Reviewerly/exHarmony huggingface dataset:
CFLIT: Coexisting Federated Learning and Information Transfer

Future wireless networks are expected to support diverse mobile services, including artificial intelligence (AI) services and ubiquitous data transmissions. Federated learning (FL), as a revolutionary learning approach, enables collaborative AI model training across distributed mobile edge devices. By exploiting the superposition property of multiple-access channels, over-the-air computation allows concurrent model uploading from massive devices over the same radio resources, and thus significantly reduces the communication cost of FL. In this paper, we study the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">coexistence</i> of over-the-air FL and traditional information transfer (IT) in a mobile edge network, where an access point (AP) coordinates a set of devices for over-the-air FL and serves multiple devices for information transfer in the meantime. We propose a <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">coexisting federated learning and information transfer (CFLIT)</i> communication framework, where the FL and IT devices share the wireless spectrum in an orthogonal frequency division multiplexing (OFDM) system. Under this framework, we aim to maximize the IT data rate and guarantee a given FL convergence performance by optimizing the long-term radio resource allocation. A key challenge that limits the spectrum efficiency of the coexisting system lies in the large overhead incurred by frequent communication between the server and edge devices for FL model aggregation. To address the challenge, we rigorously analyze the impact of the computation-to-communication ratio on the convergence of over-the-air FL in wireless fading channels. The analysis reveals the existence of an optimal computation-to-communication ratio that minimizes the amount of radio resources needed for over-the-air FL to converge to a given error tolerance. Based on the analysis, we propose a low-complexity online algorithm to jointly optimize the radio resource allocation for both the FL devices and IT devices. We further derive an analytical expression of the achievable data rate of IT users. Extensive numerical simulations verify the superior performance of the proposed design for the coexistence of FL and IT devices in wireless cellular systems.
Generating Natural Language From Logic Expressions With Structural Representation

Incorporating logic reasoning with deep neural networks (DNNs) is an important challenge in machine learning. In this article, we study the problem of converting logical expressions into natural language. In particular, given a sequential logic expression, the goal is to generate its corresponding natural sentence. Since the information in a logic expression often has a hierarchical structure, a sequence-to-sequence baseline struggles to capture the full dependencies between words, and hence it often generates incorrect sentences. To alleviate this problem, we propose a model to convert Structural Logic Expressions into Natural Language (SLEtoNL). SLEtoNL converts sequential logic expressions into structural representation and leverages structural encoders to capture the dependencies between nodes. The quantitative and qualitative analyses demonstrate that our proposed method outperforms the seq2seq model, which is based on the sequential representation, and outperforms strong pretrained language models ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">e.g.</i> , T5, BART, GPT3) with a large margin (28.6 in BLEU3) in out-of-distribution evaluation. The data and code will be available on <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com</uri> .
The Change of Knowledge Increasing Mode and Education Reform

As was stated afore, along with the coming of knowledge economy and society, the importance of knowledge, notably of new knowledge in social production and life, increases day by day, and has become one of the key factors constraining the development of contemporary society.
Visual Representation of the Internet Consumption in the European Union

The impact of internet usage on the environment is a contradictory topic. While it can help reduce carbon emissions, with smart grids or the automation of services and resources, it can also increase e-waste that end up affecting the environment. To draw attention to the impact of energy consumption on the environment, we proposed and developed a computational artifact that unites the areas of Data Aesthetics and Interaction Design. The artifact, displayed in an interactive installation, was divided into three panels: (i) the left panel, which represents the countries—from the European Union (EU)—with the lowest energy consumption impact on the environment; (ii) the central panel, which use swarming boids to represent the internet usage at the installation site and its impact; and (iii) the right panel, which represents the EU countries with the highest energy impact on the environment. The arrangement of the three panels in a single interactive installation aims to establish a visual connection between the energy consumption in the EU and the energy consumption in the installation's site and to promote awareness of its impact on the environment.
EvoDesigner: Aiding the Exploration of Innovative Graphic Design Solutions

Graphic Design (gd) artefacts, like posters on the streets or book covers on store shelves, often compete with each other to be seen, catch attention and communicate effectively. Nevertheless, due to the democratisation of gd and because finding disruptive aesthetics might be time-consuming, graphic designers often follow existing trends, lacking disruptive and catchy visual features. EvoDesigner aims to assist the exploration of distinctive gd aesthetics by employing a genetic algorithm to evolve content within two-dimensional pages. The system takes the form of an extension for Adobe InDesign, so both human designers and the machine can alternately collaborate in the creation process. In this paper, we propose a method to automatically evaluate the generated posters by assessing their dissimilarity to the output of an auto-encoder that was trained with a set of posters posted at typographicposters.com by graphic designers worldwide. The results suggest the viability of the evaluation method to recall large sets of images and therefore be used to compute an image dissimilarity degree. Furthermore, the proposed method could be used for evolving gd posters that can be deemed as new when compared to the training set.
Automatically Adding to Artistic Cultures

We consider how generative AI systems could and should evolve from being used as co-creative tools for artefact generation to co-creative collaborators for enhancing cultural knowledge, via artefact generation. We argue that while generative deep learning techniques and ethos have many drawbacks in this respect, neuro-symbolic approaches and increased AI autonomy could improve matters so that AI systems may be able to add to cultural knowledge directly. To do this, we consider what cultural knowledge is, differences between scientific and artistic applications of generative AI, stakeholders and ethical issues. We also present a case study in decision foregrounding for generative music.
Using Autoencoders to Generate Skeleton-Based Typography

Type Design is a domain that multiple times has profited from the emergence of new tools and technologies. The transformation of type from physical to digital, the dissemination of font design software and the adoption of web typography make type design better known and more accessible. This domain has received an even greater push with the increasing adoption of generative tools to create more diverse and experimental fonts. Nowadays, with the application of Machine Learning to various domains, typography has also been influenced by it. In this work, we produce a dataset by extracting letter skeletons from a collection of existing fonts. Then we trained a Variational Autoencoder and a Sketch Decoder to learn to create these skeletons that can be used to generate new ones by exploring the latent space. This process also allows us to control the style of the resulting skeletons and interpolate between different characters. Finally, we developed new glyphs by filling the generated skeletons based on the original letters' stroke width and showing some applications of the results.
LyricJam Sonic: A Generative System for Real-Time Composition and Musical Improvisation

Electronic music artists and sound designers have unique workflow practices that necessitate specialized approaches for developing music information retrieval and creativity support tools. Furthermore, electronic music instruments, such as modular synthesizers, have near-infinite possibilities for sound creation and can be combined to create unique and complex audio paths. The process of discovering interesting sounds is often serendipitous and impossible to replicate. For this reason, many musicians in electronic genres record audio output at all times while they work in the studio. Subsequently, it is difficult for artists to rediscover audio segments that might be suitable for use in their compositions from thousands of hours of recordings. In this paper, we describe LyricJam Sonic, a creative tool for musicians to rediscover their previous recordings, re-contextualize them with other recordings, and create original live music compositions in real-time. A bi-modal AI-driven approach uses generated lyric lines to find compatible audio clips from the artist's past studio recordings, and uses them to generate new lyric lines, which in turn are used to find other clips, thus creating a continuous and evolving stream of music and lyrics. The intent is to keep the artists in a state of creative flow conducive to music creation rather than taking them into an analytical/critical state of deliberately searching for past audio segments. The system can run in either a fully autonomous mode without user input, or in a live performance mode, where the artist plays live music, while the system "listens" and creates a continuous stream of music and lyrics in response (LyricJam Sonic: https://lyricjam.ai Demo videos: https://sites.google.com/view/supplementary-material-for-evo/home ).
Gender-based Cyberbullying Detection for Under-resourced Bangla Language

The present study explores the detection of gender discrimination based cyberbullying in under-resourced Bangla language. While being spoken by 230 million people globally and being rich in diversity, the Bengali language lacks computational models and annotated resources for cyberbullying detection. To address this, we created GenDisc, a corpus of Bangla Facebook comments featuring gender-based cyberbullying. This study also presents a framework for cyberbullying detection. In our proposed approach, we used four different models to train a gender-based discriminatory text classifier, followed by an ensembling technique on those four models. Then we compared the individual prediction accuracies with the ensembled prediction accuracy. While training the dataset, we followed the stratified k-fold cross validation technique. We demonstrated that integrating gender-based discrimination variables improve a classifier’s capacity to discriminate against cyberbullying. Our evaluations yielded an accuracy of 68% in gender-based speech detection during cross-validation tests.
Identifying Sentiment and Recognizing Emotion from Social Media Data in Bangla Language

Sentiment identification and emotion recognition play a significant role in many areas of natural language understanding because of their extensive usability in different applications like chatbots, text summarization, social media monitoring, customer support management, etc. In comparison with other languages, the studies done in the Bangla language are very few due to the scarcity of data and proper preprocessing tools. In this work, we present a benchmark study on different models to classify both emotion and sentiment from Bangla textual data. We collect our corpus from three online social media platforms (e.g., Youtube, Twitter, and Reddit) and perform a comparative study among several traditional machine learning and neural network-based approaches. We also perform comparative analysis among various informative feature extraction techniques. We find that the sequence model-based approach (Bidirectional GRU) with the “word embedding” feature extraction technique (word2vec) outperforms other approaches for emotion recognition and sentiment analysis.
Zero-Shot Entity Representation Learning for Temporal Knowledge Graph

Temporal Knowledge Graphs (TKG) are multi-relational graphs where time is an important dimension. The research interest in TKG is increasing very rapidly. Despite recent advancements, there still exist some unexplored areas such as, Zero-Shot Learning (ZSL) which aim’s to identify the embeddings of unseen entities. To deal with zero-shot learning we use both structural and textual descriptions of entities. For structural representation, we incorporate time directly into the vector space. For textual representation, we collect text descriptions of entities and use Convolutional Neural Networks (CNN) to capture the semantic features of the text descriptions. We utilize both of the aforementioned representations in the same vector space. Thus, we can utilize both structural and semantic representations of an entity which helps us to analyze the context of the entity. To test our model we utilized YAGO and Wikidata, two commonly used temporal knowledge graphs. We compare our method with two baseline methods. The evaluation result shows that our proposed method outperforms other methods for identifying relationships of unseen entities with proper time annotations.
SketchSynth: Cross-Modal Control of Sound Synthesis

This paper introduces a prototype of SketchSynth, a system that enables users to graphically control synthesis using sketches of cross-modal associations between sound and shape. The development is motivated by finding alternatives to technical synthesiser controls to enable a more intuitive realisation of sound ideas. There is strong evidence that humans share cross-modal associations between sound and shapes, and recent studies found similar patterns when humans represent sound graphically. Compared to similar cross-modal mapping architectures, this prototype uses a deep classifier that predicts the character of a sound rather than a specific sound. The prediction is then mapped onto a semantically annotated FM synthesiser dataset. This approach allows for a perceptual evaluation of the mapping model and gives the possibility to be combined with various sound datasets. Two models based on architectures commonly used for sketch recognition were compared, convolutional neural networks (CNNs) and recurrent neural networks (RNNs). In an evaluation study, 62 participants created sketches from prompts and rated the predicted audio output. Both models were able to infer sound characteristics on which they were trained with over 84% accuracy. Participant ratings were significantly higher than the baseline for some prompts, but revealed a potential weak point in the mapping between classifier output and FM synthesiser. The prototype provides the basis for further development that, in the next step, aims to make SketchSynth available online to be explored outside of a study environment.
Using GPT-3 to Achieve Semantically Relevant Data Sonificiation for an Art Installation

Large Language Models such as GPT-3 exhibit generative language capabilities with multiple potential applications in creative practice. In this paper, we present a method for data sonification that employs the GPT-3 model to create semantically relevant mappings between artificial intelligence-generated natural language descriptions of data, and human-generated descriptions of sounds. We implemented this method in a public art installation to generate a soundscape based on data from different systems. While common sonification approaches rely on arbitrary mappings between data values and sonic values, our approach explores the use of language models to achieve a mapping not via values but via meaning. We find our approach is a useful tool for musification practice and demonstrates a new application of generative language models in creative new media arts practice. We show how different prompts influence data to sound mappings, and highlight that matching the embeddings of texts of different lengths produces undesired behavior.
Effect of Quantum Repetition Code on Fidelity of Bell States in Bit Flip Channels

Quantum Entanglement is a key to establish secure communication between two nodes in a communication channel. Ensuring high fidelity entanglement has always been a challenging task owing to interaction with the hostile channel environment. Several methods have been proposed in this paper to mitigate the problem. It has been shown that fidelity can be improved by encoding bell pairs using Quantum Repetition Codes (QRCs). In this regard, various QRC-based techniques are also analyzed mathematically, and their performances have been compared.
Static Hand Gesture Recognition by RCDT Classifier

With technological advancement, automated hand gesture recognition has become an essential tool for a group of people who are deaf or mute, or both. In addition, the use of hand gestures provides an effective way of communication between humans and computers. In literature, most automatic hand gesture classification techniques are devised on neural network architecture. Although these techniques provide good classification accuracy, they often require the intensive use of additional GPU and larger training data sets. In this paper, we have proposed a heuristic approach to recognizing static hand gestures based on the Radon Cumulative Distribution Transform (RCDT) with a mean accuracy of 99.485%. This performance evaluation is conducted employing only 32 training samples with 10-fold cross-validation. The results can be reproduced by the MATLAB code available at https://github.com/KamrulHasan1743/rcdt_Classifier.
Cross-chain Jamming Attack with Light Client Verification Clash in IBC Protocol

Inter-Blockchain Communication (IBC) in Cosmos is a Sidechains/Relays cross-chain protocol. During the implementation of the protocol, the blockchain uses relayers to establish connections, and uses light clients to track and verify the consensus state of counterparties. However, light clients lack effective malicious behavior detection mechanisms, which easily induces the effective attacks to cross-chain communication. This paper proposes a light client jamming attack in IBC cross-chain protocol. Attackers force the victim’s light client to be in the verification state frequently by continuously generating headers containing a large number of verifier signatures. By interfering with light clients, attackers can reduce the victim’s cross-chain transaction throughput with low cost and increase the probability of cross chain transaction failure. Based on the Cosmos blockchain, this paper tests the attack, models the attack process and analyzes its security. The experiment shows that when the header constructed by the attacker contains $5.0\times 10^{4}$ signatures, the probability of successful attack can reach 90%. Finally, this paper briefly introduces some feasible defense strategies against this attack.
Composite Interference Signals Recognition Based on YOLOv5

Complex interference signal identification has been a paramount concern, yet there are very few effective methods. In this work, we propose a composite interference signal identification method based on target detection, which uses a detection box to detect the single interference signal in the composite interference signals (CISs) without separating CISs. This method based on YOLOv5 network can avoid the problem of difficult separation of CISs and be not affected by the multiple characteristics of the composite signal. It can not only effectively identify the composite signal, but also obtain the position information of each single signal. The experimental results validate our analysis. Moreover, it can be concluded that our proposed method is fault-tolerant and easy to migrate.
A password-authentication scheme based on ciphertext random sampling transmission

The traditional identity authentication approach of username and password has the security risk of password leakage in the transmission process. For the security of password transmission in the blockchain education architecture, an asymmetric encryption algorithm is used to encrypt the transmitted password. Then use an improved random sampling algorithm to randomly extract part of the ciphertext and transmit those selected ciphertexts. The authentication credentials are sampled ciphertext and the location index. At the same time, the authentication factor is generated through the Time-Stamp technology, and mutual authentication is realized based on the Response-Challenge protocol. Through security analysis and experimental analysis, the proposed scheme is proved to be secure.
Knowledge Tracing with Multi-Feature Fusion and Question Difficulty

Knowledge Tracing (KT) aims to trace students’ knowledge state and predict their performance on questions in online tutoring system. For the moment, most methods assume that all questions are equal to every student, that is to say, question itself has not any effect on students’ knowledge state. And they have not considered the effect of question difficulty (QD) on students’ knowledge state. To address this issue, some researchers have worked on the effect of question difficulty on students’ knowledge state. But, they have not considered the effect of question difficulty and knowledge concept difficulty (KCD) on students’ knowledge acquisition ability and knowledge state. In this paper, we propose a novel model called Question Difficulty Estimation Knowledge Tracing (QDEKT) model, which comprehensively considers question difficulty and knowledge concept difficulty and introduces students’ real response and predicted response on questions into the model, to evaluate students’ knowledge acquisition ability and dynamically update students’ knowledge state. Finally, extensive experiments show the effectiveness of our model.
Research on Level Set Based on CV Model for Spatial Multi-target Recognition

The edgeless active contour model can solve the initialization problem, noise problem and boundary leakage problem, but due to the use of level sets, there are still problems of low computational efficiency and difficult implementation. Combining the global effect of CV model and the local effect of RSF, this paper establishes a global and local biphasic level set model based on CV-RSF by setting weight coefficients and introducing bilevel set. By increasing the energy penalty term, the level set function maintains an approximate signed distance function during the evolution process. The comparison experiments with other level set models show that the segmentation efficiency and accuracy of this model are improved.