Reviewerly/exHarmony huggingface dataset:
Eradication of Salt and Pepper Noise from a Tumorous MRI image using SNPRB Filter

Apart from different types of noises which can be represented by categories of modeling style, we have considered a model of addictiveness and salt and pepper type of noise which belongs to impulse category in this outline. We introduce a newly built linear filterization methodology for the eradication of salt and pepper type of noise from a noisy image to achieve restoration over a degradable image. We named our methodology as SNPRB filter which is fundamentally used to deduct above mention noise category from a tumorous image, present inside the human brain and our methodology has produced a positive, desirable and effective result as we observe after experimental result and analysis. In comparison with other pre-existing concepts, our proposed invention generates a far more accurate result as we perform our methodology over multiple data set of some kind and varying noise intensities over our collected data set.
Multi-Stage HRNet: Multiple Stage High-Resolution Network for Human Pose Estimation

Human pose estimation are of importance for visual understanding tasks such as action recognition and human-computer interaction. In this work, we present a Multiple Stage High-Resolution Network (Multi-Stage HRNet) to tackling the problem of multi-person pose estimation in images. Specifically, we follow the top-down pipelines and high-resolution representations are maintained during single-person pose estimation. In addition, multiple stage network and cross stage feature aggregation are adopted to further refine the keypoint position. The resulting approach achieves promising results in COCO datasets. Our single-model-single-scale test configuration obtains 77.1 AP score in test-dev using publicly available training data.
MIM: Mutual Information Machine

We introduce the Mutual Information Machine (MIM), a probabilistic auto-encoder for learning joint distributions over observations and latent variables. MIM reflects three design principles: 1) low divergence, to encourage the encoder and decoder to learn consistent factorizations of the same underlying distribution; 2) high mutual information, to encourage an informative relation between data and latent variables; and 3) low marginal entropy, or compression, which tends to encourage clustered latent representations. We show that a combination of the Jensen-Shannon divergence and the joint entropy of the encoding and decoding distributions satisfies these criteria, and admits a tractable cross-entropy bound that can be optimized directly with Monte Carlo and stochastic gradient descent. We contrast MIM learning with maximum likelihood and VAEs. Experiments show that MIM learns representations with high mutual information, consistent encoding and decoding distributions, effective latent clustering, and data log likelihood comparable to VAE, while avoiding posterior collapse.
RANCANG BANGUN SISTEM INFORMASI MANAJEMEN DISPOSISI SURAT MENYURAT DENGAN MENGGUNAKAN MODEL RAD (RAPID APPLICATION DEVELOPMENT)

Many numbers of letters are made and accepted in the administrative administration section, so that data search will be inefficient in terms of time and energy using a manual system. So that at this time a more structured letter management administration system is needed in order to speed up the search for existing data and create reports. This filing application has the following capabilities: this filing application is run on a personal computer network in the administrative space, administrative officers can access this filing application by entering the correct user login, can add, edit, cancel, delete and save incoming mail and out, can search incoming letters and outgoing letters based on the sender and subject matter, search results can be sorted according to the letter id or date of the letter, can print reports based on search results that have been done based on the letter id or date of the letter. Using the RAD model will make it easier to design a correspondence Disposition Information System.
A landscape of the adoption of empirical evaluations in the brazilian symposium on human factors in computing systems

Context: Empirical evaluations have been widely applied as a formalism to validate and ensure the credibility of research works. In the context of Human-Computer Interaction (HCI) field, the usage of empirical methods plays a vital role due to its focus on evaluating the end-user and usability of software solutions. Even though the adoption of experimental evaluation techniques has gained popularity in recent years, its application is still questioned both qualitatively and quantitatively. Goal: To analyze how empirical research has evolved in the Human-Computer Interaction Brazilian community. Method: We performed a quasi-experiment, using published papers over the 17 editions in Brazilian Symposium on Human Factors in Computing Systems. Our experiment was divided into two phases, in order to evaluate the quality assessment and classify the type of realized empirical study, respectively. Results: From the sample of 231 studies, 113 reached satisfactory quality in the first phase. It was found that empirical studies were becoming more present in lastly years of the conference. Moreover, in the second phase, it was found with 95% confidence that the quality of the empirical studies increased, by comparing two different divisions of the 20-year conference period. Findings: The assessment questionnaire used in our research and the learned lessons about each kind of empirical study can support the conduction of new empirical evaluations in the HCI community, improving their quality.
A Concise Review on Image Dehazing Techniques

Images acquired under the influence of bad weather conditions such as haze, fog and other aerosols are deteriorated due to the dispersal of atmospheric particles which lead to color fading and contrast reduction, making it challenging for human interpretation and object feature recognition.Several methods for image haze removal have been introduced over the recent years, which consist of approaches used to extrapolate information such as contrast, scene depth, color channels and so on.In this paper, we present a concise review of the current image dehazing methods.A comprehensive assessment and development on existing methods and related techniques is conducted based on their individual characteristics and principles.Qualitative and quantitative experimental evaluation of the state-of-the-art methods are conducted and discussed in depth.The paper further puts forward an overview of future trends within the research area.
SmokEng: Towards Fine-grained Classification of Tobacco-related Social Media Text

Contemporary datasets on tobacco consumption focus on one of two topics, either public health mentions and disease surveillance, or sentiment analysis on topical tobacco products and services.However, two primary considerations are not accounted for, the language of the demographic affected and a combination of the topics mentioned above in a finegrained classification mechanism.In this paper, we create a dataset of 3144 tweets, which are selected based on the presence of colloquial slang related to smoking and analyze it based on the semantics of the tweet.Each class is created and annotated based on the content of the tweets such that further hierarchical methods can be easily applied.Further, we prove the efficacy of standard text classification methods on this dataset, by designing experiments which do both binary as well as multi-class classification.Our experiments tackle the identification of either a specific topic (such as tobacco product promotion), a general mention (cigarettes and related products) or a more fine-grained classification.This methodology paves the way for further analysis, such as understanding sentiment or style, which makes this dataset a vital contribution to both disease surveillance and tobacco use research.
Hotel2vec: Learning Attribute-Aware Hotel Embeddings with Self-Supervision

We propose a neural network architecture for learning vector representations of hotels. Unlike previous works, which typically only use user click information for learning item embeddings, we propose a framework that combines several sources of data, including user clicks, hotel attributes (e.g., property type, star rating, average user rating), amenity information (e.g., the hotel has free Wi-Fi or free breakfast), and geographic information. During model training, a joint embedding is learned from all of the above information. We show that including structured attributes about hotels enables us to make better predictions in a downstream task than when we rely exclusively on click data. We train our embedding model on more than 40 million user click sessions from a leading online travel platform and learn embeddings for more than one million hotels. Our final learned embeddings integrate distinct sub-embeddings for user clicks, hotel attributes, and geographic information, providing an interpretable representation that can be used flexibly depending on the application. We show empirically that our model generates high-quality representations that boost the performance of a hotel recommendation system in addition to other applications. An important advantage of the proposed neural model is that it addresses the cold-start problem for hotels with insufficient historical click information by incorporating additional hotel attributes which are available for all hotels.
Multiple Classifiers and Invariant Features Extraction for Digit Recognition

Extraction of feature and classification methods are important phases in recognition system.A good classifier and extraction of features that suits play a very important role in a recognition system to improving recognition rate.In this paper we propose a new system designed to recognize the ten digits of printed Arabic numerals that are the most common symbolic representation of numbers in the world today.This study has been conducted using Hu moment, number of hole and surface which are tolerate to the geometric transformations along with seven different classifiers Naive Bayesian, Multi-Layer Perceptron (MLP), Linear Discriminant Analysis (LDA), Pseudo-Inverse.Support Vector Machine (SVM), Decision Tree and K-Nearest Neighbor (KNN).Classifier combination is considered.Experimental tests demonstrated that our technique achieves good results on multi-font and multi-size printed digit dataset.
MxU

The advanced functionality requirements of modern embedded and Internet of Things (IoT) devices -- from autonomous vehicles, to city and power-grid management -- are driving an ever-increasing software complexity. At the same time, the pervasive internet connections of these systems necessitate the fundamental design of security into these devices. The isolation of complex features from those that are critical through protection domains is an effective means to constrain the scope of faults and security breaches. Common hardware-provided memory facilities to enforce protection domains through memory access control -- including Memory Management Units (MMUs) usually found in microprocessors, and Memory Protection Units (MPUs) usually found in microcontrollers -- must meet the goals of enabling flexible, efficient and dynamic management of memory , and must enable tight bounds on the worst-case execution of critical code. Unfortunately, current system memory management facilities are ill-prepared to handle this challenge: MMUs that use extensive caches to achieve strong average-case performance suffer from debilitating worst-case and even average-case behavior under hefty interference, while MPUs struggle to provide flexible memory management. This paper details MxU, a memory protection and allocation abstraction that integrates temporal specifications into the memory management subsystem, to enable portable code to achieve both predictable, tightly-bounded execution and dynamic management across both MMU- and MPU-based systems. We implement MxU in the Composite microkernel, and evaluate its flexibility and predictability over two different architectures: a MPU-based Cortex-M7 microcontroller and a MMU-based Cortex-A9 microprocessor using a suite of modern applications including neural network-based inference, SQLite, and a javascript runtime. For MMU-based systems, MxU reduces application TLB stall by up to 68.0%. For MPU-based systems, MxU enables flexible dynamic memory management often with application overheads of 1%, increasing to 6.1% under significant interference.
Towards End-to-End Learning for Efficient Dialogue Agent by Modeling Looking-ahead Ability

Learning an efficient manager of dialogue agent from data with little manual intervention is important, especially for goal-oriented dialogues.However, existing methods either take too many manual efforts (e.g.reinforcement learning methods) or cannot guarantee the dialogue efficiency (e.g.sequence-to-sequence methods).In this paper, we address this problem by proposing a novel end-to-end learning model to train a dialogue agent that can look ahead for several future turns and generate an optimal response to make the dialogue efficient.Our method is data-driven and does not require too much manual work for intervention during system design.We evaluate our method on two datasets of different scenarios and the experimental results demonstrate the efficiency of our model.
Extracting method knowledge elements from scientific literature: A rule‐based approach

ABSTRACT Knowledge elements are the basic unit of knowledge. Extracting knowledge elements from literatures enables granular knowledge organization and retrieval. This paper proposes a rule‐based framework for extracting method Knowledge Elements (KEs) in scientific literature aiming to improve the accuracy and complete extraction of method KEs. The method is divided into two stages: semi‐automated extraction of initial description rules of method KEs and automated derivation of additional description rules. In a preliminary evaluation on 415 papers, the precision and recall for the method KEs extraction are 0.77 and 0.84, respectively, indicating the effectiveness of our method.
Controlling Utterance Length in NMT-based Word Segmentation with Attention

One of the basic tasks of computational language documentation (CLD) is to identify word boundaries in an unsegmented phonemic stream. While several unsupervised monolingual word segmentation algorithms exist in the literature, they are challenged in real-world CLD settings by the small amount of available data. A possible remedy is to take advantage of glosses or translation in a foreign, well-resourced, language, which often exist for such data. In this paper, we explore and compare ways to exploit neural machine translation models to perform unsupervised boundary detection with bilingual information, notably introducing a new loss function for jointly learning alignment and segmentation. We experiment with an actual under-resourced language, Mboshi, and show that these techniques can effectively control the output segmentation length.
Cheating Prevention in Improved Extended Progressive Visual Cryptography Scheme

Securing information from unauthorized access is mandatory in day-to-day life. Secret sharing schemes deal with security of information while exchanging it with others. Visual cryptography scheme (VCS) is a secret sharing scheme for visual information like images and video. Right from the inception of VCS, various revisions are done to original VCS, like k out of n VCS, extended VCS, progressive visual cryptography scheme (PVCS), and so on. Some common issues of various VCSs are: pixel expansion, poor contrast of share images constructed, poor accuracy of reconstruction and management of noise-like random shares. Improved extended progressive visual cryptography scheme (IEPVCS) deals with all these issues, but suffers from the problem of cheating with VCS. The commonly used approach for cheating with VCS is to introduce fake shares in the system and affect the reconstruction of secret information. Very few VCS deal with cheating by fake shares introduction. A cheating prevention mechanism is introduced in IEPVCS to handle the issue of fake shares and prevent the recovery system from reconstructing wrong secret image.
CONTEXTUAL TEACHING AND LEARNING UNTUK PENINGKATAN KETERAMPILAN MENULIS NARASI SISWA KELAS IV SEKOLAH DASAR

The purpose of the study is knowing the increase of students' skills in writing narrative texts through learning activities with the contextual teaching and learning model in class IV A at Kauman 1 Malang Elementary School. Field data shows that students still write with that tend not to be coherent, short stories, and wrong in using PUEBI. This study uses the PTK method (Classroom Action Research). The research subjects were 31 students of class IV A SDN Kauman 1 Malang. The research was conducted in two cycles. Data collection uses tests and observations.The results of the study were obtained from student observations when learning activities and narrative text writing tests. The data analysis used in this study are two, qualitative descriptive and quantitative descriptive.Cycle I shows that students' skills in writing narrative texts have increased from pre-cycle test results with a class average value of 71.97 increasing to 73.84 on the results of cycle I and 77.87 tests on cycle II test results with the percentage of students completing in one class starting at 35.48% on the pre-cycle test results to 51.61% on the results of the first cycle test and 74.19% on the results of the cycle II test. The conclusion of this study is contextual teaching and learning can be used to improve students' narrative text writing skills.
Recurrent-and-Parallel GMDH Algorithms for High-Performance Computing

reCurrent-AnD-pArAllel GmDh AlGorIthms For hIGh-perFormAnCe ComputInGThe paper presents the conception, theoretical grounds and mathematical tools for designing high-performance searching and iterative GMDH algorithms on the basis of recurrent-and-parallel computing for modelling and prediction of complex processes.Its effectiveness is experimentally tested.Intelligent information technology for inductive modeling of complex processes on the basis of recurrent-and-parallel computing is constructed.
Current Picture Referencing mode with Affine Model in VVC

The next video coding standard Versatile Video Coding (VVC) is developing, and the current picture referencing (CPR) mode for processing Screen Content Video has been adopted in the third version of reference software Video Test Model (VTM3.0). It searches the block vector from the current block to the reference block in the same picture instead of searching the regular motion vector from the current picture to the reference picture. However, it only considers the translational motion compensation, using one vector to represent the movement of the whole block. To fit into more movements in the real world, intra prediction modes with affine motion model based on CPR mode called CPR-Affine mode is proposed in this paper innovatively. And it is the first to apply the affine motion model to intra prediction. First, choose the partial current picture to be the only reference picture, and the reference area must comply with some constraints to ensure that valid reference block can be obtained during decoding. Second, search a set of block-affine vectors (BVAffi) iteratively. BVAffi represents a set of the block vectors of the control points which is the top-left and the top-right corner samples of the current block, and the best BVAffi means the minimum predicted error after compensation. Meanwhile, in order to search the best BVAffi, establish one gradient filter with Sobel operation. The proposed algorithm in this paper is significant to introduce the affine motion model into intra prediction modes. Compared to VTM3.0 with CPR mode turning on, the experimental results demonstrate the efficiency of our proposed algorithm.
Using Local Knowledge Graph Construction to Scale Seq2Seq Models to Multi-Document Inputs

Query-based open-domain NLP tasks require information synthesis from long and diverse web results. Current approaches extractively select portions of web text as input to Sequence-to-Sequence models using methods such as TF-IDF ranking. We propose constructing a local graph structured knowledge base for each query, which compresses the web search information and reduces redundancy. We show that by linearizing the graph into a structured input sequence, models can encode the graph representations within a standard Sequence-to-Sequence setting. For two generative tasks with very long text input, long-form question answering and multi-document summarization, feeding graph representations as input can achieve better performance than using retrieved text portions.
Tumor Boundary Delineation Using Abnormality Outlining Box Guided Modified GVF Snake Model

Segmentation is a million dollar task in order to highlight any region of interest (ROI) such as tumor, bleed, edema, or infarct in any medical image. Disease diagnosis, prognosis, surgery, and rehabilitation aspects are guided by segmentation results in oncology. The best ideology is to use high-end active contour models for tumor detection, location, and delineation. Traditional parametric active contour model like GVF (gradient vector flow) snake model suffers from more sensitive parameters, limited capture range, more sensitive to noise, and indentation while segmenting brain tumors in medical images. In order to address and solve these problems, a modified GVF is proposed. The modified snake model proposes a new design for the snake external force to resolve these problems by inducing a strong force near edges. This paper will address how an abnormality outlining box (AOB) guides modified GVF to get rid of demerit like lack of choice of precise initial contour. AOB-guided modified GVF is an integration of AOB and modified GVF external force. The experimental results prove the modified snake model captures the tumor efficiently and quickly even in the presence of any noise artifacts.
Real Time Face Tracking and Recognition Using Efficient Face Descriptor and Features Extraction Algorithms

The real time face recognition system used to capture the real time face of end user, detect the face, and perform the recognition by preexisting features of the corresponding user. The real time face tracking and recognition is a challenging research problem since last two decades due to the various presence of various illumination conditions, low-resolutions face images, different facial expressions, etc. in capture face images. In this research work, we proposed a novel framework for the real time face tracking and recognition regardless of input facial research dataset. The proposed method is composed of four main steps such as pre-processing, face descriptor, features extraction and selection, and classification. In the first phase, we first perform the task of face tracking and cropping the detected face using the Viola–Jones (VJ) method, we further applied the Gaussian filtering to smooth the cropped face image. In the second phase, the novel face descriptor method called Tracked Directional Ternary Pattern (TDTP) was proposed to address the real time face recognition challenges. In features extraction method, we designed a novel modified DCT (MDCT) based feature extraction method to address the challenge of low-resolution face images.