Reviewerly/exHarmony huggingface dataset:
Evidence-Based Regularization for Neural Networks

Numerous approaches address over-fitting in neural networks: by imposing a penalty on the parameters of the network (L1, L2, etc.); by changing the network stochastically (drop-out, Gaussian noise, etc.); or by transforming the input data (batch normalization, etc.). In contrast, we aim to ensure that a minimum amount of supporting evidence is present when fitting the model parameters to the training data. This, at the single neuron level, is equivalent to ensuring that both sides of the separating hyperplane (for a standard artificial neuron) have a minimum number of data points, noting that these points need not belong to the same class for the inner layers. We firstly benchmark the results of this approach on the standard Fashion-MINST dataset, comparing it to various regularization techniques. Interestingly, we note that by nudging each neuron to divide, at least in part, its input data, the resulting networks make use of each neuron, avoiding a hyperplane completely on one side of its input data (which is equivalent to a constant into the next layers). To illustrate this point, we study the prevalence of saturated nodes throughout training, showing that neurons are activated more frequently and earlier in training when using this regularization approach. A direct consequence of the improved neuron activation is that deep networks are now easier to train. This is crucially important when the network topology is not known a priori and fitting often remains stuck in a suboptimal local minima. We demonstrate this property by training a network of increasing depth (and constant width); most regularization approaches will result in increasingly frequent training failures (over different random seeds), whilst the proposed evidence-based regularization significantly outperforms in its ability to train deep networks.
About russian university education export programs under sanctions

Russia is interested in attracting foreign scientists and international scientific communication. However, there are reasons hindering the development of international scientific and educational cooperation. Based on the results of the analysis of the current regulatory and methodological framework in the field of international scientific and educational cooperation and the results of the analysis of departmental statistics data of the Ministry of Education and Science of Russia, the article discusses the impact of the sanctions of “unfriendly countries” on the functioning of educational export programs of Russian universities and on the research activities of universities subordinate to the Ministry of Education and Science of Russia.
IAMonSense: Multi-level Handwriting Classification using Spatio-temporal Information

Abstract Online handwriting classification has become an open research problem as it serves as a preliminary step for handwriting recognition systems and applications in several other fields. This paper aims to extend the current trends and knowledge with multiple contributions in handwriting classification using Spatio-temporal information. Firstly, it enriches the annotations of several publicly available online handwriting datasets, SenseThePen, IAM-onDB, IAMonDo, to be used for online handwriting classification and recognition tasks, i.e., stroke, sequence, and line level.The enriched annotations of the datasets extend their functionality for online handwriting classification at different levels for further research analysis. In addition to enrichment, it also unifies the annotation level across the datasets, which enables the research community to benchmark proposed methods for comparative analysis using multiple datasets. All the datasets with enriched annotations are made publicly available for the research community as part of the IAMonSense dataset.Moreover, this paper presents a comprehensive benchmark of these datasets using multiple deep neural networks such as traditional convolutional neural networks, graph neural networks, attention-based neural networks, and transformers. These benchmarks can be used later on for further development in the field of online handwriting classification.
A Parallel Text Recognition in Electrical Equipment Nameplate Images Based on Apache Flink

Information on the equipment nameplate is important for the storage, transportation, verification and maintenance of electrical equipment. However, because a natural image of the device on the text nameplate may be multidirectional, curved, noisy or blurry, automatically recognizing the image from the device nameplate can be difficult. Meanwhile, image preprocessing methods are carried out in a serial manner, so the processing speed with regard to the above problems is slower and takes a longer time. Accordingly, this study proposes a parallel and deep-learning-based text automatic recognition method. In the proposed method, a pretreatment method comprising edge detection, morphological manipulation and projection transformation is used to obtain the corrected nameplate region. The connectionist text proposal network (CTPN) is then activated to detect text lines on the corrected nameplate area. Next, a deep-learning method is proposed to study the classification methods of convolutional recurrent neural networks and connectionist time classification for identifying text in each line of text detected by CTPN. Finally, we use Apache Flink to parallelize the above processes, including parallelization preprocessing and bidirectional long short-term memory parallelization in the process of text line detection and text recognition. Experimental results on the collected nameplate show that the proposed imaging processing method has a good recognition performance and that the parallelization method significantly reduces the data processing time cost.
360 virtual tour and escape room design as a video games-based learning process for diagnosis and strengthening of the English language

Nowadays we know it, in these last times the return to the classroom wasn´t the return to the classroom. Some students when was studying the last year, they were waiting for the time to finish class and go out to the university, now because the pandemic they and much more students didn´t return to the classroom in a normal way. The Universidad Politécnica de Juventino Rosas and the University of Guelph closed its installation in the same way that any school, college or university in the world, and nowadays we have new students that for first time in the history they started class by online mode, and they didn’t know their scholar environment. In this project we proposed a 360 virtual tour of the UPJR and we decided make more immersive and educational the experience through the escape room design as a video game-based learning process to diagnosis the English language in the first stage. The novelty of the project is to include the new TIC´s showed too in the manufacture 4.0 as the VR technology to replay in virtual way the main scholar buildings, classrooms, laboratories, library, soccer field and the cafe of the University.
Crowd counting via Localization Guided Transformer

The rapidly growing demands on real-world crowd security and commercial applications have drawn widespread attentions to crowd counting, a computer vision task that aims to count all persons that appear in a given image. Recent state-of-the-art crowd counting methods commonly follow the density map regression paradigm, where a density map is estimated from the given image and summed up as the total count. Despite achieving impressive progress, these methods are still significantly challenged by complicated scenarios with severe scale variations of persons and cluttered backgrounds. Considering that localization-based counting methods, though less accurate, are able to learn more discriminative representation of persons through locating their positions, we propose a novel Localization Guided Transformer (LGT) framework in this work. The LGT aims to use the knowledge learned from a leading localization-based method to more accurately guide the estimation on density maps for crowd counting. Specifically, our framework first exploits a point-based model with two output heads, i.e., regression head and classification head, to simultaneously predict the head point proposals and point confidence respectively. Then, an intermediate multi-scale feature map is extracted from the shared backbone network and actively fused with the point location information. Afterwards, the fused features are fed into a Transformer module to explore patch-wise interactions via the self-attention mechanism, yielding a more discriminative representation for high-quality density map estimation. Extensive experiments and comparisons with state-of-the-art methods show the effectiveness of our proposed framework. • We propose a Localization Guided Transformer (LGT) framework. • We devise a feature fusion module to fuse the intermediate features and outputs of the localization module. • We exploit the self-attention mechanism of Transformers in fused features for generating more accurate density maps.
Dynamic Resource Allocation Based on Priority in Various Data Centers Custom-Based Waiting Queue Technique in Cloud Computing

For delivering the cloud services over the internet, cloud computing has become proficient infrastructural model for hosting cloud services. Server virtualization is key technology that enables cloud computing as a service, which authorizes dynamic sharing of physical resources. Virtualization introduces the problem of virtual machine placement that increases the overheads in load balancing. Existing infrastructure needs the strategy for the VM Placement as it may create poor allocation and load balancing issues. In most of the cases, due to lack of input parameters physical machines are partially loaded that creates issue of fragmentation which leads to in sufficient resources that causes more utilization of physical machines in any infrastructure. We did extensive survey in the said domain and found that, In load balancing approach, when VM place mentis done without measuring its lifetime, that creates fragments on PM. So we propose dynamic priority based spill over technique and add the concept of short life/long life container for solving the fragmentation issue.
Medical Text Simplification Using Reinforcement Learning (TESLEA): Deep Learning–Based Text Simplification Approach

In most cases, the abstracts of articles in the medical domain are publicly available. Although these are accessible by everyone, they are hard to comprehend for a wider audience due to the complex medical vocabulary. Thus, simplifying these complex abstracts is essential to make medical research accessible to the general public.This study aims to develop a deep learning-based text simplification (TS) approach that converts complex medical text into a simpler version while maintaining the quality of the generated text.A TS approach using reinforcement learning and transformer-based language models was developed. Relevance reward, Flesch-Kincaid reward, and lexical simplicity reward were optimized to help simplify jargon-dense complex medical paragraphs to their simpler versions while retaining the quality of the text. The model was trained using 3568 complex-simple medical paragraphs and evaluated on 480 paragraphs via the help of automated metrics and human annotation.The proposed method outperformed previous baselines on Flesch-Kincaid scores (11.84) and achieved comparable performance with other baselines when measured using ROUGE-1 (0.39), ROUGE-2 (0.11), and SARI scores (0.40). Manual evaluation showed that percentage agreement between human annotators was more than 70% when factors such as fluency, coherence, and adequacy were considered.A unique medical TS approach is successfully developed that leverages reinforcement learning and accurately simplifies complex medical paragraphs, thereby increasing their readability. The proposed TS approach can be applied to automatically generate simplified text for complex medical text data, which would enhance the accessibility of biomedical research to a wider audience.
Towards Trace-based Deductive Verification (Tech Report)

Contracts specifying a procedure's behavior in terms of pre- and postconditions are essential for scalable software verification, but cannot express any constraints on the events occurring during execution of the procedure. This necessitates to annotate code with intermediate assertions, preventing full specification abstraction. We propose a logic over symbolic traces able to specify recursive procedures in a modular manner that refers to specified programs only in terms of events. We also provide a deduction system based on symbolic execution and induction that we prove to be sound relative to a trace semantics. Our work generalizes contract-based to trace-based deductive verification.
Efficient Transformers with Dynamic Token Pooling

Transformers achieve unrivalled performance in modelling language, but remain inefficient in terms of memory and time complexity. A possible remedy is to reduce the sequence length in the intermediate layers by pooling fixed-length segments of tokens. Nevertheless, natural units of meaning, such as words or phrases, display varying sizes. To address this mismatch, we equip language models with a dynamic-pooling mechanism, which predicts segment boundaries in an autoregressive fashion. We compare several methods to infer boundaries, including end-to-end learning through stochastic re-parameterisation, supervised learning (based on segmentations from subword tokenizers or spikes in conditional entropy), as well as linguistically motivated boundaries. We perform character-level evaluation on texts from multiple datasets and morphologically diverse languages. The results demonstrate that dynamic pooling, which jointly segments and models language, is both faster and more accurate than vanilla Transformers and fixed-length pooling within the same computational budget.
Deep Learning Approach for Fake Currency Recognition

In this method, the Automatic fake Currency Recognition System is used to identify fake paper money and determine if it is genuine or not. The current counterfeit issue brought on by demonetization has an impact on the financial system as well as other sectors. This strategy, which is comparatively superior to earlier image processing methods, examines a novel Convolution Neural Network approach for the identification of fake money notes through their images. This approach is based on Deep Learning, which has recently shown outstanding results in image categorization problems. Through the use of an image of the fake money note, this approach can assist both humans and machines in instantly recognizing the note. In this system original and fake notes images are used to perform training and classification operation. The proposed system achieved an accuracy of 99.46% and loss of 0.0033 using CNN algorithm.
Quantum computing is scalable on a planar array of qubits with fabrication defects

To successfully execute large-scale algorithms, a quantum computer will need to perform its elementary operations near perfectly. This is a fundamental challenge since all physical qubits suffer a considerable level of noise. Moreover, real systems are likely to have a finite yield, i.e. some non-zero proportion of the components in a complex device may be irredeemably broken at the fabrication stage. We present a threshold theorem showing that an arbitrarily large quantum computation can be completed with a vanishing probability of failure using a two-dimensional array of noisy qubits with a finite density of fabrication defects. To complete our proof we introduce a robust protocol to measure high-weight stabilizers to compensate for large regions of inactive qubits. We obtain our result using a surface code architecture. Our approach is therefore readily compatible with ongoing experimental efforts to build a large-scale quantum computer.
Interpretability Aware Model Training to Improve Robustness against Out-of-Distribution Magnetic Resonance Images in Alzheimer's Disease Classification

Owing to its pristine soft-tissue contrast and high resolution, structural magnetic resonance imaging (MRI) is widely applied in neurology, making it a valuable data source for image-based machine learning (ML) and deep learning applications. The physical nature of MRI acquisition and reconstruction, however, causes variations in image intensity, resolution, and signal-to-noise ratio. Since ML models are sensitive to such variations, performance on out-of-distribution data, which is inherent to the setting of a deployed healthcare ML application, typically drops below acceptable levels. We propose an interpretability aware adversarial training regime to improve robustness against out-of-distribution samples originating from different MRI hardware. The approach is applied to 1.5T and 3T MRIs obtained from the Alzheimer's Disease Neuroimaging Initiative database. We present preliminary results showing promising performance on out-of-distribution samples.
The Use of Squid Game Simulation for Challenging Students' Vocabulary Mastery

Applying Squid game simulation is one of the effective techniques for English teachers who want to allow their students practice in the target language. This game allows students not only to practice English forms that they already acquired, but also allow them to improve new vocabularies and English structures. Therefore, this study aims to determine the improvement of students' vocabulary mastery skills in English lessons using a squid game simulation, namely Ddakji game simulation, and Green light Red Light game simulation. The game simulation is designed to enhance students’ vocabulary mastery at elementary schools. The subject of this research is a second grader at SDN Pondok Benda 01 at the academic year 2021/2022 consisting of 30 students. The method used in this study is a qualitative method with classroom practice. The results of this study indicate that there is an increase in students' vocabulary mastery skills. The achievements in the practice class are effective, students feel happy and feel challenged to want to learn more English. The squid game simulation improves the vocabulary mastery of students in second grade at SDN Pondok Benda 01.
Speaking to Devices: Can we Use Google Assistant to Foster Students' Speaking Skills?

Teaching speaking skills needs appropriate medias to facilitate students to practice and imitate English speakers accurately. One of applications that can help students learn speaking skills is Google assistant application. This study aims to foster students’ speaking skills through the use of the Google Assistant application on devices. This study employed the application to provide students with various speaking materials. This study was designed in an experimental study which was to know whether the use of Google Assistant helps students in fostering their speaking skills or not. This study uses pre-experimental as the research design. The study involved 31 participants of eleventh-grade students in DUA MEI Senior High School, Tangerang Selatan. The data was collected from pre-test and post-test. Based on the analysis, the results show that the t-cal value (12.436) was higher than the t-table (2.042), at the 5% significant level. It indicated that the null hypothesis (H0) was rejected and the alternative hypothesis (H1) was accepted. Based on the results of the t-test calculation, this study can be concluded that Google Assistant is an effective medium to facilitate students' fostering their speaking skills.
Exponential Graph Regularized Non-Negative Low-Rank Factorization for Robust Latent Representation

Non-negative matrix factorization (NMF) is a fundamental theory that has received much attention and is widely used in image engineering, pattern recognition and other fields. However, the classical NMF has limitations such as only focusing on local information, sensitivity to noise and small sample size (SSS) problems. Therefore, how to develop the NMF to improve the performance and robustness of the algorithm is a worthy challenge. Based on the bottlenecks above, we propose an exponential graph regularization non-negative low-rank factorization algorithm (EGNLRF) combining sparseness, low rank and matrix exponential. Firstly, based on the assumption that the data is corroded, we decompose a given raw data item with a data error fitting noise matrix, applying a low-rank constraint to the denoising data. Then, we perform a non-negative factorization on the resulting low-rank matrix, from which we derive the low-dimensional representation of the original matrix. Finally, we use the low-dimensional representation for graph embedding to maintain the geometry between samples. The graph embedding terms are matrix exponentiated to cope with SSS problems and nearest neighbor sensitivity. The above three steps will be incorporated into a joint framework to validate and optimize each other; therefore, we can learn latent data representations that are undisturbed by noise and preserve the local structure of known samples. We conducted simulation experiments on different datasets and verified the effectiveness of the algorithm by comparing the proposed with the lasting ones related to NMF, low rank and graph embedding.
Utilizing Alike Neighbor Influenced Similarity Metric for Efficient Prediction in Collaborative Filter-Approach-Based Recommendation System

The most popular method collaborative filter approach is primarily used to handle the information overloading problem in E-Commerce. Traditionally, collaborative filtering uses ratings of similar users for predicting the target item. Similarity calculation in the sparse dataset greatly influences the predicted rating, as less count of co-rated items may degrade the performance of the collaborative filtering. However, consideration of item features to find the nearest neighbor can be a more judicious approach to increase the proportion of similar users. In this study, we offer a new paradigm for raising the rating prediction accuracy in collaborative filtering. The proposed framework uses rated items of the similar feature of the ’most’ similar individuals, instead of using the wisdom of the crowd. The reliability of the proposed framework is evaluated on the static MovieLens datasets and the experimental results corroborate our anticipations.
Digital Almost Nets

Digital nets (in base 2) are the subsets of [0,1]d that contain exactly the expected number of points in every not-too-small dyadic box. We construct finite sets, which we call "almost nets", such that every such dyadic box contains almost the expected number of points from the set, but whose size is exponentially smaller than the one of nets. We also establish a lower bound on the size of such almost nets.