Reviewerly/exHarmony huggingface dataset:
Deep Perceptual Image Quality Assessment for Compression

Lossy Image compression is necessary for efficient storage and transfer of data. Typically the trade-off between bit-rate and quality determines the optimal compression level. This makes the image quality metric an integral part of any imaging system. While the existing full-reference metrics such as PSNR and SSIM may be less sensitive to perceptual quality, the recently introduced learning methods may fail to generalize to unseen data. In this paper we propose the largest image compression quality dataset to date with human perceptual preferences, enabling the use of deep learning, and we develop a full reference perceptual quality assessment metric for lossy image compression that outperforms the existing state-of-the-art methods. We show that the proposed model can effectively learn from thousands of examples available in the new dataset, and consequently it generalizes better to other unseen datasets of human perceptual preference. The CIQA dataset can be found at https://github.com/googleresearch/google-research/tree/master/CIQA
An automatic refactoring framework for replacing test-production inheritance by mocking mechanism

Unit testing focuses on verifying the functions of individual units of a software system. It is challenging due to the high inter-dependencies among software units. Developers address this by mocking-replacing the dependency by a "faked" object. Despite the existence of powerful, dedicated mocking frameworks, developers often turn to a "hand-rolled" approach-inheritance. That is, they create a subclass of the dependent class and mock its behavior through method overriding. However, this requires tedious implementation and compromises the design quality of unit tests. This work contributes a fully automated refactoring framework to identify and replace the usage of inheritance by using Mockito-a well received mocking framework. Our approach is built upon the empirical experience from five open source projects that use inheritance for mocking. We evaluate our approach on four other projects. Results show that our framework is efficient, generally applicable to new datasets, mostly preserves test case behaviors in detecting defects (in the form of mutants), and decouples test code from production code. The qualitative evaluation by experienced developers suggests that the auto-refactoring solutions generated by our framework improve the quality of the unit test cases in various aspects, such as making test conditions more explicit, as well as improved cohesion, readability, understandability, and maintainability with test cases.
“Citizens Too”: Safety Setting Collaboration Among Older Adults with Memory Concerns

Designing technologies that support the cybersecurity of older adults with memory concerns involves wrestling with an uncomfortable paradox between surveillance and independence and the close collaboration of couples. This research captures the interactions between older adult couples where one or both have memory concerns—a primary feature of cognitive decline—as they make decisions on how to safeguard their online activities using a Safety Setting probe we designed, and over the course of several informal interviews and a diary study. Throughout, couples demonstrated a collaborative mentality to which we apply a frame of citizenship in opensource collaboration, specifically (a) histories of participation , (b) lower barriers to participation, and (c) maintaining ongoing contribution. In this metaphor of collaborative enterprise, one partner (or member of the couple) may be the service provider and the other may be the participant, but at varying moments, they may switch roles while still maintaining a collaborative focus on preserving shared assets and freedom on the internet. We conclude with a discussion of what this service provider-contributor mentality means for empowerment through citizenship, and implications for vulnerable populations’ cybersecurity.
Temporal convolutional networks for just-in-time design smells prediction using fine-grained software metrics

Software systems quality is strongly influenced by the presence of design problems and wrong constructs introduced during software evolution. Design smells are recognized to be indicators of such problems since their presence can indicate violations of the fundamental software design principles. This explains the interest of software researchers and developers in new approaches performing design smell early detection. In this paper, the adoption of a just-in-time design smell prediction approach is proposed. The approach uses a variant of Temporal Convolutional Networks (TCN) to predict the presence of design smells before they are introduced in the model. In comparison to other studies, in the proposed approach we focus on commit-level fine-grained product metrics and process metrics (i.e., ownership and seniority) to make deep neural networks training suitable. The validation is performed on a large dataset composed of six open source systems. The results show good prediction performance for some design smells and for all the considered design smells categories. The experiment performed shows that, with respect to baseline prediction models (i.e., LSTM,CNN and RF), the proposed TCN variant trained on the identified metrics, gives better results.
Space deformation based path planning for Mobile Robots

An excellent path planning algorithm of a robot should compromise three major criteria; low computational time, high level of smoothness and optimal length. In this work, a hybrid algorithm is developed to enable the robot to navigate smoothly in a partially known environment with a low computation time. The proposed method takes as input a global path connecting a start and a target point, then an initial optimal smoothed path is generated which is accordingly updated due to unexpected changes in the workspace. This is achieved by assuming that the robot exits on a thin metal plate which can be deformed to guarantee a convenient path for the robot. This deformation of the space continues as long as changes are detected in the environment. Numerical simulations and experimental analysis showed the high performance of the proposed algorithm, where it showed superiority in terms of smoothness and execution time.
Hybrid Predictive Ensembles: Synergies Between Human and Computational Forecasts

An increasing proportion of decisions, design choices, and predictions are being made by hybrid groups consisting of humans and artificial intelligence (AI). In this paper, we provide analytic foundations that explain the potential benefits of hybrid groups on predictive tasks, the primary use of AI. Our analysis relies on interpretive and generative signal frameworks as well as a distinction between the big data used by AI and the thick, often narrative data used by humans. We derive several conditions on accuracy and correlation necessary for humans to remain in the loop. We conclude that human adaptability along with the potential for atypical cases that mislead AI will likely mean that humans always add value on predictive tasks.
Dynamic Bayesian Network Modeling, Learning, and Inference: A Survey

Since the introduction of Dynamic Bayesian Networks (DBNs), their efficiency and effectiveness have increased through the development of three significant aspects: (i) modeling, (ii) learning and (iii) inference.However, no reviews of the literature have been found that chronicle their importance and development over time.The aim of this study is to provide a systematic review of the literature that details the evolution and advancement of DBNs, focusing in the period 1997-2019 that emphasize the aspects of modeling, learning and inference.While the literature presents temporal event networks, knowledge encapsulation, relational and time varying representations as the four predominant DBN modeling approaches, this work groups them as essential techniques within DBNs and help practitioners by associating each to various challenge that arise in pattern discovery and prediction in dynamic processes.Regarding learning, the predominant methods mainly focus on scoring with greedy search.Finally, our study suggests that the main methods used in DBN inference extend or adapt those used in static BNs, and are oriented to either optimize processing time or error rate.
StoryFacets: A design study on storytelling with visualizations for collaborative data analysis

Tracking the sensemaking process is a well-established practice in many data analysis tools, and many visualization tools facilitate overview and recall during and after exploration. However, the resulting communication materials such as presentations or infographics often omit provenance information for the sake of simplicity. This unfortunately limits later viewers from engaging in further collaborative sensemaking or discussion about the analysis. We present a design study where we introduced visual provenance and analytics to urban transportation planning. Maintaining the provenance of all analyses was critical to support collaborative sensemaking among the many and diverse stakeholders. Our system, STORYFACETS, exposes several different views of the same analysis session, each view designed for a specific audience: (1) the trail view provides a data flow canvas that supports in-depth exploration + provenance (expert analysts); (2) the dashboard view organizes visualizations and other content into a space-filling layout to support high-level analysis (managers); and (3) the slideshow view supports linear storytelling via interactive step-by-step presentations (laypersons). Views are linked so that when one is changed, provenance is maintained. Visual provenance is available on demand to support iterative sensemaking for any team member.
Access Control for RAN Slicing based on Federated Deep Reinforcement Learning

Network Slicing (NS) has been widely identified as a key architectural technology for 5G-and-beyond systems by supporting divergent requirements sustainably. With the widespread of emerging smart devices, access control becomes an essential yet challenging issue in NS-based wireless networks due to the device-base station (BS)-NS three-layer association relationship. Meanwhile, stringent data security and device privacy concerns are increasing dramatically. In this paper, we propose an efficient access control scheme for radio access network (RAN) slicing by exploiting a federated deep reinforcement learning framework, called FDRL-AC, to improve network throughput and communication efficiency while enforcing the data security and device privacy. Specifically, we use deep reinforcement learning to train local model on devices, where horizontally federated learning (FL) is employed for parameter aggregation on BS, while vertically FL is employed for feature aggregation on the encrypted party. Numerical results show that the proposed FDRL-AC scheme can achieve significant performance gain in terms of network throughput and communication efficiency in comparison with some state-of-art solutions.
Discrete and continuous representations and processing in deep learning: Looking forward

Discrete and continuous representations of content (e.g., of language or images) have interesting properties to be explored for the understanding of or reasoning with this content by machines. This position paper puts forward our opinion on the role of discrete and continuous representations and their processing in the deep learning field. Current neural network models compute continuous-valued data. Information is compressed into dense, distributed embeddings. By stark contrast, humans use discrete symbols in their communication with language. Such symbols represent a compressed version of the world that derives its meaning from shared contextual information. Additionally, human reasoning involves symbol manipulation at a cognitive level, which facilitates abstract reasoning, the composition of knowledge and understanding, generalization and efficient learning. Motivated by these insights, in this paper we argue that combining discrete and continuous representations and their processing will be essential to build systems that exhibit a general form of intelligence. We suggest and discuss several avenues that could improve current neural networks with the inclusion of discrete elements to combine the advantages of both types of representations.
Automatic Modulation Classification Using Involution Enabled Residual Networks

Automatic modulation classification (AMC) is of crucial importance for realizing wireless intelligence communications. Many deep learning based models especially convolution neural networks (CNNs) have been proposed for AMC. However, the computation cost is very high, which makes them inappropriate for beyond the fifth generation wireless communication networks that have stringent requirements on the classification accuracy and computing time. In order to tackle those challenges, a novel involution enabled AMC scheme is proposed by using the bottleneck structure of the residual networks. Involution is utilized instead of convolution to enhance the discrimination capability and expressiveness of the model by incorporating a self-attention mechanism. Simulation results demonstrate that our proposed scheme achieves superior classification performance and faster convergence speed comparing with other benchmark schemes.
Analysis of Focussed Under-Sampling Techniques with Machine Learning Classifiers

Class Imbalance Problem is the major issue in machine intelligence producing biased classifiers that work well for the majority class but have a relatively poor performance for the minority class. To ensure the development of accurate prediction models, it is essential to deal with the class imbalance problem. In this paper, the class imbalance problem is handled using focused undersampling techniques viz. Cluster Based, Tomek Link and Condensed Nearest Neighbours which equalize the number of instances of the two types of classes by undersampling the majority class based on some particular criteria. This is in contrast to random undersampling where the data samples are selected randomly from the majority class leading to underfitting and loss of some important datapoints. To fairly compare and evaluate the performance of focused undersampling approaches, prediction models are constructed using popular machine learning classifiers like K-Nearest Neighbor, Decision Tree and Naive Bayes. The results have shown that Decision Tree outperformed other machine learning techniques. Comparing and contrasting the undersampling approaches for Decision Tree concluded Condensed Nearest Neighbours to be best amongst others.
Developing bug severity prediction models using word2vec

Bug tracking systems use repositories to keep track of the bugs to improve software quality. A manual analysis of each bug and classifying it according to its severity is an unmanageable job. Therefore, it is imperative to correctly classify the severity of a bug, which otherwise might be misclassified by a laymen user. Text mining techniques have the potential to analyze such massive databases of the textual description of bug reports to classify bug severity levels adequately. Word embedding is a state-of-art text mining technique that captures the semantics of the text and group the words according to their relevance in a document. Words are embedded into real-valued vectors through word-embedding models. Word2vec is a word embedding model, proven to be effective in representing word meanings. However, the configuration of hyperparameters and feature selection affects the performance of word2vec. Tuning hyperparameter is a time-consuming process, and it is crucial to identify the correct set of parameters. The paper outlines the effectiveness of word2vec technique on the efficiency of classifiers to predict a bug severity from a bug report and examines the impact of different averaging methods, including the configuration of word embedding parameters on the classifiers’ efficiency. Results show that the bigger window size enhances the performance of classifiers; however, the influence of the minimum word count parameter was found to be mixed and depends on the selected data sets. Further, out of the classifiers used, Random Forest and Xgboost could classify the severity level for classes with few records or a rare occurrence of words specific to each class. Otherwise, Support Vector Machine and Naive Bayes classifiers performed better and worst, respectively.
Linguistic Resources for Bhojpuri, Magahi, and Maithili: Statistics about Them, Their Similarity Estimates, and Baselines for Three Applications

Corpus preparation for low-resource languages and for development of human language technology to analyze or computationally process them is a laborious task, primarily due to the unavailability of expert linguists who are native speakers of these languages and also due to the time and resources required. Bhojpuri, Magahi, and Maithili, languages of the Purvanchal region of India (in the north-eastern parts), are low-resource languages belonging to the Indo-Aryan (or Indic) family. They are closely related to Hindi, which is a relatively high-resource language, which is why we compare them with Hindi. We collected corpora for these three languages from various sources and cleaned them to the extent possible, without changing the data in them. The text belongs to different domains and genres. We calculated some basic statistical measures for these corpora at character, word, syllable, and morpheme levels. These corpora were also annotated with parts-of-speech (POS) and chunk tags. The basic statistical measures were both absolute and relative and were expected to indicate linguistic properties, such as morphological, lexical, phonological, and syntactic complexities (or richness). The results were compared with a standard Hindi corpus. For most of the measures, we tried to match the corpus size across the languages to avoid the effect of corpus size, but in some cases it turned out that using the full corpus was better, even if sizes were very different. Although the results are not very clear, we tried to draw some conclusions about the languages and the corpora. For POS tagging and chunking, the BIS tagset was used to manually annotate the data. The POS-tagged data sizes are 16,067, 14,669, and 12,310 sentences, respectively, for Bhojpuri, Magahi, and Maithili. The sizes for chunking are 9,695 and 1,954 sentences for Bhojpuri and Maithili, respectively. The inter-annotator agreement for these annotations, using Cohen’s Kappa, was 0.92, 0.64, and 0.74, respectively, for the three languages. These (annotated) corpora have been used for developing preliminary automated tools, which include POS tagger, Chunker, and Language Identifier. We have also developed the Bilingual dictionary (Purvanchal languages to Hindi) and a Synset (that can be integrated later in the Indo-WordNet) as additional resources. The main contribution of the work is the creation of basic resources for facilitating further language processing research for these languages, providing some quantitative measures about them and their similarities among themselves and with Hindi. For similarities, we use a somewhat novel measure of language similarity based on an n-gram-based language identification algorithm. An additional contribution is providing baselines for three basic NLP applications (POS tagging, chunking, and language identification) for these closely related languages.
Learning semantic priors for texture-realistic sketch-to-image synthesis

Sketch-to-image synthesis is a challenging task in the field of computer vision that generates photo-realistic images from given sketches. Existing methods of this kind are unable to discover the inherent semantic information contained in an image and use it to guide the synthesis process, substantially reduce their capacity to generate photo-realistic images. Accordingly, in this paper, we propose a novel framework that explores and leverages semantic information to generate realistic textures in synthesized images for this task. More specifically, the segmentation maps generation network is designed to learn the relationships between sketches and segmentation maps in order to obtain the semantic segmentation maps from the sketches. Taking semantic segmentation maps as the condition, a feature-wise affine transformation is then executed to change the feature maps of intermediate layers in the network, which can efficiently generate the texture required to synthesize more photo-realistic images. Extensive experiments demonstrate that when compared to other state-of-the-art sketch-to-image synthesis methods, our approach can not only synthesize images with significantly superior visual quality but is also able to achieve better results on quantitative metrics.
Efficient Hardware Implementation of PQC Primitives and PQC algorithms Using High-Level Synthesis

Efficient and quantum-resistant Post-Quantum Cryptographic (PQC) algorithms need to be built before development of large-scale quantum, which will break RSA and Elliptic Curve cryptography based existing public key infrastructure. Cryptographers are developing quantum-resistant PQC algorithms, which consist of PQC primitives. These primitives act as a basic building blocks that play a vital role in security and resource utilization. Hence, efficient and precise implementation of PQC primitives is crucial for stable PQC algorithms. This paper implements and optimizes PQC primitives using High-level synthesis. High-level synthesis produces area-optimized and speed-optimized solutions for the primitives. These solutions create efficient and constant-time PQC designs that keep the hardware secure against timing side-channel attack.
Motion Classification and Features Recognition of a Traditional Chinese Sport (Baduanjin) Using Sampled-Based Methods

This study recognized the motions and assessed the motion accuracy of a traditional Chinese sport (Baduanjin), using the data from the inertial sensor measurement system (IMU) and sampled-based methods. Fifty-three participants were recruited in two batches to participate in the study. Motion data of participants practicing Baduanjin were captured by IMU. By extracting features from motion data and benchmarking with the teacher’s assessment of motion accuracy, this study verifies the effectiveness of assessment on different classifiers for motion accuracy of Baduanjin. Moreover, based on the extracted features, the effectiveness of Baduanjin motion recognition on different classifiers was verified. The k-Nearest Neighbor (k-NN), as a classifier, has advantages in accuracy (more than 85%) and a short average processing time (0.008 s) during assessment. In terms of recognizing motions, the classifier One-dimensional Convolutional Neural Network (1D-CNN) has the highest accuracy among all verified classifiers (99.74%). The results show, using the extracted features of the motion data captained by IMU, that selecting an appropriate classifier can effectively recognize the motions and, hence, assess the motion accuracy of Baduanjin.