Reviewerly/exHarmony huggingface dataset:
A Mixed Learning Objective for Neural Machine Translation

Evaluation discrepancy and overcorrection phenomenon are two common problems in neural machine translation (NMT). NMT models are generally trained with word-level learning objective, but evaluated by sentence-level metrics. Moreover, the cross-entropy loss function discourages model to generate synonymous predictions and overcorrect them to ground truth words. To address these two drawbacks, we adopt multi-task learning and propose a mixed learning objective (MLO) which combines the strength of word-level and sentence-level evaluation without modifying model structure. At word-level, it calculates semantic similarity between predicted and ground truth words. At sentence-level, it computes probabilistic n-gram matching scores of generated translations. We also combine a loss-sensitive scheduled sampling decoding strategy with MLO to explore its extensibility. Experimental results on IWSLT 2016 German-English and WMT 2019 English-Chinese datasets demonstrate that our methodology can significantly promote translation quality. The ablation study shows that both word-level and sentence-level learning objectives can improve BLEU scores. Furthermore, MLO is consistent with state-of-the-art scheduled sampling methods and can achieve further promotion.
ANALYSIS OF PROFITABILITY RATIO AS TOOL FOR ASSESSING THE PERFORMANCE OF THE BALITAR ISLAMIC UNIVERSITY OF BLITAR PERIOD 2016-2018

This study aims to determine the financial performance of Balitar Islamic University in the financial statements for 3 periods from 2016-2018. Based on the existing problems, the research used is descriptive method by collecting data, managing data and presenting research data / financial reports so that readers of this report can understand the description of the object of research data conducted at Balitar Islamic University in Blitar. Data analyzed were in the form of financial statements for three periods from 2016-2018 using profitability ratios. The results showed the net profit margin of the Islamic University of Balitar Blitar in 2016 was 0.8%, in 2017 it was 1% and in 2018 it was 1.1%. Judging from this percentage, net income has increased even though only slightly.
Online Somatic Work: the Case of Sensory Processing Disorder

This study examines how people affected by sensory processing disorder (SPD) make their sensory experience visible and meaningful in their unprompted public blog‐writing. I analyzed 66 online narratives written by 44 people affected by SPD. The non‐normative sensory responses of people with SPD are invisible but may result in socially disapproved and challenging behavior (e.g., extreme picky eating). Minimal social and medical recognition of this condition puts people affected by SPD in the position of needing to explain their experience and account for behaviors that are culturally coded as inappropriate. To understand how people affected by SPD communicated their experience to unknown readers, I used techniques associated with grounded theory to analyze their unprompted narratives on The Mighty . Bloggers use several strategies to define SPD for readers, including making use of expert definitions, examples, and comparisons with normative sensory experience, and negative definitions that differentiated SPD from other disorders, normal childhood behavior, and misbehavior. Bloggers used these definitional strategies in conjunction with discourses of normal childhood, good parenting, and biomedicine to communicate and legitimize their experience. Moreover, bloggers did not challenge normative expectations of the human senses but instead reinforced conventional notions about how the senses are supposed to work.
Automatic Classification and Comparison of Words by Difficulty

Vocabulary knowledge is essential for both native and foreign language learning. Classifying words by difficulty helps students develop better in different stages of study and gives teachers the standard to adhere to when preparing tutorials. However, classifying word difficulty is time-consuming and labor-intensive. In this paper, we propose to classify and compare the word difficulty by analyzing multi-faceted features, including intra-word, syntactic and semantic features. The results show that our method is robust against different language environments.
Boosting component-based synthesis with control structure recommendation

Component-based synthesis is an important research field in program synthesis. API-based synthesis is a subfield of component-based synthesis, the component library of which are Java APIs. Unlike existing work in API-based synthesis that can only generate loop-free programs constituted by APIs, state-of-the-art work FrAngel can generate programs with control structures. However, for the generation of control structures, it samples different types of control structures all at random. Given the information about the desired method (such as method name and input/output types), experienced programmers can have an initial thought about the possible control structures that could be used in implementing the desired method. The knowledge about control structures in the method can be learned from high-quality projects. In this paper, we propose a novel approach of recommending control structures for API-based synthesis based on deep learning. A neural network that can jointly embed the natural language description, method name, and input/output types into high-dimensional vectors to predict the possible control structures of the desired method is proposed. We integrate the prediction model into the synthesizer to improve the efficiency of synthesis. We train our model on a codebase of high-quality Java projects from GitHub. The prediction results of the neural network are fed to the API-based synthesizer to guide the sampling process of control structures. The experimental results on 40 programming tasks show that our approach can effectively improve the efficiency of synthesis.
Usable Security Case of Remote Web Access

Various organizations such as the European Union (EU) use Remote Web Access (RWA) [1], as it enables their staff members to work from any place and at any time. Working from remote locations has its drawbacks. Cyberattacks can take place. Therefore, organizations need to secure the user identification technique (UIT). This research talks about the challenges of UIT associated with RWA and explores solutions on how to make RWA secure as well as user-friendly. However, surveys showed that users were not satisfied with these security measures, as it affected the usability of the platform. This research focuses on designing a secure and user-friendly UIT for RWA. The study involves the collection and assimilation of qualitative and quantitative data through questionnaires, focus group discussions, interviews, and document analysis. It also uses the Morea Usability testing software as a tool for assessing the user-satisfaction and usability of RWA. The preliminary assessment involved issuing 42 questionnaires to a randomly selected EU delegation to the African Union staff living in Addis Ababa. Thirty-four of them responded to the questionnaire that formed the basis of this research. About 70% of the respondents expressed confidence in the security measures, whereas 92% did not find the additional user identification steps as convenient. Nearly 64% of the people surveyed felt that the process was a lengthy one. It also affected the efficiency and memorability, thereby necessitating a change. As pointed out in [2], observing four to six participants in usability testing will uncover about 80% of a product’s usability problems. In this study, six test participants from EEAS were selected to conduct usability testing. As the study showed an inverse relationship between the usability and the perception of security and trust in RWA, the research proposes a usable UIT for RWA for making it secure and user-friendly at the same time.
Training Deep Neural Networks in Situ with Neuromorphic Photonics

We discuss an optoelectric architecture for executing the direct feedback alignment algorithm for neural network training. Using 1000 microring resonators, we can theoretically obtain speeds of 9.95 tera operations per second.
Discrimination of noise channels on the basis of quantum memory

From the point of view of communication theory, a quantum channel can be defined as a physical process mapping from initial to final state in the form of CPTP (Completely positive trace preserving) map. Quantum information theory provide a theoretical framework and some mathematical methods to accomplish this. In order to preserve information, we proposed a scheme for the protection of two-qubit state through various noise channel, where successive uses of noise channels are correlated. These successive uses of channels are called memory. Different types of noise channels with memory have been discussed in details. We compare the dynamics of entanglement of two-qubit state under different noise channel and it has been seen that one can preserve two-qubit steady state quantum correlation under noise channels with memory. The study of memory effect in quantum channel will emerge as an interesting phenomena to trace back information from the surrounding which is considered as a resource in quantum information task. However, we also define some mores measures of entanglement and their preservation. We hope that our new findings may help to study the two-qubit entanglement structure.
Novel Cluster Analytics for Analyzing COVID-19 Patient Medical Data

The ongoing global COVID-19 pandemic has caused more than 440,000 deaths among more than 8 million cases globally by Mid-June, 2020. This pandemic has caused a staggering worldwide socioeconomic impact and loss of lives. This research proposes an innovative technological approach to analyze COVID-19 patient data for new analytical insights via developing a transformative pattern identification algorithm for cluster analysis in tabular numerical data tables, e.g., patient medical data files and in disease networks. The underlying mathematics is based upon Lie algebras and continuous Markov transformations that are foundational in quantum theory, relativity, and theoretical physics. Our novel algorithm does not use an arbitrary concept of proximity or nearness but instead is based upon an information flow model where clusters are identified, and rank ordered by the matrix eigenvalues. The component clusters identify the degree of patient cluster participation by the nodal weight given by each associated eigenvector. Medical metadata tags in the tables are automatically linked to the cluster eigenvalue and eigenvectors to facilitate interpretation of the analytics. The core algorithm has been coded and will be ported to a cloud environment allowing other investigators to submit data files for cluster analytics. We plan to analyze COVID-19 patterns and expect to work with other medical research teams on pattern identification in deidentified medical patient data sets. We expect this ongoing research to lead to significant practical and theoretical insights and a greater understanding of our transformative network clustering algorithm at the individual COVID-19 patient level, hospital level and beyond.
A Recommendation Algorithm Based on Belief Propagation and Probability Matrix Factorization

Collaborative filtering recommendation algorithm is the most important recommended application methods at present. The selection of users (items) similarity measurement methods in this method may affect the final recommendation effect. Probability matrix factorization algorithm is one of the more and more methods applied in collaborative filtering algorithms. This paper proposes to convert the users (items) consumption network into a bipartite graph and use belief propagation algorithm to obtain fuzzy nearest neighbor set, improving users (items) similarity measure and incorporating it into probability matrix factorization. Experimental tests on the data set showed that the proposed method performance was good.
The Use of Think-Pair-Share (TPS) in Teaching Recount Text

This research aims to find out whether there is a difference of the eighth-grade students’ achievement in reading recount text taught by using Think Pair Share (TPS) and those who are trained by using Number Head Together (NHT). The research used a quasi-experimental design. Test and observation were used as the instrument in collecting data. The samples were students of VIII E as the experimental and VIII C as the control of SMPN 31 Banjarmasin academic year 2017/2018. The result showed that there was a difference in students’ reading achievement taught by using TPS and those who are trained by using NHT. Based on the calculation, the average score of pre-test was 67,04 for experimental and 68,80 control class. After the treatment given, the average post-test rating for the experimental class was 80,02 and control was 76,30. The calculation of t-test showed t-value was higher than t-table (2,42>1,99) at the significance level of 5%. Furthermore, the use of TPS made a better result for students’ achievement in reading. Based on the research result, the researcher recommends the English teacher to use TPS as the alternative technique in teaching since it is suitable and straightforward to begin practising cooperative learning.
Mathematical Subject Information Entity Recognition Method Based on BiLSTM-CRF

Combining language conditional random field (CRF) and bidirectional long-term and short-term memory (BiLSTM) networks, a mathematical subject information entity recognition method based on BiLSTM-CRF is constructed to extract entity information in mathematical language. Experimental results show that compared with BiLSTM, BiLSTM-CRF improves the recall rate by nearly 5%, the accuracy rate by nearly 2%, and the F1 value by nearly 4%. The results of the BERT-CRF model are also significantly better than other models.
Hybrid Loss for Improving Classification Performance with Unbalanced Data

Unbalanced data is widespread in practice and presents challenges which have been widely studied in classical machine learning. A classification algorithm trained with unbalanced data is likely to be biased towards the majority class and thus show inferior performance on the minority class. To improve the performance of deep neural network (DNN) models on poorly balanced data, we hybridized two well-performing loss functions, specially designed for learning imbalanced data, mean false error and focal loss. Since mean false error can effectively balance between majority and minority classes and focal loss can reduce the contribution of unnecessary samples, which are usually samples from the majority class, which may cause a DNN model to be biased towards the majority class when learning. We show that hybridizing the two losses can improve the classification performance of the model. Our hybrid loss function was tested with unbalanced data sets, extracted from CIFAR-100 and IMDB review datasets, and showed that, overall, it performed better than mean false error or focal loss.
Robust Controller for Permanent Magnet Linear Motor Based on Interval Matrix Theory

Abstract Parameter perturbation, end effect and external interference of AC permanent magnet linear motor affect the accuracy and performance of servo system. In this paper, the PMLSM servo system controller designed by the interval matrix theory is used to solve this problem. The uncertainty of servo system is described by interval matrix. Compared with the precise mathematical model, the controller designed by this theory has strong robustness. The simulation and experiment of the system show that the strategy has excellent dynamic and static performance, it has strong parameter robustness.
On the Unsurprising Behaviour of Kernels in High Dimensions

Kernels are employed in ML algorithms not only as a means of measuring similarity but also for their interesting theoretical interpretations and computational advantages. Among them Gaussian kernels have been extensively employed for their ease of interpretation and locality. However, it has been observed [4] that they lose many of their advantages in high dimensions. Hence a suitable modification of it has been proposed to overcome this loss. In this work, we firstly show that despite these modifications not all the lost advantages have been recovered and leads us to consider alternate kernels. However, we contend that either a change or a modification of an underlying kernel only treats the symptoms and discuss what could be the main malaise.
Comparing Academic Papers of Students and Experts in terms of Linguistic Features with Natural Language Processing

In graduate education, the quality of academic papers can reflect individual scientific research achievements. This study compared the differences in linguistic features between the papers of experts and graduate students with natural language processing. More specifically, for revealing the problems existing in the writing of graduate students, this study analyzed academic papers in a journal as experts and those of graduate students by using Chinese word segmentation. The study found that the graduate students and experts have differences in the use of various parts of speech (i.e., nouns, verbs, adjectives, pronouns, and conjunctions) and connective words (i.e., organization, comparison, summarization, etc.). The results may be helpful for further designing scaffolding to improve the writing quality of graduate students’ papers.
IoT Streaming Data Outlier Detection and Sensor Data Aggregation

Applications of today's age rely on IoT technology. As sensor data gathered from wireless sensors are large and dynamic, it is more prone to the occurrence of data outliers. Data redundancy and outliers can minimize productivity and determine the operation of IoT systems significantly. To assure safety for an IoT ecosystem, data outliers must be detected at an early stage, which in turn results in the robustness of the system. Since IoT objects have minimal resources, the nature of real data can be explored by utilizing methodologies such as classification and dimensionality reduction. Further, the number of data transmissions can be reduced via advanced data aggregation techniques to ensure data accuracy. Here, a data analysis scheme is suggested to aggregate sensor data and detect outliers. Later, the proposed methodology is compared and analyzed with an existing R-PCA based technique. Moreover, to predict future events based on observed sensor data and analyze the sensor data, an effective data analysis framework is crucial in the Cloud.
Learning and Distillating the Internal Relationship of Motion Features in Action Recognition

In the field of video-based action recognition, a majority of advanced approaches train a two-stream architecture in which an appearance stream for images and a motion stream for optical flow frames. Due to the considerable computation cost of optical flow and high inference latency of the two-stream method, knowledge distillation is introduced to efficiently capture two-stream representation while only inputting RGB images. Following this technique, this paper proposes a novel distillation learning strategy to sufficiently learn and mimic the representation of the motion stream. Besides, we propose a lightweight attention-based fusion module to uniformly exploit both appearance and motion information. Experiments illustrate that the proposed distillation strategy and fusion module achieve better performance over the baseline technique, and our proposal outperforms the known state-of-art approaches in terms of single-stream and traditional two-stream methods.
A Comparative Study for Fisheye Image Classification: SVM or DNN

The comparison between the feature-based method and the learning-based method is conducted in the training time, the accuracy and the generalization capacity, to address the optimisation for the multi-style fisheye imagery classification. We construct an srd-SIFT descriptor based SVM classifier to present the feature-based method for describing the influence of the dataset scale and the visual word scale on the classifier. The SVM classifier achieves 15.98% accuracy on the test set after 162 h training, with the condition that includes 800 images per class in 12 classes and 1500 visual words. For the learning-based method, we propose to expand training samples’ style variety, via style transformation, to facilitate the contemporary architecture retraining. Following this approach, we retrain the ResNet-50 by an artificial multi-style fisheye image dataset without complementing new training labels. The performance of the obtained ResNet classifier is evaluated on 6000 images collected in the real-world. The result shows that the retrained classifier has great generalization capacity and reaches 97.19% top-3 accuracy.
Femtojoule Technology Roadmap for TeraMAC Neuromorphic Photonic Accelerators

We analyze how neuromorphic-photonic-accelerators can provide energy-efficiencies of a few fJ/MAC and footprint-efficiencies in TeraMAC/s/mm <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> regime to surpass both analog and digital electronic alternatives, by exploiting foundry-ready photonic devices, yet without excluding lasers.
Distance Learning in Technical Education on Example of Estonia and Poland

The rapid restrictions related to the COVID-19 pandemic, also known as the coronavirus pandemic, has changed the face-to-face education principles that were developed during the centuries and became the main way to transfer knowledge in higher education institutes. The distance learning is challenging for the students and makes it hard for educators to track and evaluate the student's progress. Distance learning is more challenging for the technical education, where the practical courses plays significant role in educational process. In this paper, the modifications taken place in Department of Electrical Power Engineering and Mechatronics at Tallinn University of Technology (Tallinn, Estonia) and Department of Power Electronics, Electrical Drives and Robotics at Silesian University of Technology (Gliwice, Poland) are analyzed. The experience in preparation of additional electronical materials for the students and elements of hybrid education taken in recent years was the key factor for a fast and successful transfer of technical education into distant learning.