Reviewerly/exHarmony huggingface dataset:
Adaptive Transformer for Multilingual Neural Machine Translation

Multilingual neural machine translation (MNMT) with a single encoder-decoder model has attracted much interest due to its simple deployment and low training cost. However, the all-shared translation model often yields degraded performance due to the modeling capacity limitations and language diversity. Moreover, it has been revealed in recent studies that the shared parameters lead to negative language interference although they may also facilitate knowledge transfer across languages. In this work, we propose an adaptive architecture for multilingual modeling, which divides the parameters in MNMT sub-layers into shared and language-specific ones. We train the model to learn and balance the shared and unique features with different degrees of parameter sharing. We evaluate our model on one-to-many and many-to-one translation tasks. Experiments on IWSLT dataset show that our proposed model remarkably outperforms the multilingual baseline model and achieves comparable or even better performance compared with the bilingual model.
Spatio-Temporal Super-Resolution Reconstruction of Remote Sensing Data

We present a spatio-temporal super-resolution method for reconstructing a sequence of observations collected by imaging satellites. A sequence of observations is assumed to be defined on a low resolution spatio-temporal grid. It is further assumed that the sequence is generated by blurring of a captured scene with a spatio-temporal convolution kernel and is degraded by noise. Our method simultaneously exhibits deconvolution of the sequence of images from the effects of spatio-temporal blur, denoising of the data, and upsampling of the low-resolution sequence to a high resolution spatiotemporal grid. We perform the super-resolution in the spacetime domain, as opposed to super-resolving the sequence separately and sequentially to a higher spatial and then temporal resolution grid. Simultaneous space-time optimization achieves a more efficient and more accurate reconstruction than reconstructing a sequence frame by frame. The proposed super-resolution methodology is based on total variation regularization and computes the solution using the alternating direction method of multipliers. Numerical results show our approach to be robust and computationally efficient.
Research on 3D reconstruction of human face based on single image

With the wide application of 3D face reconstruction technology in the fields of face recognition, security early warning, and target tracking in recent years, the research of 3D face reconstruction methods has become a hot spot in the field of computer vision. Although the 3D reconstruction technology has certain theoretical support and wide application basis, how to ensure the authenticity and accuracy in the process of restoring the face is still a difficult research point for a long time. This paper focuses on the 3D face reconstruction technology, mainly focusing on the 3D face reconstruction algorithm based on the single face image as input, and conducts related research. They mainly include model-based 3D reconstruction and end-to-end reconstruction algorithms. Compared with the traditional face reconstruction algorithm based on multiple images, the use of single 2D image for 3D reconstruction has more prominent advantages in terms of economy and time cost. It makes mobile 3D face offline reconstruction possible. It will bring more real and objective application prospects to the future public safety guarantee, and make the construction of smart cities possible.
Teaching First-Year Students: Literature Review

The effectiveness of university academic process depends on how quickly and effectively a first-year student overcomes the challenges of the induction process. Creating conditions for the successful induction of students in the educational process is one of the priorities of higher education. This paper focuses on the literature review devoted to the main directions of current scientific research on the problem of adaptation of first-year students.
Stacked Semantically-Guided Learning for Image De-distortion

Image de-distortion is very important because distortions will degrade the image quality significantly. It can benefit many computational visual media applications that are primarily designed for high-quality images. In order to address this challenging issue, we propose a stacked semantically-guided network, which is the first try on this task. It can capture and restore the distortions around the humans and the adjacent background effectively with the stacked network architecture and the semantically-guided scheme. In addition, a discriminative restoration loss function is proposed to recover different distorted regions in the images discriminatively. As another important effort, we construct a large-scale dataset for image de-distortion. Extensive qualitative and quantitative experiments show that our proposed method achieves a superior performance compared with the state-of-the-art approaches.
Rancang Bangun Aplikasi Suicide Risk Idea Identification Menggunakan Rapid Application Develoment

Suicide is a deliberate act that results in death for oneself. Suicide is often associated with mental disorders such as depression, schizophrenia, bipolar disorder, and alcohol and drug abuse. Around 800 thousand people in the world are recorded to commit suicide each year, most of cases occur among teenagers. The lack of signs or symptoms in someone who wants to commit suicide is a big problem to reduce the number of deaths caused by suicide. Therefore, this research raised a theme related to these problems. research will design the Suicide Risk Idea Identification application using the Rapid Application Development method. RAD is a type of system development that is based on a series of iterative development cycles, using RAD method only takes 60-90 days. RAD has two types of methodologies, namely phase development and prototyping. The type of methodology used in this study is a prototyping methodology. Prototyping methodology applies all stages (analysis, design, and implementation) into action simultaneously. To facilitate early identification of suicidal ideation, a questionnaire is needed that discusses the severity of suicidal ideation and other attributes. SIDAS (suicidal identification attributes scale) is a questionnaire designed to identify suicidal thoughts and assess the severity of these thoughts.
ProPC: A Dataset for In-Domain and Cross-Domain Proposition Classification Tasks

Correctly identifying the types of propositions helps to understand the logical relationship between sentences, and is of great significance to natural language understanding, reasoning and generation. However, in previous studies: 1) Only explicit propositions are concerned, while most propositions in texts are implicit; 2) Only detect whether it is a proposition, but it is more meaningful to identify which proposition type it belongs to; 3) Only in the encyclopedia domain, whereas propositions exist widely in various domains. We present ProPC, a dataset for in-domain and cross-domain propositions classification. It consists of 15,000 sentences, 4 different classifications, in 5 different domains. We define two new tasks: 1) In-domain proposition classification, which is to identify the proposition type of a given sentence (not limited to explicit proposition); 2) Cross-domain proposition classification, which takes encyclopedia as the source domain and the other 4 domains as the target domain. We use the Matching, Bert and RoBERTa as our baseline methods and run experiments on each task. The result shows that machine indeed can learn the characteristics of various types of propositions from explicit propositions and classify implicit propositions, but the ability of domain generalization still needs to be strengthened. Our dataset, ProPC, is publicly available at https://github.com/NLUSoCo/ProPC.
Analysis of Online Learning Behavior Based on Multi-dimensional Features

At present, the study of personalized student models in large-scale open learning environments often selects the characteristic dimensions of students based on human experience, and there is a lack of research on whether student characteristics are effective to learning behavior analysis or whether it can affect learning effects. It is necessary to explore the impact of learning behavior characteristics on learning effects through data mining, extracting multi-dimensional features that can comprehensively and accurately reflect students’ abilities and preferences, to build a student model suitable for personalized strategy recommendations and resource recommendations. The paper uses log data of online learning platforms to explore the impact of different behavioral dimensions on students’ learning enthusiasm using correlation analysis, independent sample testing and other methods. By discovering the characteristics of different types of students, combining evaluation indicators and improved clustering algorithms, characteristics of 4 groups with different learning strategies are creatively proposed.
DEVELOPMENT OF ISLAMIC RELIGIOUS EDUCATION TEACHING MATERIALS BASED ON INTERACTIVE MULTIMEDIA WITH A SCIENTIFIC APPROACH TO CLASS IV STUDENTS OF ELEMENTARY SCHOOL

The study analyzed the feasibility and effectiveness of the product model material teachingIslamic Religious Education based on interactive multimedia with a scientific approach forfourth-grade elementary school students in West Jakarta. The research and development types ofresearch by adapting the Borg & Gall model combined with the Dick & Carey learningdevelopment steps and the Lee & Owens model with modifications. The instrument used in thisstudy was the test, interview, questionnaires, and documentation. The validity of the data wasgenerated based on the validation of material experts, media experts and linguists, and teacherand student responses. The results from this study showed the validity test results from materialexperts; an average score of 4.6 was obtained in the very good category. The validity test resultsfrom media experts obtained an average value of 4.4 with a good category. The validity testresults of linguists obtained an average value of 4.7 with a very good category. The results of theteachers’ responses showed an average of 4.6. The students’ responses in thesmall group try-outshow an average value of 3.37 (very good), and the average score of the assessment in thesignificant group test (field group try-out) of 3.5 (very good). Based on the results of the t -testthrough the independent samples test, the value of Sig. (2-tailed) of 0.016 < 0.05, whichindicated a difference in the average students learning outcomes between the experimental classusing MMI-based Islamic Education teaching materials and the control class using PowerPointmedia. The result concluded that Islamic Religious Education teaching materials based onInteractive Multimedia with a
Considerations in the development of video lessons

Video lessons are one of the main teaching resources in distance learning, representing video recordings of educational content. In the conditions of forced transfer of schools to distance learning in an emergency regime, teachers had to face this challenge, without regard to their degree of readiness to develop digital educational resources. The paper describes various features of video lessons, the professional skills that teachers must demonstrate, criteria for evaluating digital educational resources. Some aspects related to the quality of video lessons, placed in the public domain and intended for students and teachers in pre-university education are discussed.
The Key-Dependent Capacity in Multidimensional Linear Cryptanalysis

Abstract The capacity is an important parameter in multidimensional linear attack. In this paper, we firstly explore the distribution of the key-dependent capacity. Based on the magnitude of the correlation contributions, we divide the linear approximations subspace into two sets: one set consists of the strong linear approximations, and the other set consists of the weak linear approximations. We construct two statistics using the linear approximations in the two sets, respectively. Under reasonable assumptions, both of the two statistics follow Gamma distribution. Thus, the capacity is the sum of two statistics that follow Gamma distribution. Secondly, the accuracy of the model is verified by experiments on SMALLPRESENT[4]. Our experimental results show that this model can estimate the variance of the key-dependent capacity more accurately. Thus, we obtain more precise knowledge of the data complexity of the multidimensional linear attack. We derive the upper bound of the data complexity for multidimensional linear attack. Finally, based on our theoretical results, we explore the data complexity of Cho’s multidimensional linear attack on PRESENT. Our results are the smallest data complexity for the same round attack so far.
Development of a Methodology for Ultra-Short-Term Forecasting of Solar Radiation by Determining the Speed of the Cloud Shadow

Within the framework of this work, a prototype of a system for predicting the arrival of solar radiation was developed. The system is based on the calculation of the speed of the cloud shadow. Some assumptions are made in the work, which can be found in section II. The experimental installation consists of the Arduino Nano platform and three photoresistors, the software part is written in the Arduino and Python programming languages. A feature of this work is getting of data and their processing in real time. In the course of the work, experiments were carried out. The results of the experiments were analyzed and it can be said that the program code works correctly and the speed of the cloud shadow is calculated without errors. Based on the results of the experiments, options for their improvement are proposed, which in the future will allow using the forecasting system at real solar power plants.
Foreign Language Internet Environment and Its Impact on Cultural Self-Determination of Students

The relevance of the problem: In modern language education, Internet resources are intensively used, which together create a cultural and linguistic Internet environment. This environment can be defined as a virtual space in which the perception of information and the exchange of it is realized. It seems relevant to consider the issues of the influence of the Internet environment used in the process of learning foreign languages on the formation of a student's personality, and, in particular, his national and cultural self-determination.Purpose of the article: The empirical research was carried out in the process of teaching German as a second foreign language to international students of the 4th year of MGIMO. Its purpose was to consider the problem of the influence of the Internet environment on the cultural self-determination of students.Methodology: When conducting an empirical study of the influence of the Internet environment on self-determination of students, the author used methods of analysis of scientific and theoretical sources, analysis and systematization of empirical experience.Results. The study identified the advantages and controversial aspects of using the Internet environment. In order to neutralize the influence of the environment, the following recommendations were formulated: the use of pluralism of the information space, the development of critical thinking skills, the need to teach students the basics of cultural-critical analysis of the source.
Application of Blockchain technology in power material performance and settlement

The current management mode and technical means of electric power materials can not meet the increasing business volume of electric power materials. In order to solve the problem that the existing management mode can not meet the demand due to the growth of power material business volume and the improvement of power service level, this application is carried out. Through the application of blockchain, artificial intelligence, mobile Internet and other technologies, the online flow of electronic documents in power material business, the blocking points of settlement business and the 7*24 all-weather settlement business are realized. Finally, it shortens the order execution cycle, reduces the cost of manual window, improves the business efficiency, and saves expenses for power grid enterprises and suppliers, and improves the quality and efficiency of supply chain operation.
A Virtual Model of Migrant Students' Social and Living Skills Development in the Conditions of the Host University: Need Analysis of Migrant Students

Virtual reality tools and interventions have already proved their efficiency in treatment of people with disabilities. Their validation and extensive use in developing social and living skills and abilities and in psychological adaptation gave the opportunity to develop and adapt such tools to the needs of migrant students and assist them in overcoming challenges they face with in the host country. The paper is aimed at studying the process of the ‘Social Mirror’ simulator adapting and developing to the needs and purposes of social and psychological adaptation of migrant students to the receiving university environment. The new version of the simulator was named ‘Social Integrator’ and it was supposed to increase the level of migrant students' social and living skills and abilities development due to the repeated training and analysis of constructive behavioural patterns in certain virtual environments associated with learning in host institution and at the same time increasing their level of social literacy. The pilot study involved 171 migrant students. To meet the real needs of migrant students while developing such simulators a needs analysis was carried out. It revealed the number of challenges migrant students face while adapting to the host university environment and helped to develop the modules included into the scenarios for the virtual reality simulator ‘Social integrator’.
Demo Paper: Ad Hoc Search On Statistical Data Based On Categorization And Metadata Augmentation

In this paper, we describe the system of ad hoc search on statistical data based on categorization and metadata augmentation. The documents covered by this paper consist of metadata extracted from the governmental statistical data and the body of the corresponding statistical data. The metadata is characterized by the fact that its document length is short, and the main body of statistical data is almost always composed of numbers, except for titles, headers, and comments. We newly developed the categorical search that narrows the set of documents to be retrieved by category in order to properly capture the scope of the problem domain intended by the given query. In addition, to compensate for the short document length of metadata, we implemented a method of extracting the header information of the table from the main body of statistical data to augment documents to be searched. As a ranking model, we adopted BM25, which can be adjusted with few parameters to take into account term frequency and document length.
Lossy Compression with Universal Distortion

We consider a novel variant of lossy coding in which the distortion measure is revealed only to the encoder and only at run-time, as well as an extension of it in which the distortion constraint is also revealed at run-time. Two forms of rate redundancy are used to analyze the performance, and achievability results of both a pointwise and minimax nature are demonstrated. One proof uses appropriate quantization of the space of distortion measures while another uses ideas from VC dimension and growth functions.
DCT Domain Detail Image Enhancement for More Resolved Images

This paper develops a detail image signal enhancement that makes images perceived as being clearer and more resolved and so more effective for higher resolution displays. We observe that the local variant signal enhancement makes images more vivid, and the more revealed granular signals harmonically embedded on the local variant signals make images more resolved. Based on this observation, we develop a method that not only emphasizes the local variant signals by scaling up the frequency energy in accordance with human visual perception, but also strengthens the granular signals by embedding the alpha-rooting enhanced frequency components. The proposed energy scaling method emphasizes the detail signals in texture images and rarely boosts noisy signals in plain images. In addition, to avoid the local ringing artifact, the proposed method adjusts the enhancement direction to be parallel to the underlying image signal direction. It was verified through subjective and objective quality evaluations that the developed method makes images perceived as clearer and highly resolved.
Asynchronous Distributed Proximal Policy Optimization Training Framework Based on GPU

We propose a new GPU-based asynchronous DPPO training framework (GAPPO), in which the sampling part and the network update part are assigned to two different threads. The data exchange between two threads is realized by a buffer. Through coordinating the cycles of the two threads and synchronizing them, the training speed can be increased by about 1.5 times on average compared with the original DPPO algorithm. We apply this framework to five Atari games from the Arcade Learning Environment, the result indicates that compared with the DPPO algorithm, GAPPO can reach the same score in shorter time, and compared with GA3C, GAPPO can reach higher scores in four games.
A Multi-level Elastic Encryption Protection Model

Most of the existing file encryption models are based on international cryptographic algorithms, and lack of flexibility when facing the changing security environment. To solve these problems, we propose a multi-secret-level flexible encryption protection model (Muselfen-PM), which is suitable for environments with the requirement of safe storage. There are three levels of encryption protection, including low, medium and high levels of security. Domestic cryptographic algorithms SM3 and SM4 with high security are used in the model. In order to improve the security of the key, a dynamic key generation and inverse key storage mechanism are designed in the high security protection mode. The model is used in a cloud storage system, which runs well and can effectively protect the confidentiality and security of documents.
A Data-Free Approach for Targeted Universal Adversarial Perturbation

The existence of adversarial example problem puts forward high demand on the robustness of neural network. This paper proposes a universal adversarial perturbation(UAP) attack method in data-free scenario, which can realize targeted attack to any class specified by the attacker. We design a unique loss function to balance the purpose of perturbing model and targeting label. As far as we know, our method is the first UAP attack method that can achieve targeted attack in data-free scenario. Especially, in federated learning a malicious user can fool other users’ model without being noticed. We hope our attack method can inspire more researchers in the community, and enable them to better understand and defend against UAP attacks.