Reviewerly/exHarmony huggingface dataset:
Image Degradation Modeling for Real-World Super Resolution via Conditional Normalizing Flow

In recent years, deep-learning-based super-resolution (SR) methods have obtained impressive performance gains on synthetic clean datasets, but they fail to perform well in real-world scenarios due to insufficient real-world training data. To tackle this issue, we propose a conditional-normalizing-flow-based method named IDFlow for image degradation modeling that aims to generate various degraded low-resolution (LR) images for real-world SR model training. IDFlow takes image degradation modeling as a problem of learning a conditional probability distribution of LR images given the high-resolution (HR) ones, and learns the distribution from existing real-world SR datasets. It first decomposes the image degradation modeling into blur degradation modeling and real-world noise modeling. It then utilizes two multi-scale invertible networks to model these two steps, respectively. Before applied into real-world SR, IDFlow is first trained supervisedly on two real-world datasets RealSR and DPED with negative log-likelihood (NLL) loss. It is then used to generate a large number of HR-LR image pairs from an arbitrary HR image dataset for SR model training. Extensive experiments on IDFlow with RealSR and DPED are conducted, including evaluations on image degradation stochasticity, degradation modeling, and real-world super resolution. Two known SR models are trained with IDFlow and named as IDFlow-SR and IDFlow-GAN. Testing results on the RealSR and DPED testing dataset show that not only can IDFlow generate realistic degraded images close to real-world images, but it is also beneficial to real-world SR performance improvement. IDFlow-SR achieves 4× SR performance gains of 0.91 dB and 0.161 in terms of image quality assessment metrics PSNR and LPIPS. Moreover, IDFlow-GAN can super-resolve real-world images in the DPED testing dataset with richer textures and maintain clearer patterns without visible noises when compared with state-of-the-art SR methods. Quantitative and qualitative experimental results well demonstrate the effectiveness of the proposed IDFlow.
Latvian AMR Sembank

An automatically derived AMR annotation layer of the FullStack multi-layer text corpus of Latvian.First, Latvian UD Treebank (v2.5) sentences were translated to English using a state-of-the-art Latvian-English neural MT system (Hugo.lv). Second, a state-of-the-art AMR parser for English (AMREager) was applied to the MT-translated sentences. Additionally, alignment information is provided for both Latvian-English translations and English-AMR parses.
Cultivation of Modern Science-Technology-Engineering Concept for Engineering Students

In order to enhance the sensitivity, consciousness and action ability in scientific research, technological innovation and engineering practice of engineering students, a four-year and consistent cooperative training program with science-technology-engineering concept is proposed.So as to enable engineering students truly understand the definition, connotation and relationship of the science, technology and engineering, to set up correct modern science-technology-engineering concept, and to improve their own scientific, technical and engineering literacy.Through this training program, engineering students are able to effectively master scientific, technical and engineering knowledge and specialties skills, to enhance their employability and entrepreneurial capabilities, and become professional and technical talents for economic and social development.
Self-supervised adversarial learning for cross-modal retrieval.

Cross-modal retrieval aims at enabling flexible retrieval across different modalities. The core of cross-modal retrieval is to learn projections for different modalities and make instances in the learned common subspace comparable to each other. Self-supervised learning automatically creates a supervision signal by transformation of input data and learns semantic features by training to predict the artificial labels. In this paper, we proposed a novel method named Self-Supervised Adversarial Learning (SSAL) for Cross-Modal Retrieval, which deploys self-supervised learning and adversarial learning to seek an effective common subspace. A feature projector tries to generate modality-invariant representations in the common subspace that can confuse an adversarial discriminator consists of two classifiers. One of the classifiers aims to predict rotation angle from image representations, while the other classifier tries to discriminate between different modalities from the learned embeddings. By confusing the self-supervised adversarial model, feature projector filters out the abundant high-level visual semantics and learns image embeddings that are better aligned with text modality in the common subspace. Through the joint exploitation of the above, an effective common subspace is learned, in which representations of different modlities are aligned better and common information of different modalities is well preserved. Comprehensive experimental results on three widely-used benchmark datasets show that the proposed method is superior in cross-modal retrieval and significantly outperforms the existing cross-modal retrieval methods.
The Change of Paradigm in the System of Education

I find myself in the middle of the classroom.In addition to the cupboard, the podium on which my desk is placed, the flipping blackboard, and the gas stove, there should be space for individual small desks with suitable chairs, a table, and a wardrobe for the children.I tried again and again to reorganise the students' desks in different ways, but regardless of how I arranged them, they would still obstruct the children's movements.If worse comes to worst, my desk will have to go, as it serves no purpose, and the cupboard as well can be moved to the corridor […]. 26In those years, the four years of the "Magistrale" high school were still sufficient to become kindergarten and primary school teachers (the latter, at that time, was still called elementary school).From 2000 onwards, the new university Courses in Primary School Education (initially a four-year program and then a five-year program) became a requirement to teach to those age groups analogously to the requirements already in place for secondary school teachers.
Scene graph generation via multi-relation classification and cross-modal attention coordinator.

Scene graph generation intends to build graph-based representation from images, where nodes and edges respectively represent objects and relationships between them. However, scene graph generation today is heavily limited by imbalanced class prediction. Specifically, most of existing work achieves satisfying performance on simple and frequent relation classes (e.g. on), yet leaving poor performance with fine-grained and infrequent ones (e.g. walk on, stand on). To tackle this problem, in this paper, we redesign the framework as two branches, representation learning branch and classifier learning branch, for a more balanced scene graph generator. Furthermore, for representation learning branch, we propose Cross-modal Attention Coordinator (CAC) to gather consistent features from multi-modal using dynamic attention. For classifier learning branch, we first transfer relation classes' knowledge from large scale corpus, then we leverage Multi-Relationship classifier via Graph Attention neTworks (MR-GAT) to bridge the gap between frequent relations and infrequent ones. The comprehensive experimental results on VG200, a challenge dataset, indicate the competitiveness and the significant superiority of our proposed approach.
ATGW: A Machine Learning Framework for Automation Testing in Game Woody

In this paper, we present a novel approach for building an automation testing platform for a mobile game, namely Woody, by using machine learning algorithms. There are three essential components in our proposed system, including a testing platform, a game state recognition algorithm, and game agents. We implement our testing platform by using the Airtest IDE to have the possibility of testing multiple devices (e.g., tablets, iOS phones, and Android phones). For each screenshot taken on a testing device, we use the Adaptive Gaussian Thresholding algorithm to detect the game board and use the Mean Square Error function to predict the extract status of three blocks given in each turn. After that, we use a reinforcement learning approach for training three different levels of game agents to imitate the playing behaviors of different users in the game. The experimental results show that both game state recognition algorithms and game agents work very well. The results of this paper can give an additional contribution to the research community on using machine learning in the mobile gaming industry.
Control Graph Based Development of Problem Solvers for Systems with Knowledge Bases

Reducing the complexity of developing software systems that solve complex problems of science and practice, as well as ensuring their viability are among urgent tasks of software engineering. For systems with knowledge bases, as noted in many literature sources, this problem is most acute, despite the fact that significant results have been achieved in its solving. This problem is especially critical for those systems in which one of the requirements is a time limit for decision-making. Given that the used solution methods belong to the class of exhaustive ones that have a large computational complexity, parallelization is required to reduce the execution time. A methodology for using declaratively defined control graph structures for developing problem solvers for systems with knowledge bases is proposed. Special attention is paid to the processing of knowledge base fragments and the possibility of placing architectural components of the solver on different computing devices. The structure of such an information-and-control graph includes related constructs (operators), one of which is marked as "initial". These structures can be divided into two main types: computing control structures and information processing structures. The developer can group individual operators into blocks — for their repeated usage (including organization of recursive calculations). The structure of the graph also includes a description of the data.
Differentially Private Data Release: Bias Weight Perturbation Method - A Novel Approach

Differential privacy plays the important role to preserve the individual data. In this research work, discussing a novel approach of releasing private data to the public, which is differentially private, called Bias Weight Perturbation Method. The approach follow here align with principle of differential privacy, it also used concept of statistical distance and statistical sample similarity to quantify the synthetic data generation loss, which is then used to validate our results. Our proposed approach make use of the deep generative models for providing privacy and it further produce synthetic dataset which can be released to public for further use.
A systematic approach for Software refactoring based on Class and Method Level for AI application

Many researchers have investigated the different techniques to detect software code smells, which can be removed by software refactoring. In this paper, the author presents refactoring techniques a...
Review of feature extraction approaches on biomedical text classification

The overcoming volume of online biomedical literature causes congestion of data and difficulties in organizing these documents and also to retrieve the required documents from the database, especially in the Medline database. One of the solutions to surpass the overwhelming of documents is to apply classification. However, each document must be represented by a set of terminology or feature vectors. The identification of terminology or feature from biomedical literature is one of the most important and challenging tasks in text classification. This is due to a large number of new features and entities that appear in the biomedical domain. In addition, combining sets of features from different terminological resources leads to naming conflicts such as homonymous use of names and terminological ambiguities. Therefore, the purpose of this research is to investigate and evaluate the effective ways for extracting the relevant and meaningful features in order to increase the classification accuracy and improve the performance of web searches. Towards this effort, we conduct several classification experiments to evaluate and compare the effectiveness of feature extraction approaches for extracting the relevant and informative features from the biomedical literature. For our experiments, we use two different sets of features, which are a set of features that are extracted using the Genia tagger tool and set of features that are extracted by medical experts from Pusat Perubatan Universiti Kebangsaan Malaysia (PPUKM). The results show the performance of classification using features that are extracted by medical experts outperform the performance of classification using the Genia Tagger tool when applying feature selection method.
Analysis of The Utilization and Implementation of Cloud Computing Infrastructure Services on The Azure Microsoft Platform

This final project discusses cloud computing infrastructure services, where cloud computing is the evolution of various computing technologies that have existed since the early 1960s. This final project will discuss cloud computing infrastructure services on the Microsoft Azure platform. Microsoft Azure is a hyperscale cloud computing platform that has many services, one of which is infrastructure as a services. DNS server implementation will be carried out to test the functionality of the Microsoft Azure service. Analysis of the performance of both the processor compute, memory bandwidth, network throughput and storage media performance will be tested and compared with the on-premise system. The applications used for performance testing are LINPACK, STREAM, iperf and also fio. The on-premise system being compared is a virtualization-based system using the VMware vSphere platform and hardware specifications that are made closer to the server specifications on Microsoft Azure. Costs incurred for operations and investment as total cost of ownership of an on-premise infrastructure system with Microsoft Azure infrastructure services will be compared, including the costs required to implement a server on an on-premise system with implementation on Microsoft Azure.
IMPLEMENTATION OF GENETIC ALGORITHM WITH TOURNAMENT SELECTION FOR COURSE SCHEDULES

Genetic Algorithms can help human work, one of which is compiling course schedules. Preparation of course schedules, if done manually, will take a long time because you have to make a schedule where there are no schedule conflicts between one course and another. Therefore, this study will implement a Genetic Algorithm for the preparation of course schedules, so that it will speed up the preparation of course schedules compared to manual scheduling. In this study, the Genetic Algorithm with Tournament Selection was carried out with the input of control parameters, namely Population Size = 10, Crossover Rate (CR) = 0.75, and Mutation Rate (MR) = 0.01. In this study, the Genetic Algorithm has succeeded in obtaining the desired solution, namely scheduling courses where there are no schedule conflicts between one course and another. This search process took 88 generations to find the best solution.
Stuart Madnick of MIT: The Hacker Has More Advantages in a Cyber Attack

Professor Stuart Madnick, 74, is probably the world's leading expert on the effects of cyber crimes on business. Madnick researches the mishaps of technology since joining the prestigious Massachusetts Institute of Technology (MIT) as a student in 1972. Five decades later, Madnick heads a university department - the MIT Interdisciplinary Consortium for Improving Critical Infrastructure Cybersecurity (or Interdisciplinary Consortium for Improving Cyber Security Critical Infrastructure) dedicated to studying strategies for companies to create defenses against hackers.
Optimizing Recommender System: Literature Review

Recommending suitable product items to the target user is essential for the continued success of eCommerce. Today, many E-commerce systems adopt numerous recommendation techniques.  In this review paper, we analyse various hybrid recommender systems. Existing recommendation methods have challenges of data sparsity and efficiency, as the numbers of users, items, and interactions between the two in real-world applications increase fast. In this paper, we review relevant problems about a recommendation system and describe the relevant recommendation techniques used to overcome them. We also explore the evaluation process and proposed future research directions. We also identify newer challenges such as recommendations based on user profile and providing cross-domain recommendations. Network algorithm-based hybrid recommendation system is a good base with which we can respond best by discovering innovative options such as domain-specific recommendations, processing larger datasets, etc.
Un percorso di accostamento precoce al bilinguismo in ambito formativo Il modello e gli strumenti utilizzati

The advantages of teaching a foreign language from an early age has been demonstrated in the past years. However, only few schools in Italy offer bilingual programmes. Early bilingual childhood education represents a significant opportunity for the personal development of the child and for the quality of his/her language competence. This paper describes a model used to teach a foreign language to very young learners (aged 0-6) in schools and it presents and discusses some documents which have been devised in order to gather information about this model.
Comparison between fuzzy time series and box-jenkins method to forecast the paddy production in Malaysia / Nor Shafika Abd Aziz

Paddy is the main cultivation in Malaysia. This study was conducted to predict the paddy production in Malaysia using Fuzzy Time Series (FTS) and Box Jenkins Methodology and which method is more accurate to make predictions. The data used are secondary and were obtained from the Paddy Production Survey Report Department of Agriculture Malaysia from 1980 to 2019. The objective of this study is to compare between Fuzzy Time Series and Box Jenkins method of paddy production in Malaysia, to forecast the rice consumption in Malaysia using the best method between Fuzzy Time Series and the Box Jenkins method. Furthermore, R studio software has been used to calculate the Box Jenkins method. The error measure was calculated in this study which is Mean Square Error (MSE) for comparison which method gives the lowest value. The result shows that the Fuzzy Time Series method gives the lowest value of MSE which is 11826508360 as compared to Box Jenkins that is 23798176345.
Sentiment Analysis of COVID-19 Tweets: Impact of Pre-processing Step

Internet users are increasingly invited to express their opinions on various subjects in social networks, e-commerce sites, news sites, forums, etc. Much of this information, which describes feelings, becomes the subject of study in several areas of research such as: Sensing opinions and analyzing feelings. It is the process of identifying the polarity of the feelings held in the opinions found in the interactions of Internet users on the web and classifying them as positive, negative, or neutral. In this article, we suggest the implementation of a sentiment analysis tool that has the role of detecting the polarity of opinions from people about COVID-19 extracted from social media (tweeter) in the Arabic language and to know the impact of the preprocessing phase on the opinions classification. The results show gaps in this area of research, first of all, the lack of resources when collecting data. Second, Arabic language is more complexes in pre-processing step, especially the dialects in the pre-treatment phase. But ultimately the results obtained are promising.
CONTENT-BASED FEATURE FUSION REPRESENTATION FOR MARINE INVERTEBRATES

Marine species representation and retrieval is crucial for its studies and conservation. The images of these animals are usually captured underwater with complex background, at different angle, position, and size, which makes it very hard to provide a good representation with the current methods. Most of the current methods only support content-based representation for marine life images with clear background (taken in laboratory or in environments which have been set up), containing just one animal in an image, or the animal is positioned nicely at the centre of the image. Responding to these important needs, a multi-feature method for Content-based Image Retrieval (CBIR) that employs colour, shape, and texture information of marine life images is proposed. The colour feature vectors are obtained by extracting first and second order of Colour Moments. Shape information is constructed through the implementation of Discrete Wavelet transform up to four sub-bands and the extraction of Canny edge feature. Texture features are obtained with the Zernike Moments (ZM) of order four and the extraction of few Grey Level Co-occurrence Matrix properties. We conducted two experiments to determine the best order of ZM as well as to measure the retrieval performance of the proposed descriptor. Retrieval results based on marine invertebrate and Fish4Knowledge datasets clearly shown that the proposed method has effectively obtained the best precision value at 11 standard recall levels (72.42%) and MAP value (67.7%). The proposed method is further measured based on the statistical two-tailed paired t-test and has revealed a significant improvement in retrieval effectiveness.
Multi-level expression guided attention network for referring expression comprehension.

Referring expression comprehension is a task of identifying a text-related object or region in a given image by a natural language expression. In this task, it is essential to understand the expression sentence in multi-aspect and adapt it to region representations for generating the discriminative information. Unfortunately, previous approaches usually focus on the important words or phrases in the expression using self-attention mechanisms, which causes that they may fail to distinguish the target region from others, especially the similar regions. To address this problem, we propose a novel model, termed Multi-level Expression Guided Attention network (MEGA-Net). It contains a multi-level visual attention schema guided by the expression representations in different levels, i.e., sentence-level, word-level and phrase-level, which allows generating the discriminative region features and helps to locate the related regions accurately. In addition, to distinguish the similar regions, we design a two-stage structure, where we first select top-K candidate regions according to their matching scores in the first stage, then we apply an object comparison attention mechanism to learn the difference between the candidates for matching the target region. We evaluate the proposed approach on three popular benchmark datasets and the experimental results demonstrate that our model performs against state-of-the-art methods.
Compact Polyominoes

We provide a compact representation of polyominoes with n cells that supports navigation and visibility queries in constant time. Our oracle takes 3n +o(n) bits. Previous enumeration efforts indicate that at least 2.00091 n -o(n) bits (likely 2.021 n -o(n) bits) are required to distinguish polyominoes, hence confirming that our oracle is compact.