flow fields instead of hand-crafted feature correspondences. We define our problem as a probabilistic model and develop a generalized-EM formulation for the joint inference of camera motion, pixel depth, and motion-track confidence. Contrary to traditional methods assuming Gaussian-distributed observation errors, we supervise our inference framework under an (empirically validated) adaptive log-logistic distribution model. Moreover, the log-logistic residual model generalizes well to different state-of-the-art optical flow methods, making our approach modular and agnostic to the choice of optical flow estimators. Our method achieved top-ranking results on both TUM RGB-D and KITTI odometry benchmarks. Our open-sourced implementation is inherently GPU-friendly with only linear computational and storage growth.
New NCRM branding and introduction to the functionality and design of the new NCRM portal

New NCRM branding and introduction to the functionality and design of the new NCRM portal by David Martin, Co-Director of NCRM and Eva Nedbalova, NCRM Communication Manager This video was part of the 'Celebration of the new phase of the National Centre for Research Methods' https://www.youtube.com/watch?v=G2qGvZkqcTM
Penentuan Konsentrasi Pembagian Keahlian Siswa Untuk Menjadi Developer Menggunakan Metode Forward Chaining (Studi Kasus: Synapse Academy)

Choosing the concentration of expertise is not easy for students. Sometimes when these students cannot recognize their abilities and strenghts, they tend to follow the choices made by their friends, or those that are the most popular with many people. This problem of course can prevent a person from developing when the skills he takes didn’t match with his interest. Referring to this, the researher proposes to design and create a software to determine expertise to become a developer in the future.This study ains to create and design a model to determine the right skills for students acording to their interest. This study uses the forward chaining method for diagnosing the right type of expertise for students. With the input variables are the interests and talents of students. The output variable is the type of expertise to become developer, namely front-end, back-end, and database. Based on the test results, this expert system software is useful for helping and making it easier for students to determine student expertise to become the right developer according to their interests and talents.
Corrigendum to: Video Anomaly Detection Using the Optimization-Enabled Deep Convolutional Neural Network

doi:10.1093/comjnl/bxaa177 Comp J 2020;00(0): xxxx–xxxx In the originally published version of this manuscript, there was an error in the author affiliations. First and Corresponding author Felix M. Philip’s affiliation should read: “Department of Computer Science and Information Technology, JAIN(Deemed -to- be University), Kochi, Nirmal Infopark Infopark P.O Kakkanad, Kochi, Kerala, India, 682042” instead of “Department of Electronics and Communication Engineering, JAIN (Deemed-to-be University), Knowledge Park - Kochi, Nirmal Infopark Infopark P.O Kakkanad, Kochi, Kerala, India, 682042”.
A deep-learning model for semantic role labelling in medical documents

We built a model labelling the Predicate Argument Structure (PAS) for biomedical documents. PAS is an important semantic information of any document, because it reveals the main event mentioned in each sentence. Extracting PAS in a sentence is an important premise for the computer to solve a series of other problems related to the semantics in text such as event extraction, named entity extraction, question answering system… The predicate argument structure is domain dependent. Therefore, in Biomedical field, it is required to define a completely new Predicate Argument frame compared to the general field. For a machine learning model to work well with a new argument frame, identifying a new feature set is required. This is difficult, manual and requires a lot of expert labor. To address this challenge, we chose to train our model with Deep Learning method utilizing Bi-directional Long Short Term Memory. Deep learning is a machine learning method that does not require defining the feature sets manually. In addition, we also integrate Highway Connection between hidden neuron layers to minimize derivative loss. Besides, to overcome the problem of small training corpus, we integrate Deep Learning with Multi-task Learning technique. Multi-task Learning helps the main task (PAS tagging) to be complemented with knowledge learnt from a closely related task, the NER. Our model achieved F1 = 75.13% without any manually designed feature, thereby showing the prospect of Deep Learning in this domain. In addition, the experiment results also show that Multi-task Learning is an appropriate technique to overcome the problem of little training data in biomedical fields, by improving the F1 score.
STUDY OF OPTIMIZATION AND NORMALIZATION OF VILLAGE NATURAL RIVER BUILDING REJO TENGGARONG DISTRICT, KUTAI KARTANEGARA

In the area of Bangun Rejo Village in Tenggarong Seberang District, Kutai Kartanegara Regency, East Kalimantan Province is an area that often experiences flooding and is a major problem for Bangun Rejo Village Residents. Several factors cause flooding due to the not yet maximum capacity of river channels and sedimentation to drain incoming water. The existing dimensions that need to change in order to smooth the flow of river water. The method used to calculate the design rainfall is using the Gumbel method, the Log person type III method, and for the design discharge calculation using the Nakasayu Synthetic Unit Hydrograph (HSS) method. From the results of the design discharge calculations 2, 5, 10, and 25 years obtained design discharge for each river channel to be investigated, in the field measurements each river channel is calculated and obtained river dimensions and river capacity discharge, from existing analysis results river discharge is not able to accommodate the design discharge, the conclusion is needed to change the dimensions of the river for the next 25 years.
The Internalization of Immersive Virtual Teaching Approach in Management Ethics and Corporate Governance Course at Sultan Idris Education University: A Panacea to Teaching and Learning Issues during COVID-19 Pandemic

Our study examines the impact of immersive virtual teaching style on student’s performance in Management Ethics and Corporate Governance course at Sultan Idris Education University, Malaysia. We perform learning activities in Management Ethics and Corporate Governance course using (1) Interactive Teaching Video (2) Edutainment Video (3) Massive Open Online Course &amp; Facebook Group (4) Kahoot, as well as (5) Outside learning activities. We acknowledge that an issues on student’s engagement and performance are always become the center of attraction for many practitioners of e-learning, especially during COVID-19 pandemic where school and universities need to be closed and e-learning is taking over all of the traditional face to face classroom method. We therefore, intend to compare the performance of students that have been taught using (i) traditional approach of “chalk and talk” in Management Ethics and Corporate Governance course and (ii) Immersive Virtual Teaching approach where blended learning is widely executed in delivering and conveying the knowledge to the students. Our sample comprises of 71 students in Semester A171 and 134 Students in Semester A172, where in Semester A171 sample, students were exposed with traditional “chalk &amp; talk” teaching approach, while in the Semester A172, an Immersive Virtual learning approach has been employed. Our result exhibit that the student’s performance in Semester A172 is significantly higher in the various assessment evaluation that had been conducted when compared to the performance of the students in Semester A171. We also document that the utilization of Immersive Virtual Teaching approach increases student’s engagement, improve student’s motivation and boost up student’s excitement to learn more about the course.
Parallel Merging and Sorting on Linked List

We study linked list sorting and merging on the PRAM model. In this paper we show that n real numbers can be sorted into a linked list in constant time with n2+e processors or in ) time with n2 processors. We also show that two sorted linked lists of n integers in {0, 1, …, m} can be merged into one sorted linked list in O(log(c)n(loglogm)1/2) time using n/(log(c)n(loglogm)1/2) processors, where c is an arbitrarily large constant.
Audit Sistem Informasi menggunakan framework COBIT 4.1 Domain Deliver And Support (DS) (Studi Kasus: PT.Sriwijaya Internet Services)

PT Sriwijaya Internet Services is a startup company in the network sector, which was formed to fulfill all kinds of activities connected with communication access needs that other Information Technology companies have not yet been able to optimize. In this case, PT Sriwijaya Internet Services intends to provide the best service and quality in accordance with the desired IT governance. In order to fulfill the Goals that the company wants, it is necessary to measure the maturity level of IT governance in order to make it easier for IT management to find out the maturity level of the company and to be able to make decisions based on the results of the audit carried out. Based on what happened at PT Sriwijaya Internet Services, this study will measure the level of interest in IT Governance in the company. In this study, the framework used is COBIT 4.1 Domain Deliver and Support (DS). Based on the COBIT Framework, the method used is the design method stage, the interview method, and the data processing method. In PT Sriwijaya Internet Services there are two domains, namely DS11 and DS12, which have a maturity level that has met the desired target at level 4 (Managed Level). So companies are required to maintain value and develop to level 5 (Optimized Level).
Jointly Video Bitrate Adaptation and Multicast Resource Allocation in Mobile Edge Networks

Current schemes for Dynamic Adaptive Streaming over HTTP (DASH) are mainly client-driven. Thus, in the scenario of multiple users watching the same video, repeated subscription and data transmission results in an under-utilization of network bandwidth resources. Additionally, competition for limited network resources of individual users may motivate selfish behaviors, which leads to unfairness and sub-optimal utility of video services. In this paper, Multimedia Broadcast Multicast Service (MBMS) in mobile edge networks for multi-bitrate video sessions is applied to overcome these limitations. We formulate a non-linear integer programming (NLIP) model, which jointly optimize bitrate adaptation and resource allocation for multiple users. This model takes video quality, playback interruptions, and quality oscillations as linear constraints to maximize multicast users' Quality of Experience (QoE). Due to NP-Hardness of this problem, we propose a heuristic greedy algorithm, which can work out the optimal or near-optimal solution with low time complexity. The evaluation results demonstrate that our method can achieve Pareto Optimality of the system utility, and maximize users' QoE while ensuring fairness.
Noise Attention based Spectrum Anomaly Detection Method for Unauthorized Bands

Spectrum anomaly detection is of great importance in wireless communication to secure safety and improve spectrum efficiency. However, spectrum anomaly detection faces many difficulties, especially in unauthorized frequency bands. For example, the composition of unauthorized frequency bands is very complex and the abnormal usage patterns are unknown in prior. In this paper, a noise attention method is proposed for unsupervised spectrum anomaly detection in unauthorized bands. First of all, we theoretically prove that the anomalies in unauthorized bands will raise the noise floor of spectrogram after VAE reconstruction. Then, we introduce a novel anomaly metric named as noise attention score to more effectively capture spectrum anomaly. The effectiveness of the proposed method is experimentally verified in 2.4 GHz ISM band. Leveraging the noise attention score, the AUC metric of anomaly detection is increased by 0.193. The proposed method is beneficial to reliably detecting abnormal spectrum while keeping low false alarm rate.
A user-centred focus on augmented reality and virtual reality in AEC: Opportunities and barriers identified by industry professionals

This paper presents insights into the opportunities and barriers for using augmented reality (AR) and virtual reality (VR) in the architecture, engineering and construction (AEC) industry by contextualizing how their adoption is leveraged in practices. Based on a review of literature, a qualitative study using semi-structured interviews was conducted with thirteen participants from AEC industries between five and thirty years of experience. Interviews were conducted face-to-face and virtually using questions focusing on participants’ experiences, perceptions of, and opinions about the use of AR/VR in AEC practice. Qualitative dissemination of key insights highlighted immediate and future possibilities for AR/VR, with current limitations that require future investigation from a user-centred perspective. Suggesting a XR-PACT framework, this paper frames key directions for future research to address current limitations and explore new opportunities that positively impact architecture and other professions, communities of building users.
Temporal Object Tracking in Large-Scale Production Facilities using Bayesian Estimation

Moving towards comprehensive digitalization of production facilities, it is critical to know the location of work pieces, charges, or other objects of interest that change location over time during production. For the case of a limited traceability of these objects, we first present a theoretical approach that performs a recursive Bayesian estimation of the object’s location over time based on typical passage measurements in production (e. g. light barriers or RFID systems). The probabilistic method is based on a directed acyclic graph modeling the transfer and sojourn of the objects in the production network. Subsequently, the method is validated on simulated data while varying both size and measurement conditions of the process. The results show the benefit of the proposed method against a single estimation and demonstrate its potential for the application in real time scenarios.
Conversational Semantic Role Labeling

Semantic role labeling (SRL) aims to extract the arguments for each predicate in an input sentence. Traditional SRL can fail to analyze dialogues because it only works on every single sentence, while ellipsis and anaphora frequently occur in dialogues. To address this problem, we propose the conversational SRL task, where an argument can be the dialogue participants, a phrase in the dialogue history or the current sentence. As the existing SRL datasets are in the sentence level, we manually annotate semantic roles for 3,000 chit-chat dialogues (27,198 sentences) to boost the research in this direction. Experiments show that while traditional SRL systems (even with the help of coreference resolution or rewriting) perform poorly for analyzing dialogues, modeling dialogue histories and participants greatly helps the performance, indicating that adapting SRL to conversations is very promising for universal dialogue understanding. Our initial study by applying CSRL to two mainstream conversational tasks, dialogue response generation and dialogue context rewriting, also confirms the usefulness of CSRL.
Research Guides: Research Impact: Responsible Metrics: COVID-19

This guide provides an overview of the Responsible Metrics movement and provides resources to support the responsible and equitable evaluation of scholarly work, particularly in the wake of unpredictable challenges.
Protection Of Moral And Material Rights Of Copyright Subjects

This topic deals with the most common questions about the transfer of copyright to third parties. Violations in the field of copyright in terms of the participation of various subjects and their relationship to objects of copyright legal assessment of the interaction of the author of a work in the field of science, literature and art. Prevention of offenses in the field of copyright.
Increasing interoperability in the Web of Things using Autonomous Agents (Position Paper)

The Web of Things (WoT) aims to enable interoperability across the Internet of Things (IoT) and application domains. However, the number and heterogeneity of connected devices continues to grow. The integration of multiple Things with different interaction methods requires human effort and intervention, and is increasingly challenging as their heterogeneity grows. Some form of automation of this task, for example by delegating new service compositions to autonomous agent, would require mechanisms for uniform access and interaction among Things. In this position paper, we investigate how Semantic Interoperability Solutions (SIS) can be combined with multi-agent systems (MAS), to allow agents to autonomously interact with these WoT resources. We use as a starting point the work of the Web of Things Working Group at W3C (World Wide Web Consortium). We identify and describe issues related to i) the representation, ii) the discovery and selection of Things, and iii) the interaction between agents and Things. We claim that these issues are the core tenets to ensure this integration using MAS, WoT and SIS.
Robot Position Control Based on Uncalibrated Visual Servo

Mobile robot visual servo is based on visual feedback information to directly control the movement of the mobile robot. This paper designs an uncalibrated visual servo control method for mobile robots based on feature point recognition to achieve high-precision position control of mobile robots. The overall scheme of the system is determined firstly, and a robust feature point pattern and recognition algorithm is proposed. Aiming at the visual servo input requirements, the feature points of the mobile robot composed of positioning points and coding points are designed. The uncalibrated visual servo algorithm is studied to realize the stabilization and trajectory tracking control of the mobile robot, and the control accuracy is verified through experiments.
Probabilistic Box Embeddings for Uncertain Knowledge Graph Reasoning

Knowledge bases often consist of facts which are harvested from a variety of sources, many of which are noisy and some of which conflict, resulting in a level of uncertainty for each triple. Knowledge bases are also often incomplete, prompting the use of embedding methods to generalize from known facts, however, existing embedding methods only model triple-level uncertainty, and reasoning results lack global consistency. To address these shortcomings, we propose BEUrRE, a novel uncertain knowledge graph embedding method with calibrated probabilistic semantics. BEUrRE models each entity as a box (i.e. axis-aligned hyperrectangle) and relations between two entities as affine transforms on the head and tail entity boxes. The geometry of the boxes allows for efficient calculation of intersections and volumes, endowing the model with calibrated probabilistic semantics and facilitating the incorporation of relational constraints. Extensive experiments on two benchmark datasets show that BEUrRE consistently outperforms baselines on confidence prediction and fact ranking due to its probabilistic calibration and ability to capture high-order dependencies among facts.
Sentence Alignment with Parallel Documents Helps Biomedical Machine Translation.

The existing neural machine translation system has achieved near human-level performance in general domain in some languages, but the lack of parallel corpora poses a key problem in specific domains. In biomedical domain, the parallel corpus is less accessible. This work presents a new unsupervised sentence alignment method and explores features in training biomedical neural machine translation (NMT) systems. We use a simple but effective way to build bilingual word embeddings (BWEs) to evaluate bilingual word similarity and transferred the sentence alignment problem into an extended earth mover's distance (EMD) problem. The proposed method achieved high accuracy in both 1-to-1 and many-to-many cases. Pre-training in general domain, the larger in-domain dataset and n-to-m sentence pairs benefit the NMT model. Fine-tuning in domain corpus helps the translation model learns more terminology and fits the in-domain style of text.
The Computer Program Development to Determine The Shares of Inheritance for Three Types of Heir

Abstract: This research studied an Acehnese manuscript table about inheritance distribution. The purpose of the study was to create the general rules among the heir types in the table such that its share priorities were obtained. With the existence of these priorities, the relations among the portions were known which make it was possible to develop an algorithm and the computerized table for three types of the heir. This table consists of the shares for three types of an heir from the domain of ten types of heir selected in this research. The computer program was built by first determining the more important ones between the two related shares. From the total 120 cases investigated, four cases did not follow the developed priorities. The governing table for the three types of heir is expected to be the foundation to the determination of the shares for three types of the heir.Abstrak: Penelitian ini mengkaji isi sebuah tabel manuskrip Aceh tentang pembagian warisan sehingga didapatkan suatu ketentuan umum tentang prioritas dari bagian-bagian warisan tersebut. Dengan adanya prioritas ini, diperoleh suatu hubungan antar bagian warisan sehingga dapat dibangun suatu algoritma untuk komputerisasi tabel bagian tiga jenis ahli waris. Tabel ini berisi bagian tiga jenis ahli waris dari sepuluh jenis ahli waris yang dipilih sebagai domain. Program komputer ini dibangun dengan menentukan bagian yang lebih diprioritaskan di antara dua bagian. Dari total 120 kasus, terdapat empat kasus yang tidak mengikuti prioritas yang telah dibangun. Hasil berupa tabel tiga jenis ahli waris ini diharapkan menjadi dasar untuk penentuan tiga jenis ahli waris dari seluruh jenis ahli waris yang berjumlah 30 tersebut.