Reviewerly/exHarmony huggingface dataset:
Deep Learning-Based Automated Background Removal for Structural Exterior Image Stitching

This paper presents a deep learning-based automated background removal technique for structural exterior image stitching. In order to establish an exterior damage map of a structure using an unmanned aerial vehicle (UAV), a close-up vision scanning is typically required. However, unwanted background objects are often captured within the scanned digital images. Since the unnecessary background objects often cause serious distortion on the image stitching process, they should be removed. In this paper, the automated background removal technique using deep learning-based depth estimation is proposed. Based on the fact that the region of interest has closer working distance than the background ones from the camera, the background region within the digital images can be automatically removed using a deep learning-based depth estimation network. In addition, an optimal digital image selection based on feature matching-based overlap ratio is proposed. The proposed technique is experimentally validated using UAV-scanned digital images acquired from an in-situ high-rise building structure. The validation test results show that the optimal digital images obtained from the proposed technique produce the precise structural exterior map with computational cost reduction of 85.7%, while raw scanned digital images fail to construct the structural exterior map and cause serious stitching distortion.
The Perceived Importance of Cybercrime Control among Police Officers: Implications for Combatting Industrial Espionage

The current research explored the predictors of how police officers perceived the importance of combatting cybercrime. This is an era in which industrial security is threatened by perpetrators who use advanced techniques to steal information online. Understanding how law enforcement officers view the control of cybercrimes, especially those that steal confidential business information, can inform industrial espionage prevention and help maintain a nation’s industrial competitiveness in the world market. We surveyed a convenience sample of South Korean police officers attending training at the Police Human Resources Development Institute (PHRDI) using a paper-and-pencil questionnaire. The results indicated that the officers’ perceptions of colleagues’ and organizational views on cybercrime control significantly impacted their attitudes. Additionally, officers’ perceptions of the seriousness of online theft (in this paper, we use the terms online theft and property cybercrime interchangeably) and their computer proficiency were also found to affect their views on the importance of combatting cybercrimes. We conclude by suggesting that the police take a proactive organizational approach to prevent and respond to online property crimes through education and public awareness programs, which could positively impact the prevention of industrial espionage.
Inferring object properties from human interaction and transferring them to new motions

Abstract Humans regularly interact with their surrounding objects. Such interactions often result in strongly correlated motions between humans and the interacting objects. We thus ask: “Is it possible to infer object properties from skeletal motion alone, even without seeing the interacting object itself?” In this paper, we present a fine-grained action recognition method that learns to infer such latent object properties from human interaction motion alone. This inference allows us to disentangle the motion from the object property and transfer object properties to a given motion. We collected a large number of videos and 3D skeletal motions of performing actors using an inertial motion capture device. We analyzed similar actions and learned subtle differences between them to reveal latent properties of the interacting objects. In particular, we learned to identify the interacting object, by estimating its weight, or its spillability. Our results clearly demonstrate that motions and interacting objects are highly correlated and that related object latent properties can be inferred from 3D skeleton sequences alone, leading to new synthesis possibilities for motions involving human interaction. Our dataset is available at http://vcc.szu.edu.cn/research/2020/IT.html .
A Deep Learning method for effective channel allotment for SDN based IOT

Due to advances in the field of internet of things (IoT), the transmission speed become very important and need to be discussed. Doing proper assignment of appropriate channels to the generated traffic in SDN based IoT can affect transmission speed enormously. Software Defined Networking has been evolved as a supporting technology to improve the performance of IoT networks and to increase transmission quality. Different machine learning algorithm can be used for prediction of network traffic and allocation of the channel is done for better assignment. Hence, in this paper CNNs based network traffic prediction and allocation of channel technique is proposed. This technique significantly improves the network performance.
El Volumen Louder Por Favor: Code-switching in Task-oriented Semantic Parsing

Arash Einolghozati, Abhinav Arora, Lorena Sainz-Maza Lecanda, Anuj Kumar, Sonal Gupta. Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume. 2021.
A protocol to communicate seismic risk in schools: design, test and assessment in Italy

The best chance to achieve a future disaster-resilient society is through risk education in School: it has a great potential to strengthen capacity of communities to mitigate risks. The KnowRISK (Know your city, Reduce seISmic risK through non-structural elements) project took this opportunity and implemented a risk communication campaign for schools in Portugal, Italy, and Iceland. The idea was that suitably changes in people’s knowledge and attitude can trigger best practices. Crucial to reach such target is the raise of awareness on meaningful issues. The main challenge of the campaign was how to effectively address the mitigation of the vulnerability to earthquakes of non-structural elements, which is an issue considered to be of low priority even in the building regulations of many countries around the world. The campaign stood on a communication strategy that was systematized within a protocol, for 13- 15 years old students, that specifies goals, contents, learning strategy, support material, and relies on face-to-face intervention of scientists in the classroom. This protocol had training sessions bounded by assessment sessions, ex-ante and ex-post, that allowed to validate its efficacy. The training made large use of flipped learning and Episode of Situated Learning (EAS) strategy to raise student’s motivation and increase achievements. To ensure its replicability, the protocol was tested in zones matching a wide range of seismic hazard in Italy. The assessment showed the protocol be effective and ready for a wide dissemination.
Development of a Graphic Design Learning Module for the Deaf and Hard-of-Hearing (DHH) Students Based on Technology and Learning Style in Malaysian TVET institutions.

Graphic Design is used in many industries and involves both the knowledge and skills in creating artefacts. Many Deaf and hard-of-hearing (DHH) students, especially in the Technical and Vocational Education and Training (TVET) institutions are attracted in pursuing studies in this field, perhaps due to the visual nature of the course. However, learning graphic design is challenging for DHH students due to the technologies and teaching approach applied in these institutions. A module which was designed with experts, was implemented in a selected polytechnic which offered the graphic design course for the deaf and non-deaf students. The module used videos and other resources for the different learning styles, as well as a discussion platform on Padlet, and quizzes. An exploratory implementation study on 15 DHH students who volunteered to use the module was conducted. The students’ interactions were monitored and the results of their pre and post-tests during the implementation of the module was analysed to determine if the module was effective for learning. The findings indicate that students could use the module based on their learning styles and were active interacting on Padlet. The results of the pre and post-tests show a significant increase in scores, indicating the effectiveness of the module. Hence, the module has the potential to improve understanding in graphic design among DHH students. The findings have implications on the design of modules for DHH and special needs students. This is in line with TVET aspiration, which is to provide equal access to quality education for all. Further investigation should be done to determine whether the module could be used for other programs involving special needs students.
Sentiment Analysis - An Assessment of Online Public Opinion: A Conceptual Review

This conceptual paper discusses sentiment analysis as a technique of research. It is a tool support decision for textual data collection and analysis available on the internet. It is also considered as a technique of data mining. It uses machine learning language to evaluate textual content. As a method of research, it is computational by nature and identify and categories opinions in the form of text. It targets a large data without any delay and hurdle and also facilitates the collection of data and its analysis. It helps domain leaders to collect real time data about emotions, opinion and attitude, without compromising, validity, reliability and generalizability. The paper also presents this as a way to divide quantitative and qualitative data through real time innovative ways of collection and analysis of data. The paper also discusses limitations one experience when applying this in their domain of research.
Towards optimal cropping: breast and liver tumor classification using ultrasound images

In tumor diagnostics from Ultrasound scan images, the region of interest is often determined by marking the boundary of the suspect mass by experts, simply by clicking on sufficient number of tumor boundary points. To determine whether the tumor is malignant or benign, clinical experts who are trained for long time on how to interpret image information from the marked tumor region and from the surrounding area. In contrast, in designing automatic computer aided diagnosis system using both traditional and conventional machine learning, the relevant image features are generally obtained by cropping the tumor as region of interest (RoI) without considering the periphery of the tumor that might contain important discriminative information for better classification accuracy. In this work, we investigate the impact on classification accuracy of different types of tumors by the cropping strategy where the tumor area will be augmented by a proportion of the surrounding region of the ROI. The required optimal proportion need to be determined so that the cropped ROIs encapsulate information about posterior echo and shadow of the tumor in addition to internal texture and echo that has mainly been used as classification indicators. Recently proposed cropping techniques use the best fitting ellipse of the tumor and examine the proportion by which the ellipse is expanded to improve accuracy. Unfortunately, the fitting ellipse may not reflect the shape of the tumor. Here, we investigate a number of alternative approaches of cropping the ROIs using the concept of convex hull shape(s) determined from the tumor boundary points selected by radiologists. Initially, we check several expansion ratio scales of the convex hull ranging from 0.6 to 4.0 against the cropped tumor without margin. Several classification methods including handcrafted features and deep learning methods are adopted for breast and liver tumors using ultrasound images. We shall demonstrate the importance of optimal cropping for breast and liver ultrasound tumor classification. Furthermore, optimal margin depends on the cancer type and classification method as well.
Cooperative Use of Recurrent Neural Network and Siamese Region Proposal Network for Robust Visual Tracking

In order to solve the problems of unbalanced sample data and the lack of consideration of temporal information in existing Siamese-based trackers, this paper proposes a Siamese recurrent neural network and region proposal network (Siamese R-RPN), which can be trained in an end-to-end manner. Siamese R-RPN is consisted of Siamese network, recurrent neural network and region proposal network. Image features extracted by the Siamese network are strengthened by the channel and spatial attention mechanisms, and are sent to the RPN for classification and regression. Temporal information is processed by a recurrent neural network-based Long Short-Term Memory (LSTM) to predict the rough location of the target, it is mapped to the anchor feature map of the RPN for anchor selection. This makes the positive and negative samples participating in the training procedure to become more balanced and representative. Because of the collaborative use of temporal and spatial information, the tracker proposed in this paper has achieved state-of-the-art performance on three large tracking benchmarks—OTB 2015, VOT2016 and VOT 2018—where this verifies its effectiveness.
Fully autocompensating high-dimensional quantum cryptography by quantum degenerate four-wave mixing

We present a bidirectional quantum communication system based on optical phase conjugation for achieving fully autocompensating high-dimensional quantum cryptography. We prove that random phase shifts and couplings among 2N spatial and polarization optical modes described by SU(2N) transformations due to perturbations are autocompensated after a single round trip between Alice and Bob. Bob can use a source of single photons or, alternatively, coherent states and then Alice attenuates them up to a single photon level, and thus non-perturbated 1-qudit states are generated for high-dimensional QKD protocols, as the BB84 one, of a higher security.
A Novel Framework for Multi-Document Temporal Summarization (MDTS)

Internet or Web consists of a massive amount of information, handling which is a tedious task. Summarization plays a crucial role in extracting or abstracting key content from multiple sources with its meaning contained, thereby reducing the complexity in handling the information. Multi-document summarization gives the gist of the content collected from multiple documents. Temporal summarization concentrates on temporally related events. This paper proposes a Multi-Document Temporal Summarization (MDTS) technique that generates the summary based on temporally related events extracted from multiple documents. This technique extracts the events with the time stamp. TIMEML standards tags are used in extracting events and times. These event-times are stored in a structured database form for easier operations. Sentence ranking methods are build based on the frequency of events occurrences in the sentence. Sentence similarity measures are computed to eliminate the redundant sentences in an extracted summary. Depending on the required summary length, top-ranked sentences are selected to form the summary. Experiments are conducted on DUC 2006 and DUC 2007 data set that was released for multi-document summarization task. The extracted summaries are evaluated using ROUGE to determine precision, recall and F measure of generated summaries. The performance of the proposed method is compared with particle swarm optimization-based algorithm (PSOS), Cat swarm optimization-based summarization (CSOS), Cuckoo Search based multi-document summarization (MDSCSA). It is found that the performance of MDTS is better when compared with other methods. Doi: 10.28991/esj-2021-01268 Full Text: PDF
Simple Type Theory is not too Simple: Grothendieck’s Schemes Without Dependent Types

Formulae display:?Mathematical formulae have been encoded as MathML and are displayed in this HTML version using MathJax in order to improve their display. Uncheck the box to turn MathJax off. This feature requires Javascript. Click on a formula to zoom.
Benchmarking Machine Reading Comprehension: A Psychological Perspective

Machine reading comprehension (MRC) has received considerable attention as a benchmark for natural language understanding.However, the conventional task design of MRC lacks explainability beyond the model interpretation, i.e., reading comprehension by a model cannot be explained in human terms.To this end, this position paper provides a theoretical basis for the design of MRC datasets based on psychology as well as psychometrics, and summarizes it in terms of the prerequisites for benchmarking MRC.We conclude that future datasets should (i) evaluate the capability of the model for constructing a coherent and grounded representation to understand contextdependent situations and (ii) ensure substantive validity by shortcut-proof questions and explanation as a part of the task design.
Student experience of using mobile devices for learning chemistry

Mobile devices have been utilized as an emerging learning tool in the 21st century to support the learning of chemistry. This study aimed to identify matriculation students’ learning experience with regards to the use of mobile devices for learning chemistry. The study employed a qualitative case study approach, which was carried out in a matriculation college. Online diaries and interviews were utilized as the instruments of the study. A total of 84 students initially reflected their learning journey using an online diary, whereas 17 students who were active in mobile learning were purposely selected for the interview. The findings revealed that students have positive and negative experiences towards the use of mobile devices for learning chemistry. They positively perceived that using a mobile device for learning chemistry promotes knowledge construction, visualization of abstract concepts, self-control in learning and intellectual discourse. However, the issue of learning resources quality challenged their available cognitive capacity for learning chemistry. This study offered educators with insights about the importance of incorporating mobile devices for learning chemistry. Educators can plan the appropriate instructional strategies for chemistry based on the students’ learning needs.
Adaptation of Back-translation to Automatic Post-Editing for Synthetic Data Generation

Automatic Post-Editing (APE) aims to correct errors in the output of a given machine translation (MT) system.Although data-driven approaches have become prevalent also in the APE task as in many other NLP tasks, there has been a lack of qualified training data due to the high cost of manual construction.eSCAPE, a synthetic APE corpus, has been widely used to alleviate the data scarcity, but it might not address genuine APE corpora's characteristic that the post-edited sentence should be a minimally edited revision of the given MT output.Therefore, we propose two new methods of synthesizing additional MT outputs by adapting back-translation to the APE task, obtaining robust enlargements of the existing synthetic APE training dataset 1 .Experimental results on the WMT English-German APE benchmarks demonstrate that our enlarged datasets are effective in improving APE performance.
Agile Approach in Training Future Primary School Teachers for Resolving Complex Pedagogical Situation

must constantly change and adapt to modern conditions.This is the concept of VUCA world (U.S. Army Heritage and Education Center, 2019
An Analysis of Emotional Tendency Under the Network Public Opinion: Deep Learning

Network public opinion refers to the common opinion with tendency and influence formed by the public on certain social events through the Internet.Due to the complexity of interest relations, network public opinion is likely to cause difficulties for individuals, enterprises, or governments.To control the public's emotional tendency to social events, this study designed an OCC sentiment rule system to label the network public opinion case base.The text representation method is Word2Vec in deep learning, and the convolution neural network is used to construct the sentiment tendency analysis model under the network public opinion.Taking the case of Dolce & Gabbana humiliation incident, Xiangshui explosion incident, and baixiangguo girl's murder as the research cases, the accuracy of the model in identifying the above three events was 85.87%, 73.65%, and 85.87%, respectively, under the optimal parameters setting.The experimental results show that the proposed method can improve the accuracy of emotion recognition by 3.00% ~ 8.00% compared with the manual annotation method, i.e., the network public opinion sentiment orientation recognition model constructed in this study has a high recognition accuracy and can be used to assist relevant departments in detecting network public opinion.
Modelling of pedagogical technologies on the basis of activity approach

The article identifies the main starting points for modelling pedagogical technologies based on the activity approach, namely: understanding of pedagogical technology as a scientifically sound activity aimed at forming the personality of the learner during the educational process by means of certain educational material; interpretation of pedagogical modelling as a process of creating such a reflection of the educational process that could reproduce a pedagogical object or process and replace it; the result of pedagogical modelling are images, forms of objects of pedagogical design. These are the relevant documents: concepts, state educational standards, curricula and programs, lesson plans, algorithms for action in lessons, diagrams, tables, etc.; drawing up a model of pedagogical technology based on the activity approach is an algorithm of actions, which includes: determining the subject of activity of the teacher and the student, selection of means of their activity, determination of conditions of pedagogical process, establishment of activity results. The generalized algorithm of drawing up of pedagogical technology model on the basis of the activity approach is presented by such steps: definition of a subject of activity for the teacher and the student separately; selection of means of their activity of the teacher; drawing up the process of educational activity of the teacher and the cognitive process of the student; determining the conditions for this process and establishing the results of this activity. The technology of creating a model for the implementation of interdisciplinary links based on the activity approach is represented by two stages: the creation of a model of their content and a model of the implementation process. The result of modelling the content of interdisciplinary links is to set goals for the use of knowledge from one subject in the study of another. The result of modelling the process of realization of interdisciplinary connections is the integral knowledge of the object of study formed in the student.
Translations from Discretised PDDL+ to Numeric Planning

Hybrid PDDL+ models are amongst the most advanced models of systems and the resulting problems are notoriously difficult for planning engines to cope with. An additional limiting factor for the exploitation of PDDL+ approaches in real-world applications is the restricted number of domain-independent planning engines that can reason upon those models. With the aim of deepening the understanding of PDDL+ models, in this work we study a novel mapping between a time discretisation of PDDL+ and numeric planning as for PDDL2.1 (level 2). The proposed mapping not only clarifies the relationship between these two formalisms, but also enables the use of a wider pool of engines, thus fostering the use of hybrid planning in real-world applications. Our experimental analysis shows the usefulness of the proposed translation, and demonstrates the potential of the approach for improving the solvability of complex PDDL+ instances.