Reviewerly/exHarmony huggingface dataset:
Privacy-Preserving Public Cloud Audit Scheme Supporting Dynamic Data for Unmanned Aerial Vehicles

As one of the most popular applications in recent years, cloud storage has been gradually integrated into all walks of life.In the field of communication techniques for the unmanned aerial vehicles (UAVs), UAVs use sensors to upload collected data to cloud servers during various exploration activities, but UAVs can only store and calculate valid information currently collected due to the limited storage and computing performance.In the actual exploration, UAVs need not only to upload complete data to the cloud server, but also to support the data dynamic update efficiently.Moreover, due to security requirements, the privacy of data uploaded by UAVs must be protected.However, the existing auditing schemes for dynamic data and integrity have many problems, such as high computation cost, low efficiency of dynamic update, low privacy and security.For this reason, we propose a public cloud audit scheme that supports dynamic data and privacy protection based on distributed string equality check protocol and Merkle-hash tree multi-level index structure.First of all, a third-party server (TPS) is set between the cloud service provider and users, which complete digital signature, integrity auditing, and data dynamic operations significantly in place of users to reduce the local computing cost.Users then locally upload data which has been encrypted to the TPS.Secondly, to further improve the security of the scheme, TPS implement signature for encrypted data based on the distributed string equality check protocol.By designing authorizations with time constraints, it is guaranteed that only the legitimate TPS with time constraints can operate with cloud servers.Finally, we implement dynamic data operation efficiently based on MHT multi-level index structure.The security proof and performance analysis show that our proposed scheme is safe and effective.
Incremental Learning for End-to-End Automatic Speech Recognition

In this paper, we propose an incremental learning method for end-to-end Automatic Speech Recognition (ASR) which enables an ASR system to perform well on new tasks while maintaining the performance on its originally learned ones. To mitigate catastrophic forgetting during incremental learning, we design a novel explainability-based knowledge distillation for ASR models, which is combined with a response-based knowledge distillation to maintain the original model's predictions and the “reason” for the predictions. Our method works without access to the training data of original tasks, which addresses the cases where the previous data is no longer available or joint training is costly. Results on a multi-stage sequential training task show that our method outperforms existing ones in mitigating forgetting. Furthermore, in two practical scenarios, compared to the target-reference joint training method, the performance drop of our method is 0.02% Character Error Rate (CER), which is 97% smaller than the drops of the baseline methods.
Predictive and interpretable models via the stacked elastic net

Abstract Motivation Machine learning in the biomedical sciences should ideally provide predictive and interpretable models. When predicting outcomes from clinical or molecular features, applied researchers often want to know which features have effects, whether these effects are positive or negative and how strong these effects are. Regression analysis includes this information in the coefficients but typically renders less predictive models than more advanced machine learning techniques. Results Here, we propose an interpretable meta-learning approach for high-dimensional regression. The elastic net provides a compromise between estimating weak effects for many features and strong effects for some features. It has a mixing parameter to weight between ridge and lasso regularization. Instead of selecting one weighting by tuning, we combine multiple weightings by stacking. We do this in a way that increases predictivity without sacrificing interpretability. Availability and implementation The R package starnet is available on GitHub (https://github.com/rauschenberger/starnet) and CRAN (https://CRAN.R-project.org/package=starnet).
Odia character recognition system: A study on feature extraction and classification techniques

Optical Character Recognition (OCR) is the most successful application for automatic pattern recognition. OCR has been a widely studied field of research and development for more than last 50 years. It is one of the many important gifts given by computer science to the mankind. OCR means “a technique of recognition of machine printed or hand written text by computer and its conversion to an editable form as per the requirement”. During the recent decades, handwritten character recognition has been a subject of study because of both its theoretical importance in recognition system with numerous possible applications. This paper presents an extensive study of numerous publications by a number of researchers working for years in the field of Odia character recognition (one of the many regional languages used in India and spoken by more than 30 million people worldwide). The main focus has been on feature extraction, data set size, classification techniques and recognition accuracy. A number of selective articles published in journals and conference proceedings on character recognition have been included in this work (2005 to 2020). The review process has been divided into two parts. In the first part a study of various works by researchers is presented with detailed description. In the second part, various feature extraction and classification techniques have been presented and summarized. The findings are presented in the analysis section.
Multi-agent system application in accordance with game theory in bi-directional coordination network model

The multi-agent system is the optimal solution to com- plex intelligent problems.In accordance with the game theory, the concept of loyalty is introduced to analyze the relationship between agents' individual income and global benefits and build the logical architecture of the multi-agent system.Besides, to verify the feasibility of the method, the cyclic neural network is optimized, the bi-directional coordination network is built as the training network for deep learning, and specific training scenes are simulated as the training background.After a certain number of training iterations, the model can learn simple strategies autonomously.Also, as the training time increases, the complexity of learning strategies rises gradually.Strategies such as obstacle avoidance, firepower distribution and collaborative cover are adopted to demonstrate the achievability of the model.The model is verified to be realizable by the examples of obstacle avoidance, fire distribution and cooperative cover.Under the same resource background, the model exhibits better convergence than other deep learning training networks, and it is not easy to fall into the local endless loop.Furthermore, the ability of the learning strategy is stronger than that of the training model based on rules, which is of great practical values.
Peculiarities Of Student Distance Learning In Emergency Situation Condition

The article outlines the contemporary issues of distance learning and the use of the Internet by students during the outbreak of pandemic. The conducted research demonstrates student attitude towards distance learning, its advantages and disadvantages, student perception and acquisition of lecture and seminar material, implementation of online trainings and practical classes. The purpose of this article is to investigate student behavioral, cognitive and emotional reactions to forced distance learning conditions. The authors assume that students, being members of Generation Z, can easily adapt to the new learning environment, quickly organize the learning process, as well as choose preferable online learning platforms. The research proves that 66% of students need from 2 to 4 hours for distance learning; 22% spend from 4 to 6 hours studying remotely and only 12% spend less than 2 hours a day studying in a new way. One third of students (36%) consider the distance learning system quite comfortable, 8% – very comfortable, while a quarter of the respondents (25%) have neutral attitude towards online learning technologies. Students choose the following distance learning platforms the most often: Google Meet (94%) and Moodle (70%). They also use Zoom, Skype, Viber and Telegram in order to keep in touch with teachers and fulfil studying purposes. 19% of students regard distance learning as of a high quality, whereas 75% are currently neutral about this way of learning and only 6% of the respondents consider these necessary innovations ineffective.
Platform philanthropy, ‘public value’, and the COVID-19 pandemic moment

This commentary examines the COVID-19 pandemic as a moment of strategic advancement for digital platforms. Focusing on the redeployment of Google Maps data for tracking adherence to mobility restrictions, I argue that demonstrating such ‘public value’ uses of Google’s vast geolocation and behavioral data forms part of a larger effort to strengthen their economic and political position in the face of two existential threats—declining advertising revenues and platform regulation.
ECML: An Ensemble Cascade Metric-Learning Mechanism Toward Face Verification

Face verification can be regarded as a two-class fine-grained visual-recognition problem. Enhancing the feature's discriminative power is one of the key problems to improve its performance. Metric-learning technology is often applied to address this need while achieving a good tradeoff between underfitting, and overfitting plays a vital role in metric learning. Hence, we propose a novel ensemble cascade metric-learning (ECML) mechanism. In particular, hierarchical metric learning is executed in a cascade way to alleviate underfitting. Meanwhile, at each learning level, the features are split into nonoverlapping groups. Then, metric learning is executed among the feature groups in the ensemble manner to resist overfitting. Considering the feature distribution characteristics of faces, a robust Mahalanobis metric-learning method (RMML) with a closed-form solution is additionally proposed. It can avoid the computation failure issue on an inverse matrix faced by some well-known metric-learning approaches (e.g., KISSME). Embedding RMML into the proposed ECML mechanism, our metric-learning paradigm (EC-RMML) can run in the one-pass learning manner. The experimental results demonstrate that EC-RMML is superior to state-of-the-art metric-learning methods for face verification. The proposed ECML mechanism is also applicable to other metric-learning approaches.
Mapping ultramafic complexes using airborne imaging spectroscopy and spaceborne data in Arctic regions with abundant lichen cover, a case study from the Niaqornarssuit complex in South West Greenland

ABSTRACT This study investigates the usage of HyMAP airborne hyperspectral and Sentinel-2, ASTER and Landsat-8 OLI spaceborne multispectral data for detailed mapping of mineral resources in the Arctic. The EnMAP Geological Mapper (EnGeoMAP) and Iterative Spectral Mixture Analysis (ISMA) approaches are tested for mapping of mafic-ultramafic rocks in areas covered by abundant lichen. Using the Structural Similarity Index Measure (SSIM), the output classification results from airborne data are quantitatively compared to the available geological map and to the HyMAP reference data in case of using spaceborne dataset. Results demonstrate the capability of both airborne and spaceborne data to provide large-scale reconnaissance mapping of geologic materials over vast arctic regions where field access is limited. The distributions of three ultramafic units (dunite, peridotite, pyroxenite) and one mafic unit (gabbro) are mapped based on analyzing specific visible and near-infrared and short-wave-infrared spectral features. The extent of peridotite and dunite units mapped using both approaches is consistent with geological map, whereas pyroxenite abundance maps show different patterns in their distribution as compared to the geological map. The results suggest that EnGeoMAP method has a better performance than ISMA method for mapping the dunite unit, whilst ISMA performs better for mapping peridotite and pyroxenite rocks.
Text Classification with Negative Supervision

Advanced pre-trained models for text representation have achieved state-of-the-art performance on various text classification tasks.However, the discrepancy between the semantic similarity of texts and labelling standards affects classifiers, i.e. leading to lower performance in cases where classifiers should assign different labels to semantically similar texts.To address this problem, we propose a simple multitask learning model that uses negative supervision.Specifically, our model encourages texts with different labels to have distinct representations.Comprehensive experiments show that our model outperforms the stateof-the-art pre-trained model on both singleand multi-label classifications, sentence and document classifications, and classifications in three different languages.
Iterative-Deepening Conflict-Based Search

Conflict-Based Search (CBS) is a leading algorithm for optimal Multi-Agent Path Finding (MAPF). CBS variants typically compute MAPF solutions using some form of A* search. However, they often do so under strict time limits so as to avoid exhausting the available memory. In this paper, we present IDCBS, an iterative-deepening variant of CBS which can be executed without exhausting the memory and without strict time limits. IDCBS can be substantially faster than CBS due to incremental methods that it uses when processing CBS nodes.
Learning Architectures from an Extended Search Space for Language Modeling

Neural architecture search (NAS) has advanced significantly in recent years but most NAS systems restrict search to learning architectures of a recurrent or convolutional cell.In this paper, we extend the search space of NAS.In particular, we present a general approach to learn both intra-cell and inter-cell architectures (call it ESS).For a better search result, we design a joint learning method to perform intra-cell and inter-cell NAS simultaneously.We implement our model in a differentiable architecture search system.For recurrent neural language modeling, it outperforms a strong baseline significantly on the PTB and Wiki-Text data, with a new state-of-the-art on PTB.Moreover, the learned architectures show good transferability to other systems.E.g., they improve state-of-the-art systems on the CoNLL and WNUT named entity recognition (NER) tasks and CoNLL chunking task, indicating a promising line of research on large-scale prelearned architectures.
On Understanding Data Worker Interaction Behaviors

Understanding how data workers interact with data and various pieces of information (e.g., code snippet examples) is key to design systems that can better support them in exploring a given dataset. To date, however, there is a paucity of research studying information seeking patterns and the strategies adopted by data workers as they carry out data curation activities. In this work, we aim at understanding the behaviors of data workers in discovering data quality issues, and how these behavioral observations relate to their performance. Specifically, we investigate how data workers use information resources and tools to support their task completion. To this end, we collect a multi-modal dataset through a data-driven experiment that relies on the use of eye-tracking technology with a purpose-designed platform built on top of iPython Notebook. The collected data reveals that: (i) searching in external resources is a prevalent action that can be leveraged to achieve better performance; (ii) 'copy-paste-modify' is a typical strategy for writing code to complete tasks; (iii) providing sample code within the system could help data workers to get started with their task; and (iv) surfacing underlying data is an effective way to support exploration. By investigating the behaviors prior to each search action, we also find that the most common reasons that trigger external search actions are the need to seek assistance in writing or debugging code and to search for relevant code to reuse. Our findings provide insights into patterns of interactions with various system components and information resources to perform data curation tasks. This bears implications on the design of domain-specific IR systems for data workers like code-base search.
Learning Source Phrase Representations for Neural Machine Translation

The Transformer translation model (Vaswani et al., 2017) based on a multi-head attention mechanism can be computed effectively in parallel and has significantly pushed forward the performance of Neural Machine Translation (NMT).Though intuitively the attentional network can connect distant words via shorter network paths than RNNs, empirical analysis demonstrates that it still has difficulty in fully capturing long-distance dependencies (Tang et al., 2018).Considering that modeling phrases instead of words has significantly improved the Statistical Machine Translation (SMT) approach through the use of larger translation blocks ("phrases") and its reordering ability, modeling NMT at phrase level is an intuitive proposal to help the model capture long-distance relationships.In this paper, we first propose an attentive phrase representation generation mechanism which is able to generate phrase representations from corresponding token representations.In addition, we incorporate the generated phrase representations into the Transformer translation model to enhance its ability to capture long-distance relationships.In our experiments, we obtain significant improvements on the WMT 14 English-German and English-French tasks on top of the strong Transformer baseline, which shows the effectiveness of our approach.Our approach helps Transformer Base models perform at the level of Transformer Big models, and even significantly better for long sentences, but with substantially fewer parameters and training steps.The fact that phrase representations help even in the big setting further supports our conjecture that they make a valuable contribution to long-distance relations.
Software defect prediction: A multi-criteria decision-making approach

Failure of software systems as a result of software testing is very much rampant as modern software systems are large and complex. Software testing which is an integral part of the software development life cycle (SDLC), consumes both human and capital resources. As such, software defect prediction (SDP) mechanisms are deployed to strengthen the software testing phase in SDLC by predicting defect prone modules or components in software systems. Machine learning models are used for developing the SDP models with great successes achieved. Moreover, some studies have highlighted that a combination of machine learning models as a form of an ensemble is better than single SDP models in terms of prediction accuracy. However, the efficiency of machine learning models can change with diverse predictive evaluation metrics. Thus, more studies are needed to establish the effectiveness of ensemble SDP models over single SDP models. This study proposes the deployment of Multi-Criteria Decision Method (MCDM) techniques to rank machine learning models. Analytic Network Process (ANP) and Preference Ranking Organization Method for Enrichment Evaluation (PROMETHEE) which are types of MCDM techniques are deployed on 9 machine learning models with 11 performance evaluation metrics and 11 software defects datasets. The experimental results showed that ensemble SDP models are best appropriate SDP models as Boosted SMO and Boosted PART ranked highest for each of the MCDM techniques. Besides, the experimental results also validated the stand of not considering accuracy as the only performance evaluation metrics for SDP models. Conclusively, more performance metrics other than predictive accuracy should be considered when ranking and evaluating machine learning models.&#x0D; Keywords: Ensemble; Multi-Criteria Decision Method; Software Defect Prediction
The Academic Profession as Perceived by Faculty

This paper analyzes the findings from a study of faculty’s perceptions of teaching as the most significant component of the academic profession. In a broader context, it investigates into the transformation of the academic profession, meaning that professors in Russia as well as around the world tend to perform research, administrative, and expert functions more and more often. Previously, Russian researchers observed a conflict between faculty’s perception of teaching as the most important profession component and administrators’ publication productivity requirements. A number of publications present strategies that professors use to adjust to the changing administrative requirements. However, the existing findings mostly reflect administrators’ perception of the academic profession, on which the faculty’s perspective is considered to be implicitly dependent. Available literature offers little evidence of how professors perceive the content, meaning, and goals of the academic profession. The present study was designed to find out by which goals and norms faculty members are guided in doing their work and to determine the logic behind the way they allocate their efforts among various aspects of teaching. Data was collected using focused in-depth interviews carried out in a Yekaterinburg university. Research was performed within a methodological framework of grounded theory and narrative analysis which traces its origin to hermeneutics. Findings indicate that professors share similar perceptions of the skills necessary to do their work, the goals of teaching, and the criteria for maintaining educational quality. Meanwhile, there is no platform for building those perceptions, faculty’s attitudes being shaped under the influence of their mentors and personal experience. The study also evaluates the impact of other university actors (students, colleagues, administrators) on the respondents’ perceptions of the teaching process. Finally, a rationale for building professional solidarity within the university’s academic community is provided.
Deep-water framework: The Swiss army knife of humans working with machine learning models

Working with machine learning models has become an everyday task not only for software engineers, but for a much wider spectrum of researchers and professionals. Training such models involves finding the best learning methods and their best hyper-parameters for a specific task, keeping track of the achieved performance measures, comparing the results visually, etc. If we add feature extraction methods – that precede the learning phase and depend on many hyper-parameters themselves – into the mixture, like source code embedding that is quite common in the field of software analysis, the task cries out for supporting tools. We propose a framework called Deep-Water that works similarly to a configuration management tool in the area of software engineering. It supports defining arbitrary feature extraction and learning methods for an input dataset and helps in executing all the training tasks with different hyper-parameters in a distributed manner. The framework stores all circumstances, parameters and results of training, which can be filtered and visualized later. We successfully used the tool in several software analysis based prediction tasks, like vulnerability or bug prediction, but it is general enough to be applicable in other areas as well, e.g. NLP, image processing, or even other non-IT fields.