OpenRaiser/Intern-Atlas huggingface dataset:
Cloud Big Data Mining and Analytics: Bringing Greenness and Acceleration in the Cloud
Venue: arXiv cs.DC 2021
Year: 2021

Big data is gaining overwhelming attention since the last decade. Almost all the fields of science and technology have experienced a considerable impact from it. The cloud computing paradigm has been targeted for big data processing and mining in a more efficient manner using the plethora of resources available from computing nodes to efficient storage. Cloud data mining introduces the concept of performing data mining and analytics of huge data in the cloud availing the cloud resources. But can we do better? Yes, of course! The main contribution of this chapter is the identification of four game-changing technologies for the acceleration of computing and analysis of data mining tasks in the cloud. Graphics Processing Units can be used to further accelerate the mining or analytic process, which is called GPU accelerated analytics. Further, Approximate Computing can also be introduced in big data analytics for bringing efficacy in the process by reducing time and energy and hence facilitating greenness in the entire computing process. Quantum Computing is a paradigm that is gaining pace in recent times which can also facilitate efficient and fast big data analytics in very little time. We have surveyed these three technologies and established their importance in big data mining with a holistic architecture by combining these three game-changers with the perspective of big data. We have also talked about another future technology, i.e., Neural Processing Units or Neural accelerators for researchers to explore the possibilities. A brief explanation of big data and cloud data mining concepts are also presented here.
Group Surfing: A Pedestrian-Based Approach to Sidewalk Robot Navigation
Venue: arXiv cs.RO 2021
Year: 2021

In this paper, we propose a novel navigation system for mobile robots in pedestrian-rich sidewalk environments. Sidewalks are unique in that the pedestrian-shared space has characteristics of both roads and indoor spaces. Like vehicles on roads, pedestrian movement often manifests as linear flows in opposing directions. On the other hand, pedestrians also form crowds and can exhibit much more random movements than vehicles. Classical algorithms are insufficient for safe navigation around pedestrians and remaining on the sidewalk space. Thus, our approach takes advantage of natural human motion to allow a robot to adapt to sidewalk navigation in a safe and socially-compliant manner. We developed a \textit{group surfing} method which aims to imitate the optimal pedestrian group for bringing the robot closer to its goal. For pedestrian-sparse environments, we propose a sidewalk edge detection and following method. Underlying these two navigation methods, the collision avoidance scheme is human-aware. The integrated navigation stack is evaluated and demonstrated in simulation. A hardware demonstration is also presented.
Dynamic Texture Synthesis by Incorporating Long-range Spatial and Temporal Correlations
Venue: arXiv cs.CV 2021
Year: 2021

The main challenge of dynamic texture synthesis lies in how to maintain spatial and temporal consistency in synthesized videos. The major drawback of existing dynamic texture synthesis models comes from poor treatment of the long-range texture correlation and motion information. To address this problem, we incorporate a new loss term, called the Shifted Gram loss, to capture the structural and long-range correlation of the reference texture video. Furthermore, we introduce a frame sampling strategy to exploit long-period motion across multiple frames. With these two new techniques, the application scope of existing texture synthesis models can be extended. That is, they can synthesize not only homogeneous but also structured dynamic texture patterns. Thorough experimental results are provided to demonstrate that our proposed dynamic texture synthesis model offers state-of-the-art visual performance.
Experiments of ASR-based mispronunciation detection for children and adult English learners
Venue: arXiv cs.CL 2021
Year: 2021

Pronunciation is one of the fundamentals of language learning, and it is considered a primary factor of spoken language when it comes to an understanding and being understood by others. The persistent presence of high error rates in speech recognition domains resulting from mispronunciations motivates us to find alternative techniques for handling mispronunciations. In this study, we develop a mispronunciation assessment system that checks the pronunciation of non-native English speakers, identifies the commonly mispronounced phonemes of Italian learners of English, and presents an evaluation of the non-native pronunciation observed in phonetically annotated speech corpora. In this work, to detect mispronunciations, we used a phone-based ASR implemented using Kaldi. We used two non-native English labeled corpora; (i) a corpus of Italian adults contains 5,867 utterances from 46 speakers, and (ii) a corpus of Italian children consists of 5,268 utterances from 78 children. Our results show that the selected error model can discriminate correct sounds from incorrect sounds in both native and nonnative speech, and therefore can be used to detect pronunciation errors in non-native speech. The phone error rates show improvement in using the error language model. The ASR system shows better accuracy after applying the error model on our selected corpora.
Fairness in Rankings and Recommendations: An Overview
Venue: arXiv cs.DB 2021
Year: 2021

We increasingly depend on a variety of data-driven algorithmic systems to assist us in many aspects of life. Search engines and recommender systems amongst others are used as sources of information and to help us in making all sort of decisions from selecting restaurants and books, to choosing friends and careers. This has given rise to important concerns regarding the fairness of such systems. In this work, we aim at presenting a toolkit of definitions, models and methods used for ensuring fairness in rankings and recommendations. Our objectives are three-fold: (a) to provide a solid framework on a novel, quickly evolving, and impactful domain, (b) to present related methods and put them into perspective, and (c) to highlight open challenges and research paths for future work.
OCM3D: Object-Centric Monocular 3D Object Detection
Venue: arXiv cs.CV 2021
Year: 2021

Image-only and pseudo-LiDAR representations are commonly used for monocular 3D object detection. However, methods based on them have shortcomings of either not well capturing the spatial relationships in neighbored image pixels or being hard to handle the noisy nature of the monocular pseudo-LiDAR point cloud. To overcome these issues, in this paper we propose a novel object-centric voxel representation tailored for monocular 3D object detection. Specifically, voxels are built on each object proposal, and their sizes are adaptively determined by the 3D spatial distribution of the points, allowing the noisy point cloud to be organized effectively within a voxel grid. This representation is proved to be able to locate the object in 3D space accurately. Furthermore, prior works would like to estimate the orientation via deep features extracted from an entire image or a noisy point cloud. By contrast, we argue that the local RoI information from the object image patch alone with a proper resizing scheme is a better input as it provides complete semantic clues meanwhile excludes irrelevant interferences. Besides, we decompose the confidence mechanism in monocular 3D object detection by considering the relationship between 3D objects and the associated 2D boxes. Evaluated on KITTI, our method outperforms state-of-the-art methods by a large margin. The code will be made publicly available soon.
LioNets: A Neural-Specific Local Interpretation Technique Exploiting Penultimate Layer Information
Venue: arXiv cs.LG 2021
Year: 2021

Artificial Intelligence (AI) has a tremendous impact on the unexpected growth of technology in almost every aspect. AI-powered systems are monitoring and deciding about sensitive economic and societal issues. The future is towards automation, and it must not be prevented. However, this is a conflicting viewpoint for a lot of people, due to the fear of uncontrollable AI systems. This concern could be reasonable if it was originating from considerations associated with social issues, like gender-biased, or obscure decision-making systems. Explainable AI (XAI) is recently treated as a huge step towards reliable systems, enhancing the trust of people to AI. Interpretable machine learning (IML), a subfield of XAI, is also an urgent topic of research. This paper presents a small but significant contribution to the IML community, focusing on a local-based, neural-specific interpretation process applied to textual and time-series data. The proposed methodology introduces new approaches to the presentation of feature importance based interpretations, as well as the production of counterfactual words on textual datasets. Eventually, an improved evaluation metric is introduced for the assessment of interpretation techniques, which supports an extensive set of qualitative and quantitative experiments.
On the efficiency of polar-like decoding for symmetric codes
Venue: arXiv cs.IT 2021
Year: 2021

The recently introduced polar codes constitute a breakthrough in coding theory due to their capacityachieving property. This goes hand in hand with a quasilinear construction, encoding, and successive cancellation list decoding procedures based on the Plotkin construction. The decoding algorithm can be applied with slight modifications to Reed-Muller or eBCH codes, that both achieve the capacity of erasure channels, although the list size needed for good performance grows too fast to make the decoding practical even for moderate block lengths. The key ingredient for proving the capacity-achieving property of Reed-Muller and eBCH codes is their group of symmetries. It can be plugged into the concept of Plotkin decomposition to design various permutation decoding algorithms. Although such techniques allow to outperform the straightforward polar-like decoding, the complexity stays impractical. In this paper, we show that although invariance under a large automorphism group is valuable in a theoretical sense, it also ensures that the list size needed for good performance grows exponentially. We further establish the bounds that arise if we sacrifice some of the symmetries. Although the theoretical analysis of the list decoding algorithm remains an open problem, our result provides an insight into the factors that impact the decoding complexity.
IRS-aided MIMO Systems over Double-scattering Channels: Impact of Channel Rank Deficiency
Venue: arXiv cs.IT 2021
Year: 2021

Intelligent reflecting surfaces (IRSs) are promising enablers for next-generation wireless communications due to their reconfigurability and high energy efficiency in improving poor propagation condition of channels, e.g., limited scattering environment. However, most existing works assumed full-rank channels requiring rich scatters, which may not be available in practice. To analyze the impact of rank-deficient channels and mitigate the ensued performance loss, we consider a large-scale IRS-aided MIMO system with statistical channel state information (CSI), where the double-scattering channel is adopted to model rank deficiency. By leveraging random matrix theory (RMT), we first derive a deterministic approximation (DA) of the ergodic rate with low computational complexity and prove the existence and uniqueness of the DA parameters. Then, we propose an alternating optimization algorithm for maximizing the DA with respect to phase shifts and signal covariance matrices. Numerical results will show that the DA is tight and our proposed method can effectively mitigate the performance loss induced by channel rank deficiency.
Feature-Oriented Defect Prediction: Scenarios, Metrics, and Classifiers
Venue: arXiv cs.SE 2021
Year: 2021

Several software defect prediction techniques have been developed over the past decades. These techniques predict defects at the granularity of typical software assets, such as components and files. In this paper, we investigate feature-oriented defect prediction: predicting defects at the granularity of features -- domain-entities that represent software functionality and often cross-cut software assets. Feature-oriented defect prediction can be beneficial since: (i) some features might be more error-prone than others, (ii) characteristics of defective features might be useful to predict other error-prone features, and (iii) feature-specific code might be prone to faults arising from feature interactions. We explore the feasibility and solution space for feature-oriented defect prediction. Our study relies on 12 software projects from which we analyzed 13,685 bug-introducing and corrective commits, and systematically generated 62,868 training and test datasets to evaluate classifiers, metrics, and scenarios. The datasets were generated based on the 13,685 commits, 81 releases, and 24, 532 permutations of our 12 projects depending on the scenario addressed. We covered scenarios such as just-in-time (JIT) and cross-project defect prediction. Our results confirm the feasibility of feature-oriented defect prediction. We found the best performance (i.e., precision and robustness) when using the Random Forest classifier, with process and structure metrics. Surprisingly, single-project JIT and release-level predictions had median AUC-ROC values greater than 95% and 90% respectively, contrary to studies that assert poor performance due to insufficient training data. We also found that a model trained on release-level data from one of the twelve projects could predict defect-proneness of features in the other eleven projects with median AUC-ROC of 82%, without retraining.
The AppChk Crowd-Sourcing Platform: Which third parties are iOS apps talking to?
Venue: arXiv cs.CY 2021
Year: 2021

In this paper we present a platform which is usable by novice users without domain knowledge of experts. The platform consisting of an iOS app to monitor network traffic and a website to evaluate the results. Monitoring takes place on-device; no external server is required. Users can record and share network activity, compare evaluation results, and create rankings on apps and app-groups. The results are used to detect new trackers, point out misconduct in privacy practices, or automate comparisons on app-attributes like price, region, and category. To demonstrate potential use cases, we compare 75 apps before and after the iOS 14 release and show that we can detect trends in app-specific behavior change over time, for example, by privacy changes in the OS. Our results indicate a slight decrease in tracking but also an increase in contacted domains. We identify seven new trackers which are not present in current tracking lists such as EasyList. The games category is particularly prone to tracking (53% of the traffic) and contacts on average 36.2 domains with 59.3 requests per minute.
Tight Lower Bound for Average Number of Terms in Optimal Double-base Number System
Venue: arXiv cs.DM 2021
Year: 2021

We show in this note that the average number of terms in the optimal double-base number system is in Omega(n / log n). The lower bound matches the upper bound shown earlier by Dimitrov, Imbert, and Mishra (Math. of Comp. 2008).
Learning to recover orientations from projections in single-particle cryo-EM
Venue: arXiv cs.LG 2021
Year: 2021

A major challenge in single-particle cryo-electron microscopy (cryo-EM) is that the orientations adopted by the 3D particles prior to imaging are unknown; yet, this knowledge is essential for high-resolution reconstruction. We present a method to recover these orientations directly from the acquired set of 2D projections. Our approach consists of two steps: (i) the estimation of distances between pairs of projections, and (ii) the recovery of the orientation of each projection from these distances. In step (i), pairwise distances are estimated by a Siamese neural network trained on synthetic cryo-EM projections from resolved bio-structures. In step (ii), orientations are recovered by minimizing the difference between the distances estimated from the projections and the distances induced by the recovered orientations. We evaluated the method on synthetic cryo-EM datasets. Current results demonstrate that orientations can be accurately recovered from projections that are shifted and corrupted with a high level of noise. The accuracy of the recovery depends on the accuracy of the distance estimator. While not yet deployed in a real experimental setup, the proposed method offers a novel learning-based take on orientation recovery in SPA. Our code is available at https://github.com/JelenaBanjac/protein-reconstruction
A Non-sequential Approach to Deep User Interest Model for CTR Prediction
Venue: arXiv cs.IR 2021
Year: 2021

Click-Through Rate (CTR) prediction plays an important role in many industrial applications, and recently a lot of attention is paid to the deep interest models which use attention mechanism to capture user interests from historical behaviors. However, most current models are based on sequential models which truncate the behavior sequences by a fixed length, thus have difficulties in handling very long behavior sequences. Another big problem is that sequences with the same length can be quite different in terms of time, carrying completely different meanings. In this paper, we propose a non-sequential approach to tackle the above problems. Specifically, we first represent the behavior data in a sparse key-vector format, where the vector contains rich behavior info such as time, count and category. Next, we enhance the Deep Interest Network to take such rich information into account by a novel attention network. The sparse representation makes it practical to handle large scale long behavior sequences. Finally, we introduce a multidimensional partition framework to mine behavior interactions. The framework can partition data into custom designed time buckets to capture the interactions among information aggregated in different time buckets. Similarly, it can also partition the data into different categories and capture the interactions among them. Experiments are conducted on two public datasets: one is an advertising dataset and the other is a production recommender dataset. Our models outperform other state-of-the-art models on both datasets.
Neuro-Symbolic VQA: A review from the perspective of AGI desiderata
Venue: arXiv cs.LG 2021
Year: 2021

An ultimate goal of the AI and ML fields is artificial general intelligence (AGI); although such systems remain science fiction, various models exhibit aspects of AGI. In this work, we look at neuro-symbolic (NS)approaches to visual question answering (VQA) from the perspective of AGI desiderata. We see how well these systems meet these desiderata, and how the desiderata often pull the scientist in opposing directions. It is my hope that through this work we can temper model evaluation on benchmarks with a discussion of the properties of these systems and their potential for future extension.
Knowledge Distillation For Wireless Edge Learning
Venue: arXiv cs.NI 2021
Year: 2021

In this paper, we propose a framework for predicting frame errors in the collaborative spectrally congested wireless environments of the DARPA Spectrum Collaboration Challenge (SC2) via a recently collected dataset. We employ distributed deep edge learning that is shared among edge nodes and a central cloud. Using this close-to-practice dataset, we find that widely used federated learning approaches, specially those that are privacy preserving, are worse than local training for a wide range of settings. We hence utilize the synthetic minority oversampling technique to maintain privacy via avoiding the transfer of local data to the cloud, and utilize knowledge distillation with an aim to benefit from high cloud computing and storage capabilities. The proposed framework achieves overall better performance than both local and federated training approaches, while being robust against catastrophic failures as well as challenging channel conditions that result in high frame error rates.
On Generalized Expanded Blaum-Roth Codes
Venue: arXiv cs.IT 2021
Year: 2021

Expanded Blaum-Roth (EBR) codes consist of $n\times n$ arrays such that lines of slopes $i$, $0\leq i\leq r-1$ for $2\leq r<n$, as well as vertical lines, have even parity. The codes are MDS with respect to columns, i.e., they can recover any $r$ erased columns, if and only if $n$ is a prime number. Recently a generalization of EBR codes, called generalized expanded Blaum-Roth (GEBR) codes, was presented. GEBR codes consist of $pτ\times (k+r)$ arrays, where $p$ is prime and $τ\geq 1$, such that lines of slopes $i$, $0\leq i\leq r-1$, have even parity and every column in the array, when regarded as a polynomial, is a multiple of $1+x^τ$. In particular, it was shown that when $p$ is an odd prime number, 2 is primitive in $GF(p)$ and $τ= p^j$, $j\geq 0$, the GEBR code consisting of $pτ\times (p-1)τ$ arrays is MDS. We extend this result further by proving that GEBR codes consisting of $pτ\times pτ$ arrays are MDS if and only if $τ= p^j$, where $0\leq j$ and $p$ is any odd prime.
Research on innovation in China and Latin America: Bibliometric insights in the field of business, management and decision sciences
Venue: arXiv cs.DL 2021
Year: 2021

China and Latin America (LATAM) are now key players in global research production. This study presents a comparative study on research on innovation in management and decision sciences based on data from Scopus and Web of Knowledge (WoS) between China and LATAM. Findings showed significant differences between regions regarding journals citation dependent measures, and between the number of authors and journal reputation, public universities have been leading producers, and China showed a particular interest in research topics such as commerce and industry, while LATAM in sustainable development and bio-technology.
Research on innovation in business and management about China and Latin America: bibliometrics insights using Google Scholar, Dimensions and Microsoft Academic
Venue: arXiv cs.DL 2021
Year: 2021

Trade and investment between developing regions such as China and Latin America (LATAM) are growing prominently. However, insights on crucial factors such as innovation in business and management (iBM) about both regions have not been scrutinized. This study presents the research output, impact, and structure of iBM research published about China and LATAM in a comparative framework using Google Scholar, Dimensions, and Microsoft Academic. Findings showed i) that iBM topics of both regions were framed within research and development management, and technological development topics, ii) significant differences in output and impact between regions, and iii) the same case for platforms.