Reviewerly/exHarmony huggingface dataset:
ANTHEM

Product search is a fundamentally challenging problem due to the large-size of product catalogues and the complexity of extracting semantic information from products. In addition to this, the black-box nature of most search systems also hamper a smooth customer experience. Current approaches in this area utilize lexical and semantic product information to match user queries against products. However, these models lack (i) a hierarchical query representation, (ii) a mechanism to detect and capture inter-entity relationships within a query, and (iii) a query composition method specific to e-commerce domain. To address these challenges, in this paper, we propose an AtteNTive Hyperbolic Entity Model (ANTHEM), a novel attention-based product search framework that models query entities as two-vector hyperboloids, learns inter-entity intersections and utilizes attention to unionize individual entities and inter-entity intersections to predict product matches from the search space. ANTHEM utilizes the first and second vector of hyperboloids to determine the query's semantic position and to tune its surrounding search volume, respectively. The attention networks capture the significance of intra-entity and inter-entity intersections to the final query space. Additionally, we provide a mechanism to comprehend ANTHEM and understand the significance of query entities towards the final resultant products. We evaluate the performance of our model on real data collected from popular e-commerce sites. Our experimental study on the offline data demonstrates compelling evidence of ANTHEM's superior performance over state-of-the-art product search methods with an improvement of more than 10% on various metrics. We also demonstrate the quality of ANTHEM's query encoder using a query matching task.
CNN-based Automatic Modulation Classification Over Underwater Acoustic Channels

Automatic modulation classification (AMC) aims to recognize modulation schemes from received communication signals. Such a task is especially challenging over underwater acoustic (UWA) channels due to their harsh conditions including long multipath, high Doppler effect, and so on. In recent years, deep learning for AMC has attracted increasing attentions for its powerful feature-extraction capability. In this paper, we explore the feasibility and performance of a convolutional neural network (CNN)-based AMC method over UWA channels. Three transmission modes: single-carrier (SC), orthogonal frequency-division multiplexing (OFDM), direct sequence spread spectrum (DSSS), are employed. The modulation schemes include four coherent modulations: BPSK, QPSK, 8PSK and 16QAM, and two non-coherent modulations: BFSK, QFSK. In total, fourteen classes of communication signals are considered for classification. It showed recognition of single-carrier coherent signal is more difficult than others and to improve the classification accuracy, two hard example mining mechanisms were adopted. Numerical simulations showed the proposed scheme achieves decent recognition performance.
An Empirical Study of Yanked Releases in the Rust Package Registry

Cargo, the software packaging manager of Rust, provides a yank mechanism to support release-level deprecation, which can prevent packages from depending on yanked releases. Most prior studies focused on code-level (i.e., deprecated APIs) and package-level deprecation (i.e., deprecated packages). However, few studies have focused on release-level deprecation. In this study, we investigate how often and how the yank mechanism is used, the rationales behind its usage, and the adoption of yanked releases in the Cargo ecosystem. Our study shows that 9.6% of the packages in Cargo have at least one yanked release, and the proportion of yanked releases kept increasing from 2014 to 2020. Package owners yank releases for other reasons than withdrawing a defective release, such as fixing a release that does not follow semantic versioning or indicating a package is removed or replaced. In addition, we found that 46% of the packages directly adopted at least one yanked release and the yanked releases propagated through the dependency network, which leads to 1.4% of the releases in the ecosystem having unresolved dependencies.
Non-separable rotation moment invariants

In this paper, we introduce new rotation moment invariants, which are composed of non-separable Appell moments. We prove that Appell polynomials behave under rotation as monomials, which enables easy construction of the invariants. We show by extensive tests that non-separable moments may outperform the separable ones in terms of recognition power and robustness thanks to a better distribution of their zero curves over the image space.
An Efficient Procedure for Identifying the Similarity Between French and English Languages with Sequence Matcher Technique

Through this research article, we have compared multi-lingual document which are used in plagiarism detection, bilingual lexicon extraction and NLP application. The comparison of multi-lingual text is done using a parallel corpus which is a collection of aligned sentences and sentences which are translation of each other. Here, we have used sequence matcher in order to retrieve the similarity of sentences, lexicons, and words in a multi-lingual content. The technique sequence matcher is found to perform better over the other existing techniques.
Time-Space Transformers for Video Panoptic Segmentation

We propose a novel solution for the task of video panoptic segmentation, that simultaneously predicts pixel-level semantic and instance segmentation and generates clip-level instance tracks. Our network, named VPS-Transformer, with a hybrid architecture based on the state-of-the-art panoptic segmentation network Panoptic-DeepLab, combines a convolutional architecture for single-frame panoptic segmentation and a novel video module based on an instantiation of the pure Transformer block. The Transformer, equipped with attention mechanisms, models spatio-temporal relations between backbone output features of current and past frames for more accurate and consistent panoptic estimates. As the pure Transformer block introduces large computation overhead when processing high resolution images, we propose a few design changes for a more efficient compute. We study how to aggregate information more effectively over the space-time volume and we compare several variants of the Transformer block with different attention schemes. Extensive experiments on the Cityscapes-VPS dataset demonstrate that our best model improves the temporal consistency and video panoptic quality by a margin of 2.2%, with little extra computation.
Practical Ethics

As ethical issues become increasingly important and problematic in research, this chapter reflects on our own investigations into some of the ethical considerations involved in long-term research ‘in the wild’. In particular, we consider two relevant issues: the effect of a long-term relationship with a community, and the delineation and relevance of ‘practical’ ethics in the process. What becomes clear from our interrogation of the data is that issues of responsibility, including those of how we identify what our responsibilities might be; who holds them; what they entail, and how we discharge them are matters of the negotiated order. In a context where research relationships ‘in the wild’ are predicated on lasting commitments, they are not, and cannot be, determined by the researchers alone. They evolve over time and in delicate relation to the needs and desires of our fellow research partners and participants—this is what ‘practical ethics’ entails.
Deep neural architectures for dialect classification with single frequency filtering and zero-time windowing feature representations

The goal of this study is to investigate advanced signal processing approaches [single frequency filtering (SFF) and zero-time windowing (ZTW)] with modern deep neural networks (DNNs) [convolution neural networks (CNNs), temporal convolution neural networks (TCN), time-delay neural network (TDNN), and emphasized channel attention, propagation and aggregation in TDNN (ECAPA-TDNN)] for dialect classification of major dialects of English. Previous studies indicated that SFF and ZTW methods provide higher spectro-temporal resolution. To capture the intrinsic variations in articulations among dialects, four feature representations [spectrogram (SPEC), cepstral coefficients, mel filter-bank energies, and mel-frequency cepstral coefficients (MFCCs)] are derived from SFF and ZTW methods. Experiments with and without data augmentation using CNN classifiers revealed that the proposed features performed better than baseline short-time Fourier transform (STFT)-based features on the UT-Podcast database [Hansen, J. H., and Liu, G. (2016). "Unsupervised accent classification for deep data fusion of accent and language information," Speech Commun. 78, 19-33]. Even without data augmentation, all the proposed features showed an approximate improvement of 15%-20% (relative) over best baseline (SPEC-STFT) feature. TCN, TDNN, and ECAPA-TDNN classifiers that capture wider temporal context further improved the performance for many of the proposed and baseline features. Among all the baseline and proposed features, the best performance is achieved with single frequency filtered cepstral coefficients for TCN (81.30%), TDNN (81.53%), and ECAPA-TDNN (85.48%). An investigation of data-driven filters, instead of fixed mel-scale, improved the performance by 2.8% and 1.4% (relatively) for SPEC-STFT and SPEC-SFF, and nearly equal for SPEC-ZTW. To assist related work, we have made the code available ([Kethireddy, R., and Kadiri, S. R. (2022). "Deep neural architectures for dialect classification with single frequency filtering and zero-time windowing feature representations," https://github.com/r39ashmi/e2e_dialect (Last viewed 21 December 2021)].).
Research on face tracking Algorithm Based on Detection and Supervision Tracking

Abstract Accurate face detection and tracking is widely used in many social life scenes. However, the uncontrollable background noise and random illumination change in the application scene will reduce the detection accuracy of the tracked target, and the rotation, occlusion and overlap of the tracked target will also affect the accuracy and success rate of the tracking algorithm. In order to solve the above problems, this paper proposes a method that uses deep learning detection method to supervise the correlation filter tracking algorithm to improve the success rate of de-tection and tracking. First of all, in the first frame of the picture, we use the deep learning SSD (Single Shot MultiBox Detector) algorithm to detect the face, and take the detected face as the tracking target, and use the correlation filtering algorithm DSST (Discriminative Scale Space Tracker) Tracking in the process of tracking, face detection is continuously carried out on the tracking target, and the detection results are used to monitor the tracking results, so as to reduce the target drift caused by the boundary effect, thus improving the accuracy of the tracking algorithm. The algorithm is tested and verified on OTB100 data set. The final experimental results show that the accuracy of this algorithm is obviously better than the mainstream classical algorithm, and the frame rate meets the real-time re-quirements.
Imagineering a new way of governing: the blockchain and res publica

Blockchain technologies loom large among the controversial topics of public debate. Like any technology, blockchain offers various ways to imagine alternative models of politics and society. Arguably, the most common interpretation treats it as the technology for techno-anarchism and a tool for total decentralization. This paper focuses on an overlooked genealogy of the politics of the blockchain – the classic republican theory. We consider whether republican practices could shed light on certain aspects of blockchain communities and understand their governance better. Our paper offers a historical analysis of governance visions inscribed in Bitcoin and Ethereum, arguably the most influential applications.
ForeSI: Success-Aware Visual Navigation Agent

In this work, we present a method to improve the efficiency and robustness of the previous model-free Reinforcement Learning (RL) algorithms for the task of object-goal visual navigation. Despite achieving state-of-the-art results, one of the major drawbacks of those approaches is the lack of a forward model that informs the agent about the potential consequences of its actions, i.e., being model-free. In this work, we augment the model-free RL with such a forward model that can predict a representation of a future state, from the beginning of a navigation episode, if the episode were to be successful. Furthermore, in order for efficient training, we develop an algorithm to integrate a replay buffer into the model-free RL that alternates between training the policy and the forward model. We call our agent ForeSI; ForeSI is trained to imagine a future latent state that leads to success. By explicitly imagining such a state, during the navigation, our agent is able to take better actions leading to two main advantages: first, in the absence of an object detector, ForeSI presents a more robust policy, i.e., it leads to about 5% absolute improvement on the Success Rate (SR); second, when combined with an off-the-shelf object detector to help better distinguish the target object, our method leads to about 3% absolute improvement on the SR and about 2% absolute improvement on Success weighted by inverse Path Length (SPL), i.e., presents higher efficiency.
Tableaux for Logics of Content Relationship and Set-Assignment Semantics

Abstract In the paper, we examine tableau systems for R. Epstein’s logics of content relationship: $$\mathbf {D}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mi>D</mml:mi> </mml:math> (Dependence Logic), $$\mathbf {DD}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mi>DD</mml:mi> </mml:math> (Dual Dependence Logic), $$\mathbf {Eq}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mi>Eq</mml:mi> </mml:math> (Logic of Equality of Content), $$\mathbf {S}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mi>S</mml:mi> </mml:math> (Symmetric Relatedness Logic) and $$\mathbf {R}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mi>R</mml:mi> </mml:math> (Nonsymmetric Relatedness Logic) (Epstein in The semantic foundations of Logic, Springer Science + Business Media, Dordrecht, (1990), cf. Epstein in Philos Stud 36:137–173, 1979, Epstein in Rep. Math. Logic 21:19–34, 1987, Klonowski in Logic Log Philos, accepted for publication, Krajewski in J Non Class Logic 8:7–33, 1991). The first tableau systems for those logics were defined by Carnielli (Rep Math Logic 21:35–46, 1987). However, his approach has some limitations, for example, it requires a proof of functional completeness and axiomatization. Notwithstanding the first two constraints, it does not include all Epstein logics, e.g., logic $$\mathbf {Eq}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mi>Eq</mml:mi> </mml:math> . Unlike Carnielli’s approach, here we use set-assignment semantics to determine those logics. Since syntax and semantics of a given logic usually determine a minimal syntax and structure of a tableau system for the logic along with other properties, we propose a uniform tableau framework for the logics determined by set-assignment semantics. What distinguishes our tableau systems is that they combine the features of tableaux for propositional logics and syllogistic logics when the problem of content of propositions is analysed in tableau proofs. To denote the content of propositions in the proofs, we use generalised labels (explored in the syllogistic context in Jarmużek and Goré (In: Fitting (ed.) Landscapes in Logic, College Publications, London, accepted)).
Neural‐augmented two‐stage Monte Carlo tree search with over‐sampling for protein folding in HP Model

This paper proposes a novel Monte Carlo tree search (MCTS) algorithm to solve the protein folding problem in HP model. There are two main challenges. First, the problem is proved to be NP‐complete. The solution space is large and it is hard to find a good solution via a search algorithm without prior knowledge of the HP model. We tackle this challenge by formulating the problem as a deterministic Markov decision process (MDP) and solve it in an AlphaZero's manner. The difference is that we design a MCTS algorithm with two stages: neural exploitation stage and random exploration stage. In the first stage, the search algorithm utilizes the knowledge from previous experience by evaluating the states with a trained neural network, while in the second stage, the states are evaluated by fast and random rollouts. It effectively reduces the number of neural inferences and computational cost. The second challenge is that the evaluation of typical MCTS cannot preserve the correct preference over the actions in our task. To address this challenge, we propose an over‐sampling mechanism that encourages the agent to search more on those actions with high rollout values. The proposed method is tested and compared in a series of experiments. Experimental results have confirmed the effectiveness of the proposed method empirically. Besides, we also visualize the obtained the best conformations and verify the proposed technical designs through an ablation study. © 2022 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC.
A Hybrid Method to Measure Distribution Consistency of Mixed-Attribute Datasets

Random sample partition (RSP) is a newly developed data management and processing model for Big Data processing and analysis. To apply the RSP model for Big Data computation tasks, it is very important to measure the distribution consistency of different datasets. Existing measurement methods for continuous-attribute and discrete-attribute datasets cannot directly deal with mixed-attribute datasets. In this article, we design a hybrid method to measure the distribution consistency among different mixed-attribute datasets by using a multilayer extreme learning machine (MLELM) and the generalized maximum mean discrepancy (GMMD) criterion, abbreviated as MLELM-GMMD. MLELM is first used to transform original mixed-attribute datasets into corresponding deep encoding datasets. Then, the GMMD criterion is applied to check the distribution consistency of the deep encoding datasets. Four experiments have been done to validate the feasibility and effectiveness of MLELM-GMMD, i.e., the impact of MLELM on the amount of information during mixed-attribute data transformation, the impact of MLELM on distributions of mixed-attribute data, the distribution consistencies of RSP and non-RSP data blocks, and the comparison with other measurement methods. Experimental results show that the proposed MLELM-GMMD method can measure the distribution consistency of mixed-attribute datasets more accurately than one-hot encoding-based methods.
Do connections pay off in the bitcoin market?

This paper identifies the bitcoin investor network and studies the relationship between connections and returns. Using transaction data recorded in the bitcoin blockchain from 2015 to 2020, we reach three conclusions. First, connectedness is not strongly correlated with higher returns in the first four years. However, the correlation becomes strong and significant in 2019 and 2020. Second, returns also differ among those connected addresses. By dividing the connected addresses into ten decile groups based on their centrality, we find that the top 20% most-connected addresses earn higher returns than their peers during most of our sample period. Third, eigenvector centrality is more related to higher returns than degree centrality for the top 20% most-connected addresses, implying that the quality of connections may matter more than quantity among those highly connected addresses.
Effective Bug Triage Based on a Hybrid Neural Network

With the increasing scale and complexity of open source software, the quality of software has become a focus to which repairers pay close attention. Due to the inevitable existence of some known or unknown bugs in software,under certain conditions, software bugs may directly cause program running errors, and then produce abnormal running results and wrong program behavior, which will cause huge economic losses. Therefore, software defect repair is an important part of software evolution and quality assurance. Quickly and efficiently assigning defect reports to the right repairer for repair,to ensure efficiency and reduce the cost of open-source software development is an important problem that must be solved in software quality improvement. In this study, we propose a new defect report repair recommendation algorithm, RCNN, which can effectively learn the features of the defect report and recommend the appropriate repairer according to the feature. The proposed algorithm uses a CNN convolution kernel to capture the local information of the text and RNN is used to capture the sequence information of the text. The attention mechanism is introduced to learn the contribution ratio of each part of the text to the overall semantic information of the text. Thus, to a certain extent, it makes up for the defect that RNN cannot effectively learn and monitor remote information. Through experiments on the Eclipse and Mozilla datasets, compared with NB (naive Bayes), SVM (support vector machines), LeeCNN and DBRNNA, the RCNN model can effectively find the appropriate bug repairer among many repairers, and achieve higher classification accuracy.
MARE: an Active Learning Approach for Requirements Classification

Several studies indicate that poor requirements practices, that result in incomplete or inaccurate requirements, poorly managed requirement changes, and missed requirements, are the most common factors in project failure. Possible solutions for better requirements definition include better requirements documentation, and requirements reuse. In this paper, we present a novel application of machine learning and active learning to classify the requirements of a given dataset. This approach can accelerate project development. By organizing the requirements into categories, developers can easily see what requirements were already implemented, and where they need to focus on the next step of development.
Auto QA: The Question Is Not Only What, but Also Where

Visual Question Answering can be a functionally relevant task if purposed as such. In this paper, we aim to investigate and evaluate its efficacy in terms of localization-based question answering. We do this specifically in the context of autonomous driving where this functionality is important. To achieve our aim, we provide a new dataset, Auto-QA. Our new dataset is built over the Argoverse dataset and provides a truly multi-modal setting with seven views per frame and point-cloud LIDAR data being available for answering a localization-based question. We contribute localized attention adaptations of most popular VQA baselines and evaluate them on this task. We also provide joint point-cloud and image-based baselines that perform well on this task. An additional evaluation that we perform is to analyse whether the attention module is accurate or not for the image-based VQA baselines. To summarize, through this work we thoroughly analyze the localization abilities through visual question answering for autonomous driving and provide a new benchmark task for the same. Our best joint baseline model achieves a useful 74.8% accuracy on this task. We release our dataset and source code for our baseline modules in the following webpage: https: //delta-lab-iitk.github.io/AUTO-QA/