Reviewerly/exHarmony huggingface dataset:
Towards Bootstrapping Biomedical Named Entity Recognition using Reinforcement Learning

Named entity recognition is one of the most fundamental problems in knowledge graph. In biomedical field, labeling high-quality biomedical entities requires plenty of linguistic knowledge due to abbreviation and specificity. Using dictionary is the simplest way for labeling, but it is difficult to obtain a versatile dictionary and usually a dictionary for one corpus is not suitable for another corpus due to bad transferability. Current mainstream recognition methods require lots of manpower, which is time-consuming and laborious. To handle this challenge, we present a novel approach to automatically recognize new biomedical entities. First, we use a small number of manually labeled biomedical entities as seeds to label some biomedical texts and learn their features autonomously. Then by using a tagger based on supervised learning and an instance selector based on reinforcement learning, we iteratively generate new biomedical entities. Experiment results demonstrate that our method can deal with biomedical named entity recognition and obtain significant performances in both English and Chinese biomedical datasets.
Learning Path Recommendation using Hybrid Particle Swarm Optimization

Revised Bloom's Taxonomy (RBT) is proposed in general to look more forward in responding to the demands of the developing educational community, including how students develop and learn and how teachers prepare Learning Objects (LO).The variety of characteristics of students' abilities in a class has always been a problem that is often faced by a teacher. Unfortunately, cognitive classifications to develop students' knowledge to a high level have not been used to plan a learning path that is appropriate for their cognitive level. The purpose of this study is to recommend a learning path that matches the cognitive abilities of students from a learning object ontology. The method used in this research is Hybrid Particle Swarm Optimization (HPSO) which integrates Binary Particle Swarm Optimization (BPSO) and Discrete Particle Swarm Optimization (DPSO). The Connection Weight (CW) function is used to test the quality of the connection between the learning objects of an ontologysubject controlled by the cognitive class.Based on experimental studies, the HPSO method can recommend a suitable learning path for cognitive classes, namely Low Cognitive (CL), Medium Cognitive (CM), and High Cognitive (CH).The similarity of the sequence of learning paths based on population in CL-class is 87.5%, CM class 75%, and CH class 87.5%.
Can a Differential Attack Work for an Arbitrarily Large Number of Rounds?

Differential cryptanalysis is one of the oldest attacks on block ciphers. Can anything new be discovered on this topic? A related question is that of backdoors and hidden properties. There is substantial amount of research on how Boolean functions affect the security of ciphers, and comparatively, little research, on how block cipher wiring can be very special or abnormal. In this article we show a strong type of anomaly: where the complexity of a differential attack does not grow exponentially as the number of rounds increases. It will grow initially, and later will be lower bounded by a constant. At the end of the day the vulnerability is an ordinary single differential attack on the full state. It occurs due to the existence of a hidden polynomial invariant. We conjecture that this type of anomaly is not easily detectable if the attacker has limited resources.
Digital Processing of Blood Image by Applying Two-Dimensional Haar Wavelets

In this work, compression and zero coefficients have been determined by using digital processing of the blood image that is 1 μl (approximately one drop of blood) in the Haar wavelet. Also using this amount, an algorithm is proposed to define the number of leukocytes in human blood. We know that there are currently two types of methods for determining the number of leukocytes: grease and counting by using a microscope. This is a loss of time, causes a number of errors and inconveniences. By using this algorithm, a program has been developed to calculate the number of leukocytes with high accuracy in a blood image of 1 μl (about one drop of blood).
Lévy-Flight Intensified Current Search for Multimodal Function Minimization

This paper proposes the novel trajectory-based metaheuristic algorithm named the Lévy-flight intensified current search (LFICuS) for multimodal function minimization. The proposed LFICuS is the new modified version of the intensified current search (ICuS) initiated from the electrical current flowing through the electric networks. The random number drawn from the Lévy-flight distribution and the adjustable search radius mechanism are conducted to improve the search performance. To perform its effectiveness, the proposed LFICuS is tested against ten selected standard multimodal benchmark functions for minimization. Results obtained by the LFICuS will be compared with those obtained by the ICuS. As simulation results, it was found that the proposed LFICuS is much more efficient for function minimization than the ICuS.
Research on rapid planning of intelligent aircraft trajectory under multiple constraints

Abstract Intelligent aircraft trajectory planning is an important support to ensure the successful completion of the mission of the aircraft, and it is also a key component of the intelligent aircraft mission planning system. Due to system performance constraints and environmental constraints, the positioning system of the intelligent aircraft cannot be accurately positioned during flight, which will cause flight errors. Excessive flight errors will lead to the failure of the mission. Therefore, error correction plays an increasingly important role in trajectory planning. This paper studies the rapid trajectory planning of intelligent aircraft under the limitation of system positioning accuracy. Based on the optimization theory, with the error generation rules and correction rules of the intelligent aircraft in flight as constraints, establishes the optimization model, and uses the fast non-dominated multi-objective optimization algorithm (NSGA-II) with elite retention strategy. This paper corrects the vertical and horizontal errors during the flight, and finally designs a reasonable intelligent aircraft trajectory that meets the minimum trajectory length and the minimum number of corrections during the flight.
Interchanging the Mode of Display Between Desktop and Immersive Headset for Effective and Usable On-line Learning

In this new era of “untact”, the on-line solutions for social gatherings and meetings have become important more than ever. In particular, immersive 3D VR based environments have emerged as a possible alternative to the video based tele-conferencing solutions for their potential improved learning effects. However, the usability issues stand in its way for it to be fully spread and utilized by the mass. In this poster, we study for another alternative, the mixed usage of desktop (or equally 2D video tele-conferencing) and immersive modes as necessary. The desktop mode is convenient for the user and allows the usual interaction in the most familiar way (e.g. note taking), while the immersive mode offers a more direct contact and interplay among the teacher and other learners. We conducted a pilot experiment comparing the user experience among the three modes: (1) desktop, (2) I3D VR/HMD and (3) mixed. For the mixed mode, we designed a flexible fixture for conveniently wearing/donning the head-set and switch between the desktop and I3D mode quickly. Partici- pants much preferred the mixed mode usage due to the aforementioned ad- vantages of bringing the bests of both worlds.
Pedestrian Detection method based on Multi-Scale Fusion Inception-SSD Model

Pedestrian detection is widely used in daily life. many fields require high accuracy and fast speed of pedestrian detection is an urgent problem to be solved. the depth, convolution kernel size, and feature layer selection of neural networks have a great impact on the performance of target detection. in this paper, based on Single Shot MultiBox Detector (SSD), a pedestrian detection method based on Inception-SSD of sparse connections and multi-scale fusion is proposed. this algorithm achieves good performance in both detection speed and detection accuracy. Through comparing the experimental data on the PASCAL VOC and CUHK Occlusion image data sets, it shows that some of the optimized designs adopted in this paper have higher accuracy than the original algorithm, and the detection speed reaches 31 fps to meet the real-time requirements.
IterefinE: Iterative KG Refinement Embeddings using Symbolic Knowledge

Knowledge Graphs (KGs) extracted from text sources are often noisy and lead to poor performance in downstream application tasks such as KG-based question answering. While much of the recent activity is focused on addressing the sparsity of KGs by using embeddings for inferring new facts, the issue of cleaning up of noise in KGs through KG refinement task is not as actively studied. Most successful techniques for KG refinement make use of inference rules and reasoning over ontologies. Barring a few exceptions, embeddings do not make use of ontological information, and their performance in KG refinement task is not well understood. In this paper, we present a KG refinement framework called IterefinE which iteratively combines the two techniques – one which uses ontological information and inferences rules, viz.,PSL-KGI, and the KG embeddings such as ComplEx and ConvE which do not. As a result, IterefinE is able to exploit not only the ontological information to improve the quality of predictions, but also the power of KG embeddings which (implicitly) perform longer chains of reasoning. The IterefinE framework, operates in a co-training mode and results in explicit type-supervised embeddings of the refined KG from PSL-KGI which we call as TypeE-X. Our experiments over a range of KG benchmarks show that the embeddings that we produce are able to reject noisy facts from KG and at the same time infer higher quality new facts resulting in upto 9% improvement of overall weighted F1 score.
U.S. Data Diplomacy: Imperatives, Apparatus, and Scope

U.S. data diplomacy applies of analysis of social networks and target audiences to conduct effective digital campaigns and detect hostile information of adversaries in social media.
A Study on the Usability Test Method of Collaborative Robot Based on ECG Measurement

Collaborative robots can work with operator, it used situations that handing things over or moving heavy objects next to the workers. It is most important to ensure the safety of users as a condition for using collaborative robots. Therefore, the use of ECG sensors, which can measure the body’s autonomic nervous system reactions, in usability evaluations is a way to derive objective results from users. In this study, we tried to develop an objective usability evaluation index for collaborative robot products by conducting a usability evaluation of collaborative robot products based on heart rate measurement. We recruited 12 undergraduates with engineering majors (F = 6, M = 6, 23.75 ± 2.60 yrs). When the subjects expressed their intention to learn enough about how to use the collaborative robot, the subjects provided a 10-minute break to adjust the learning effect. Subjects wore ECG sensor (Bioharness, ZephyrTM Co.) to measure heart rate during break time. Subjects performed usability test of collaborative robot after the break (10 min) and continuously measured heart rate during the usability test. In comparisons between break time and the usability test of the collaborative robot, more heart rate was increased in usability test of collaborative robot than break time (p = 0.01). A significant positive correlation between the change value of heart rate and the time of usability test was shown (r = 601, p = 0.024). We found that heart rate can express the user’s autonomic nervous system response to product use in usability evaluation, and check the convenience of using collaborative robot.
Stemming Javanese: Another Adaptation of the Nazief-Adriani Algorithm

Javanese is the regional language with the most speakers in Indonesia. Not only in the Java island, but the Javanese is also spoken by people outside Indonesia, such as in Malaysia and Suriname. Besides Bahasa Indonesia, which is the national language that must be learned by Indonesian society, the regional languages, like Javanese, also must be preserved. One of the preservation methods is by using an information retrieval system. One of the popular preprocessing methods in information retrieval is called stemming. It is used to reduce differences in word forms so that the information retrieval process becomes effective. Since Javanese has its uniqueness, the language morphology is different from Bahasa Indonesia. Thus, the stemming process of Javanese words also differs from Bahasa Indonesia. The stemming algorithm in this paper is developed by adapting the Nazief-Adriani algorithm, a well-known Bahasa Indonesia stemmer method. The algorithm in this paper is made based on the rules of Javanese language morphology. It removes ater-ater (prefixes), seselan (infixes), penambang (suffixes), bebarengan (confixes), and tembung rangkep (repeated word).
Human ability enhancement for reading mammographic masses by a deep learning technique

The usefulness of taking mammography has widely been recognized, but screening mammography occasionally results in an excessive recommendation for subsequent biopsy causing many women inconvenience and severe anxiety. Especially, there is a high chance of unnecessary biopsy recommendation for those findings which are difficult to be classified into malignancy and benignancy. However, few have focused on the computer-aided diagnosis (CAD) performance for such difficult cases. To address this problem, we developed a deep learning based classification technique to aid the difficult diagnosis. We evaluated 100 benign and malignant masses of the breast imaging-reporting and data system (BI-RADS) Category 4 that are generally difficult to be classified into malignant and benign. Five certificated doctors participated in the experiments where each doctor reads the 100 images alone first and a week later reads again with the proposed CAD system. The area under the receiver operating characteristic curve (AUC-ROC) for the CAD system was 0.79. This is greater than 0.65, the average value of the human readers' AUC-ROCs, while the average value of the human readers' AUC-ROCs reached the best value of 0.8 when they used the CAD system. These results suggest that the proposed CAD system is able to not only outperform human readers in classifying the masses, but also enhance the human performance in this difficult task.
IMPROVING THE EFFECTIVENESS OF TEACHERS USING ICT

The article will consider information and communication technologies used in business processes, inter-preted for effective work in the educational process, of a teacher with students. Also, the article will describe several business management tools that have worked well in the Russian market: Bitrix24 and the Planfix management system. The author analyzes the entities that contribute to increasing the profit of enterprises having these automated information systems that will be used as enhancing the effectiveness of students, that is, students and the teacher will be considered as clients and the company negotiating through CRM or ERP systems. Since it is such cooperation that sets them apart and leads them to the leaders in the Russian and international markets. An analysis of 30 students divided into two groups will be conducted. A teacher works with one group using an unconventional method, using business tools, while the process proceeds in the other with traditional teaching methods. The key objective of this study is to show related aspects of the functioning of ICTs that are subtle to business processes in the education system. Since a growing number of universities are instilling their teachers to work on various cloud systems, for example, DGUNH has a Cisco platform that allows teachers to track students' actions at various intervals.
Pattern Matching in Meter Detection of Arabic Classical Poetry

Arabic classical poetry meter is a sequence of patterns. A poetry verse is characterized by a meter and consists of two parts. Detecting classical poetry meter is important for teaching purposes, for poetry and prose categorization, for authorship recognition and for computational aesthetics. Automatically detecting the meter of any single verse written as a normal sentence is challenging. We need a global approach that processes phonological verse preparation, deals with distinguishing the first part of the verse from the second, handles meters disambiguation and covers the verse parts concordance. To tackle this challenge, we introduce a novel solution called the Arabic Meters Identification System (AMIS) that combines an exhaustive pattern data set, pattern matching and similarity. We evaluate our system on a vocalized poetry corpus and reach a precision of 99.3%.
Chaotic Brain Storm Optimization Algorithm in Objective Space for Medical Image Registration

Image registration is the basis of medical image analysis whichhas profound significance in the field of medical image processing. In order to improve its accuracy, a method based on chaotic brain storm optimization algorithm in objective space(CBSO-OS) is proposed. The CBSO-OS increases its ability of global exploitation by replacing random function with guass map. Compared with other four optimization algorithms in image registration experiments, the root mean square error of the proposed algorithm is reduced by 0.69%, 2.26%, 3.54% and 1.37%, respectively. Meanwhile, the translation error of the proposed registration algorithm is the smallest. Experimental results show that the proposed registration algorithm effectively reduces the registration error of medical images so as to provide doctors with high-precision registered images.
Studying Affect Dynamics Using Epistemic Networks

The study of how affect develops and manifests over time during learning is a popular area of research called affect dynamics. Students’ affective states are recorded in authentic settings like classrooms using direct observations by culturally sensitive, trained, and certified coders. A popular approach to studying affect dynamics in the last decade involved a transition metric called the L statistic. However, recent studies have reported statistical errors and other discrepancies with L statistic leading to questions about its reliability. Thus, we turn to epistemic network analysis (ENA), an emerging technique that is gaining popularity in studying the structure of temporal interconnections between codes. In this paper, we present an alternative approach to study affect dynamics by extending ENA to include directionality in the network edges to capture transitions. We also propose a new approach to running significance tests on network edges to identify significantly likely transitions. Then, we apply the two techniques – L statistic and ENA - to a previously collected affect dataset from a middle school math class, in order to better understand the trade-offs between these methods. Our analysis revealed that ENA could be a promising new approach to conduct affect dynamics analysis. In addition to avoiding statistical errors seen in L statistic, ENA offers better visualization which better emphasizes the magnitude of a transition’s strength. We discuss the assumptions in ENA that need to be vetted further and the possibility for new kinds of analysis in the future for affect dynamics research using ENA.
Text Orientation Detection Based on Multi Neural Network

Optical character recognition (OCR) is an important research area in the field of pattern recognition, such as Vehicle License Plate Recognition. With it, we can extract textual information from the images to facilitate digital processing. However, most existing systems are designed to detect or recognize horizontal (or near-horizontal) texts and can't be applied to recognize texts of varying orientations. Text Orientation Detection is an important but challenging task. It can be used as a pre-processing for previous researches, and allows them to be adapted to more complex situations. Once we get angle of the text, we can use a series of transformations to get horizontal text. Although this problem is a multiclass classification, the results using common multiclass classification methods are not ideal. Our algorithm is inspired by the human behavior of recognizing texts. In this paper, we propose a new algorithm containing three neural networks to detect text orientation. The first is for capturing the abstract information of text image, and trained on multi-orientation, synthetic texts. Then the second is for evaluating the correctness of meaning of texts and trained on horizontal texts. The output of these two neural networks serves as the input to the last. In this way, the last neural network can obtain information about the image of the text as well as its meaning, and finally evaluate and output the angle of the text. According to the results, our proposed method can guarantee a high accuracy of text orientation detection.
Non-conditional Contracting Connectives

It has been claimed that contracting connectivesContracting connective are conditionalsConditional. Our modest aim here is to show that the conditional-like features of a contracting connectiveContracting connective depend on the defining features of the conditionalConditional in a particular logic, yes, but they also depend on the underlying notion of logical consequence and the structure of the collection of truth values. More concretely, we will show that under P-consequenceP-consequence and suitable satisfiability conditions for the conditionalConditional, conjunctionsConjunction are contracting connectivesContracting connective for some logics without thereby being conditional-ish.
Recommendation of Academic Papers based on Heterogeneous Information Networks

The rapid advance in science and technology is made possible by research conduct and breakthroughs in a wide range of fields, which have resulted in a large number of academic papers. Searching through the enormous literature to find relevant information of one's research interest has become an increasingly important yet challenging problem for many researchers. Most existing methods for academic paper recommendation are based on the analysis of paper contents and only meet with limited success. We propose a novel method based on heterogeneous information networks for academic paper recommendation, referred to as HNPR. This method considers the citation relationship between papers, the collaboration relationship between authors, and the research area information of papers to construct two types of heterogeneous information networks. In such networks, a random walk-based strategy is used to simulate natural sentences for the discovery of relevance between two papers according to a mature natural language processing model. Extensive experimental results using real data in public digital libraries show that HNPR significantly improves the accuracy of academic paper recommendation in comparison with traditional content-based recommendation methods.