Reviewerly/exHarmony huggingface dataset:
A Survey of Text Representation Methods and Their Genealogy

In recent years, with the advent of highly scalable artificial-neural-network-based text representation methods the field of natural language processing has seen unprecedented growth and sophistication. It has become possible to distill complex linguistic information of text into multidimensional dense numeric vectors with the use of the distributional hypothesis. As a consequence, text representation methods have been evolving at such a quick pace that the research community is struggling to retain knowledge of the methods and their interrelations. We contribute threefold to this lack of compilation, composition, and systematization by providing a survey of current approaches, by arranging them in a genealogy, and by conceptualizing a taxonomy of text representation methods to examine and explain the state-of-the-art. Our research is a valuable guide and reference for artificial intelligence researchers and practitioners interested in natural language processing applications such as recommender systems, chatbots, and sentiment analysis.
Remembering past and predicting future: a hybrid recurrent neural network based recommender system

Traditional recommender systems (RS) assume users' taste to be static (taste remains same over time) and reactive (a change in taste cannot be predicted and is observed only after it occurs). Further, traditional RS restricts the recommendation process to candidate items generation. This work aims to explore two phases of RS, i.e., Candidate Generation as well as Candidate Ranking. We propose a RS from a multi-objective (short-term prediction, long-term prediction, diversity, and popularity bias) perspective which was previously overlooked. The sequential and non-sequential behavior of users is exploited to predict future behavioral trajectories with the consideration of short-term and long-term prediction using recurrent neural networks and nearest neighbors approach. Further, a novel candidate ranking method is introduced to prevent users from being entangled in recommended items. On multiple datasets, largest being MovieLens (ML) 1M, our model shows excellent results achieving a hit rate and short-term prediction success of 58% and 71% respectively on ML 1M. Further, it implicitly handles two important parameters, i.e., diversity and item popularity with a success rate of 59.22% and 34.28% respectively.
Pre-trained Speech Representations as Feature Extractors for Speech Quality Assessment in Online Conferencing Applications

Speech quality in online conferencing applications is typically assessed through human judgements in the form of the mean opinion score (MOS) metric. Since such a labor-intensive approach is not feasible for large-scale speech quality assessments in most settings, the focus has shifted towards automated MOS prediction through end-to-end training of deep neural networks (DNN). Instead of training a network from scratch, we propose to leverage the speech representations from the pre-trained wav2vec-based XLS-R model. However, the number of parameters of such a model exceeds task-specific DNNs by several orders of magnitude, which poses a challenge for resulting fine-tuning procedures on smaller datasets. Therefore, we opt to use pre-trained speech representations from XLS-R in a feature extraction rather than a fine-tuning setting, thereby significantly reducing the number of trainable model parameters. We compare our proposed XLS-R-based feature extractor to a Mel-frequency cepstral coefficient (MFCC)-based one, and experiment with various combinations of bidirectional long short term memory (Bi-LSTM) and attention pooling feedforward (AttPoolFF) networks trained on the output of the feature extractors. We demonstrate the increased performance of pre-trained XLS-R embeddings in terms a reduced root mean squared error (RMSE) on the ConferencingSpeech 2022 MOS prediction task.
GFB-Net: A Global Context-Guided Feature Balance Network for Arbitrary-Oriented SAR Ship Detection

Recently, deep learning techniques have been successfully applied to SAR ship detection. However, SAR ship detection is still a challenging task. First, ship targets in SAR images are characterized by arbitrary orientation and densely arranged. Traditional horizontal bounding box based detection is extremely unfriendly to such densely aligned scenes and often results in inaccurate localization. Secondly, the ship targets in SAR images are multi-scale, which makes detection difficult. This paper proposes an arbitrary-oriented SAR ship detection network called global context-guided feature balance network (GFB-Net) to solve the above problems. First, we introduce an efficient Oriented R-CNN detector. Unlike the horizontal bounding box detector, the rotated bounding box based detector can locate the ship target more finely and reduce the background interference. Additionally, we propose a global context-guided feature balanced pyramid to improve the detection performance of multi-scale ships by balancing different levels of feature and learning context information. Finally, we conducted experiments on SSDD dataset, and the results show that the proposed GFB-Net has a better performance compared to other detection methods.
Volatility Interdependence Between Cryptocurrencies, Equity, and Bond Markets

This paper investigates (i) the return-volatility spillover between Bitcoin, Ethereum, Ripple, and Litecoin, (ii) the interdependence between cryptocurrencies' volatility and the US equity and bond markets' volatility, and (iii) the impact of the Covid-19 outbreak on the cryptocurrencies' return-volatility. A two-step estimation approach is considered where Univariate General Autoregressive Conditional Heteroskedastic models are estimated to model the volatility of the four cryptocurrencies then a Simultaneous Equation Model is estimated to model the interconnection between the cryptocurrency volatilities, the US equity and bond markets' volatility, and Covid-19 outbreak. We show that return-volatility spillovers exist among Bitcoin, Ethereum, and Litecoin while Ripple is the main transmitter of shocks. We find that the cryptocurrency market is detached from the US stock market but not from the US bond market. Finally, we show that a high economic and financial uncertainty in the US stock market due to pandemic outbreaks affects the price of Litecoin, Bitcoin, and Ethereum. However, shocks are short-lived. Our findings have practical implications; as the evidence of volatility spillovers among cryptocurrencies and their relative isolation from the majority of mainstream assets should be factored into the valuation and portfolio diversification strategies of investors. In crisis times such as those induced by Covid-19, investors who seek protection from downward movements in bond markets could benefit from taking a position in Ethereum. Policymakers can also rely on our findings to time their intervention to stabilize markets and control uncertainties inherent to stressful periods.
SERF: Interpretable Sleep Staging using Embeddings, Rules, and Features

The accuracy of recent deep learning based clinical decision support systems is promising. However, lack of model interpretability remains an obstacle to widespread adoption of artificial intelligence in healthcare. Using sleep as a case study, we propose a generalizable method to combine clinical interpretability with high accuracy derived from black-box deep learning. Clinician-determined sleep stages from polysomnogram (PSG) remain the gold standard for evaluating sleep quality. However, PSG manual annotation by experts is expensive and time-prohibitive. We propose SERF, interpretable Sleep staging using Embeddings, Rules, and Features to read PSG. SERF provides interpretation of classified sleep stages through meaningful features derived from the AASM Manual for the Scoring of Sleep and Associated Events. In SERF, the embeddings obtained from a hybrid of convolutional and recurrent neural networks are transposed to the interpretable feature space. These representative interpretable features are used to train simple models like a shallow decision tree for classification. Model results are validated on two publicly available datasets. SERF surpasses the current state-of-the-art for interpretable sleep staging by 2%. Using Gradient Boosted Trees as the classifier, SERF obtains 0.766 $\kappa$ and 0.870 AUC-ROC, within 2% of the current state-of-the-art black-box models.
Creating new chaotic signals with reservoir computers

While there have been many publications on potential applications of chaos to fields such as communications, radar, sonar, random signal generation, channel equalization and others, designing continuous chaotic systems is still an unsolved problem. There are a number of well known chaotic systems used for applications, but if any application is to become widely used, some way of generating many different chaotic signals is necessary. This work shows that one may use a reservoir computer to create a set of chaotic signals that are correlated but easily distinguishable from one chaotic signal with desirable properties. The ability to distinguish the new signals is demonstrated with a simple communications example.
IoT device identification based on network communication analysis using deep learning

Attack vectors for adversaries have increased in organizations because of the growing use of less secure IoT devices. The risk of attacks on an organization’s network has also increased due to the bring your own device (BYOD) policy which permits employees to bring IoT devices onto the premises and attach them to the organization’s network. To tackle this threat and protect their networks, organizations generally implement security policies in which only white-listed IoT devices are allowed on the organization’s network. To monitor compliance with such policies, it has become essential to distinguish IoT devices permitted within an organization’s network from non-white-listed (unknown) IoT devices. In this research, deep learning is applied to network communication for the automated identification of IoT devices permitted on the network. In contrast to existing methods, the proposed approach does not require complex feature engineering of the network communication, because the ’communication behavior’ of IoT devices is represented as small images which are generated from the device’s network communication payload. The proposed approach is applicable for any IoT device, regardless of the protocol used for communication. As our approach relies on the network communication payload, it is also applicable for the IoT devices behind a network address translation (NAT) enabled router. In this study, we trained various classifiers on a publicly accessible dataset to identify IoT devices in different scenarios, including the identification of known and unknown IoT devices, achieving over 99% overall average detection accuracy.
Clustering by Direct Optimization of the Medoid Silhouette

The evaluation of clustering results is difficult, highly dependent on the evaluated data set and the perspective of the beholder. There are many different clustering quality measures, which try to provide a general measure to validate clustering results. A very popular measure is the Silhouette. We discuss the efficient medoid-based variant of the Silhouette, perform a theoretical analysis of its properties, and provide two fast versions for the direct optimization. We combine ideas from the original Silhouette with the well-known PAM algorithm and its latest improvements FasterPAM. One of the versions guarantees equal results to the original variant and provides a run speedup of $O(k^2)$. In experiments on real data with 30000 samples and $k$=100, we observed a 10464$\times$ speedup compared to the original PAMMEDSIL algorithm.
Small-Object Sensitive Segmentation Using Across Feature Map Attention

Semantic segmentation is an important step in understanding the scene for many practical applications such as autonomous driving. Although Deep Convolutional Neural Networks-based methods have significantly improved segmentation accuracy, small/thin objects remain challenging to segment due to convolutional and pooling operations that result in information loss, especially for small objects. This paper presents a novel attention-based method called Across Feature Map Attention (AFMA) to address this challenge. It quantifies the inner-relationship between small and large objects belonging to the same category by utilizing the different feature levels of the original image. The AFMA could compensate for the loss of high-level feature information of small objects and improve the small/thin object segmentation. Our method can be used as an efficient plug-in for a wide range of existing architectures and produces much more interpretable feature representation than former studies. Extensive experiments on eight widely used segmentation methods and other existing small-object segmentation models on CamVid and Cityscapes demonstrate that our method substantially and consistently improves the segmentation of small/thin objects.
How Masterly Are People at Playing with Their Vocabulary?

In this paper, we describe adaptation of a simple word guessing game that occupied the hearts and minds of people around the world.There are versions for all three Baltic countries and even several versions of each.We specifically pay attention to the Latvian version and look into how people form their guesses given any already uncovered hints.The paper analyses guess patterns, easy and difficult word characteristics, and player behaviour and response.
Intersemiotic translation of contracts into digital environments

An intersemiotic translation is any form of translation that involves at least two different semiotic codes; for example, the translation from words to images, to numerical code, or to non-verbal sounds. One of the most widespread examples of intersemiotic translation in the contemporary world is transposing natural language into machine language in digital environments. In this case, if the source text is a legal text, we encounter a particular type of intersemiotic translation, namely an intersemiotic legal translation in a digital environment. This paper will focus on the intersemiotic legal translation of contracts in digital environments, and is divided into two parts. In the first part (Section Ways of intersemiotically translating a contract using digital tools), we will analyze four possible uses of the intersemiotic translation of contracts in a digital context. In particular, we will highlight the technical characteristics of intersemiotic translation, its limitations, and its potential in different phases of contract management, namely the drafting of the document, the agreement, the archiving of the document, and the execution of contractual clauses. We will examine different digital tools that exploit intersemiotic translation, such as contract drafting tools and online platforms that allow for the conclusion of electronic contracts, document archiving in blockchains, and building smart contracts. When analyzing these uses of intersemiotic translation in the digital environment, we will highlight four types of output that can represent the product of intersemiotic translation in the digital environment: epistemic effects, legal effects, digital effects, and economic effects. In the second part (Section A tool for translating the contract intersemiotically), we will describe a hypothetical prototype that, in light of the four potential uses of intersemiotic translation, could represent a support tool to simplify the communication between professionals and clients through the drafting of legal documents with the aid of dynamic forms and, eventually, with the help of artificial intelligence (AI). Beyond facilitating the dialogue between legal professionals and their clients, we use interfaces to allow clients to create their own drafts of their documents and the lawyer to work on the drafts drawn up by the customer, correct them, and structure them in order to guarantee the validity of the document. The system can also be designed to archive legal documents and private deeds securely and entrust them to a professional by using blockchain technology and automating the execution of some contractual clauses via smart contract protocols.
IoT-Based English Translation Teaching from the Perspective of Artificial Intelligence

In recent years, with the development of artificial intelligence, the Internet of Things (IoT) has become a research hotspot in industry and academia. At the same time, as a derivative tool of artificial intelligence, machine translation based on the IoT is constantly being applied to English translation and its teaching. In teaching, helping students learn English translation has been the focus of machine translation in recent years. Compared with human translation, machine translation is more efficient and convenient. However, machine translation also has some problems. Compared with traditional human translation, it cannot meet the requirements of faithfulness, expressiveness, and elegance of translation. In many fields, neural network translation is comparable to human translation. In the field of English translation teaching, neural network translation has broad prospects. With the gradual maturity of neural network translation, we should think about how to use neural network translation to make it a powerful English translation teaching tool instead of sticking to traditional teaching and cultivating students who are completely unable to compare it with neural network translation. Therefore, in the context of rapid AI iteration, college English translation teaching should also keep pace with the times. With the help of tools such as neural network translation, English translation talents who can skillfully use AI technology can be trained quickly and efficiently so that they can keep pace with the times and master the power of AI. The research in this paper provides important guidance for the application of artificial intelligence and the Internet of Things, especially for intelligent relaying.
Evaluation and comparison of federated learning algorithms for Human Activity Recognition on smartphones

Pervasive computing promotes the integration of smart devices in our living spaces to develop services providing assistance to people. Such smart devices are increasingly relying on cloud-based Machine Learning, which raises questions in terms of security (data privacy), reliance (latency), and communication costs. In this context, Federated Learning (FL) has been introduced as a new machine learning paradigm enhancing the use of local devices. At the server level, FL aggregates models learned locally on distributed clients to obtain a more general model. In this way, no private data is sent over the network, and the communication cost is reduced. Unfortunately, however, the most popular federated learning algorithms have been shown not to be adapted to some highly heterogeneous pervasive computing environments. In this paper, we propose a new FL algorithm, termed FedDist, which can modify models (here, deep neural network) during training by identifying dissimilarities between neurons among the clients. This permits to account for clients' specificity without impairing generalization. FedDist evaluated with three state-of-the-art federated learning algorithms on three large heterogeneous mobile Human Activity Recognition datasets. Results have shown the ability of FedDist to adapt to heterogeneous data and the capability of FL to deal with asynchronous situations.
Real-time neural network inference on extremely weak devices

With the wide adoption of AI applications, there is a pressing need of enabling real-time neural network (NN) inference on small embedded devices, but deploying NNs and achieving high performance of NN inference on these small devices is challenging due to their extremely weak capabilities. Although NN partitioning and offloading can contribute to such deployment, they are incapable of minimizing the local costs at embedded devices. Instead, we suggest to address this challenge via agile NN offloading, which migrates the required computations in NN offloading from online inference to offline learning. In this paper, we present AgileNN, a new NN offloading technique that achieves real-time NN inference on weak embedded devices by leveraging eXplainable AI techniques, so as to explicitly enforce feature sparsity during the training phase and minimize the online computation and communication costs. Experiment results show that AgileNN's inference latency is >6X lower than the existing schemes, ensuring that sensory data on embedded devices can be timely consumed. It also reduces the local device's resource consumption by >8X, without impairing the inference accuracy.
A Novel Approach to Maritime Image Dehazing Based on a Large Kernel Encoder–Decoder Network with Multihead Pyramids

With the continuous increase in human–robot integration, battlefield formation is experiencing a revolutionary change. Unmanned aerial vehicles, unmanned surface vessels, combat robots, and other new intelligent weapons and equipment will play an essential role on future battlefields by performing various tasks, including situational reconnaissance, monitoring, attack, and communication relay. Real-time monitoring of maritime scenes is the basis of battle-situation and threat estimation in naval battlegrounds. However, images of maritime scenes are usually accompanied by haze, clouds, and other disturbances, which blur the images and diminish the validity of their contents. This will have a severe adverse impact on many downstream tasks. A novel large kernel encoder–decoder network with multihead pyramids (LKEDN-MHP) is proposed to address some maritime image dehazing-related issues. The LKEDN-MHP adopts a multihead pyramid approach to form a hybrid representation space comprising reflection, shading, and semanteme. Unlike standard convolutional neural networks (CNNs), the LKEDN-MHP uses many kernels with a 7 × 7 or larger scale to extract features. To reduce the computational burden, depthwise (DW) convolution combined with re-parameterization is adopted to form a hybrid model stacked by a large number of different receptive fields, further enhancing the hybrid receptive fields. To restore the natural hazy maritime scenes as much as possible, we apply digital twin technology to build a simulation system in virtual space. The final experimental results based on the evaluation metrics of the peak signal-to-noise ratio, structural similarity index measure, Jaccard index, and Dice coefficient show that our LKEDN-MHP significantly enhances dehazing and real-time performance compared with those of state-of-the-art approaches based on vision transformers (ViTs) and generative adversarial networks (GANs).
Representative Points from a Mixture of Two Normal Distributions

In recent years, the mixture of two-component normal distributions (MixN) has attracted considerable interest due to its flexibility in capturing a variety of density shapes. In this paper, we investigate the problem of discretizing a MixN by a fixed number of points under the minimum mean squared error (MSE-RPs). Motivated by the Fang-He algorithm, we provide an effective computational procedure with high precision for generating numerical approximations of MSE-RPs from a MixN. We have explored the properties of the nonlinear system used to generate MSE-RPs and demonstrated the convergence of the procedure. In numerical studies, the proposed computation procedure is compared with the k-means algorithm. From an application perspective, MSE-RPs have potential advantages in statistical inference.Our numerical studies show that MSE-RPs can significantly improve Kernel density estimation.