Reviewerly/exHarmony huggingface dataset:
Extremely Compact Non-local Representation Learning

In contrast to regular convolutions with local receptive fields, non-local operations have widely proven an effective method for modeling long-range dependencies. Although lots of prior works have been proposed, prohibitive computation and GPU memory occupation are still the major concerns. Different from that carrying out non-local operations pixel-wise or channel-wise in a computation intensive way, we argue that we can achieve effective non-local operation using a more compact high-order statistic, which can be computed more efficiently and may convey some high-level information. In this paper, we propose an extremely compact non-local learning module (CoNL) with high-order reasoning based on a graph convolution as the core. In our CoNL, a global Hadamard pooling (GHP) as a non-local operation is used to extract a compact second-order feature vector from the input tensor. With the help of a light-weight graph convolution network (GCN), this high-order compact vector is further refined with high-level reasoning. After the GCN refinement, the compact high-order vector intuitively indicates some global semantic characteristics, and is eventually applied to enhance the input tensor through a channel scaling operation. The CoNL module is designed easily pluggable to upgrade existing networks. Extensive experiments on a wide range of tasks demonstrate the effectiveness and efficiency of our work. The proposed CoNL can achieve comparable or superior performance over previous state-of-the-art baselines on video recognition, semantic segmentation, object detection and instance segmentation tasks. For a 96 x 96 x 2048 input, our block consumes 13.6 x less in computational cost than non-local block while 7.6 x smaller in GPU memory occupation.
Deep Learning Approach Towards Unstructured Text Data Utilization

With the meteoric development of the big data, internet of things (IoT), the very first question is about how to scout the massive volume of data from heterogeneous data sources. The modern tools and techniques commonly used in successful transmuting of well-structured data into business intelligence (BI) clearly don't work when started on unlabeled data. It is needed to have effective and efficient mapping processes to transform unstructured text data to structured text data with assigned categories to make rapid decisions. With the support of modern automatic text mining tools, the decision-making process can now be done efficiently and cost-effectively. The deep learning concept has attained state-of-the-art results. The mapping operation of unstructured text data to classified structured text data will symbolize unstructured data as renewable assets that are well arranged, useful, and meaningful to serve organizational operations. This chapter presented a comparison of conventional and deep learning methods for unstructured text data classification and its challenges.
BlockDrive: A Deduplication framework for Cloud using edge-level blockchain

Data deduplication is an important data compression technique to eliminate duplicate copies of data and has been widely used in cloud storage to reduce the amount of storage space and save bandwidth. However, even though most cloud service providers already employ deduplication techniques, they consume a lot of overhead bandwidth. Hence, we came up with a solution to reduce this overhead bandwidth by creating a middleware deduplication layer that checks the originality of a file without the need to communicate with the cloud. This results in reducing a substantial amount of internet overhead and at the same time making the deduplication process much quicker since it is closer to the edge device. We implement the deduplication layer using a decentralized blockchain structure due to its inherent security and scalability features.To protect the confidentiality of sensitive data while supporting deduplication, the convergent encryption techniques such as double hashing has been proposed to ensure that the user account credentials are secure and not misused. We implement our blockchain layer using Ganache that provides us with an Ethereum powered blockchain to test and deploy our prototype. Hence all the file transactions i.e., uploading, sharing and deletion of files will go through ganache and get added to our local blockchain network. We define Smart contracts to communicate with the ganache layer which mines a block every time a new file is uploaded or shared with another user and file metadata is stored in each of these blocks. Since every unique file will have its own block, the file metadata will help in achieving deduplication at this layer.
Thermal-to-Visible Face Synthesis and Recognition

Face is one of the most widely used biometrics. One key advantage of using faces as a biometric is that they do not require the cooperation of the test subject. Various face recognition (FR) systems have been developed over the last two decades. Recent advances in machine learning and computer vision methods have provided robust systems that achieve significant gains in performance of face recognition systems [5, 19]. Deep learning methods, enabled by the vast improvements in processing hardware coupled with the ubiquity of face data and algorithmic development, have led to significant improvements in face recognition accuracy, particularly in unconstrained scenarios [4, 5, 19, 20, 27]. Also, largely driven by social network companies, progress in face recognition research, development, and deployment have focused on faces collected in visible regimes of the electromagnetic spectrum.
Measurement error caused by quarter-wave plate in the dynamic interferometer: Theory and correction method

Quarter-wave plate (QWP) is one of the most important components of dynamic interferometer and its retardation can influence the measurement accuracy, which is usually manifested as periodic ripple error in the measurement results. Based on Jones matrix, the error formula is deduced in this paper, and the error correction method by rotating the half-wave plate (HWP) to change the intensity distribution in the four phase-shifted interferograms is proposed. In the experiment, the ripple error can be effectively suppressed when the background intensity of four interferograms tend to be the same. The performance of dynamic interferometer can be significantly improved through the proposed method.
Meta-Learned Feature Critics for Domain Generalized Semantic Segmentation

How to handle domain shifts when recognizing or segmenting visual data across domains has been studied by learning and vision communities. In this paper, we address domain generalized semantic segmentation, in which the segmentation model is trained on multiple source domains and is expected to generalize to unseen data domains. We propose a novel meta-learning scheme with feature disentanglement ability, which derives domain-invariant features for semantic segmentation with domain generalization guarantees. In particular, we introduce a class-specific feature critic module in our framework, enforcing the disentangled visual features with domain generalization guarantees. Finally, our quantitative results on benchmark datasets confirm the effectiveness and robustness of our proposed model, performing favorably against state-of-the-art domain adaptation and generalization methods in segmentation.
Criticalities and Advantages of the Use of Artificial Intelligence in Research

In recent years, artficial intelligence, through the rapid development of machine learning and deep learning, has started to be used in different sectors, even in academic research. The objective of this study is a reflection on the possible errors that can occur when the analysis of human behavior and the development of academic research rely on artificial intelligence. To understand what errors artificial intelligence can make more easily, three cases have been analyzed: the use of the IMPACT system for the evaluation of school system in the District of Columbia Public Schools (DCPS) in Washington, the face detection system, and the “writing” of the first scientific text by artificial intelligence. In particular, this work takes into consideration the systematic errors due to the polarization of data with which the machine learning models are trained, the absence of feedback and the problem of minorities who cannot be represented through the use of big data.
The implementation of Mind Mapping technique in teaching writing at the second grade of Junior High School 3 Trimurjo

The objectives of this research are to find out: 1) Whether there is any significant improvement of students’ writing ability of descriptive text after being taught through mind mapping technique, 2) Which aspect of writing improved the most after being taught by using mind mapping technique and 3) What is the students’ perception towards the implementation of mind mapping technique in descriptive text writing. This research was a quantitative research used a one group pretest-posttest design. The subjects were 23 students of class VIII A at SMP N 3 Trimurjo in 2019/2020 academic year. Writing tests and questionnaires were administered as the instrument of the research. The data were analyzed by using Paired Sample T-test in which the significance level was determined by p&lt;0.05. The result of the research showed that the value of significant level was 0.00&lt;0.05. It means that there was an improvement in students’ writing ability of descriptive text after the implementation of mind mapping technique and the aspect of writing that improved the most after the implementation of mind mapping technique was content. Furthermore, the result of questionnaires showed that most students (75.86%) gave positive towards the implementation of mind mapping technique in descriptive text writing. Most of the students agree that mind mapping technique can help them in composing writing, make them enjoyed and relaxed in composing writing, make them more interested and motivated in writing. But, almost of them disagree that by using mind mapping technique, they do not have any difficulties to make writing anymore. It means that even they have positive perception towards the implementation of mind mapping technique in descriptive text writing, they still have another difficulty in composing it. Keywords: mind mapping technique, writing ability, students’ perception
Fast quantum circuit simulation using hardware accelerated general purpose libraries

Quantum circuit simulators have a long tradition of exploiting massive hardware parallelism. Most of the times, parallelism has been supported by special purpose libraries tailored specifically for the quantum circuits. Quantum circuit simulators are integral part of quantum software stacks, which are mostly written in Python. Our focus has been on ease of use, implementation and maintainability within the Python ecosystem. We report the performance gains we obtained by using CuPy, a general purpose library (linear algebra) developed specifically for CUDA-based GPUs, to simulate quantum circuits. For supremacy circuits the speedup is around 2x, and for quantum multipliers almost 22x compared to state-of-the-art C++-based simulators.
Internet plus based teaching mode of English information in Colleges and Universities

Abstract China’s education attaches great importance to the learning of English courses. Many parents pay attention to students’ English learning. With the gradual increase of international communication activities, many schools carry out English teaching activities [1]. In this process, the topic of English teaching has been pushed to the upsurge. With the rapid development of computer teaching technology, people gradually realize the important role of computer. The mode of English information teaching based on Internet will be opened soon. This paper analyzes the current situation of traditional English teaching. According to the current trend, this paper puts forward the main application of English information teaching mode.
Applying fuzzy Delphi Method to Construct Evaluation Criteria for Online English Teaching

After the outbreak of COVID-19, traditional offline teaching is not carried out normally. However, education cannot be stopped. In this case, many teachers are facing challenges, not subject to geographical restrictions, diverse ways, and other advantages. Thus, online teaching has become an important teaching method in this special period. Based on the literature review, this paper summarizes the evaluation criteria of different levels of online English teaching. Using the questionnaire survey, experts and scholars were consulted for several rounds. After the validity of the questionnaire by using a fuzzy Delphi method, the evaluation criteria system of English online teaching was established, and the importance, order, and grade of the evaluation criteria were evaluated. This system allows teachers to formulate teaching strategies to optimize and improve English online teaching. The findings of this paper shed some light on efficient learning strategies and ensure the effect of online English teaching.
Benchmarking of Natural Scene Image Dataset In Degraded Conditions For Visibility Enhancement

Poor visibility due to existence of fog and other associated particles in the atmosphere is the most fundamental problem for current vision applications. Recently, techniques for visibility enhancement of images have received a significant attention. However, validation of the existing techniques remains scare due to the lack of balanced distribution on the existing datasets. In this paper, a newly designed dataset entitled “SAMEER-TU Outdoor Dataset” is proposed. The dataset contains 5880 images of urban scenes in fog, poor illumination and clear conditions. Also, ground truths are provided in terms of meteorological weather parameters and corresponding clear scene images of the degraded images. On the designed dataset, quantitative analysis of existing visibility enhancement techniques (i. e., conventional and deep learning techniques) are performed based on qualitative evaluation metrics. It comes as no surprise that the existing visibility enhancement techniques and there still existing significant for further improvement.
Performance evaluation of automotive product development team members based on a PLM system: A case study of M Automotive Products Company

Based on product lifecycle management (PLM) theory and social comparison theory, this paper constructs a performance ranking model of automobile product development project team members, uses an active server provider (ASP) to develop the system, realizes the online operation and real-time analysis of the performance management system, and solves the problems of the low efficiency, low morale and unfair assessment of product development team members caused by traditional performance assessment. The performance management platform for the team members of the automotive product development project uses a PLM system to realize a systematic and standardized list of project functions and provide a performance appraisal management system that can be evaluated and fairly compared for project team members. Based on the performance appraisal practice of automotive product development project team members of M Automotive Products Company, this study verified the feasibility of the ranking model’s transformation to work habits based on the predetermined and result data of the mission completion rate, the punctuality rate and the degree of improvement of the management level of product development project team. Through the tracking of the team performance ranking function, it is found that the model can solve the problems of untimely assessment and insufficient incentives in the current traditional performance appraisal, enhance the overall enthusiasm of the team, and give full play to the subjective initiative of the team.
FORMATION OF LOGIC - ALGORITHMIC CULTURE OF STUDENTS IN THE PROCESS OF TEACHING NATURAL SCIENTIFIC DISCIPLINES

The paper substantiates the need for the formation of a logical and algorithmic culture of students, which contributes to the conscious perception of educational material, which is adequate to modern school and university education in order to instill information and computer literacy in the younger generation. Teaching students to generalize, systematize, apply logical knowledge in a new situation, transfer the acquired knowledge from one subject to another is one of the main tasks of a modern school. At the same time, the logical and algorithmic level of their assimilation provides such a quality of knowledge as completeness and effectiveness, i.e. the student can list all the leading elements of knowledge, define each of them, characterize their main features, and also perform tasks on the topic using the acquired knowledge and skills. The generalization of the practical experience of the organization of the educational process allowed us to offer methodological recommendations for the organization of the formation of the logical and algorithmic culture of students on the basis of interdisciplinary connections in the process of teaching natural science disciplines. In the article, this technique is implemented with the help of one of the new forms of organization of classroom work at the university – complex seminars. The plans of conducting complex seminars on the topics "Harmonic oscillations" and "Integral and its application in physics" are given.
Expert Classification: Resource-Based Approach

Classification expertise is considered in the context of effective resource usage ensuring work of an expert group. A set of indicators allowing estimation of income amount from the expertise associated with the correct solving of the classification problem, just like possible expenses due to errors made by experts. The selection problem of optimal set of experts has been formulated in terms of dependency on the prior distribution of objects in classes.
Hashing A Face

Face recognition methods have made great progress in the recent years. These methods most of the time represent a face image as a high-dimensional real-valued feature, often obtained using a deep network. However, comparisons of this high-dimensional feature can be computationally expensive. Furthermore, when dealing with large face images database this representation can lead to prohibitive storage requirements. Also, in a context where the capture of the face image is performed on a mobile device or in a separate location from the face verificationFace verification or search process, the amount of data that needs to be transmitted over the network should be minimized.
Research on Evaluation of Composite Insulator Companies Based on the Randomized Procedure

According to the production characteristics of composite insulator companies, the independent random states had been calculated in the productions of composite insulator by the statics which had been get from the supervision of UHV projects on the base of stochastic process theory. The method of signal statics chain about the period by the project and time has been brought up and the distribution of normal state and other states of production have been showed. The production capacity has been described by the stratification of Markov chain with the Cascade model of the normal state and other states of production. The stratification rule is that the mutual independence had been regarded as so that the each production capacity of composite insulator companies could been compared.
Web-Based Expert System for Diagnosing Gastric Disease Using Bayes Theorem Method

Health is a very expensive thing, so it takes an awareness to protect it. The human body has many limbs, one of which is the stomach. Gastric disease is a disease that cannot be accommodated, because if left untreated it can cause other diseases to arise and can also cause death if it is not immediately supported. Gastric disease can be caused by an inappropriate diet, a burden on the mind and also an infection caused by bacteria. The author took the initiative to create a web-based gastric disease diagnosis expert system using the Bayes theorem method which is useful for gastric disease sufferers in diagnosing their own gastric disease. The method used by researchers is the Bayes theorem method. The data collection technique used is quantitative data techniques. The results that have been achieved from this research are that it can prevent and minimize the risk of gastric disease in sufferers or the community.
‘Making Life Lively’: Co-estrangement in live electroacoustic improvisation

The use of live electronic processing to extend, modify or transform an acoustic musical instrument has its roots in the recording and broadcast technologies that were developed in the first few decades of the twentieth century. In the second half of the century these tools were adopted by composers and musicians in many musical genres and have become commonplace and in some musics, ubiquitous. The perceived musical relationship between instrument and its electronic ‘other’ has been discussed largely from the point of view of listener and composer. This paper focuses on the performers ’ perspective through reflection on and discussion of the author’s working methods in improvising duo contexts. The author suggests ‘estrangement’ as a term to describe and understand aspects of the performer’s experience of live transformation and discusses how this estrangement might influence the relationship between musicians and the resulting musical interaction in improvisation, and finally offers ‘co-estrangement’ as a description of his shared experience in such improvising duos.
Improved ECC-Based Image Encryption with 3D Arnold Cat Map

Maintaining the secrecy and safety of images while sharing digital data online is a huge challenge. Numerous encryption schemes use the Elliptic Curve Cryptography(ECC) to encrypt and decrypt the images as ECC provides higher security with shorter key sizes. The researchers also suggest the use of the popular chaotic maps for added strength of the encryption process. In this paper, a novel encryption scheme for digital images based on ECC and 3D Arnold cat map is proposed. The 3D Arnold cat map scrambles the position of pixels in the image and then transforms the values of pixels. The transformed pixel values are encrypted and decrypted using the Elliptic Curve Analogue ElGamal Encryption Scheme (ECAEES). The proposed model is implemented using Python. We get an average entropy value of 7.9992, NPCR of 99.6%, UACI of 33.3% and PSNR of 27.89. The correlation coefficient values between adjacent pixels of cipher images are minimized. The improved performance proves that the model put forward is more secure and resilient than the existing noteworthy schemes.Parida, Priyansi Pradhan, Chittaranjan
Learning Skip Map For Efficient Ultra-High Resolution Image Segmentation

The pattern distribution of ultra-high resolution images is usually unbalanced. While part of an image contains complex and fine-grained patterns such as boundaries, most areas are composed of simple and repeated patterns. In this work, we propose to learn a skip map, which can guide a segmentation network to skip simple patterns and hence reduce computational complexity. Specifically, the skip map highlights simple-pattern areas that can be down-sampled for processing at a lower resolution, while the remaining complex part is still segmented at the original resolution. Applied on the state-of-the-art ultra-high resolution image segmentation network GLNet, our proposed skip map saves more than 30% computation while maintaining comparable segmentation performance.