Reviewerly/exHarmony huggingface dataset:
Does the century matter? Machine learning methods to attribute historical periods in an Italian literary corpus

This study aims to analyse an Italian literary corpus from a diachronic perspective using machine learning methods. With reference to a basis of texts written between the 16th and the 21st century, the aim is to apply a well-known robust machine learning (ML) algorithm (Random Forest - RF) in order to see how the texts are classified in four different partitions, representing periodizations theorized by four Italian literature scholars. The corpus we employed for training the ML algorithm includes 420 Italian texts: 100 texts from the 16th century, 27 from the 17th, 57 from the 18th, 100 from the 19th, 100 from the 20th, and 36 from the 21st. In order to vectorize the texts, we used the Author’s Multilevel N-gram Profile (AMNP) (Mikros and Perifanos, 2013; Cortelazzo, Mikros, and Tuzzi, 2018), a document representation method that takes into account a diverse set of linguistic features (i.e., ngrams of increasing length - unigrams, bigrams, trigrams - and ngrams of increasing level - character, word). Each text was split into text chunks of 2000 words in length, and then it was transformed into AMNP vectors. The results of this research have shown an impressive accuracy in classification with the Random Forest algorithm since the precision in the four periodizations reached a minimum value of 89% in the partition-based Migliorini’s theories and a maximum value of 97% in the partition based on Cella’s ones. Looking at the misclassification cases, particularly in Migliorini’s training, it’s interesting to notice that when Random Forest makes a mistake in classifying text chunks into a century, its error is usually of +/- 1 century.
Object Detection Using Deep Learning CenterNet Model with Multi-Head External Attention Mechanism

Deep learning algorithms are highly effective at handling complex and challenging tasks such as image classification and detection. Over the past few decades, there have been a variety of convolutional neural networks (CNNs) with varying architectures to improve accuracy in object detection. However, a variety of factors, including brightness, enclosures, viewing distances, and background components, can affect the appearance, size, and shape of objects making the object detection task even more challenging. A multi-head external attention mechanism-based CenterNet model has been proposed to enhance the accuracy in detection of objects. The feature extraction process is carried out using Hourglass-104 network and Adaptive Feature Pyramid Network (AFPN). High-level features are derived through contextual modeling using an adaptive feature fusion (AFF) module with multi-head external attention. A hybrid feature selection method called adaptive hybrid feature selection (AHFS) determines the best features followed by prediction of the objects by the improved CenterNet. In order to assess the object detection accuracy, the experiment was conducted using the MS-COCO dataset on average precision (AP), mean average precision (mAP), and average recall (AR) metrics. Our proffered method achieves 64.76% on the MS-COCO dataset, improving the accuracy by 2.5% compared to other state-of-the-art models.
Multimodal Knowledge Graph for Power Equipment Defect Data

Most traditional knowledge graph methods only learn knowledge representations from structured triples, ignoring the rich visual information in power images. To address this problem, this paper constructs a multimodal knowledge graph for power equipment defect data. The knowledge graph of power equipment images and defect texts is developed, and the InteractiveGraph graph database is used to realize knowledge storage, construct a multi-modal knowledge graph for power equipment defect data, and realize knowledge query and auxiliary decision-making.
Aggregating pairwise semantic differences for few-shot claim verification

As part of an automated fact-checking pipeline, the claim verification task consists in determining if a claim is supported by an associated piece of evidence. The complexity of gathering labelled claim-evidence pairs leads to a scarcity of datasets, particularly when dealing with new domains. In this article, we introduce Semantic Embedding Element-wise Difference (SEED), a novel vector-based method to few-shot claim verification that aggregates pairwise semantic differences for claim-evidence pairs. We build on the hypothesis that we can simulate class representative vectors that capture average semantic differences for claim-evidence pairs in a class, which can then be used for classification of new instances. We compare the performance of our method with competitive baselines including fine-tuned Bidirectional Encoder Representations from Transformers (BERT)/Robustly Optimized BERT Pre-training Approach (RoBERTa) models, as well as the state-of-the-art few-shot claim verification method that leverages language model perplexity. Experiments conducted on the Fact Extraction and VERification (FEVER) and SCIFACT datasets show consistent improvements over competitive baselines in few-shot settings. Our code is available.
Differential-quadrature-phase-shift quantum digital signature

A novel quantum digital signature (QDS) scheme, called "differential quadrature phase-shift QDS," is presented. A message sender broadcasts a weak coherent pulse train with four phases of {0, π/2, π, 3π/2} and recipients create their own authentication keys from the broadcasted signal. Unlike conventional QDS protocols, there is no post-processing of information exchange between the sender and recipients and that between the recipients. Therefore, secured channels and/or authenticated channels for information exchange are not needed, and the key creation procedure is simpler than that of conventional QDS. Security issues are also discussed, using binominal distributions instead of Hoeffding's inequality utilized in conventional QDS studies, and calculation examples for system conditions achieving the QDS function are presented.
Trustless architecture and the V-form organization

Blockchain (distributed ledger technology) is an institutional technology that allows trust to be manufactured instead of being earned. Trust is an important component of business and trade and has previously been subsumed into information costs. It is only now that the importance of trust is being fully appreciated. Arun Sundarajaran has suggested that the creation of new forms of trust has driven the expansion economic activity throughout history. In this chapter we argue that the industrialization of trust is going, again, to drive a massive expansion in economic activity through the emergence of new organizational forms that will deliver high-powered market incentives deep into what would appear to be hierarchical organizations. We are labelling these (as yet speculative) organizations forms the "V-form" organization. In this chapter we discuss the importance of trust, the evolution of trust, and the industrialization of trust. We argue that current organizational forms have exhausted the levels of trust that have previously sustained them. Blockchain technology offers a new industrialized form of trust that can drive further economic activity.
Regulating disinformation on Twitter and Facebook

The spread of disinformation in recent years has caused the international community concerns, particularly around its impact on electoral and public health outcomes. When one considers how disinformation can be contained, one often looks to new laws imposing more accountability on prominent social media platforms. While this narrative may be consistent with the fact that the problem of disinformation is exacerbated on social media platforms, it obscures the fact that individual users hold more power than is acknowledged and that shaping user norms should be accorded high priority in the fight against disinformation. In this article, I examine selected legislation implemented to regulate the spread of disinformation online. I also scrutinise two selected social media platforms – Twitter and Facebook – to anchor my discussion. In doing so, I consider what these platforms have done to self and co-regulate. Thereafter, I consider the limitations on regulation posed by certain behavioural norms of users. I argue that shaping user norms lie at the heart of the regulatory approaches discussed and is pivotal to regulating disinformation effectively.
THE RELATIONSHIP BETWEEN FAN TOKENS AND CRYPTOCURRENCIES: AN EMPIRICAL EVIDENCE

In recent years, as a result of the increasing popularity of crypto-assets all over the world, an increasing number of innovative crypto assets are coming to the market. In this context, Fan Tokens, which is a type of "utility token", are also discussed in this research as a prominent crypto asset in recent years. As developments in both cryptocurrency and sports sector increase the interest in Fan Tokens day by day, the financial volume of the system is constantly growing. With the growing volume, the risks taken by those who buy Fan Tokens are also increasing. Although there are studies on the risks of crypto assets in the literature, since the issue is not addressed specifically for Fan Tokens, it is deemed worthy of our review. The relationship between the price movements of Fan Tokens and the movements of the dominant crypto assets has been examined. It has been determined by the regression and correlation analyzes in the research that the movements in the crypto money exchanges have an effect on the Fan Token exchanges. Therefore, the developments in the crypto money exchanges should be followed especially and carefully for those who see the Fan Tokens as an investment tool.
An Improvement Object Detection Method Findcontour with Fuzzy Logic for Detect Balinese Script Object

The function of OpenCV can be used as a method for detecting Balinese script objects . Experiments to detect Balinese script using this function are able to detect all objects that are on palm leaf media. Objects that have been detected are literal objects and non-literal objects. This study aims to improve the detection method with the findcontour function by adding a fuzzy algorithm, to separate Balinese script objects from non-Balinese characters. The data in this study were in the form of Balinese script written on palm leaves. The Balinese manuscripts used as data in the research are new manuscripts that were deliberately written for research purposes. Fuzzy logic in this study is used to determine objects that have been detected, including script objects or not. The results are visible from the detected objects, fuzzy logic is able to eliminate 400 - 5000 objects that are not needed. Meanwhile, in terms of time, the filtering process with fuzzy logic has an impact on the ROI process being faster with an average time of less than 1 second.
Circuit-QED based time-averaged dispersive readout of a semiconductor charge qubit

Maturing hybrid circuit quantum electrodynamics architectures composed of semiconductor quantum dots and on-chip microwave resonators promise an effective approach for quantum nondemolition readouts in semiconductor systems. Here, we study quantum coherence in a semiconductor charge qubit coupled with a high-impedance resonator. The qubit is controlled by a periodic pulse sequence, and a dispersive measurement is performed by measuring the time-averaged resonator response. From the Rabi oscillation and Ramsey fringe measurements, the coherence time of the charge qubit is determined as T2, Rabi ≈2.7 ns and T2, Ramsey ≈3.5 ns. Compared with the time-resolved (pulsed) measurement, this time-averaged dispersive measurement process eliminates the limitation caused by the resonator response time, enabling a high repetition rate of the pulse sequence in the experiments.
Image Captioning with Synergy-Gated Attention and Recurrent Fusion LSTM

Long Short-Term Memory (LSTM) combined with attention mechanism is extensively used to generate semantic sentences of images in image captioning models.However, features of salient regions and spatial information are not utilized sufficiently in most related works.Meanwhile, the LSTM also suffers from the problem of underutilized information in a single time step.In the paper, two innovative approaches are proposed to solve these problems.First, the Synergy-Gated Attention (SGA) method is proposed, which can process the spatial features and the salient region features of given images simultaneously.SGA establishes a gated mechanism through the global features to guide the interaction of information between these two features.Then, the Recurrent Fusion LSTM (RF-LSTM) mechanism is proposed, which can predict the next hidden vectors in one time step and improve linguistic coherence by fusing future information.Experimental results on the benchmark dataset of MSCOCO show that compared with the state-of-the-art methods, the proposed method can improve the performance of image captioning model, and achieve competitive performance on multiple evaluation indicators.
Weibo Disaster Rumor Recognition Method Based on Adversarial Training and Stacked Structure

To solve the problems existing in the process of Weibo disaster rumor recognition, such as lack of corpus, poor text standardization, difficult to learn semantic information, and simple semantic features of disaster rumor text, this paper takes Sina Weibo as the data source, constructs a dataset for Weibo disaster rumor recognition, and proposes a deep learning model BERT_AT_Stacked LSTM for Weibo disaster rumor recognition.First, add adversarial disturbance to the embedding vector of each word to generate adversarial samples to enhance the features of rumor text, and carry out adversarial training to solve the problem that the text features of disaster rumors are relatively single.Second, the BERT part obtains the word-level semantic information of each Weibo text and generates a hidden vector containing sentencelevel feature information.Finally, the hidden complex semantic information of poorlyregulated Weibo texts is learned using a Stacked Long Short-Term Memory (Stacked LSTM) structure.The experimental results show that, compared with other comparative models, the model in this paper has more advantages in recognizing disaster rumors on Weibo, with an F1_Socre of 97.48%, and has been tested on an open general domain dataset, with an F1_Score of 94.59%, indicating that the model has better generalization.
Autonomous driving system using proximal policy optimization in deep reinforcement learning

&lt;span&gt;Autonomous driving is one solution that can minimize and even prevent accidents. In autonomous driving, the vehicle must know the surrounding environment and move under the provisions and situations. We build an autonomous driving system using proximal policy optimization (PPO) in deep reinforcement learning, with PPO acting as an instinct for the agent to choose an action. The instinct will be updated continuously until the agent reaches the destination from the initial point. We use five sensory inputs for the agent to accelerate, turn the steer, hit the brakes, avoid the walls, detect the initial point, and reach the destination point. We evaluated our proposed autonomous driving system in a simulation environment with several branching tracks, reflecting a real-world setting. For our driving simulation purpose in this research, we use the Unity3D engine to construct the dataset (in the form of a road track) and the agent model (in the form of a car). Our experimental results firmly indicate our agent can successfully control a vehicle to navigate to the destination point. &lt;/span&gt;
Rethinking the Importance of Quantization Bias, Toward Full Low-Bit Training

Quantization is a promising technique to reduce the computation and storage costs of DNNs. Low-bit (≤ 8 bits) precision training remains an open problem due to the difficulty of gradient quantization. In this paper, we find two long-standing misunderstandings of the bias of gradient quantization noise. First, the large bias of gradient quantization noise, instead of the variance, is the key factor of training accuracy loss. Second, the widely used stochastic rounding cannot solve the training crash problem caused by the gradient quantization bias in practice. Moreover, we find that the asymmetric distribution of gradients causes a large bias of gradient quantization noise. Based on our findings, we propose a novel adaptive piecewise quantization method to effectively limit the bias of gradient quantization noise. Accordingly, we propose a new data format, Piecewise Fixed Point (PWF), to present data after quantization. We apply our method to different applications including image classification, machine translation, optical character recognition, and text classification. We achieve approximately 1.9~3.5× speedup compared with full precision training with an accuracy loss of less than 0.5%. To the best of our knowledge, this is the first work to quantize gradients of all layers to 8 bits in both large-scale CNN and RNN training with negligible accuracy loss.
HCI for general aviation

research-article HCI for general aviation: current state and research challenges Authors: Sebastian S. Feger LMU Munich LMU MunichView Profile , Felix Ehrentraut Lufthansa Aviation Training Lufthansa Aviation TrainingView Profile , Christopher Katins Humboldt University of Berlin Humboldt University of BerlinView Profile , Philippe Palanque IRIT IRITView Profile , Thomas Kosch Humboldt University of Berlin Humboldt University of BerlinView Profile Authors Info & Claims InteractionsVolume 29Issue 6November - December 2022 pp 60–65https://doi.org/10.1145/3564040Published:03 November 2022Publication History 1citation91DownloadsMetricsTotal Citations1Total Downloads91Last 12 Months91Last 6 weeks11 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access
Incremental Machine Learning: Incremental Classification

One of the research topics in machine learning is incremental machine learning. The ever-increasing data size and variety in response to the limited memory and processing power make incremental learning approaches mandatory. In this study, focal changes in the data are determined for incremental classification algorithms by defining the general framework of the incremental machine learning approach. In addition, along with the theoretical definition of incremental machine learning, the existing machine learning algorithms that are suitable for incremental machine learning are defined. It is mentioned that in terms of which features of these algorithms are suitable for incremental learning. This study provides a detailed definition for researchers who will work on incremental machine learning issues, as well as defines the incremental classification characteristics and gives information about the focus changes in the data.
Analysis of ECG Image File Encryption using ECDH and AES-GCM Algorithm

Image data is used in various fields, including the health sector. One of the image data in the health sector is an electrocardiogram (ECG). The ECG contains the identifying information of a person which must be guarded and secured. ECG image data security can be done by encrypting image data using encryption algorithms. The encryption algorithms used in this thesis are ECDH and AES-GCM. ECDH is used to generate key pairs which are then used as keys for AES-GCM encryption and decryption. The encryption and decryption process are carried out in the python programming language. The results of the encryption time and decryption time increase because of the dimensions and size of the ECG image file. The nonce value and authentication tag are checked to be able to perform the decryption process. The histogram test results prove the uniformity of pixels in the encrypted ECG image file. PSNR and SSIM test results prove the difference between the initial and encrypted ECG image files. The results of the NIST Statistical Test Suite test prove that the algorithm used produces random output so that it can be used to secure ECG image files.
Anti-Phishing Game Framework Based on Extended Design Play Experience (DPE) Framework as an Educational Media

The main objective of this research is to increase security awareness against phishing attacks in the education sector by teaching users about phishing URLs. The educational media was made based on references from several previous studies that were used as basic references. Development of antiphishing game framework educational media using the extended DPE framework. Participants in this study were vocational and college students in the technology field. The respondents included vocational and college students, each with as many as 30 respondents. To assess the level of awareness and understanding of phishing, especially phishing URLs, participants will be given a pre-test before playing the game, and after completing the game, the application will be given a posttest. A paired t-test was used to answer the research hypothesis. The results of data analysis show differences in the results of increasing identification of URL phishing by respondents before and after using educational media of the anti-phishing game framework in increasing security awareness against URL phishing attacks. More serious game development can be carried out in the future to increase user awareness, particularly in phishing or other security issues, and can be implemented for general users who do not have a background in technology.
Clustered Bert Model for predicting Retweet Popularity

This work aims to predict retweet popularity of covid19 tweet corpus. Our work fuses unsupervised and supervised learning techniques to create retweet popularity model. In the first phase, we use a Clustered Bert model, which works on clustering the Bert embeddings using clustering algorithms on the textual data to generate novel and meaningful feature set for our model. In the second phase, we use the output of Clustered Bert model as an input to the Supervised Regression models intending to predict retweet popularity. Our work also draws a comparison between features from numeric model; emotions/sentiment model; and Clustered Bert model. Three different Regression models, belonging to different categories: Nearest Neighbors, Ensemble and Stacked models are then tested on the final feature-set to generate predictions for our model. The results show higher accuracy when the Clustered Bert model is used in combination with numerical and emotion/sentiment model. The experiment shows better results for Stacked Regression models out of all the three regressors used for our study.
Object Detection for Low Light Images

Object detection is a computer vision method for locating objects in images. Although, it has surpassed human performance and it has been considered practically solved, there are still considerable challenges, such as when photos are captured under suboptimal lighting conditions due to environmental and/or technical constraints. On the other hand, a variety of methods have been developed to enhance low light images, which can boost an object detector’s performance. In this work, we apply different image enhancement methods and study how they affect the efficacy of a well known detector (You Only Look Once, YOLO). A statistical analysis between YOLO’s performance for each enhancing algorithm, using a low light imaging dataset, is also presented, proving that for these kind of images, enhancement is a valuable step.