Reviewerly/exHarmony huggingface dataset:
Investigating Semi-Supervised Learning Algorithms in Text Datasets

Using large training datasets enhances the generalization capabilities of neural networks. Semi-supervised learning (SSL) is useful when there are few labeled data and a lot of unlabeled data. SSL methods that use data augmentation are most successful for image datasets. In contrast, texts do not have consistent augmentation methods as images. Consequently, methods that use augmentation are not as effective in text data as they are in image data. In this study, we compared SSL algorithms that do not require augmentation; these are self-training, co-training, tri-training, and tri-training with disagreement. In the experiments, we used 4 different text datasets for different tasks. We examined the algorithms from a variety of perspectives by asking experiment questions and suggested several improvements. Among the algorithms, tri-training with disagreement showed the closest performance to the Oracle; however, performance gap shows that new semi-supervised algorithms or improvements in existing methods are needed.
EDUCATIONAL ASPECTS OF SYSTEM AND SITUATIONAL APPROACHES TO COLLEGE MANAGEMENT IN MODERN CONDITIONS

In the article the author expresses his ideas for ensuring the success of modern college management. Based on his own experience in managing a college at the Kazan Innovative University named after V.G. Timiryasov, the author reveals the advantage of combining systematic and situational management approaches, proves that such a symbio- sis is necessary when working with older teenagers who pass through a difficult period of their development during their college years. At the same time it is shown that if operational management is important in solving everyday problems, then a systematic approach is necessary in ensuring the strategic development of an institution in the system of vocational education.
Data Visualization Design of Sports Journalism Based on Narrative Perspective A Case Study: Sigma Awards (2020-2021)

With the development of big data, data visualization makes data easily understood.Data visualization has become an essential presentation of sports journalism.In this paper, we present a survey of the 18 listed projects concerning sports in the Sigma Awards.We have analyzed the types and sources of data, application areas and visualization techniques.Through content analysis, trends in the use of data and visualization techniques in sports journalism are shown: the degree of openness of data sources is high; the subject categories are relatively diverse; the visualization techniques are various.
Pengembangan Media Pembelajaran Interaktif Berbasis Moodle pada Mata Pelajaran Bahasa Inggris

The present research aims to create an interactive learning media based on Moodle in English subjects at SMK Muhammadiyah 7 Gondanglegi. There are a number of reasons why this research was conducted. The results of observations and analysis of the initial needs show that there is no learning media that can connect students and teachers when students have on the job training class outside for approximately 3 months. So such an interactive media is needed that can bridge those needs. This research was conducted by adopting the Robert Maribe Branch Development Research design with the ADDIE model. This model is implemented into the following procedures, namely, (1) analysis, (2) design, (3) development, (4) implementation, and (5) evaluation. In this study, researchers reached the development stage and the final product without carrying out trials or implementations due to the limited time of research. The results of this study is an interactive web-based media based on Moodle. The results of the media expert validation of the teachers of SMK Muhammdiyah 7 Gondanglegi reached 87% with very good category. The results of the material expert validation from lecturers of IKIP Budi Utomo Malang achieved 95% with the very good category too. Finally, it can be concluded that web-based teaching media with Moodle are suitable media for the needs of English classes at SMK Muhamadiyah 7 Gondanglegi.
The philosophy of the material in the figurative-formative structure of the works by “Blue Rose” artists

This article is dedicated to Moscow symbolist art group “Blue Rose”, named after the exhibition from 1907 and known for their special attention to the semantical and metaphysical attributes of the artistic material. Paint, canvas, primers, paper, brush stroke techniques – all of those components played a particular role in the creation of the figurative-formative system. The members of “Blue Rose” were inspired by creative genius of V.E. Borisov-Musatov and M.A. Vrubel as well as traditions of the Old Russian icon painting. The latter found its new beginning at the turn of the century, in the era of Symbolism. Icon as an example of the union between material matter and spiritual concepts significantly influenced the core ideas in the art of symbolism. The “Blue Rose” followed the path of combining traditions and innovation, further affecting the viewer’s perception of the art work by specific organization of its material structure. Sacralization and spiritualization of materialistic principles of art are closely related to the problem of the symbol, in which contradictions between materialistic and spiritual as well as worldly and divine are resolved.
Flexible Fine-grained Data Access Management for Hyperledger Fabric

As blockchains go beyond cryptocurrencies into applications in multiple industries such as Insurance, Healthcare and Banking, handling personal or sensitive data, data access control becomes increasingly relevant. Access control mechanisms proposed so far are mostly based on requester identity, particularly for permissioned blockchain platforms, and are limited to binary, all-or-nothing access decisions. This is the case with Hyperledger Fabric's native access control mechanisms and, as permission updates require consensus, these fall short regarding the flexibility required to address GDPR-derived policies and client consent management. We propose SDAM, a novel access control mechanism for Fabric that enables fine-grained and dynamic control policies, using both contextual and resource attributes for decisions. Instead of binary results, decisions may also include mandatory data transformations as to conform with the expressed policy, all without modifications to Fabric. Results show that SDAM's overhead w.r.t baseline Fabric is acceptable. The scalability of the approach w.r.t to the number of concurrent clients is also evaluated and found to follow Fabric's.
Tackling Italian University Assessment Tests with Transformer-Based Language Models

Masked language modeling (MLM) pre-training methods such as BERT corrupt the input by replacing some tokens with [MASK] and then train a model to reconstruct the original tokens.While they produce good results when transferred to downstream NLP tasks, they generally require large amounts of compute to be effective.As an alternative, we propose a more sample-efficient pre-training task called replaced token detection.Instead of masking the input, our approach corrupts it by replacing some tokens with plausible alternatives sampled from a small generator network.Then, instead of training a model that predicts the original identities of the corrupted tokens, we train a discriminative model that predicts whether each token in the corrupted input was replaced by a generator sample or not.Thorough experiments demonstrate this new pre-training task is more efficient than MLM because the task is defined over all input tokens rather than just the small subset that was masked out.As a result, the contextual representations learned by our approach substantially outperform the ones learned by BERT given the same model size, data, and compute.The gains are particularly strong for small models; for example, we train a model on one GPU for 4 days that outperforms GPT (trained using 30x more compute) on the GLUE natural language understanding benchmark.Our approach also works well at scale, where it performs comparably to RoBERTa and XLNet while using less than 1/4 of their compute and outperforms them when using the same amount of compute.
Sentiment Analysis on the Perception and Mindset of the People of Indonesia on the Use of Vaccines to Deal with the Covid-19 Pandemic using the Text Mining Method

Right now, the world is busy with the COVID-19 pandemic. Coronavirus disease (COVID-19) itself is an infectious caused by a new variant of the newly discovered coronavirus. One way to deal with the virus is to get vaccinated against COVID-19. The government through the Indonesian Ministry of Health is also promoting the procurement of this COVID-19 vaccine by bringing various types of this COVID-19 vaccine. This research was conducted to know the sentiment and perception of the Indonesian people about the COVID-19 vaccination program. To find out, this research uses the Text Mining technique using Twitter as a data source. Data processing and analysis in this research used the Naive Bayes Classifier method using Python software. The results of this study show that the sentiment and perception of the Indonesian people to vaccination against COVID-19 is positive, as evidenced by the Confession Matrix value leaning towards True Positive.
An IoT based smart suitcase embedded with IRIS recognition and object detection technique

The proposed system integrates comfort and security that can be provided to the users. This system is not only automatic but it uses a smartphone to communicate with the suitcase via Wi-Fi. The directions, along with the speed at which the suitcase moved can be controlled using the mobile app. The system uses an image/object detection algorithm to make sure it’s completely aware of the environment. The proposed system uses convolutional neural networks (CNN) image recognition techniques with image processing capability from a computer vision library called OpenCV to detect the back view of the user and closely follow them. It captures the rear view of the person to verify it's in a safe position. Since we use advanced object tracking technology, this will work well even in a crowded place. The key feature of this system is its IRIS recognition module thereby providing extreme safety to the contentsinside of the suitcase.
A Deep Neural Network for Hand Gesture Recognition from Rgb Image in Complex Background

The research in deep learning becomes extensively popular recently, and can be applied in many applications such as image pre-processing, segmentation, object recognition, and semantic analysis. Deep learning has gradually replaced traditional algorithms. In hand gesture recognition, the traditional algorithm mostly depends on depth information to recognize it, and yet the accuracy is not good. This paper proposes a method using a deep neural network for hand gesture recognition. The proposed model only needs a single CMOS camera to recognize hand gestures in a complex background. In the neural network design, a depthwise separable convolutional is used to establish a model which is divided into segmentation and recognition. By training the segmentation model as the attention model to improve the recognition rate, the hand gesture recognition tasks can be performed by only using the recognition model and a part of the segmentation model. As our proposed single-stage model, we avoid the use of the whole model and thus reduce the number of weights and calculations. Additionally, in the training phase, the data augmentation and iterative training strategy further increase recognition accuracy. The results show that the proposed work uses little parameter usage while still have high gesture recognition rate than the other works.
Resorting to Context-Aware Background Knowledge for Unveiling Semantically Related Social Media Posts

Social media networks have become a prime source for sharing news, opinions, and research accomplishments in various domains, and hundreds of millions of posts are announced daily. Given this wealth of information in social media, finding related announcements has become a relevant task, particularly in trending news (e.g., COVID-19 or lung cancer). To facilitate the search of connected posts, social networks enable users to annotate their posts, e.g., with hashtags in tweets. Albeit effective, an annotation-based search is limited because results will only include the posts that share the same annotations. This paper focuses on retrieving context-related posts based on a specific topic, and presents PINYON, a knowledge-driven framework, that retrieves associated posts effectively. PINYON implements a two-fold pipeline. First, it encodes, in a graph, a CORPUS of posts and an input post; posts are annotated with entities for existing knowledge graphs and connected based on the similarity of their entities. In a decoding phase, the encoded graph is used to discover communities of related posts. We cast this problem into the Vertex Coloring Problem, where communities of similar posts include the posts annotated with entities colored with the same colors. Built on results reported in the graph theory, PINYON implements the decoding phase guided by a heuristic-based method that determines relatedness among posts based on contextual knowledge, and efficiently groups the most similar posts in the same communities. PINYON is empirically evaluated on various datasets and compared with state-of-the-art implementations of the decoding phase. The quality of the generated communities is also analyzed based on multiple metrics. The observed outcomes indicate that PINYON accurately identifies semantically related posts in different contexts. Moreover, the reported results put in perspective the impact of known properties about the optimality of existing heuristics for vertex graph coloring and their implications on PINYON scalability.
DTLF: Deep Transfer Learning for Website Fingerprinting

In anonymous networks, users can be tracked by implementing website fingerprinting (WF) attacks, which can prevent users from browsing illegal websites or performing illegal actions. With deep learning technologies, WF attacks achieve higher than 98% accuracy against powerful anonymity Tor network. However, these attacks rely on enormous encrypted traffic data, which are time-consuming to collect. Moreover, the large-scale encrypted traffic data collected also needs to be updated frequently to adapt for changes in website content. This paper proposes a new method, Deep Transfer Learning For Website Fingerprinting (DTLF), which combines the deep transfer learning framework with model DResNet. This method obtains useful and rich feature representations by training on a large number of existing labeled datasets to apply to few encrypted traffic. The results over multiple datasets show that the DTLF can achieve 96.5% accuracy with 5 labeled samples. In the 56-day experimental setting against concept drift, the accuracy is only 2.6% lower. In addition, the DTLF can consume less time overhead to perform WF attack. When training on the target dataset, it often only takes tens of seconds which is less than 0.1 times of other methods.
What is a Multi-Modal Knowledge Graph: A Survey

With the explosive growth of multi-modal information on the Internet, the multi-modal knowledge graph (MMKG) has become an important research topic in knowledge graphs to meet the needs of data management and application. Most research on MMKG has taken image-text data as the research object and used the multi-modal deep learning approach to process multimodal data. In comparison, the structure of the MMKG is no uniform statement. This paper focuses on MMKG, introduces the related theories of multi-modal knowledge, and analyzes several common ideas about its construction. The survey also explains the structural evolution, proposes mirror node alignment to represent cross-modal knowledge for MMKG, lists some tasks’difficulties, and ultimately gives a sample MMKG for the news scene.
A Data-to-Text Generation Model with Deduplicated Content Planning

Texts generated in data-to-text generation tasks often have repetitive parts. In order to get higher quality generated texts, we choose a data-to-text generation model with content planning, and add coverage mechanisms to both the content planning and text generation stages. In the content planning stage, a coverage mechanism is introduced to remove duplicate content templates, so as to remove sentences with the same semantics in the generated texts. In the text generation stage, the coverage mechanism is added to remove the repeated words in the texts. In addition, in order to embed the positional association information in the data into the word vectors, we also add positional encoding to the word embedding. Then the word vectors are fed to the pointer network to generate content template. Finally, the content template is inputted into the text generator to generate the descriptive texts. Through experiments, the accuracy of the content planning and the BLEU of the generated texts have been improved, which verifies the effectiveness of our proposed data-to-text generation model.
Conclusion and Outlook

In this chapter, we summarize the primary contributions of this thesis work and offer a short perspective on the possible extensions of each study, concluding with a discussion of outstanding challenges and emerging approaches in the field.
SecretGen: Privacy Recovery on Pre-trained Models via Distribution Discrimination

Transfer learning through the use of pre-trained models has become a growing trend for the machine learning community. Consequently, numerous pre-trained models are released online to facilitate further research. However, it raises extensive concerns on whether these pre-trained models would leak privacy-sensitive information of their training data. Thus, in this work, we aim to answer the following questions: “Can we effectively recover private information from these pre-trained models? What are the sufficient conditions to retrieve such sensitive information?” We first explore different statistical information which can discriminate the private training distribution from other distributions. Based on our observations, we propose a novel private data reconstruction framework, SecretGen, to effectively recover private information. Compared with previous methods which can recover private data with the ground truth label of the targeted recovery instance, SecretGen does not require such prior knowledge, making it more practical. We conduct extensive experiments on different datasets under diverse scenarios to compare SecretGen with other baselines and provide a systematic benchmark to better understand the impact of different auxiliary information and optimization operations. We show that without prior knowledge about true class prediction, SecretGen is able to recover private data with similar performance compared with the ones that leverage such prior knowledge. If the prior knowledge is given, SecretGen will significantly outperform baseline methods. We also propose several quantitative metrics to further quantify the privacy vulnerability of pre-trained models, which will help the model selection for privacy-sensitive applications. Our code is available at: https://github.com/AI-secure/SecretGen .
Using Machine Learning for Inter-smell Detection: A Feasibility Study

Code smells may degrade code quality owing to the unnoticed introduction of side effects. Various approaches for identifying code smells have been developed since the term “code smell” was coined. Furthermore, the available literature suggests that code smells may be connected, and there is theory concerning smell interactions such as collocated, coupled smells, inter smell and so on. Machine learning has proven to be one of the most effective ways for detecting code smell. However, the capability of machine learning to detect such code smell relations has yet to be demonstrated. This paper proposes a new novel classification of smell interactions and investigates the feasibility of machine learning techniques to detect four inter-smells by employing seven widely used machine learning algorithms on four publicly available datasets and employing commonly used performance measures such as recall, accuracy, precision, and Matthews coefficient (MCC). A low MCC value (0.5) for all four inter-smells indicates a poor inter-smell relationship and contradicts the facts provided in the literature. As a result, machine learning may not be a viable option for inter-smell detection.
The Regulation of AI Trading from an AI Life Cycle Perspective

Among innovative technologies, Artificial Intelligence (AI) is often avouched as the game changerin the provision of financial services. In this regard, the algorithmic trading domain is no exception.The impact of AI in the industry is a catalyst for transformation in the operations and the structure ofcapital markets. In effect, AI adds a further layer of system complexity, given its potential to alter the composition and behaviour of market actors, as well as the relationships among them.Despite the many expected benefits, the wide use of AI could also impose new and unprecedented risks to market participants and financial stability. Specifically, owing to the potential of AI trading to disrupt markets and cause harm, global financial regulators are faced today with the daunting task of how best to approach its regulation in order to foster innovation and competition without sacrificing market stability and integrity. While there are common challenges, each market player faces problems unique to the context-specific use of AI. In other words, there are no one-size-fits-all solutions for regulating AI in automated trading. Rather, any effective and future-proof AI-targeting regulation should be proportionate to the particular and additional risks arising from specific applications (eg, due to the specific AI methods applied with their respective capability, validity and criticality). Therefore, financial regulators face a multi-faceted challenge. They must first define the additional risks posed by specific use cases that call for more in-depth scrutiny and, hence, identify the technical specificities that can facilitate the occurrence of those risks. Based on this assessment, they finally need to determine which AI characteristics require special regulatory treatment.Inspired by the EU AI Act proposal, this paper examines the advantages of a ‘rule-based’ and ‘risk-oriented’ regulatory approach, combining both ex-ante and ex-post regulatory measures, that needs to be put in perspective with the ‘AI life cycle’. By advocating for a multi-stakeholder engagement in AI regulatory governance, it proposes a way forward to assist financial regulators and industry players – but even actors in public education – in understanding, identifying and mitigating the risks associated with automated trading through an engineering approach for the purpose of complexity mastering.
A fast and accurate method for identifying the equipment fingerprint of distribution secondary system

During the operation of the distribution secondary system attackers will use the uncertainty of device identity to obtain device fingerprint information by scanning the network to associate vulnerabilities and invade the system thus exposing the system to great risks. A device fingerprint is a set of characteristic information that uniquely identifies a device. Therefore, a device fingerprint can be used to discover malicious devices and system vulnerabilities. In this paper the passive device fingerprint identification method based on TCP/IP protocol is proposed to solve the problem of inaccurate matching in the device fingerprint database of the distribution secondary system and the low efficiency of the existing device fingerprint identification method. The method combines the RIPPER algorithm to build the device fingerprint identification classification model. The automatic fingerprint library based on HTTP User-Agent is used to recognize the device fingerprint.