Reviewerly/exHarmony huggingface dataset:
Real-Time Resume Classification System Using LinkedIn Profile Descriptions

In the domain of online job recruitment, accurate job and resume classification is vital for both the seeker and the recruiter. We have built an automatic text classification system that utilizes various techniques like Term frequency-inverse document frequency with Machine Learning and Convolution Neural network for training the model with texts and classifying them into labels and finally to compare their results. Using resume data of applicants, we have categorized them into different categories. Due to the sensitive nature of resume data, we have used domain adaptation. A classifier is trained on a large dataset of job description snippet, which is then used to classify resume data. Despite having a small dataset, consistent classification performance is seen. The primary filter for this type of work is the efficiency the system can provide. We aim to compare the results obtained by various algorithms that are generated using the same data so that the efficiency of each algorithm can be evaluated. From the result, it is evident that character-level CNN gives a better F1 score compared to other models.
Gundapusunil at SemEval-2020 Task 8: Multimodal Memotion Analysis

Recent technological advancements in the Internet and Social media usage have resulted in the evolution of faster and efficient platforms of communication.These platforms include visual, textual and speech mediums and have brought a unique social phenomenon called Internet memes.Internet memes are in the form of images with witty, catchy, or sarcastic text descriptions.In this paper, we present a multi-modal sentiment analysis system using deep neural networks combining Computer Vision and Natural Language Processing.Our aim is different than the normal sentiment analysis goal of predicting whether a text expresses positive or negative sentiment; instead, we aim to classify the Internet meme as a positive, negative, or neutral, identify the type of humor expressed and quantify the extent to which a particular effect is being expressed.Our system has been developed using CNN and LSTM and outperformed the baseline score.
Optimize Student Learning via Random Forest-Based Adaptive Narrative Game

This paper presents an adaptive narrative game system that focuses on sequential logic design. The system adapts a random forest machine learning model to estimate a student's current level of domain knowledge relative to the problem presented to him through his game-playing behavior data, such as time taken to find solutions, errors in solutions, and emotional indicators. Hints, prompts, and/or individualized lessons are then offered to the player to guide their learning in a positive and productive direction. Our preliminary pilot study demonstrates that the model can make accurate classifications, from which proper assistance can then be provided to individual students as they play.
Knowledge-based generation of a plant-specific reinforcement learning framework for energy reduction of production plants

In research, there are more and more successful approaches to operate production plants more resource-efficiently and more productively with the help of reinforcement learning. An important point is the reduction of the energy demand of production plants. Instead of manually implementing complex standby strategies rigidly in a PLC, an intelligent system can train to derive decisions about the optimal energetic state of each component autonomously. Since learning in a virtual environment has decisive advantages, simulation models with sufficient accuracy are necessary. Many of the previous implementations of reinforcement learning approaches in an industrial environment are usually tailored to a specific plant. In particular, the agent's scope of action and its connection to the environment must be adapted manually for a new plant. The presented solution shows an intelligent system which automatically optimizes the virtual learning environment with the support of plant knowledge and adapts the reinforcement learning agent to the respective production plant.
Expeditious banking using Blockchain Technology

Block chain has an interesting support of bit coin, the digital crypto currency with an ever increasing sphere of users worldwide. But, block chain in itself is much more than just bit coin, it is the new generation security system encapsulating processes in series of blocks to provide a secure way of recording transactions and it is circulated among signatories, or any target group being the participants in the process. It draws its appeal out of the fact that it achieves this without the need of any central authority. Current banking architecture is largely centralized and therefore vulnerable to load defaults and frauds like the PNB scam, Videocon case, Kingfisher scam and many more. Banking all over the world has adopted block chain technologies and it is the need of the hour for regulation and avoidance of such scams. Thus, we are using block chain technology for the decentralized working of banks and the complete removal of authoritarian interception. The model which we are proposing includes block chain encapsulated in the process of NEFT (National Electronic Fund Transfer) using IFSC (Indian Financial System Code) incorporating the protocols set down by RBI for secure and decentralized fund transfer. Our blocks will consist of the process computed in java micro services. The ledger will be interconnected within themselves using consensus algorithms.
MedZip: 3D Medical Images Lossless Compressor Using Recurrent Neural Network (LSTM)

As scanners produce higher-resolution and more densely sampled images, this raises the challenge of data storage, transmission and communication within healthcare systems. Since the quality of medical images plays a crucial role in diagnosis accuracy, medical imaging compression techniques are desired to reduce scan bitrate while guaranteeing lossless reconstruction. This paper presents a lossless compression method that integrates a Recurrent Neural Network (RNN) as a 3D sequence prediction model. The aim is to learn the long dependencies of the voxel's neighbourhood in 3D using Long Short-Term Memory (LSTM) network then compress the residual error using arithmetic coding. Experiential results reveal that our method obtains a higher compression ratio achieving 15% saving compared to the state-of-the-art lossless compression standards, including JPEG-LS, JPEG2000, JP3D, HEVC, and PPMd. Our evaluation demonstrates that the proposed method generalizes well to unseen modalities CT and MRI for the lossless compression scheme. To the best of our knowledge, this is the first lossless compression method that uses LSTM neural network for 16-bit volumetric medical image compression.
Factors Affecting Disaster Resilience in Oman: Integrating Stakeholder Analysis and Fuzzy Cognitive Mapping

Planning for community resilience to disasters is a process that involves co‐ordinated action within and between relevant organizations and stakeholders, with the goal of reducing disaster risk. The effectiveness of this process is influenced by a range of factors, both positively and negatively, that need to be identified and understood so as to develop organizational capacity to build community resilience to disaster. This study investigates disaster planning and management in Oman, a country facing significant natural hazards, and with a relatively new system of institutional disaster management. Fuzzy cognitive mapping integrated with stakeholder analysis is used to identify relevant factors and their inter‐relationships, and hence provides an improved understanding of disaster governance. Developing an improved understanding of the complexity of this institutional behavior allows identification of opportunities to build greater resilience to disaster through improved planning and emergency response. We make recommendations for improved disaster management in Oman relating to governance (including improved plan dissemination and closer working with community organizations), risk assessment, public education, built environment development, and financing for disaster resilience.
The K-Medoids Clustering Method for Learning Applications during the COVID-19 Pandemic

A disease that is currently widespread today is caused by the spread of the coronavirus disease or what is commonly called COVID 19. This virus is very dangerous to health because it attacks organs in the human body from various sources, either from the air or direct touch. With the existence of COVID 19, it has an impact on all countries, especially the State of Indonesia, which consists of various islands, which are also affected by the COVID 19 virus. So that the central government takes a policy to carry out social distancing to every one to break the chain of spreading this virus, with this social distancing it has an impact on all activities that occur every day. As an impact on the learning process that usually takes place in class, it turns into online learning that uses several supporting applications in the learning process during the COVID 19 pandemic. With online learning from various applications, it attracts researchers to research with the K-Medoid Clustering Algorithm in using applications during the pandemic COVID 19.
Inertias of entanglement witnesses

Entanglement witnesses (EWs) are a fundamental tool for the detection of entanglement. We study the inertias of EWs, i.e., the triplet of the numbers of negative, zero, and positive eigenvalues respectively. We focus on the EWs constructed by the partial transposition of states with non-positive partial transposes. We provide a method to generate more inertias from a given inertia by the relevance between inertias. Based on that we exhaust all the inertias for EWs in each qubit-qudit system. We apply our results to propose a separability criterion in terms of the rank of the partial transpose of state. We also connect our results to tripartite genuinely entangled states and the classification of states with non-positive partial transposes. Additionally, the inertias of EWs constructed by X-states are clarified.
Toward an Epistemological Mutation in the Humanities and Social Sciences

The ambition of this text is to contribute to the definition of a new ‘digital enlightenment’ around the scholarly uses of the Internet. After recalling some of the main principles of the open science movement and the need for users to reclaim their data, I draw up an epistemological–political project aimed at making knowledge creation operations transparent for the community of researchers in the social sciences and humanities. This program is divided into three points, according to the tripartition of language acts by Austin. On the locutory level, I propose the use of a language with computable semantics (Information Economy MetaLanguage, IEML) as a documentary metalanguage. On the illocutionary level, I propose a formalization of the multiple roles and rules of communication as well as the types of reference implemented by the scholarly community. The transparency of the first two stages of the production of meaning would make it possible to trace, with the commitment of researchers, the perlocutionary processes of authority building. This program aims at the emergence of a reflexive collective intelligence loop in the scholarly community whose exemplarity should allow for percolation and progressive dissemination to other communities.
Research on the Flight Anomaly Detection During Take-off Phase Based on FOQA Data

Aiming at the high risk of fatal accidents during the flight's take-off phase, a data-driven method for flight anomaly detection is proposed; firstly, the key performance parameters of the take-off phase are chosen; secondly, Airbus A320 aircraft is taken as the research object, and the corresponding QAR data in FOQA station are divided into training set and testing set; thirdly, the training set is clustered by one-class SVM method to get the anomaly detection model, then the model is used to detect the outlier of the testing set; finally, the flight anomaly detection examples of two-dimensional, three-dimensional and multi-dimensional parameters are given respectively, and the outlier flights deviating from the group characteristics can be accurately marked, which provides important decision support information for the flight safety management.
Recent advance on phase measuring deflectometry for obtaining 3D shape of specular surface

Phase measuring deflectometry (PMD) is a superior technique to obtain 3D shape information of specular surfaces because of its advantages of large dynamic range, non-contact operation, full-field measurement, fast acquisition, high precision and automatic data processing. This paper reviews the recent advance on PMD. First, the basic principle of PMD is introduced following several fringe reflection methods. Then, a direct PMD (DPMD) method is presented for measuring 3D shape of specular objects having discontinuous surfaces. The DPMD method builds the relationship between phase and depth data, without gradient integration procedure. Next, an infrared PMD (IR-PMD) method is reviewed to measure specular objects. Because IR light is used as a light source, the IR-PMD method is insensitive to the effect of ambient light and has high measurement accuracy. The following will analyze the effects of error sources, including nonlinear influence, lens distortion of imaging and projecting system, geometric calibration error, on the measurement results and evaluate the performance of the 3D shape measurement system. Finally, the future research directions of PMD will be discussed.
Edge Preserving Filter Selection for Noise Removal and Histogram Equalization

Enhancement of image contrast can be done efficiently and simply using the Histogram Equalization (HE) technique. Noise amplification, over-enhancement, inconsistency in brightness and other such drawbacks are faced during HE along with loss of structure information. A novel edge preserving filter-selection scheme is proposeding this paper to simultaneously overcome these drawbacks. Along with certain regularizations, the generic data-fitting components and variational energies are reduced using a filter-based approach. However, the total energy required by the system is maintained. Total variation, Mean Curvature, Bernstein and Gaussian Curvature filters are the fast discrete filters presented in this paper that ensures that energy variation regularizes. Filter selection is performed based on the output of the comparison algorithm. Noise amplification and over enhancement are avoided and both qualitative, as well as quantitative evaluation of the filters, is performed. The energy of the overall model can be reduced rapidly using these filters. The experimental results prove the efficiency of these filters in regularization models.
A game-theoretic approach to storage offloading in PoC-based mobile blockchain mining

Proof of Capacity (PoC) is an eco-friendly alternative to Proof of Work for consensus in blockchains since it determines mining rights based on miners' storage rather than computation. In PoC, for every block, a miner executes hashing on part of his dedicated storage. The miner that comes up with the smallest hash value among all miners will win the block. PoC has yet to be applied to mobile applications, due to the storage limitation of mobile devices. Storage offloading can be a viable solution that allows miners to offload mining all files to a cloud storage. In each mining round, a miner can decide whether to mine on his local device or by a cloud virtual machine (VM). Self-mining requires no extra cost but it incurs download delay, which will reduce the chance of winning. Cloud-mining experiences no delay but it brings cost on VMs. This delay-cost tradeoff challenges each miner to determine a ratio between self-mining and cloud-mining to maximize his utility. We model interactions among miners as a non-cooperative game and formulate a Nash equilibrium problem to investigate the effects of offloading on miners' utilities. We analyze the existence and uniqueness of equilibrium and propose a distributed algorithm to achieve the equilibrium in a uniform-delay setting. Further, we extend our results to non-uniform delays since miners may choose different network settings, e.g. 5G, 4G, or 3G. Both numerical evaluation and testbed experiments on Burstcoin are conducted to show the feasibility of storage offloading and to validate the proposed models and theoretical results.
Object detection and classification by cascade object training

Computer Vision (CV) has become ubiquitous in smart systems for detecting and labeling objects, starting from social media platforms to autonomous vehicles. It requires extensive computation and image processing. In this paper, a model is processed and used to detect various colored cups with saucers from a set of different objects. The system is trained using Cascade Trainer Graphical User Interface (GUI), and the testing is done utilizing MATLAB, discussed in detail. Finally, the model is tested for its efficacy on the S32V234 Evaluation Board (EVB). Our proposed system accomplished its goal by identifying and tagging the objects of interest with maximum possible accuracy.
Semisupervised Classification With Novel Graph Construction for High-Dimensional Data

Graph-based methods have achieved impressive performance on semisupervised classification (SSC). Traditional graph-based methods have two main drawbacks. First, the graph is predefined before training a classifier, which does not leverage the interactions between the classifier training and similarity matrix learning. Second, when handling high-dimensional data with noisy or redundant features, the graph constructed in the original input space is actually unsuitable and may lead to poor performance. In this article, we propose an SSC method with novel graph construction (SSC-NGC), in which the similarity matrix is optimized in both label space and an additional subspace to get a better and more robust result than in original data space. Furthermore, to obtain a high-quality subspace, we learn the projection matrix of the additional subspace by preserving the local and global structure of the data. Finally, we intergrade the classifier training, the graph construction, and the subspace learning into a unified framework. With this framework, the classifier parameters, similarity matrix, and projection matrix of subspace are adaptively learned in an iterative scheme to obtain an optimal joint result. We conduct extensive comparative experiments against state-of-the-art methods over multiple real-world data sets. Experimental results demonstrate the superiority of the proposed method over other state-of-the-art algorithms.
Continuous quality improvement in statistical code: avoiding errors and improving transparency

Clear communication of statistical approaches can ensure healthcare research is well understood, reduce major errors and promote the advancement of science. Yet in contrast to the increasing complexity of data and analyses, published methods sections are at times insufficient for describing necessary details. Therefore, ensuring the quality, transparency and reproducibility of statistical approaches in healthcare research is essential.1 2 Such concerns are not just theoretical and have direct implications for research in the quality and safety field. For example, the Hospital Readmissions Reduction Program was instituted in 2012 by the US Centers for Medicare & Medicaid Services (CMS) and imposed financial penalties on hospitals with high readmission rates. Subsequent studies sought to determine the extent to which this programme was successful in reducing readmissions without promoting unintended consequences, such as increased mortality. Clearly defining the success or failure of this programme is essential, but in 2018 two prominent articles using the same CMS data set presented opposing results.3 4 These conflicts are undoubtedly due to differences in analytical choices, but specific differences are challenging to reconcile given statistical code was unavailable to readers. In another recent example, a major article was retracted due to the discovery of a statistical coding error that reversed the categorisation of treatment and control groups.5 This clinical trial examined a support programme for hospitalised patients with chronic obstructive pulmonary disease, originally reporting a lower risk of hospitalisation and emergency department visits, but in actuality demonstrating the support programme was associated with harm. Both cases demonstrate how better practices with statistical coding sharing at the time of publication may improve the quality of research.While the utility of statistical code sharing may seem self-evident, it occurs more infrequently than one would expect.2 We believe a principal barrier to statistical code sharing is …
A Comparative Study of Transfer Learning Approaches for Video Anomaly Detection

Recent research has shown that features obtained from pretrained Convolutional Neural Network (CNN) models can be promptly applied to a variety of problems they were not originally designed to solve. This concept, often referred to as Transfer Learning (TL), is a common practice when labeled data is limited. In some fields, such as video anomaly detection, TL is still an underexplored subject in the sense that it is not clear whether the architecture of the pretrained CNN model impacts on the video anomaly detection performance. In order to clarify this issue, we perform an extensive benchmark using 12 different pretrained CNN models on ImageNet as feature extractors and apply the features obtained to seven video anomaly detection benchmark datasets. This work presents some interesting findings about video anomaly detection using TL. The highlights of our findings were revealed by our experiments, which have shown that a simple classification process using One-Class Support Vector Machines yields similar results to state-of-the-art models. Moreover, a statistical analysis suggests that architectural differences are negligible when choosing a pretrained model for video anomaly detection, since all models presented similar performance. At last, we present an in-depth visual analysis of the Avenue dataset, and reveal several aspects that may be limiting the performance of state-of-the-art video anomaly detection methods.
Security Control Components for Epidemic Prevention Donation Management Blockchain

After the outbreak of the new coronavirus pneumonia, it has been broadcasting soon. Medical supplies are out of used. Chinese people have provided medical supplies and raised donations to help Hubei Province, China. While, Red Cross Society of Hubei Branch and Wuhan Branch were suspected for unfair distribution of donated materials, the backlog of donated items, and inefficient assignment. These problems exposed public welfare organizations to the public sights. For information sharing, Jiangsu Province developed a new COVID-19 Donation Management Blockchain System (CDMBS). The system improves the transparency and credibility of donation information with the blockchain. Based on the overall architecture of Jiangsu's new coronavirus charity donation management blockchain, this paper designs various types of security control components. The components include the middleware for secure connection to protect the user certificate security. We designed CA certificate user management middleware to realize more secure registration and query functions for users with extended attributes. There is also a blockchain query middle with access control Software to ensure that important blockchain information is viewed securely. Experiments have verified the effectiveness and applicability of these security control components. It can be applied into a variety of scenarios in the CDMBS.