Reviewerly/exHarmony huggingface dataset:
Experimental Results of SAR Imaging Using a Low-Cost X-band Radar with a PLL with Direct Modulation for UAV Applications

This paper presents the system design, imaging algorithm, and first trial results of an X-band frequency modulated continuous wave Synthetic Aperture Radar (SAR) with the transmitted waveform synthesized by a Phase Locked Loop (PLL) with direct modulation. The rapid growth of autonomous vehicle technology puts a demand on sensors, precision, and affordability. The latest navigation and avionics trends lead to miniaturization, highly appreciated in Unmanned Aerial Vehicle (UAV) applications. In particular, low cost, small size, and high precision of remote sensors find a wide range of applications in civilian and military domains (e.g., autonomous delivery systems, driving assistance systems, self-driving cars, military surveillance missiles, or fire guidance, among others). The SAR sensor presented in this paper was designed to be a small and affordable radar sensor dedicated to small UAV platforms and radar imaging. System architecture and a brief description of the signal processing algorithms are illustrated in this paper. The utility of the system is supported by preliminary results from ground-based and airborne trials.
An Outsourced Decryption ABE Model using ECC in Internet of Things

Internet of Things (IoT) is a modern technology that is applicable almost everywhere nowadays. Everything is connected to the Internet in the modern digital era. IoT is a collection of things that are interconnected to share information. Devices connected to IoT networks have some limitations in performing heavy computational tasks because of the availability of less computational and battery power. Attribute Based Encryption (ABE) is a modern public-key cryptographic technique that provides privacy with access control. The bilinear map is an expensive operation that is used in most of the ABE schemes. Elliptic Curve Cryptography (ECC) is an alternative for bilinear pairing to reduce the computation of encryption and decryption in ABE schemes. The process of encryption and decryption of ABE is a heavy computational task for resource-constrained devices. In this paper, an outsourcing-based decryption of ABE using ECC is proposed to reduce the decryption overhead of devices that have limited computational resources. Our scheme divides the computation of the decryption of ABE into two stages: first, the partial decryption of ciphertext in the cloud server, and second, the final decryption of partially decrypted ciphertext by the data user to retrieve the original message. This scheme is secure against the malicious cloud server by adding a blinding factor into the secret to be shared. The blinding factor is shared with intended users through attribute authority. The experimental results demonstrate that our scheme can reduce the decryption complexity and save the computational time of devices, compared to the existing schemes. Thus the proposed scheme is applicable for lightweight devices used in IoT.
Understanding the Limits of Unsupervised Domain Adaptation via Data Poisoning

Unsupervised domain adaptation (UDA) enables cross-domain learning without target domain labels by transferring knowledge from a labeled source domain whose distribution differs from the target. However, UDA is not always successful and several accounts of negative transfer have been reported in the literature. In this work, we prove a simple lower bound on the target domain error that complements the existing upper bound. Our bound shows the insufficiency of minimizing source domain error and marginal distribution mismatch for a guaranteed reduction in the target domain error, due to the possible increase of induced labeling function mismatch. This insufficiency is further illustrated through simple distributions for which the same UDA approach succeeds, fails, and may succeed or fail with an equal chance. Motivated from this, we propose novel data poisoning attacks to fool UDA methods into learning representations that produce large target domain errors. We evaluate the effect of these attacks on popular UDA methods using benchmark datasets where they have been previously shown to be successful. Our results show that poisoning can significantly decrease the target domain accuracy, dropping it to almost 0\% in some cases, with the addition of only 10\% poisoned data in the source domain. The failure of UDA methods demonstrates the limitations of UDA at guaranteeing cross-domain generalization consistent with the lower bound. Thus, evaluation of UDA methods in adversarial settings such as data poisoning can provide a better sense of their robustness in scenarios unfavorable for UDA.
Sustainability in the Web 3.0: How to Assess ESG Characteristics on Smart-Contract Blockchain Ecosystems

The object of study in current Environmental, Social and Governance (ESG) research is often the firm or its management. Of particular interest is also the link between ESG and its impact on financial market performance. I extend existing literature introducing concepts to measure ESG issues for smart-contract blockchains and their ecosystems. Legally, such platforms are often foundations, not for-profit companies. Rather than company management’s decisions and the analysis of related risks, three novel elements emerge: technology, mechanism design and community, take centre-stage in the decentralized context. I propose one environmental metric (Consensus Algorithm), a social metric (Gini coefficient) and one governance metric (Validator risk index) to start the discourse around assessment of ESG issues on smart contract blockchains and their ecosystems.
Stylometric Literariness Classification: the Case of Stephen King

This paper applies stylometry to quantify the literariness of 73 novels and novellas by American author Stephen King, chosen as an extraordinary case of a writer who has been dubbed both "high" and "low" in literariness in critical reception.We operationalize literariness using a measure of stylistic distance (Cosine Delta) based on the 1000 most frequent words in two bespoke comparison corpora used as proxies for literariness: one of popular genre fiction, another of National Book Award-winning authors.We report that a supervised model is highly effective in distinguishing the two categories, with 94.6% macro average in a binary classification.We define two subsets of texts by King-"high" and "low" literariness works as suggested by critics and ourselves-and find that a predictive model does identify King's Dark Tower series and novels such as Dolores Claiborne as among his most "literary" texts, consistent with critical reception, which has also ascribed postmodern qualities to the Dark Tower novels.Our results demonstrate the efficacy of Cosine Delta-based stylometry in quantifying the literariness of texts, while also highlighting the methodological challenges of literariness, especially in the case of Stephen King.The code and data to reproduce our results are available at https://github.com/andreasvc/
Configuration-aware Model Predictive Motion Planning in Narrow Environment for Autonomous Tractor-trailer Mobile Robot

A novel collision-free motion planner was proposed for tractor-trailer mobile robots (TTMRs) in a narrow environment with consideration of the polygonal shape of the TTMR and obstacles. The motion planner was designed as an iterative nonlinear optimization problem with a receding horizon similar to the model predictive control. Collision-free constraints with a configuration of the TTMRs were derived from the Farkas’ lemma with simplification, which were the hard constraints in the optimization problem. As such, the proposed method guarantees collision avoidance in its motion planning. The presented modified Farkas’ lemma stabled the fluctuated calculation time during the optimization. Numerical simulations confirmed the validity of the proposed method. A thorough evaluation of the dynamic environment with a moving obstacle was also carried out.
A divided and prioritized experience replay approach for streaming regression

In the streaming learning setting, an agent is presented with a data stream on which to learn from in an online fashion. A common problem is catastrophic forgetting of old knowledge due to updates to the model. Mitigating catastrophic forgetting has received a lot of attention, and a variety of methods exist to solve this problem. In this paper, we present a divided and prioritized experience replay approach for streaming regression, in which relevant observations are retained in the replay, and extra focus is added to poorly estimated observations through prioritization. Using a real-world dataset, the method is compared to the standard sliding window approach. A statistical power analysis is performed, showing how our approach improves performance on rare, important events at a trade-off in performance for more common observations. Close inspections of the dataset are provided, with emphasis on areas where the standard approach fails. A rephrasing of the problem to a binary classification problem is performed to separate common and rare, important events. These results provide an added perspective regarding the improvement made on rare events.•We divide the prediction space in a streaming regression setting•Observations in the experience replay are prioritized for further training by the model's current error.
How Do Neural Sequence Models Generalize? Local and Global Cues for Out-of-Distribution Prediction

After a neural sequence model encounters an unexpected token, can its behavior be predicted?We show that RNN and transformer language models exhibit structured, consistent generalization in out-of-distribution contexts.We begin by introducing two idealized models of generalization in next-word prediction: a local context model in which generalization is consistent with the last word observed, and a global context model in which generalization is consistent with the global structure of the input.In experiments in English, Finnish, Mandarin, and random regular languages, we demonstrate that neural language models interpolate between these two forms of generalization: their predictions are well-approximated by a log-linear combination of local and global predictive distributions.We then show that, in some languages, noise mediates the two forms of generalization: noise applied to input tokens encourages global generalization, while noise in history representations encourages local generalization.Finally, we offer a preliminary theoretical explanation of these results by proving that the observed interpolation behavior is expected in log-linear models with a particular feature correlation structure.These results help explain the effectiveness of two popular regularization schemes and show that aspects of sequence model generalization can be understood and controlled.
Delexicalised Multilingual Discourse Segmentation for DISRPT 2021 and Tense, Mood, Voice and Modality Tagging for 11 Languages

This paper describes our participating system for the Shared Task on Discourse Segmentation and Connective Identification across Formalisms and Languages.Key features of the presented approach are the formulation as a clause-level classification task, a languageindependent feature inventory based on Universal Dependencies grammar, and compositeverb-form analysis.The achieved F1 is 92% for German and English and lower for other languages.The paper also presents a clauselevel tagger for grammatical tense, aspect, mood, voice and modality in 11 languages.
Towards Reliable Spatial Memory Safety for Embedded Software by Combining Checked C with Concolic Testing

In this paper we propose to combine the safe C dialect Checked C with concolic testing to obtain an effective methodology for attaining safer C code. Checked C is a modern and backward compatible extension to the C programming language which provides facilities for writing memory-safe C code. We utilize incremental conversions of unsafe C software to Checked C. After each increment, we leverage concolic testing, an effective test generation technique, to support the conversion process by searching for newly introduced and existing bugs.Our RISC-V experiments using the RIOT Operating System (OS) demonstrate the effectiveness of our approach. We uncovered 4 previously unknown bugs and 3 bugs accidentally introduced through our conversion process.
DNN gradient lossless compression: Can GenNorm be the answer?

In this paper, the problem of optimal gradient lossless compression in Deep Neural Network (DNN) training is considered. Gradient compression is relevant in many distributed DNN training scenarios, including the recently popular federated learning (FL) scenario in which each remote users are connected to the parameter server (PS) through a noiseless but rate limited channel. In distributed DNN training, if the underlying gradient distribution is available, classical lossless compression approaches can be used to reduce the number of bits required for communicating the gradient entries. Mean field analysis has suggested that gradient updates can be considered as independent random variables, while Laplace approximation can be used to argue that gradient has a distribution approximating the normal (Norm) distribution in some regimes. In this paper we argue that, for some networks of practical interest, the gradient entries can be well modelled as having a generalized normal (GenNorm) distribution. We provide numerical evaluations to validate that the hypothesis GenNorm modelling provides a more accurate prediction of the DNN gradient tail distribution. Additionally, this modeling choice provides concrete improvement in terms of lossless compression of the gradients when applying classical fix-to-variable lossless coding algorithms, such as Huffman coding, to the quantized gradient updates. This latter results indeed provides an effective compression strategy with low memory and computational complexity that has great practical relevance in distributed DNN training scenarios.
A New Dataset and Efficient Baselines for Document-level Text Simplification in German

The task of document-level text simplification is very similar to summarization with the additional difficulty of reducing complexity. We introduce a newly collected data set of German texts, collected from the Swiss news magazine 20 Minuten (‘20 Minutes’) that consists of full articles paired with simplified summaries. Furthermore, we present experiments on automatic text simplification with the pretrained multilingual mBART and a modified version thereof that is more memory-friendly, using both our new data set and existing simplification corpora. Our modifications of mBART let us train at a lower memory cost without much loss in performance, in fact, the smaller mBART even improves over the standard model in a setting with multiple simplification levels.
Using Principal Component Analysis on Crypto Correlations to Build a Diversified Portfolio

A simple look at cryptoassets’ historical can lead us think that in recent years most have followed Bitcoin’s wake. If so, it would be very difficult to build an exposure to this market without being highly exposed to Bitcoin, and on the other hand a portfolio with many cryptos poses a great operational risk due to the lack of institutional custody. To this aim, this paper presents an updated correlation analysis of 31 crypto assets, among them and with some equity and gold indices. Furthermore, we conduct a PCA to identify the group of cryptos that present different correlation patterns and may help us build a diversified portfolio. The correlation update shows that these cryptoassets, which account for aprox. 80% of the market, have been positively correlated since 2017 and Ether has been the asset with the highest results. These correlations increase during bear markets, especially in the current bear period started in April 2021. When analyzing Bitcoin against equity markets, we confirmed that correlation is very volatile and swings from positive to negative continuously, which makes it very difficult to use Bitcoin as an equity hedge. As a closing, we have observed that the only times that Bitcoin presented negative correlation with equity indexes coincides with times when gold also showed negative correlation, which could reveal the use of the digital asset as a store of value.Finally, the PCA show a great number of assets from different category, size and design around a single, highly concentrated cluster. This confirms the great speculation that exists in the market, which moves all the assets en masse. When using the PCA to build a diversified portfolio we achieved better results in terms of return, risk-adjusted return and with a lower correlation to Bitcoin.
Efficiency of the project method in the development of professional competencies in future teachers

The objective is to study the effectiveness of the project method in the formation of professional competencies of future employees in the field of education. Methods: surveys and questionnaires, methods of remote collection and processing of information (GOOGLE Forms) the main methods used in the study were the method of questionnaires, the method of synthesis and analysis, the method of expert evaluations. The chi-square criterion, the feature combination criterion, and Pearson's correlation coefficient were used to calculate the results of the study. Results: The results of the study showed high indicators of the level of formation of students' subjectivity when using the project method in both the main and control groups. The introduction of the project method has an unconditional favourable effect in the process of forming the professional competencies of future teachers. Together with traditional teaching methods, the project method is an effective tool f or training future teachers. It is stated that the reliability of the obtained results is proved by the chi-square criterion. Its level is 0.01, on the basis of which it can be concluded that the proposed model is appropriate. Conclusion: Thus, after the analysis of the obtained results, the expediency of applying the project method in the training of future teachers was established.
Spatio-Temporal Variational Gaussian Processes

We introduce a scalable approach to Gaussian process inference that combines spatio-temporal filtering with natural gradient variational inference, resulting in a non-conjugate GP method for multivariate data that scales linearly with respect to time. Our natural gradient approach enables application of parallel filtering and smoothing, further reducing the temporal span complexity to be logarithmic in the number of time steps. We derive a sparse approximation that constructs a state-space model over a reduced set of spatial inducing points, and show that for separable Markov kernels the full and sparse cases exactly recover the standard variational GP, whilst exhibiting favourable computational properties. To further improve the spatial scaling we propose a mean-field assumption of independence between spatial locations which, when coupled with sparsity and parallelisation, leads to an efficient and accurate method for large spatio-temporal problems.
Self-supervised Visual Representation Learning for Fine-Grained Ship Detection

The success of deep learning has greatly promoted the development of object detection algorithms. However, the performance of object detection algorithms in specific fields still needs to be improved. We focus on the field of fine-grained ship detection in our work. For fine-grained ship detection, labelling requires some professional knowledge, and it is difficult for the model to identify some less distinguishable categories. In this paper, a siamese network is proposed for self-supervised visual representation learning to reduce the reliance on a large amount of labelled data. Meanwhile, we collect and label a fine-grained ship dataset called HarborShips. We pretrain the backbone model in a self-supervised method to learn effective visual features by optimizing contrastive loss. The feature extractor is further fine-tuned for the downstream task of ship detection in a supervised way. In the task of ship detection, our model improves the mAP by 8.9% on our Harborships dataset.
Unsupervised Text Style Transfer with Content Embeddings

The style transfer task (here style is used in a broad "authorial" sense with many aspects including register, sentence structure, and vocabulary choice) takes text input and rewrites it in a specified target style preserving the meaning, but altering the style of the source text to match that of the target.Much of the existing research on this task depends on the use of parallel datasets.In this work we employ recent results in unsupervised cross-lingual language modeling (XLM) and machine translation to effect style transfer while treating the input data as unaligned.First, we show that adding "content embeddings" to the XLM which capture human-specified groupings of subject matter can improve performance over the baseline model.Evaluation of style transfer has often relied on metrics designed for machine translation which have received criticism of their suitability for this task.As a second contribution, we propose the use of a suite of classical stylometrics as a useful complement for evaluation.We select a few such measures and include these in the analysis of our results.
Learning Large Graph-Based MDPs With Historical Data

Weconsider learning the dynamics and measurement model parameters of a graph-based Markov decision process (GMDP) given a history of measurements. Graph-based models have been used in modeling many data-based applications, such as recognition tasks, disease epidemics, forest wildfires, freeway traffic, and social networks. We leverage the expectation–maximization framework and develop an algorithm that optimizes the measurement likelihood and has favorable complexity for large models. In contrast to prior work, we directly consider GMDPs with significantly large discrete state spaces, arbitrary coupling structure, and long measurement sequences. We also consider a special structural property called Anonymous Influence, which we use to test hypotheses and gain insights into the data. We demonstrate the effectiveness of our learning algorithm by considering two real-world data sets on the 2020 Novel Coronavirus (COVID-19) pandemic in California and on user interactions on Twitter. Our results show that the learned GMDP models better explain the data compared to an uncoupled model assumption.
Aspects of quantum states asymmetry for the magnetic dipolar interaction dynamics

We investigate the asymmetry properties of quantum states in relation to the Hamiltonian responsible for the magnetic dipolar interaction (MDI) dynamics, and we evaluate its relationship to entanglement production. We consider some classes of pure and mixed quantum states of two qubits evolved under MDI and, using the asymmetry measure defined via the Wigner–Yanase skew information, we describe the asymmetry dependence on the Hamiltonian parameters and initial conditions of the system. In addition, we define and calculate the dynamics of the asymmetry of local states, characterizing their temporal and interaction parameters dependence. Finally, because the MDI Hamiltonian has a null eigenvalue, the group generator-based asymmetry measure does not adequately quantify the state susceptibility with respect to the action of the subspace generated by the eigenvectors associated with this eigenvalue. For this reason, we also define and study the group element-based asymmetry measure with relation to the unitary operator associated with the MDI Hamiltonian.