Reviewerly/exHarmony huggingface dataset:
Application of Decision Tree C4.5 Algorithm in Student Achievement Evaluation Information System

The application of decision tree C4.5 algorithm in student performance evaluation system can improve teachers’ work efficiency and student performance, and provide effective information for school administrators. This paper mainly introduces the feasibility analysis of using this method in student achievement measurement information system, the design and implementation of data acquisition function module and so on. Firstly, this paper describes the concept and characteristics of student achievement evaluation information, and then studies the application of decision tree C4.5 algorithm, and designs a student achievement evaluation information system based on this algorithm, and carries out simulation experiment to test the algorithm performance of the system. The final test results show that decision tree C4.5 algorithm can process and generate performance evaluation information quickly and effectively, its query information and processing information time is less than 3s, and its classification accuracy is above 85%.
CUE Vectors: Modular Training of Language Models Conditioned on Diverse Contextual Signals

We propose a framework to modularize the training of neural language models that use diverse forms of sentence-external context (including metadata) by eliminating the need to jointly train sentence-external and withinsentence encoders.Our approach, contextual universal embeddings (CUE), trains LMs on one set of context, such as date and author, and adapts to novel metadata types, such as article title, or previous sentence.The model consists of a pretrained neural sentence LM, a BERT-based context encoder, and a masked transformer decoder that estimates LM probabilities using sentence-internal and sentenceexternal information.When context or metadata are unavailable, our model learns to combine contextual and sentence-internal information using noisy oracle unigram embeddings as a proxy.Real contextual information can be introduced later and used to adapt a small number of parameters that map contextual data into the decoder's embedding space.We validate the CUE framework on a NYTimes text corpus with multiple metadata types, for which the LM perplexity can be lowered from 36.6 to 27.4 by conditioning on context.Bootstrapping a contextual LM with only a subset of the context/metadata during training retains 85% of the achievable gain.Training the model initially with proxy context retains 67% of the perplexity gain after adapting to real context.Furthermore, we can swap one type of pretrained sentence LM for another without retraining the context encoders, by only adapting the decoder model.Overall, we obtain a modular framework that allows incremental, scalable training of context-enhanced LMs.
Logistics &amp; Supply Chain Management Using the Power of Blockchain

A large portion of supply chain networks have experienced hardships when attempting to incorporate all parts and partners, which make supply chain management network the board framework experiencing an absence of effectiveness and straightforwardness that make a consistent expansion in product misrepresentation and customer’s mistake. All data about product creation, stockpiling and transportation should stream obviously during all phases of the production network by following and validating to keep away from item’s tampering in the organization. Current software is based on top of brought together engineering and this leaves a hole for altering and bogus data particularly in metropolitan region, yet additionally the current arrangements can’t permit the data to be imparted to authorities or buyers to successfully survey and guarantee recognizability and straightforwardness. This paper proposes use of blockchain to store details of product and logistics level to provide assurance to consumer regarding authenticity of product.
Unveiling User Behavior on Summit Login Nodes as a User

We observe and analyze usage of the login nodes of the leadership class Summit supercomputer from the perspective of an ordinary user—not a system administrator—by periodically sampling user activities (job queues, running processes, etc.) for two full years (2020–2021). Our findings unveil key usage patterns that evidence misuse of the system, including gaming the policies, impairing I/O performance, and using login nodes as a sole computing resource. Our analysis highlights observed patterns for the execution of complex computations (workflows), which are key for processing large-scale applications.
A Novel Deep Learning SFR Model for FR-SSPP at Varied Capturing Conditions and Illumination Invariant

Face recognition systems attempt to identify individuals of interest as they appear through a network of cameras. Application like immigration management, fugitive tracing, and video surveillance dominates the technique of face recognition-single sample per person (FR-SSPP) which has become an important research topic in academic era. The issue of face recognition can be divided with two groups. The first is recognition of face with multiple samples per person, also known as conventional face recognition. The second method is to recognize faces using only single sample per person (SSPP). Predictions of variations like illumination, disguise in facial images have limitations with SSPP. Pose, illumination, low resolution, and blurriness are considered to be the challenges that face recognition system encounters. All these problems related to face recognition with single sample per person will be dealt with the proposed synthesized face recognition (SFR) model. The SFR model initially pre-processes the input facial image followed by the techniques like 4X generative adversarial network (4XGAN) to enhance the resolution and sharp generative adversarial network (SharpGAN) technique to sharpen the images. In image formation, 3D virtual synthetic images are generated consisting of various poses, and position regression map network technique (PRN) provides the dense alignment of the generated face images. Finally, with face detection and the deep feature extraction using convolution neural network, the proposed SFR model provides a better solution to the problems involved with recognition of face with single sample per person. Triplet loss function helps to recognize or identify aged faces which yields more importance to achieve a good functioning face recognition system which also overcomes the facial features changes. The model will achieve a greater accuracy and creates a vision to evaluate a detailed analysis which includes the conditions of environment and requirements of the application needed.
There’s a Time and Place for Reasoning Beyond the Image

Images are often more significant than only the pixels to human eyes, as we can infer, associate, and reason with contextual information from other sources to establish a more complete picture.For example, in Figure 1, we can find a way to identify the news articles related to the picture through segment-wise understandings of the signs, the buildings, the crowds, and more.This reasoning could provide the time and place the image was taken, which will help us in subsequent tasks, such as automatic storyline construction, correction of image source in intended effect photographs, and upper-stream processing such as image clustering for certain location or time.In this work, we formulate this problem and introduce TARA: a dataset with 16k images with their associated news, time, and location, automatically extracted from New York Times 1 (NYT), and an additional 61k examples as distant supervision from WIT (Srinivasan et al., 2021).On top of the extractions, we present a crowdsourced subset in which we believe it is possible to find the images' spatiotemporal information for evaluation purpose.We show that there exists a 70% gap between a state-of-the-art joint model and human performance, which is slightly filled by our proposed model that uses segment-wise reasoning, motivating higher-level vision-language joint models that can conduct open-ended reasoning with world knowledge.
Rule-based Text Extraction for Multimodal Knowledge Graph

Textual information is widely integrated in visual tasks such as object/scene detection and image annotation.However, the textual information is not fully exploited, overlooking the wide background knowledge available for Web images.This work proposes a multimodal knowledge graph (KG) to represent the knowledge extracted from unstructured Web image surrounding text and to integrate the relationship between image and text entities.Existing multimodal KG works have mainly focused on advanced visual processes for extracting entities and relations from images, and only employed standard text processing techniques such as tokenization, stop word removal, and part-of-speech (POS) tagging to capture nouns only or basic subject-verb-object from text in the semantic enrichment process.Adversely, neglecting other rich information in the text.Thus, the proposed approach attempts to address this as an automatic relation extraction (RE) problem to extract all possible triples from the text information from simple to complex sentences, in constructing the multimodal KG which eventually can be used as a training seed for visual tasks.A linguistic analysis is performed on a set of Web news articles consisting of news images and their related text.The dependency relations and POS information obtained are used to formulate a set of domainagnostic entity-relation extraction rules.A triple extractor incorporating these rules, is developed to extract the triples from a news articles dataset and construct the proposed MKG.The Precision and Recall metrics are used to evaluate the extractor's performance.The evaluation results show that the proposed approach can extract entities and relations in the dataset with the precision score of 0.90 and recall score of 0.60.While the results are promising, the extraction rules can still be improved to capture all the knowledge.
Why Accuracy is Not Enough: The Need for Consistency in Object Detection

Object detectors are vital to many modern computer vision applications. However, even state-of-the-art object detectors are not perfect. On two images that look similar to human eyes, the same detector can make different predictions because of small image distortions like camera sensor noise and lighting changes. This problem is called inconsistency. Existing accuracy metrics do not properly account for inconsistency, and similar work in this area only targets improvements on artificial image distortions. Therefore, we propose a method to use nonartificial video frames to measure object detection consistency over time, across frames. Using this method, we show that the consistency of modern object detectors ranges from 83.2% to 97.1% on different video datasets from the multiple object tracking challenge. We conclude by showing that applying image distortion corrections such as WEBP Image Compression and Unsharp Masking can improve consistency by as much as 5.1%, with no loss in accuracy.
Extracting Latent Steering Vectors from Pretrained Language Models

Prior work on controllable text generation has focused on learning how to control language models through trainable decoding, smartprompt design, or fine-tuning based on a desired objective.We hypothesize that the information needed to steer the model to generate a target sentence is already encoded within the model.Accordingly, we explore a different approach altogether: extracting latent vectors directly from pretrained language model decoders without fine-tuning.Experiments show that there exist steering vectors, which, when added to the hidden states of the language model, generate a target sentence nearly perfectly (> 99 BLEU) for English sentences from a variety of domains.We show that vector arithmetic can be used for unsupervised sentiment transfer on the Yelp sentiment benchmark, with performance comparable to models tailored to this task.We find that distances between steering vectors reflect sentence similarity when evaluated on a textual similarity benchmark (STS-B), outperforming pooled hidden states of models.Finally, we present an analysis of the intrinsic properties of the steering vectors.Taken together, our results suggest that frozen LMs can be effectively controlled through their latent steering space. 1
Research on Intelligent Natural Language Texts Classification

Natural language texts widely exist in many aspects of social life, and classification is of great significance to its efficient use and normalized preservation.Manual texts classification has the problems such as labor intensive, experience dependent and error prone, therefore, the research on intelligent classification of natural language texts has great social value.In recent years, machine learning technology has developed rapidly, and related researchers have carried out a lot of works on the texts classification based on machine learning, the research methods show the characteristic of diversification.This paper summarizes and compares the texts classification methods mainly from three aspects, including technical routes, text vectorization methods and classification information processing methods, in order to provide references for further research and explore the development direction of the texts classification.
A Multi-modal Seq2seq Chatbot Framework

Abstract The pandemic has forced young people to stay away from school and friends, complete online learning at home and live at home. Therefore, various mental illnesses such as anxiety and depression occur more frequently. Chatbot is a communication method that is more acceptable to young people. This paper proposes a multi-modal chatbot seq2seq framework, which divides the mental state of young people into different types through multi-modal information such as text and images entered by users in the chatbot. This model combines image description and text summarization modules with the attention mechanism in a multi-modal model to control related content in different modalities. Experiments on multi-modal data sets show that this method has 70% average accuracy and real users who use this system also believe that this method has good judgment ability.
Literature History of Marxism as a Philosophy of Science in Foreign Countries

As the dual attribute of both science and revolution, Marxism has not achieved a reasonable and balanced development in the domestic academic circle, and its value interpretation has been continuously demonstrated, while its scientific nature has not attracted enough attention in China.As a scientific interpretation, Marxist research presents the following characteristics abroad: the deep interpretation of the philosophy of science in the classical text of Marxist International and communist International; the repositioning of the relationship between Marxism and contemporary philosophy of science; the tentative exploration of Western Marxism.
Distributed Heterogeneous Parallel Computing Framework Based on Component Flow

Abstract Single processor has limited computing performance, slow running speed and low efficiency, which is far from being able to complete complex computing tasks, while distributed computing can solve such huge computational problems well. Therefore, this paper carried out a series of research on the heterogeneous computing cluster based on CPU+GPU, including component flow model, multi-core multi processor efficient task scheduling strategy and real-time heterogeneous computing framework, and realized a distributed heterogeneous parallel computing framework based on component flow. The results show that the CPU+GPU heterogeneous parallel computing framework based on component flow can make full use of the computing resources, realize task parallel and load balance automatically through multiple instances of components, and has the characteristics of good portability and reusability.
Feature Extraction and Representation Learning via Deep Neural Network

Selection of a text characteristic is a prerequisite for text mining and information retrieval. Traditional techniques of feature extraction demand the use of custom features that must be made by hand. For new applications, deep learning allows the acquisition of new effective feature representations from training data rather than having to spend a lengthy time developing an effective feature by hand. Deep learning has made tremendous strides in text mining as a new feature extraction technique. This means that instead of using handmade features that strongly rely on designers' prior knowledge and cannot be fully utilised with big data, deep learning employs features from massive datasets. Massive datasets, containing millions of parameters, can be used to train deep learning models automatically to represent those datasets' features. This work initially describes the most prevalent text feature extraction approaches and then goes into greater depth on how deep learning is regularly utilised in text feature extraction, as well as how it can be used in future.
Construction of minimal linear codes with few weights from weakly regular plateaued functions

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The Influences of Public and Institutional Pressure on Firms’ Cybersecurity Disclosures

Cybersecurity disclosures in reports filed with the US Securities and Exchange Commission (SEC) inform investors about firms’ cybersecurity incidents, risks, and related risk management efforts. Firms have traditionally chosen to communicate such information on a quarterly or annual basis, if at all, and prior research on the topic has largely focused on regulatory factors as driving forces. In this paper, we focus on timely disclosures (via 8-K filings) and derive hypotheses regarding the influences of two alternate forms of pressure as drivers of cybersecurity disclosures—(1) public pressure following a firm’s data breach and (2) pressure arising from the breaches of industry peers, which we cast as “institutional pressure.” We also theorize on how the source of the breach (internal or external) influences these forms of pressure. Our results suggest that firms’ cybersecurity disclosure practices are influenced by public pressure following a data breach and that this pressure is more acute for external breaches than for internal breaches. By contrast, breaches by industry peers, as a form of institutional pressure, appear to prompt fewer cybersecurity disclosures, except when the focal firm suffers its own external breach. From a theoretical perspective, our study supports a nuanced application of legitimacy theory in the cybersecurity disclosure context, especially in the midst of public and institutional pressure, such that the source of a data breach determines whether firms attempt to address the resultant legitimacy gap. From a practical perspective, our results may be viewed as alarming in that firms are not reacting to internal breaches with the same degree of communicative effort about cybersecurity as for external breaches, at least in terms of the timely disclosures we consider in this study. Our findings also point to certain levers that can promote timely cybersecurity disclosures, and thus have important policy implications.
Convolutional Neural Networks for Real and Fake Face Classification

This paper deals with the problem of classifying images of real and fake faces as it is impossible to distinguish them with the bare eye.Two different convolutional neural networks architecture models are applied.The first one is pre-trained VGG16 model, where transfer learning method is applied on our dataset.The architecture of the second model is based on VGG16 and represents its smaller and lighter version.Techniques such as learning rate decay, dropout and batch normalization was applied in training process.Comparison of obtained results of both models is made.
Proctoring and Non-proctoring Systems

This research describes learning achievement assessment technology, especially proctor technology.This study compares and contrasts proctoring and non-proctoring procedures used for online exams.The sample case used was the test scores of students enrolled in Hasanuddin University's Indonesian Arabic translation course.The research method used was a non-experimental quantitative method that compared students' online test results using proctoring and non-proctoring systems during online exams.The test scores of 101 students (40 male and 61 female students) from two different classes were sampled.The results of the tests for both classes were collected six times: three times using the proctoring method and three times using the non-proctoring system.A trend analysis was performed on the data.SPSS 26 was used to analyze the data via the two-way ANOVA procedure.The results indicate that the online proctoring system resulted in lower test scores than the online non-proctoring system, while the variables of class and gender did not affect the learning results.
Should We Trust This Summary? Bayesian Abstractive Summarization to The Rescue

We explore the notion of uncertainty in the context of modern abstractive summarization models, using the tools of Bayesian Deep Learning.Our approach approximates Bayesian inference by first extending stateof-the-art summarization models with Monte Carlo dropout and then using them to perform multiple stochastic forward passes.Based on Bayesian inference we are able to effectively quantify uncertainty at prediction time.Having a reliable uncertainty measure, we can improve the experience of the end user by filtering out generated summaries of high uncertainty.Furthermore, uncertainty estimation could be used as a criterion for selecting samples for annotation, and can be paired nicely with active learning and human-in-the-loop approaches.Finally, Bayesian inference enables us to find a Bayesian summary which performs better than a deterministic one and is more robust to uncertainty.In practice, we show that our Variational Bayesian equivalents of BART and PEGASUS can outperform their deterministic counterparts on multiple benchmark datasets.
Study on the Method of Adversarial Example Attack Based on MI-FGSM

Mao, Guanqiao Li, Lu Wang, Qingyu Li, JunbaoDeep neural network outperformed human beings in many fields such as image classification [1–3], object detection [4, 5], and image semantic segmentation [6] in recent years. But this model has security risks; for instance, it can be considered as a black box and can hardly change to improve its performance when the training is complete, nobody is able to explain the exact meaning of the weights of a model. Those fatal flaws above produce a real problem, deep neural network is vulnerable and will be attacked by adversarial examples [7–9]. In this paper, we use MI-FGSM algorithm on three typical convolution neural network and produce three sets of adversarial example. Then we use these three sets of adversarial examples to attack three typical convolutional neural networks mentioned above, called white box attack. Next, we train three new convolutional neural network and attack them, called black box attack. The attack described above at military target dataset has a significant effect.