Reviewerly/exHarmony huggingface dataset:
Multi-Granularity Semantic Collaborative Reasoning Network for Visual Dialog

A visual dialog task entails an agent engaging in a multiple round conversation about an image. Notably, one of the main issues is capturing the semantic associations of multiple inputs, such as the questions, dialog history, and image features. Many of the techniques use a token or a sentence granularity semantic representation of the question and dialog history to model semantic associations; however, they do not perform collaborative modeling, which limits their efficacy. To overcome this limitation, we propose a multi-granularity semantic collaborative reasoning network to properly support a visual dialog. It employs different granularity semantic representations of the question and dialog history to collaboratively identify the relevant information from multiple inputs based on attention mechanisms. Specifically, the proposed method collaboratively reasons the question-related information from the dialog history based on its granular semantic representations. Then, it collaboratively locates the question-related visual objects in the image by leveraging refined question representations. The experimental results conducted on the VisDial v.1.0 dataset verify the effectiveness of the proposed method, showing the improvements of the best normalized discounted cumulative gain score from 59.37 to 60.98 with a single model, from 60.92 to 62.25 with ensemble models, and from 63.15 to 64.13 with performing multitask learning.
Assessing Distributed Solar Power Generation Potential under Multi-GCMs: A Factorial-Analysis-Based Random Forest Method

The development of renewable energy is important for climate change mitigation and socioeconomic sustainability, and the prediction of renewable energy potential (e.g., solar) under the consideration of climate change impact is challenged. In this study, a factorial-analysis-based random forest (FARF) method is developed for the distributed solar power generation (DSPG) predication under multiple global climate models (GCMs). FARF has advantages in (i) downscaling large-scale climate variables to local scales, (ii) avoiding the problem of overfitting in traditional models; and (iii) reflecting the main and interactive effects of climate variables on solar radiation intensity (SRI). Then, the FARF method is applied to the Jing-Jin-Ji region of China to predict the DSPG potential under three GCMs and two emission scenarios (RCP4.5 and 8.5). Multiple validation coefficients prove that the FARF method is effective and feasible. Major findings are as follows: (i) during 2021–2100, the regional SRI would increase under all GCMs and RCPs, and the southern region is obviously higher than the northern region; (ii) the main impact factors are temperature (contribution >51%) and humidity (contribution >28%), and the interactive effects of multiple factors are insignificant; (iii) the regional DSGP would continuously rise and its contribution to electricity consumption would continue to increase; and (iv) under all GCMs, SRI and DSPG under RCP8.5 would be higher than those under RCP4.5. The findings can help decision makers to use the desired strategies for promoting renewable energy utilization and energy system sustainable development.
Degeneracy engineering for classical and quantum annealing: A case study of sparse linear regression in collider physics

Classical and quantum annealing are computing paradigms that have been proposed to solve a wide range of optimization problems. In this paper, we aim to enhance the performance of annealing algorithms by introducing the technique of degeneracy engineering, through which the relative degeneracy of the ground state is increased by modifying a subset of terms in the objective Hamiltonian. We illustrate this novel approach by applying it to the example of ${\ensuremath{\ell}}_{0}$-norm regularization for sparse linear regression, which is, in general, an $\mathsf{NP}$-hard optimization problem. Specifically, we show how to cast ${\ensuremath{\ell}}_{0}$-norm regularization as a quadratic unconstrained binary optimization (QUBO) problem, suitable for implementation on annealing platforms. As a case study, we apply this QUBO formulation to energy flow polynomials in high-energy collider physics, finding that degeneracy engineering substantially improves the annealing performance. Our results motivate the application of degeneracy engineering to a variety of regularized optimization problems.
Joint one‐sided synthetic unpaired image translation and segmentation for colorectal cancer prevention

Abstract Deep learning has shown excellent performance in analysing medical images. However, datasets are difficult to obtain due privacy issues, standardization problems, and lack of annotations. We address these problems by producing realistic synthetic images using a combination of 3D technologies and generative adversarial networks. We propose CUT‐seg, a joint training where a segmentation model and a generative model are jointly trained to produce realistic images while learning to segment polyps. We take advantage of recent one‐sided translation models because they use significantly less memory, allowing us to add a segmentation model in the training loop. CUT‐seg performs better, is computationally less expensive, and requires less real images than other memory‐intensive image translation approaches that require two stage training. Promising results are achieved on five real polyp segmentation datasets using only one real image and zero real annotations. As a part of this study we release Synth‐Colon, an entirely synthetic dataset that includes 20,000 realistic colon images and additional details about depth and 3D geometry: https://enric1994.github.io/synth-colon
Decoherence and classicalization of continuous-time quantum walks on graphs

Abstract We address decoherence and classicalization of continuous-time quantum walks (CTQWs) on graphs. In particular, we investigate three different models of decoherence and employ the quantum-classical (QC) dynamical distance as a figure of merit to assess whether, and to which extent, decoherence classicalizes the CTQW, i.e. turns it into the analogue classical process. We show that the dynamics arising from intrinsic decoherence, i.e. dephasing in the energy basis, do not fully classicalize the walker and partially preserves quantum features. On the other hand, dephasing in the position basis, as described by the Haken–Strobl master equation or by the quantum stochastic walk (QSW) model, asymptotically destroys the quantumness of the walker, making it equivalent to a classical random walk. We also investigate how fast is the classicalization process and observe a larger rate of convergence of the QC-distance to its asymptotic value for intrinsic decoherence and the QSW models, whereas in the Haken–Strobl scenario, larger values of the decoherence rate induce localization of the walker.
Mechanizing Matching Logic In Coq

Matching logic is a formalism for specifying, and reasoning about, mathematical structures, using patterns and pattern matching.Growing in popularity, it has been used to define many logical systems such as separation logic with recursive definitions and linear temporal logic.In addition, it serves as the logical foundation of the K semantic framework, which was used to build practical verifiers for a number of real-world languages.Despite being a fundamental formal system accommodating substantial theories, matching logic lacks a general-purpose, machine-checked formalization.Hence, we formalize matching logic using the Coq proof assistant.Specifically, we create a new representation of matching logic that uses a locally nameless encoding, and we formalize the syntax, semantics, and proof system of this representation in the Coq proof assistant.Crucially, we prove the soundness of the formalized proof system and provide a means to carry out interactive matching logic reasoning in Coq.We believe this work provides a previously unexplored avenue for reasoning about matching logic, its models, and the proof system.
The boundaries of meaning: a case study in neural machine translation

The success of deep learning in natural language processing raises intriguing questions about the nature of linguistic meaning and ways in which it can be processed by natural and artificial systems. One such question has to do with subword segmentation algorithms widely employed in language modeling, machine translation, and other tasks since 2016. These algorithms often cut words into semantically opaque pieces, such as ‘period’, ‘on’, ‘t’, and ‘ist’ in ‘period|on|t|ist’. The system then represents the resulting segments in a dense vector space, which is expected to model grammatical relations among them. This representation may in turn be used to map ‘period|on|t|ist’ (English) to ‘par|od|ont|iste’ (French). Thus, instead of being modeled at the lexical level, translation is reformulated more generally as the task of learning the best bilingual mapping between the sequences of subword segments of two languages; and sometimes even between pure character sequences: ‘p|e|r|i|o|d|o|n|t|i|s|t’ → ‘p|a|r|o|d|o|n|t|i|s|t|e’. Such segmentations and alignments are at work in highly efficient end-to-end machine translation systems, despite their allegedly opaque nature. But do they have linguistic or philosophical plausibility? I attempt to cast light on this question, in the spirit of making artificial intelligence more transparent and explainable.
Zero-Shot Topic Labeling for Hazard Classification

Topic classification is the task of mapping text onto a set of meaningful labels known beforehand. This scenario is very common both in academia and industry whenever there is the need of categorizing a big corpus of documents according to set custom labels. The standard supervised approach, however, requires thousands of documents to be manually labelled, and additional effort every time the label taxonomy changes. To obviate these downsides, we investigated the application of a zero-shot approach to topic classification. In this setting, a subset of these topics, or even all of them, is not seen at training time, challenging the model to classify corresponding examples using additional information. We first show how zero-shot classification can perform the topic-classification task without any supervision. Secondly, we build a novel hazard-detection dataset by manually selecting tweets gathered by LINKS Foundation for this task, where we demonstrate the effectivenes of our cost-free method on a real-world problem. The idea is to leverage a pre-trained text-embedder (MPNet) to map both text and topics into the same semantic vector space where they can be compared. We demonstrate that these semantic spaces are better aligned when their dimension is reduced, keeping only the most useful information. We investigated three different dimensionality reduction techniques, namely, linear projection, autoencoding and PCA. Using the macro F1-score as the standard metric, it was found that PCA is the best performing technique, recording improvements for each dataset in comparison with the performance on the baseline.
Quantifying Unsharpness of Observables in an Outcome-Independent way

Recently, in the paper (Liu and Luo, Phys. Rev. A 104, 052227, 2021) a very beautiful measure of the unsharpness (fuzziness) of the observables constructed via uncertainty is discussed. This measure does not depend on the values of the outcomes and can measure the intrinsic unsharpness of the observables. In this work, we also quantify the unsharpness of observables in an outcome-independent way. But our approach is operationally motivated and different than the approach of the paper (Liu and Luo, Phys. Rev. A 104, 052227, 2021). In this work, at first, we construct two Lüders instrument-based unsharpness measures and provide the tight upper bounds of those measures. Then we prove the monotonicity of our proposed measures under a class of fuzzifying processes (processes that make the observables more fuzzy). This is consistent with the resource-theoretic framework. Then we relate our approach to the approach of the paper (Liu and Luo, Phys. Rev. A 104, 052227, 2021). Next, we try to construct two instrument-independent unsharpness measures. In particular, we define two instrument-independent unsharpness measures and provide the tight upper bounds of those measures and then we derive the condition for the monotonicity of those measures under a class of fuzzifying processes and prove the monotonicity for dichotomic qubit observables. Then we show that for an unknown measurement, the values of all of these measures can be determined experimentally. Finally, we present the idea of the resource theory of the sharpness of the observables and discuss an example where the sharpness of the observables can be considered as a resource.
How Do Viewers Synthesize Conflicting Information from Data Visualizations?

Scientific knowledge develops through cumulative discoveries that build on, contradict, contextualize, or correct prior findings. Scientists and journalists often communicate these incremental findings to lay people through visualizations and text (e.g., the positive and negative effects of caffeine intake). Consequently, readers need to integrate diverse and contrasting evidence from multiple sources to form opinions or make decisions. However, the underlying mechanism for synthesizing information from multiple visualizations remains under-explored. To address this knowledge gap, we conducted a series of four experiments (N = 1166) in which participants synthesized empirical evidence from a pair of line charts presented sequentially. In Experiment 1, we administered a baseline condition with charts depicting no specific context where participants held no strong belief. To test for the generalizability, we introduced real-world scenarios to our visualizations in Experiment 2 and added accompanying text descriptions similar to online news articles or blog posts in Experiment 3. In all three experiments, we varied the relative direction and magnitude of line slopes within the chart pairs. We found that participants tended to weigh the positive slope more when the two charts depicted relationships in the opposite direction (e.g., one positive slope and one negative slope). Participants tended to weigh the less steep slope more when the two charts depicted relationships in the same direction (e.g., both positive). Through these experiments, we characterize participants' synthesis behaviors depending on the relationship between the information they viewed, contribute to theories describing underlying cognitive mechanisms in information synthesis, and describe design implications for data storytelling.
Impact of using different-sized touch keyboards on free-text keystroke dynamics authentication in the Arabic language

Authentication using keystroke dynamics (KD) has become an active research area due to its usability and security aspects. Nowadays, the scale of touch keyboard use has expanded to include most modern devices. Although KD typically focuses on a single device at a particular time, authentication systems are adjustable within their environments, as these systems' users frequently switch between multiple devices. Thus, this paper assesses users' typing behavior on different tablet devices with varying touch keyboard sizes. In particular, we empirically assess whether the validation results from free-text KD authentication vary depending on the touch keyboard size. The results reveal interesting research directions for future feasibility studies on changing the dynamic keystroke typing-pad effect on user-security and trust-authentication analysis.
Individuals’ willingness to provide geospatial global positioning system (GPS) data from their smartphone during the COVID-19 pandemic

This study aims to evaluate people's willingness to provide their geospatial global positioning system (GPS) data from their smartphones during the COVID-19 pandemic. Based on the self-determination theory, the addition of monetary incentives to encourage data provision may have an adverse effect on spontaneous donation. Therefore, we tested if a crowding-out effect exists between financial and altruistic motivations. Participants were randomized to different frames of motivational messages regarding the provision of their GPS data based on (1) self-interest, (2) pro-social benefit, and (3) monetary compensation. We also sought to examine the use of a negative versus positive valence in the framing of the different armed messages. 1055 participants were recruited from 41 countries with a mean age of 34 years on Amazon Mechanical Turk (MTurk), an online crowdsourcing platform. Participants living in India or in Brazil were more willing to provide their GPS data compared to those living in the United States. No significant differences were seen between positive and negative valence framing messages. Monetary incentives of $5 significantly increased participants' willingness to provide GPS data. Half of the participants in the self-interest and pro-social arms agreed to provide their GPS data and almost two-thirds of participants were willing to provide their data in exchange for $5. If participants refused the first framing proposal, they were followed up with a "Vickrey auction" (a sealed-bid second-priced auction, SPSBA). An average of $17 bid was accepted in the self-interest condition to provide their GPS data, and the average "bid" of $21 was for the pro-social benefit experimental condition. These results revealed that a crowding-out effect between intrinsic and extrinsic motivations did not take place in our sample of internet users. Framing and incentivization can be used in combination to influence the acquisition of private GPS smartphone data. Financial incentives can increase data provision to a greater degree with no losses on these intrinsic motivations, to fight the COVID-19 pandemic.
The Multibillion Dollar Software Supply Chain of Ethereum

The rise of blockchain technologies has triggered tremendous research interest, coding efforts, and monetary investments in the last decade. Ethereum is the single largest programmable blockchain platform today. It features cryptocurrency trading, digital art, and decentralized finance through smart contracts. So-called Ethereum nodes operate the blockchain, relying on a vast supply chain of third-party software dependencies maintained by diverse organizations. These software suppliers have a direct impact on the reliability and the security of Ethereum. In this article, we perform an analysis of the software supply chain of Java Ethereum nodes and distill the challenges of maintaining and securing this blockchain technology.
Adaptive threshold based SIFT image registration algorithm

The adaptive threshold based SIFT image registration algorithm (AT-SIFT) is proposed to improve the traditional SIFT algorithm which has the problems of large calculation amount, single matching constraint, and low registration accuracy. Feature points are detected and described by combining FAST operators with and SIFT descriptors. The FLANN algorithm based on adaptive threshold and the improved RANSAC algorithm are used to remove the mis-matching points and solve the transformational matrix. The experimental results show that the algorithm not only improves the matching speed and accuracy but also has relatively strong robustness.
Data Exploratory Analysis and Feature Selection of Low-Speed Wind Tunnel Data for Predicting Force and Moment of Aircraft

This paper discusses exploratory data analysis (EDA) and feature selection of aircraft test results in Indonesia's low-speed wind tunnels (ILST). First, we briefly explain input and output parameters and data processing to make readable and higher accurate data. Then, we used feature selection using embedded and random forest methods to find parameters that most affect the force coefficient of aircraft. The research activities carried out in this study are to review literature from either scientific journals, the internet, or books and interview with an engineer who tests aircraft models at ILST. Then create a program for processing data from test results, such as data extraction, data cleaning, exploratory data analysis, and feature selection with python. After applying the feature selection method, we found that all the methods show similar results and have succeeded in separating the powerful features from the weak ones with a significant score difference. We decide to use the Random Forest method. The three most strongest features in the coefficient of an aircraft model in the ILST test (CL, CD, CM25, CYAW, CROLL and CY) are the following: for CL are ALFA (0.984), T0 (0.008), P0 (0.004), on for CD is are ALFA (0.965), T0 (0.009), RE (0.007), in CM25 are ALFA (0.416), P0 (0.285), T0 (0.168), in CYAW are BETA (0.44), T0 (0.141), ALFA (0.141), in CROLL is BETA (0.79), ALFA (0.091), P0 (0.036), and in CY are BETA (0.842), ALFA (0.114) and T0 (0.014). The results of this paper can be used to help build a model for the coefficient of aircraft design using machine learning based on the data from the ILST test more effectively and efficiently.
Adaptive Behavior Cloning Regularization for Stable Offline-to-Online Reinforcement Learning

Offline reinforcement learning, by learning from a fixed dataset, makes it possible to learn agent behaviors without interacting with the environment.However, depending on the quality of the offline dataset, such pre-trained agents may have limited performance and would further need to be fine-tuned online by interacting with the environment.During online fine-tuning, the performance of the pre-trained agent may collapse quickly due to the sudden distribution shift from offline to online data.We propose to adaptively weigh the behavior cloning loss during online fine-tuning based on the agent's performance and training stability.Moreover, we use a randomized ensemble of Q functions to further increase the sample efficiency of online fine-tuning by performing a large number of learning updates.Experiments show that the proposed method yields state-of-the-art offline-to-online reinforcement learning performance on the popular D4RL benchmark.
Multitask Learning for Low Resource Spoken Language Understanding

We explore the benefits that multitask learning offer to speech processing as we train models on dual objectives with automatic speech recognition and intent classification or sentiment classification.Our models, although being of modest size, show improvements over models trained end-to-end on intent classification.We compare different settings to find the optimal disposition of each task module compared to one another.Finally, we study the performance of the models in lowresource scenario by training the models with as few as one example per class.We show that multitask learning in these scenarios compete with a baseline model trained on text features and performs considerably better than a pipeline model.On sentiment classification, we match the performance of an end-to-end model with ten times as many parameters.We consider 4 tasks and 4 datasets in Dutch and English.