Reviewerly/exHarmony huggingface dataset:
Ranking facts for explaining answers to elementary science questions

Abstract In multiple-choice exams, students select one answer from among typically four choices and can explain why they made that particular choice. Students are good at understanding natural language questions and based on their domain knowledge can easily infer the question’s answer by “connecting the dots” across various pertinent facts. Considering automated reasoning for elementary science question answering, we address the novel task of generating explanations for answers from human-authored facts. For this, we examine the practically scalable framework of feature-rich support vector machines leveraging domain-targeted, hand-crafted features. Explanations are created from a human-annotated set of nearly 5000 candidate facts in the WorldTree corpus. Our aim is to obtain better matches for valid facts of an explanation for the correct answer of a question over the available fact candidates. To this end, our features offer a comprehensive linguistic and semantic unification paradigm . The machine learning problem is the preference ordering of facts, for which we test pointwise regression versus pairwise learning-to-rank. Our contributions, originating from comprehensive evaluations against nine existing systems, are (1) a case study in which two preference ordering approaches are systematically compared, and where the pointwise approach is shown to outperform the pairwise approach, thus adding to the existing survey of observations on this topic; (2) since our system outperforms a highly-effective TF-IDF-based IR technique by 3.5 and 4.9 points on the development and test sets, respectively, it demonstrates some of the further task improvement possibilities (e.g., in terms of an efficient learning algorithm, semantic features) on this task; (3) it is a practically competent approach that can outperform some variants of BERT-based reranking models; and (4) the human-engineered features make it an interpretable machine learning model for the task.
The Development of Smart Parcel Receiver Box

Abstract: Online shopping has become a new trend shopping recently. However, the recipient is often not at home when the items are delivered. This study aims to design, develop, and evaluate a Smart Parcel Receiver Box. The goal of this study is to help recipients keep their packages safe during delivery time. The researcher has used the Engineering Design Project (EDP). EDP includes 5 phases: identification of the problem, analysis of the problem, brainstorming alternative design, modelling the best solution, refinement and retesting the model. To open the box, the sender should ask permission from the receiver by scanning the QR code displayed on the box door. The receiver will permit it by unlocking the door of the box remotely using apps, namely web pages.  Overall, the project achieved the objectives of the study. However, there is still space for improvement in terms of product design and apps used. Keywords: Smart Parcel Receiver Box, Engineering Design Process Model, Web page, QR codes
Incomplete Label Distribution Learning by Exploiting Global Sample Correlation

In recent years, label distribution learning (LDL) has become a new learning paradigm in the field of machine learning. LDL is mainly designed to solve the problem of ambiguity among labels. Although LDL has been successful in many applications, most of these efforts are centered around complete supervised information. However, in reality, the supervised information is often incomplete due to the huge cost of label annotation. To address this problem, this paper proposes a novel incomplete LDL approach by utilizing the global sample correlation (IncomLDL-GSC). The label correlation is also considered to improve the performance of the model. Extensive experiments are conducted on 13 data sets to demonstrate the effectiveness of our proposed method.
First-class environments in R

The R programming language is widely used for statistical computing. To enable interactive data exploration and rapid prototyping, R encourages a dynamic programming style. This programming style is supported by features such as first-class environments. Amongst widely used languages, R has the richest interface for programmatically manipulating environments. With the flexibility afforded by reflective operations on first-class environments, come significant challenges for reasoning and optimizing user-defined code. This paper documents the reflective interface used to operate over first-class environment. We explain the rationale behind its design and conduct a large-scale study of how the interface is used in popular libraries.
MS-RANAS: Multi-Scale Resource-Aware Neural Architecture Search

Neural Architecture Search (NAS) has proved effective in offering outperforming alternatives to handcrafted neural networks. In this paper we analyse the benefits of NAS for image classification tasks under strict computational constraints. Our aim is to automate the design of highly efficient deep neural networks, capable of offering fast and accurate predictions and that could be deployed on a low-memory, low-power system-on-chip. The task thus becomes a three-party trade-off between accuracy, computational complexity, and memory requirements. To address this concern, we propose Multi-Scale Resource-Aware Neural Architecture Search (MS-RANAS). We employ a one-shot architecture search approach in order to obtain a reduced search cost and we focus on an anytime prediction setting. Through the usage of multiple-scaled features and early classifiers, we achieved state-of-the-art results in terms of accuracy-speed trade-off.
Wireless UV light UAV inter-aircraft avoidance algorithm based on target trajectory prediction

In the complex electromagnetic environment of the battlefield, it is particularly important to ensure the communication network of the UAV cluster and the safety of the UAV. This paper proposes an algorithm for avoidance wireless UAVs in the UAV cluster. First, the enhanced vector field histogram algorithm is improved by combining the characteristics of airborne wireless UV coverage and the characteristics of UAV motion. Secondly, using a traceless transformation to improve the one-step prediction in the Gaussian mixture probability hypothesis density filtering algorithm. Simulation results show that compared with the A * -based enhanced vector field histogram algorithm, the overall trajectory of the algorithm in this paper is smoother and there is no obvious jitter when avoiding. Prove the feasibility of this algorithm.
Goal-Oriented Models for Teaching and Understanding Data Structures

Most computer science curricula include a compulsory course on data structures. Students are prone to memorise facts about data structures instead of understanding the essence of underlying concepts. This can be explained by the fact that learning the basics of each data structure, the difference with each other, and the adequacy of each of them to the most appropriate context of use, is far from trivial. This paper explores the idea of providing adequate levels of abstractions to describe data structures from an intentional point of view. Our hypothesis is that adopting a goal-oriented perspective could emphasise the main goals of each data structure, its qualities, and its relationships with the potential context of use. Following this hypothesis, in this paper we present the use of iStar2.0 to teach and understand data structures. We conducted a comparative quasi-experiment with undergraduate students to evaluate the effectiveness of the approach. Significant results show the great potential of goal modeling for teaching technical courses like data structures. We conclude this paper by reflecting on further teaching and conceptual modeling research to be conducted in this field.
Design of the Autonomous Path Planning System for Mining Robots Based on Stereo Vision

Rock crushing task for excavators has harsh working environments. With the development of vision sensors and intelligent algorithms, it is possible to achieve autonomous intelligent mining for unmanned excavators. This paper presents an autonomous path planning system for mining robots based on stereo vision. For semantic obstacles such as excavators and loaders in the quarry environment, a recognition network for construction vehicles is trained. The positions and sizes of the construction vehicles are determined by stereo vision. To avoid the rollover hazard that may occur, a gradient map is derived from the elevation map. The traffic cost considering the energy consumption is adopted to build a graph, where the Dijkstra graph search algorithm is used to obtain the path with the lowest cost. Finally, ROS and Gazebo are utilized to simulate and verify the autonomous path planning system. The results show that the proposed system based on stereo vision can complete the autonomous intelligent navigation task in the quarry environment.
Behavior-Rule Specification-based IDS for Safety-Related Embedded Devices in Smart Home

The rapid development of Information & Communication Technology (ICT) has accelerated the development of Internet of Things (IoT) devices, which in turn increases the application of these devices to build a safe home environment for all. However, there are security concerns that need to be resolved to overcome the possible invasion of privacy (e.g., IoT camera) and malicious access to safety-related devices. If the protection against external intrusion is weak, it can pose a fatal threat. Hence, this paper researched the security dedicated to the smart home environment. To date, as a representative security solution suitable for the limited-resource nature of IoT, there exist an Intrusion Detection System (IDS). But, by careful analysis of this existing IDS, it was found that there are limitations in supporting security in resource-constrained IoT-assisted smart homes. In this paper, we designed a behavior-rule specification-based IDS to provide security suitable for the said environment. The proposed approach does not only copes with unknown threats, which is a disadvantage of the existing IDS but also lowers the false positive rate. Finally, the IDS performance was validated in MATLAB tool, then compared with the detection accuracy of contemporary classification algorithms, namely Support Vector Machine and Decision Tree.
A Deep Learning model capable of producing heatmap probabilities for Characters in Natural Scenes.

Text appearing in Natural settings come in all shapes, sizes and textures. Classical methods have often failed at extracting accurately the text present in naturally occurring scenes. Text appearing in the wild presents itself in forms of hierarchy organized as sentences, words and characters. Methods for detecting Text from everyday scenes of the real world have found success. Most real world datasets available are annotated on a word level or line level thereby limiting detection to words and not characters. Inspired by the works of Naver Labs on CRAFT [2] and Microsoft Research and Baidu Research's work on WordSup [5] by training models in a weakly supervised manner to gain character level predictions. We propose a computationally efficient architecture capable of providing similar results. Thus our model, once capable of producing character level annotation trained on Synthetic text can be used to fine tune for text appearing in natural settings. The methods discussed prove to be robust enough to identify text that could be curved or somewhat deformed appearing in natural settings. Our approach includes the generation of probabilities of the location of characters and the gaps between characters of which constitute a word, such that it becomes easier to localize characters and words. Our method goes to show comparable results as to CRAFT [2] with only 30% of the number of learnable parameters required.
Task 1 - Argumentative Text Understanding for AI Debater (AIDebater)

Debate refers to the behavior of humans using certain reasons to explain their views on things or issues, exposing their contradictions, and finally obtaining a common understanding and opinions. In the current era of massive information and misleading culture, we expect that an AI system that realizes fully autonomous debate can promote the development of intelligent debate, help establish more reasonable arguments, and make more informed decisions. Based on artificial intelligence technology, this task uses deep learning-based algorithms to identify the attributes of input debate topics and claims, including support, opposition, and neutrality. In this task, we transform the Argumentative Text Understanding problem into a classification problem, firstly compared roberta-large, macbert-large, nezha-large, and nezha-large-wwm, and finally chose to use nezha-large with the highest accuracy rate, and achieve 90.2% accuracy in the second stage.
Adversarial Networks with Circular Attention Mechanism for Fine-Grained Domain Adaptation

Fine-grained Image Analysis (FGIA) as a branch of the image analysis tasks has received more and more attention in recent years. Compared with ordinary image analysis tasks, FGIA requires more detailed human data annotation, which not only requires the annotator to have professional knowledge, but also requires greater labor costs. An effective solution is to apply the domain adaptation (DA) method to transfer knowledge from existing fine-grained image datasets to massive unlabeled data. This paper presents the circular attention mechanism to cyclically extract deep-level image features to match the label hierarchy from coarse to fine. What is more, the networks effectively improve the distinguishability and transferability of fine-grained features based on the adversarial learning framework. Experimental results show that our proposed method achieves excellent transfer performance on three fine-grained recognition benchmarks.
UC-Secure Cryptographic Reverse Firewall–Guarding Corrupted Systems with the Minimum Trusted Module

Nowadays, mass-surveillance is becoming an increasingly severe threat to the public’s privacy. The PRISM and a series of other events showed that inner attacks such as subversion attacks may exist in the current network extensively. As an important strategy to defend users’ privacy against these attacks, cryptographic reverse firewall (CRF) is designed to be a middle-box, modifying all the messages coming in and out of a computer. However, the current formal definition of CRFs merely considers a single protocol session. If such a CRF applies to multiple entities, the security of every entity could not be deduced directly, which leads to an application limitation. In this work, we re-define the notion of CRF from a new perspective based on UC-emulation. Our new definition expresses all expected properties of a CRF in a more brief way, under the universal composition environment. We present a composition theorem which enables deploying one CRF for a local area of network rather than a single computer, and this can significantly reduce the number of CRFs used in practical applications.As another part of this work, under the new definition, we present the first deterministic CRF construction. Compared with existing CRFs, our construction only requires secure randomness in its initial phase rather than every protocol session, and such randomness can be acquired from a public resource. Noting that the probabilistic algorithms are the main targets of subversion attacks, our work makes it much easier to realize a trusted CRF, and thus, pushes CRFs from a concept to application with one more step.
RoKGDS: A Robust Knowledge Grounded Dialog System

In this paper, we propose a pre-training based Robust Know-ledge Grounded Dialog System (RoKGDS) to enhance the performance of the model in unknown scenarios, which is easily generalized to various knowledge grounded dialog tasks, such as persona dialog, knowledge dialog, recommendation dialog. We use a bucket encoder to efficiently extract all kinds of knowledge information (e.g. profile, knowledge graph, and dialog goal). To improve the robustness of the model, we develop a hybrid decoder with a hybrid attention and a copy mechanism. The hybrid attention is an adaptation scheme to apply the pre-trained language model to our model and the copy mechanism is a gate mechanism to control generating a word from generic vocabulary or the input knowledge. Experiments show that our model is more robust than the other baseline models. Furthermore, we use visualization to explain the effectiveness of the hybrid attention compared to other two adaptation schemes. In the 2021 Language and Intelligence Challenge: Multi-Skill Dialog task, our best model ranked 3rd in the automatic evaluation stage and 5th in the human evaluation stage.
On-demand Action Detection System using Pose Information

Human action detection is a very important yet difficult task for various multimedia applications such as safety surveillance, sports video analysis and video editing in media industry. Most existing methods proposed for action detection are machine learning based approaches, however, highly time- and cost-consuming to prepare training data with annotations. Thus, it is still very difficult to apply these methods for industrial applications where the actions of interests might happen rarely in real scenarios such as criminal or suspicious behaviors, because it is impossible to collect a large number of such training data for target actions. In this paper, we disruptively abandon these conventional methods, alternatively, adopting an on-demand retrieval approach using pose information to handle the action detection task. We introduce a demo system that can detect similar actions immediately by specifying a few second sample video without any training process. The system demonstrates the usability and efficacy of our on-demand approach for human action detection. The experimental results are reported to show that our approach outperforms the state-of-the-art method in higher precision and recall, up to 11% and 6.1% improvement, respectively.
Application and Development of Educational Big Data in University Course Learning

Big data in education plays a pivotal role in university course learning at present, and it is also the trend of future education development.This paper introduces the application functions of big data in the teaching and learning of university subjects: the effective data analysis of big data in education, the resource-sharing function of big data in education, and the the evaluation of a variety of students' schoolwork.At the same time, this paper studies the development and application of intelligent platform based on big data, discusses the construction and application of automatic writing evaluation system based on "big data" and "artificial intelligence" technology.
Fast Text Classification by Leveraging Class Feature Words

Text classification is an important task in the NLP field, which aims to map documents into a set of predefined classes. Supervised machine learning or deep learning models have achieved great success in this field. However, these methods require a large number of high-quality labeled data and high performance machines for adequate training. In many cases, we cannot meet these conditions. In this paper, we propose a text classification algorithm leveraging class feature words. This method simulates the human’s intuition of document classification. First, we establish the feature word list of each class, then calculate the similarity between the document and feature word lists of classes and classify the document. The innovation of our method is that it doesn’t need external knowledge or a huge pre-trained language model. By establishing a class feature vocabulary and similarity analysis, we achieve the classification of words and documents. In order to verify its generality and robustness, we conduct experiments on 4 mainstream datasets and compare with the supervised text classification algorithms. The experiments show that our algorithm has extremely speed advantage, comparable to the performance of the supervised classification algorithm, and good interpretability.
INCREASING STUDENT MOTIVATION THROUGH DIFFERENTIATED TEACHING IN FOREIGN LANGUAGE CLASSES

The article intends to determine the impact of differentiated teaching on student incentive in foreign language classes. Motivation is a crucial factor affecting reaching learning goals. It is necessary to create such conditions that let weaker and gifted learners gain in-depth knowledge and feel satisfied while doing activities and participate in the lessons. In this case differentiated teaching is observed, because it is one of the most efficient approaches which allow meeting individual needs and recognizing the diversity of learners. In this research took part a group of 25 learners from a public school in Almaty city. Survey questions empower the investigator to find the proper answer to the research question in terms of affecting differentiation to the motivation. Data were analyzed via quantitative method; findings let identify how elements of differentiated teaching influence more on student motivation and define the importance of teacher’s role in the educational process. Differentiated teaching makes teaching more straightforward and effective, providing students’ flexibility and freedom.
Educator's perception on students 'communication competences

Didactic communication is part of human communication, but it has a peculiar specificity, it implies a premeditation and a clear intentionality in the sense of producing profound and long-term changes in the individual and in society. The nature of this type of communication is intensely persuasive, which puts into action a whole set of means, channels and procedures that can and must be combined in order to achieve the goals set. This is why it is important to be able to assess the student's communication skills, for having to an appropriate perception of their level, in order to assure and streamline communication in the classroom.
Παράγοντες που επηρεάζουν τη διαγλωσσική μεταφορά κατά την κατάκτηση της τρίτης γλώσσας: Δεδομένα από μία πειραματική προσέγγιση

In the scientific debate on third language (L3) acquisition a prominent discussion topic has been the role played in this process by the various (linguistic and individual) factors affecting the crosslinguistic transfer. In this article, we will discuss the most important of these factors in terms of their impact both on the theoretical discussion on the acquisition of L3 as well as in experimental studies conducted on this topic. More specifically we will elaborate on their impact on the acquisition of German as a L3, by native speakers of Greek, who have already acquired English as a second language .
SCALABLE MASKS FOR CORNER DETECTION IN THREE-DIMENSIONAL IMAGES

We consider scalable masks of the detection of corners in the three-dimensional images used for processing by a sliding window on the image. Unlike well-known algorithms, the large-size matrices of masks are constructed by simply adding rows, columns and sides of smaller masks, leaving the submatrices unchanged.
Antithesis “Human - Inhuman” as an Ideological and Meaningful Basis of F. I. Chudakov’s Story “Buzuy”

The paper aims to reveal ideological meaning of the story “Buzuy” by F. I. Chudakov (1888-1918). Scientific originality of the study is conditioned by the fact that F. I. Chudakov’s story was not previously analyzed from such a perspective. The article examines story architectonics, figurative system, personages’ characteristics, which allows clarifying author’s intention. The findings indicate that author’s intention is realized through the opposition of moral concepts “human” - “inhuman”. This opposition manifests itself through the conflict between the main character and his antagonists; personages are described in terms of opposition “friend - foe”, “human - bestial”; contrast also largely determines story composition.