Reviewerly/exHarmony huggingface dataset:
Challenges in Procedural Multimodal Machine Comprehension: A Novel Way To Benchmark

We focus on Multimodal Machine Reading Comprehension (M3C) where a model is expected to answer questions based on given passage (or context), and the context and the questions can be in different modalities. Previous works such as RecipeQA have proposed datasets and cloze-style tasks for evaluation. However, we identify three critical biases stemming from the question-answer generation process and memorization capabilities of large deep models. These biases makes it easier for a model to overfit by relying on spurious correlations or naive data patterns. We propose a systematic framework to address these biases through three Control-Knobs that enable us to generate a test bed of datasets of progressive difficulty levels. We believe that our benchmark (referred to as Meta- RecipeQA) will provide, for the first time, a fine grained estimate of a model&#x2019;s generalization capabilities. We also propose a general M<sup>3</sup>C model that is used to realize several prior SOTA models and motivate a novel hierarchical transformer based reasoning network (HTRN). We perform a detailed evaluation of these models with different language and visual features on our benchmark. We observe a consistent improvement with HTRN over SOTA (~ 18% in Visual Cloze task and ~ 13% in average over all the tasks). We also observe a drop in performance across all the models when testing on RecipeQA and proposed Meta&#x2013;RecipeQA (e.g. 83.6% versus 67.1% for HTRN), which shows that the proposed dataset is relatively less biased. We conclude by highlighting the impact of the control knobs with some quantitative results.
A Surface Fitting Image Super-Resolution Algorithm Based on Triangle Mesh Partition

High-frequency information such as image edges and textures have an important influence on the visual effect of the super-resolution images. Therefore, it is vital to maintain the edge and texture features of the super-resolution image. A surface fitting image super-resolution algorithm based on triangle mesh partitions is proposed in this study. Different from the traditional image interpolation algorithm using quadrilateral mesh, this method reconstructs the fitting surface on the triangle mesh to approximate the original scene surface. LBP algorithm and second-order difference quotient are combined to divide the triangular mesh accurately, and the edge angle is utilized as a constraint to makes the edge of the constructed surface patch more informative. By the area coordinates as weighting coefficients to perform weighted averaging on the surface patches at the vertices of the triangle mesh, the cubic polynomial surface patches are obtained on the triangle mesh. Finally, a global structure sparse regularization strategy is adopted to optimize the initially super-resolution image and further eliminate the artifacts at the image edges and textures. Since the new method proposed in this study utilizes numerous information about local feature (e.g. edges), compared to other state-of-the-art methods, it provides clear edges and textures, and improves the image quality greatly.
User Identity Verification Strategy of Virtual Power Plant Node Based on Improved Zero-knowledge Technology

The application of virtual power plants makes up for the shortcomings of new energy sources and provides ideas for the development of low-carbon power systems. As the whole society attaches importance to information security issues, user information security issues based on blockchain trading platforms have brought challenges to the development of virtual power plants. This paper proposes a virtual power plant user identity verification method based on improved zero-knowledge proof. In order to solve the problem of increased node users and increased resource consumption for identity verification, the article proposes a distributed parallel verification strategy. The test results show that the method meets the field requirements in terms of accuracy and recall of identity verification. And compared with other verification methods, it has certain performance advantages.
Impacts of Trust Measurements on the Reputation-Based Mining Paradigm

Trust and reputation play an essential role in the success of the reputation-based mining paradigm, and therefore, it is important to have a mechanism for monitoring, analyzing, and labeling participants' behavior as a measurement of trust. To improve the existing reputation-based mining paradigm, we designed and implemented a trust model that takes into account real-world trust forming habits and incentivizes participants to commit to honest mining strategies in the cryptocurrency system. While detection of dishonest mining strategies can be challenging, this trust model considers past behavior and it is defection sensitive, i.e., making it more difficult to attain a high reputation value, the more one commits to dishonest mining. We also observed that the success of this trust model relies on the performance of attacks' detection, which is a reasonable observation. Our trust model can be used in cryptocurrency simulations to promote cooperation among miners and create a trustworthy environment for all participants.
A Competition of Shape and Texture Bias by Multi-view Image Representation

There are mainly two views on the interpretation of high efficiency of Convolutional Neural Networks (CNNs) for the task of image classification: shape bias and texture bias. This is critical to the causality and reliability of CNN models in real applications. In this work, we try to explore the power of CNNs and reconcile the hypothesis contradiction of CNNs from a multi-view image representation. Firstly, we assume an image is generated from object shape representation, object texture representation, and background information. Secondly, we segment and recombine the object shape, texture and image background through two losses: image reconstructed loss and feature discrepancy loss. Finally, the classification loss is combined by shape, texture and background contributions weighted by multi-view features. Comprehensive experiments conducted on real-world datasets show that, first, CNNs generally do not have texture or shape bias, which change with the internal bias of data; second, CNNs are learning knowledge in a lazy way, i.e., high level knowledge is learned only if low level knowledge does not satisfy the task requirements. Our findings might benefit the interpretability of CNNs and provide insight of more robust design.
Enhancing Top-N Item Recommendations by Peer Collaboration

Deep neural networks (DNN) based recommender models often require numerous parameters to achieve remarkable performance. However, this inevitably brings redundant neurons, a phenomenon referred to as over-parameterization. In this paper, we plan to exploit such redundancy phenomena for recommender systems (RS), and propose a top-N item recommendation framework called PCRec that leverages collaborative training of two recommender models of the same network structure, termed peer collaboration. We first introduce two criteria to identify the importance of parameters of a given recommender model. Then, we rejuvenate the unimportant parameters by copying parameters from its peer network. After such an operation and retraining, the original recommender model is endowed with more representation capacity by possessing more functional model parameters. To show its generality, we instantiate PCRec by using three well-known recommender models. We conduct extensive experiments on two real-world datasets, and show that PCRec yields significantly better performance than its counterpart with the same model (parameter) size.
An Easy-to-Integrate IP Design of AHB Slave Bus Interface for the Security Chip of IoT

Information security is fundamental to the Internet of things (IoT) devices, in which security chip is an important means. This paper proposes an Advanced High-performance Bus Slave Control IP (AHB-SIP), which applies to cryptographic accelerators in IoT security chips. Composed by four types of function registers and AHB Interface Control Logic (AICL), AHB-SIP has a simple and easy-to-use structure. The System on Chip (SoC) design can be realized by quickly converting the nonstandard interface of the security module to the AHB slave interface. AHB-SIP is applied to the security accelerators of SM2, SM3, and SM4 and random number generator (RNG). Combined with a low-power embedded CPU, TIMER, UART, SPI, IIC, and other communication interfaces, a configurable SoC can be integrated. Moreover, SMIC 110 nm technology is employed to tape out the SoC on a silicon chip. The area of AHB-SIP is 0.072 mm2, only occupying 6‰ of the chip (3.45 <math xmlns="http://www.w3.org/1998/Math/MathML" id="M1"> <msup> <mrow /> <mrow> <mi>∗</mi> </mrow> </msup> </math> 3.45 mm2), and the power consumption of encryption modules combined with AHB-SIP is lower than that combined with AXI interface, which is decreased up to 61.0% and is ideal for the application of IoT.
Detecting Phishing of Short Uniform Resource Locators using classification techniques

Phishing is a technique wherein a hacker clones a legitimate website, email id, graphic design and asks the target to act on the deceptive link for getting sensitive company and personal data. Approximately 78 per cent of cyber-attacks consist of Phishing techniques. There are websites which store the Uniform Resource Locators and domain name of phished sites. However, ample amounts of clones emerge every day. Hence it takes time up to several days to update the websites. There are many techniques in phishing that even experienced technologists cannot detect. This cyber-security threat is not easily identifiable by looking. Association rule mining in Uniform Resource Locators detects phishing. However, with an increase in the shortened Uniform Resource Locators- less information is derived from the uniform resource locator. We use machine learning algorithms to detect phished shorten Uniform Resource Locators. Naive Bayes Theorem, Logistic Regression and Random Forests Algorithm produced an accuracy of 99.4, 96 and 98 per cent respectively.
A News Title Generation Method Based on UNILM Framework via Adversarial Training

Text generation is now a very mature task. Many methods have been applied to text generation and achieved good results. This paper uses the pre-trained model Unilm with adversarial training to generate news headlines from the Thucnews dataset. We fine tune the methods and parameters in the model and produced some results for reference or comparison
Distance teaching and learning as an element of vocational education: some models, assessments, and tools for effective results in future accomplishments

This research paper applies a mixed-method approach for the investigation of challenges and eligible solutions for the online teaching and learning techniques, as well as, their impact on the professional identification of the educational system, students, and professors. The discussion would be about the outcomes of both quantitative and qualitative approaches for detailed examination of the revealing problems in digital learning, by using the explanatory type of research supplied with both statistics and descriptive analysis of various categories, approaches, models and types. Statistics of further mentioned surveys and examinations underline the existing issues in the online teaching &amp; learning platform, which is the difficulty in the management of their time (both students and instructors); less interest in studies; less gained help from instructors; technology disruptions or unawareness of their usage. In addition to this, the research emphasizes the approaches/models and categories for resolving the indicated challenges by illustrating three essential techniques for online teaching and learning: course design, the interaction of both students and instructors, and the level of preparedness of the professors for the distance teaching. Moreover, the analysis of the results and discussions of the research findings underline the possible affirmative results, the flexibility of studying designs and their outcomes, also professors' and students' capability of interfacing for mutually positive outcomes. Due to the COVID-19 pandemic, teaching and learning activities in the classroom have been permanently halted. Students in their last years of high school and university are in an unusual position that makes it extremely difficult to observe the upcoming future. The duration of the pandemic, as well as its impacts on everyday living, costs, and other financial struggles, might have a serious influence on college and adolescents' ability to continue their studies. The insecurity generated by disruptions in the academic sphere is concerning. Undergraduate and graduate students' circumstances have created adverse situations such as the need to drop out of school.
A Tree-based Construction for Verifiable Diplomas with Issuer Transparency

Still to this day, academic credentials are primarily paper-based, and the process to verify the authenticity of such documents is costly, time-consuming, and prone to human error and fraud. Digitally signed documents facilitate a cost-effective verification process. However, vulnerability to fraud remains due to reliance on centralized authorities that lack full transparency. In this paper, we present the mechanisms we designed to create secure and machine-verifiable academic credentials. Our protocol models a diploma as an evolving set of immutable credentials. The credentials are built as a tree-based data structure with linked time-stamping, where portions of credentials are distributed over a set of smart contracts. Our design prevents fraud of diplomas and eases the detection of degree mills, while increasing the transparency and trust in the issuer's procedures. Our evaluation shows that our solution offers a certification system with strong cryptographic security and imposes a high level of transparency of the certification process. We achieve these benefits with acceptable costs compared to existing solutions that lack such transparency.
A semantic parsing pipeline for context-dependent question answering over temporally structured data

We propose a new setting for question answering in which users can query the system using both natural language and direct interactions within a graphical user interface that displays multiple time series associated with an entity of interest. The user interacts with the interface in order to understand the entity's state and behavior, entailing sequences of actions and questions whose answers may depend on previous factual or navigational interactions. We describe a pipeline implementation where spoken questions are first transcribed into text which is then semantically parsed into logical forms that can be used to automatically extract the answer from the underlying database. The speech recognition module is implemented by adapting a pre-trained LSTM-based architecture to the user's speech, whereas for the semantic parsing component we introduce an LSTM-based encoder-decoder architecture that models context dependency through copying mechanisms and multiple levels of attention over inputs and previous outputs. When evaluated separately, with and without data augmentation, both models are shown to substantially outperform several strong baselines. Furthermore, the full pipeline evaluation shows only a small degradation in semantic parsing accuracy, demonstrating that the semantic parser is robust to mistakes in the speech recognition output. The new question answering paradigm proposed in this paper has the potential to improve the presentation and navigation of the large amounts of sensor data and life events that are generated in many areas of medicine.
Image Augmentation based on GAN deep learning approach with Textual Content Descriptors

Computer vision, also known as computational visual perception, is a branch of artificial intelligence that allows computers to interpret digital pictures and videos in a manner comparable to biological vision. It entails the development of techniques for simulating biological vision. The aim of computer vision is to extract more meaningful information from visual input than that of a biological vision. Computer vision is exploding due to the avalanche of data being produced today. Powerful generative models, such as Generative Adversarial Networks (GANs), are responsible for significant advances in the field of picture creation. The focus of this research is to concentrate on textual content descriptors in the images used by GANs to generate synthetic data from the MNIST dataset to either supplement or replace the original data while training classifiers. This can provide better performance than other traditional image enlarging procedures due to the good handling of synthetic data. It shows that training classifiers on synthetic data are as effective as training them on pure data alone, and it also reveals that, for small training data sets, supplementing the dataset by first training GANs on the data may lead to a significant increase in classifier performance.
Eye Movement Events Detection with KNN-GA and Prior Knowledge

Aiming at the problems of difficulty in threshold adjustment and low detection accuracy and efficiency, an eye movement event detection method combining K-Nearest Neighbor and genetic algorithm (KNN-GA) and prior knowledge is proposed. Firstly, design the absolute amplitude feature of eye movement to describe the eye movement event characteristics of PSOs, and then the KNN is used to pre-detect eye movement events based on the minimum feature subset generated by genetic algorithm; After that, screening rules based on prior knowledge are ted to further adjust and optimize the pre-detection results. Experimental results show that this algorithm avoids threshold adjustment, and its execution efficiency is equivalent to simple IVT and NH, meanwhile the detection accuracy of fixation, saccade, and PSOs are increased by at least 3.4%, 4.7%, and 12.7%, respectively, and the detection performance is robust under different noise levels.
A New GA-PSO Optimization Methodology with Fuzzy Adaptive Inertial Weight

In this paper, a new optimization methodology is proposed concerning to hybridization involving Genetic Algorithm and Particle Swarm Optimization (GA-PSO) with fuzzy adaptive inertial weight. In order to optimize multimodal problems with fast and non-premature convergence, hybridization is performed by combining the desirable features of GA and PSO. However, a slow and premature convergence can still occur due to the inefficient trade-off between global and local search. To this end, in this paper, a Mamdani fuzzy system is used for parametric adaptation of the inertial weight of the PSO, since through the size of the inertial weight it is possible to define whether the search will occur global or local manner.
Lifelong learning for text retrieval and recognition in historical handwritten document collections

This chapter provides an overview of the problems that need to be dealt with when constructing a lifelong-learning retrieval, recognition and indexing engine for large historical document collections in multiple scripts and languages, the Monk system. This application is highly variable over time, since the continuous labeling by end users changes the concept of what a 'ground truth' constitutes. Although current advances in deep learning provide a huge potential in this application domain, the scale of the problem, i.e., more than 520 hugely diverse books, documents and manuscripts precludes the current meticulous and painstaking human effort which is required in designing and developing successful deep-learning systems. The ball-park principle is introduced, which describes the evolution from the sparsely-labeled stage that can only be addressed by traditional methods or nearest-neighbor methods on embedded vectors of pre-trained neural networks, up to the other end of the spectrum where massive labeling allows reliable training of deep-learning methods. Contents: Introduction, Expectation management, Deep learning, The ball-park principle, Technical realization, Work flow, Quality and quantity of material, Industrialization and scalability, Human effort, Algorithms, Object of recognition, Processing pipeline, Performance,Compositionality, Conclusion.
Exploration of College English Teaching Reform Based on Evidence-based Teaching Concept

The concept of evidence-based teaching has become more and more mature, and has been successfully applied to the field of education, especially college English teaching, which provides a new theoretical guidance for educational research and teaching practice. This paper analyzes the important role of evidence-based teaching in college English teaching reform, and discusses its specific application and teaching reform for reference.
A Reinforcement-Learning-Based Deployment Strategy for GPP Components

This article presents a deployment strategy to optimally manage the computing resource in the Software Defined Radio (SDR) equipment. The proposed solution is a combination of Reinforcement Learning and the run-time resource data of the communication system. Two scenarios, based on the traditional method and Reinforcement Learning, are made through the SDR virtual platform. The simulation results show that the proposed method expands the new module for General Purpose Processor (GPP) resources management and improves the performance of Cognitive Radio (CR). The method innovatively optimizes component deployment in multi-processor platforms and adapts to the CR equipment with the new generation heterogeneous chips so that to implement more applications when resources are limited.
A Tool to Assist the Compiler Construction Instructor in Checking the Equivalence of Specifications Based on Regular Expressions

This paper presents CheRegES (CHEcking REGular Expression-based Specifications), a tool that assists the Compiler Construction instructor in checking the equivalence of computer language lexical specifications based on regular expressions. The tool allows the comparison of a reference specification, provided by the instructor, with the specification proposed by the student. As a result, the tool can report that: (i) both specifications are equivalent (and, therefore, the specification proposed by the student can be considered correct); (ii) there are discrepancies between the specification proposed by the student and the one provided by the instructor (and, therefore, the specification proposed by the student can be considered incorrect); or (iii) the result of the comparison is inconclusive. Also, in case discrepancies are discovered, the tool provides sentences that allow differentiation between the two specifications, and that help the instructor to diagnose the problems underlying the student’s specification. The paper motivates the need for the tool, describes its functionality, briefly summarizes its internals, and presents a preliminary evaluation of the tool that makes the usefulness of CheRegES as a tool to support assessment in Compiler Construction courses apparent.
An Addiction Detection Application Playing Free Fire Games Based on Android Using Tsukamoto's Fuzzy Logic

Online games that have a battle royal genre nowadays are very much the inantantnya. One of them is Free Fire which has been downloaded by more than 100 million people. The high number indicates that a person's high interest in playing online games can have a negative impact on one of them. For that, there is an app that can detect how much fun a person has in playing Free Fire games. By utilizing the fuzzy logic method tsukamoto allows it to detect the level of addiction of a person which is an android-based application. By utilizing the data owned by each player as an input variable that will produce output in the form of an addiction level percentage. Based on the result of the test carrieed out, the precentageresult were 83.34% which indicated a good category. Utilization of fuzzy tsukamoto method is expected to provide the right conclusions and in accordance with the expected.