Reviewerly/exHarmony huggingface dataset:
NIL: large-scale detection of large-variance clones

A code clone (in short, clone) is a code fragment that is identical or similar to other code fragments in source code. Clones generated by a large number of changes to copy-and-pasted code fragments are called large-variance (modifications are scattered) or large-gap (modifications are in one place) clones. It is difficult for general clone detection techniques to detect such clones and thus specialized techniques are necessary. In addition, with the rapid growth of software development, scalable clone detectors that can detect clones in large codebases are required. However, there are no existing techniques for quickly detecting large-variance or large-gap clones in large codebases. In this paper, we propose a scalable clone detection technique that can detect large-variance clones from large codebases and describe its implementation, called NIL. NIL is a token-based clone detector that efficiently identifies clone candidates using an N-gram representation of token sequences and an inverted index. Then, NIL verifies the clone candidates by measuring their similarity based on the longest common subsequence between their token sequences. We evaluate NIL in terms of large- variance clone detection accuracy, general Type-1, Type-2, and Type- 3 clone detection accuracy, and scalability. Our experimental results show that NIL has higher accuracy in terms of large-variance clone detection, equivalent accuracy in terms of general clone detection, and the shortest execution time for inputs of various sizes (1–250 MLOC) compared to existing state-of-the-art tools.
Cost-effective Batch-mode Multi-label Active Learning

Active learning aims to select the most valuable unlabeled instances for annotations and thus to maximize the performance of a learner with the selected instances. Batch-mode active learning methods can select a batch of unlabeled instances with informativeness and low redundancy at each iteration, they are more efficient than myopic active learning methods, which typically have to retrain the model after querying each instance. However, Batch-mode Multi-label Active Learning is not well explored yet, since it is quite difficult to select a batch of instances or instance-label pairs with high informativeness but low redundancy for query and budget saving. In this paper, we propose a novel approach CBMAL (Cost-effective Batch-mode Multi-label Active Learning) to address this challenge. CBMAL firstly selects a batch of informative instance-label pairs using uncertainty, label correlation and label space sparsity. Next, CBMAL leverages the information from the feature and label dimensions to pick out a small batch of instance-label pairs with the highest information and lowest redundancy from the first batch. These instance-label pairs are then queried and used to update the learner for the next iteration. Experimental results on six benchmark datasets demonstrate that CBMAL can reduce both the query and time costs while achieve a better performance than state-of-the-art methods.
Privacy and E-Learning: A Pending Task

Most educational software programs use and gather personal information and metadata from students. Additionally, most of the educational software programs are no longer operated by the learning institutions but are run by third-party agencies. This means that in the decade since 2020, information about students is stored and handled outside premises and control of learning institutions. The personal information about students and their activity while they interact with learning management systems and online learning tools is increasingly in custody of cloud computing platforms, software-as-a-service providers, and learning tool vendors. There is an increasing will to use all the data and metadata from the activity of the students for research, to develop education management strategies, pedagogy approaches, and develop behavior control tools or learning tools informed by behavior analysis from learning analytics. Many times, these studies lack the ethical and moral perspective. In addition, there is an increasing number of cases in which this information has leaked or has been used in a shady way. Additionally, this information will be around for a long time, tied to the future digital profiles of the students whose data has been leaked. This paper hypothesizes that there has been an ongoing process of technological evolution that leads to a loss of control over personal information, which makes it even more difficult to protect user confidentiality and ensuring privacy, that data surveillance has entered the world of education, and that the current legal frameworks are not enough to really protect the student’s personal information. The paper analyzes how this situation came to pass, and why this is wrong. We conclude with some proposals to address it from its different root dimensions: technical, cultural, legal, and organizational.
Fast and Robust UAV to UAV Detection and Tracking From Video

Unmanned Aerial Vehicle (UAV) technology is being increasingly used in a wide variety of applications ranging from remote sensing, to delivery and security. As the number of UAVs increases, there is a growing need for UAV to UAV detection and tracking systems for both collision avoidance and coordination. Among possible solutions, autonomous “see-and-avoid” systems based on low-cost high-resolution video cameras offer important advantages in terms of light weight and low power consumption. However, in order to be effective, camera based “see-and-avoid” systems require sensitive, robust, and computationally efficient algorithms for autonomous detection and tracking of UAVs from a moving camera. In this article, we propose a general architecture for a highly accurate and computationally efficient UAV to UAV detection and tracking (U2U-D&T) algorithm from a camera mounted on a moving UAV platform. The system is based on a computationally efficient pipeline consisting of a moving target detector, followed by a target tracker. The algorithm is validated using video data collected from multiple fixed-wing UAVs that is manually ground-truthed and is publicly available. Results indicate that the proposed algorithm can be implemented on commodity hardware and robustly achieves highly accurate detection and tracking of even distant and faint UAVs. Open source code for the U2U-D&T algorithm is available at: <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><uri>https://github.com/jingliinpurdue/Fast-and-Robust-UAV-to-UAV-Detection-and-Tracking.git</uri>.</i>
Perceptual Indistinguishability-Net (PI-Net): Facial Image Obfuscation with Manipulable Semantics

With the growing use of camera devices, the industry has many image datasets that provide more opportunities for collaboration between the machine learning community and industry. However, the sensitive information in the datasets discourages data owners from releasing these datasets. Despite recent research devoted to removing sensitive information from images, they provide neither meaningful privacy-utility trade-off nor provable privacy guarantees. In this study, with the consideration of the perceptual similarity, we propose perceptual indistinguishability (PI) as a formal privacy notion particularly for images. We also propose PI-Net, a privacy-preserving mechanism that achieves image obfuscation with PI guarantee. Our study shows that PI-Net achieves significantly better privacy utility trade-off through public image data.
Computer Vision-Driven Evaluation System for Assisted Decision-Making in Sports Training

Computer vision has become a fast-developing technology in the field of artificial intelligence, and its application fields are also expanding, thanks to the rapid development of deep learning. It will be of great practical value if it is combined with sports. When a traditional exercise assistance system is introduced into sports training, the athlete’s training information can be obtained by monitoring the exercise process through sensors and other equipment, which can assist the athlete in retrospectively analyzing the technical actions. However, the traditional system must be equipped with multiple sensor devices, and the exercise information provided must be accurate. This paper proposes a motion assistance evaluation system based on deep learning algorithms for human posture recognition. The system is divided into three sections: a standard motion database, auxiliary instruction, and overall evaluation. The standard motion database can be customized by the system user, and the auxiliary teaching system can be integrated. The user’s actions are compared to the standard actions and intuitively displayed to the trainers as data. The system’s overall evaluation component can recognize and display video files, giving trainers an intelligent training platform. Simulator tests are also available. It also demonstrates the efficacy of the algorithm used in this paper.
lifeXplore at the Lifelog Search Challenge 2021

Since its first iteration in 2018, the Lifelog Search Challenge (LSC) continues to rise in popularity as an interactive lifelog data retrieval competition, co-located at the ACM International Conference on Multimedia Retrieval (ICMR). The goal of this annual live event is to search a large corpus of lifelogging data for specifically announced memories using a purposefully developed tool within a limited amount of time. As long-standing participants, we present our improved lifeXplore -- a retrieval system combining chronologic day summary browsing with interactive combinable concept filtering. Compared to previous versions, the tool is improved by incorporating temporal queries, advanced day summary features as well as usability improvements.
An integrated FCM-FBWM approach to assess and manage the readiness for blockchain incorporation in the supply chain

Despite the initial hype surrounding blockchain applications in the supply chain, real-world implementation of this disruptive technology faces significant challenges. To manage these challenges and ensure a smooth implementation, supply chain organizations must perform various activities to acquire readiness. This paper proposes a readiness assessment and management approach for blockchain implementation in the supply chain. The proposed approach allows supply chain decision-makers to (1) identify the readiness-relevant activities for blockchain implementation, (2) model the causal relationships among the identified activities, (3) assess the activities’ contribution weights to the overall readiness, and (4) develop an effective readiness improvement plan by prioritizing those activities with the most impact on the overall readiness. Fuzzy cognitive maps (FCMs) are employed to model the causal relationships between the activities. The fuzzy best–worst​ method (FBWM) is adopted to establish the contribution weights of the activities to the supply chain’s overall readiness. The FCM inference process is also used to incorporate feedback loops among the activities. The proposed approach is then illustrated through an empirical study.
Distance learning in Ukraine in COVID-19 emergency

The global outbreak of COVID-19, subsequent lockdown of universities, and suspension of on-campus learning have caught many higher educational institutions off-guard, challenging their ability to adapt to a new delivery system. Distance learning has come under the spotlight as the only option to avoid the disruption of the teaching-learning process irreversibly. The present study outlines the problems facing Ukrainian universities after the quarantine imposition due to the COVID-19 emergency. The research reveals that the emergency transition of Ukrainian universities to distance learning has not been smooth; both professor-tutors and students have been put at an unfair disadvantage. Additionally, the study intends to analyse the specifics of distance learning in the system of higher education of Ukraine in the emergency and activities of Ukrainian universities concerning the problems of transition to distance learning and utilisation of educational online platforms. An internet survey was conducted among Ukrainian university professor-tutors and students to explore the changes to the teaching-learning process under the COVID-19 emergency quarantine. Interviews with senior officials of leading Ukrainian universities were held to explore how they addressed the issue of the disruption of the education process due to the COVID-19 quarantine.
High Fidelity Fingerprint Generation: Quality, Uniqueness, And Privacy

In this work, we utilize progressive growth-based Generative Adversarial Networks (GANs) to develop the Clarkson Fingerprint Generator (CFG). We demonstrate that the CFG is capable of generating realistic, high fidelity, $512 \times 512$ pixels, full, plain impression fingerprints. Our results suggest that the fingerprints generated by the CFG are unique, diverse, and resemble the training dataset in terms of minutiae configuration and quality, while not revealing the underlying identities of the training data. We make the pre-trained CFG model and the synthetically generated dataset publicly available at https://github.com/keivanB/Clarkson_Finger_Gen
Active learning with extreme learning machine for online imbalanced multiclass classification

Active learning (AL) can significantly reduce the cost of labeling instances. Extreme learning machine (ELM) has low computational cost, extremely fast training speed and strong generalization ability. Previous studies have shown that the combination of them can generate efficient learning models. Nevertheless, these researches did not focus on multiclass imbalanced data. Cold start may occur and the performance of classifier is also reduced due to the imbalanced distribution of categories. Moreover, there is no framework for processing stream-based data. To address these problems, an improved framework called AL for class incremental and weighted sequential ELM (AI-WSELM) is proposed in this paper, and its advantages are as follows: (1) similarity query and margin sampling were used to alleviate cold start and select uncertain instances, respectively, (2) an improved weighting strategy was used to tackle stream-based multiclass imbalanced distribution, (3) a class incremental mechanism was added to deal with new categories appeared in the subsequent batches, and (4) AI-WSELM greatly reduced the cost of labeling samples when ensuring classification performance. The simulation results show that the proposed model has satisfactory performance compared to the existing ELMs and the other related algorithms, which indicates the feasibility and effectiveness of AI-WSELM.
Scanning error detection and compensation algorithm for white-light interferometry

As the primary error source in white-light interferometry (WLI), the error of scanning steps directly affects coherence peak sensing and greatly lowers the measurement accuracy. Based on the least-squares iterative algorithm, we present an algorithm to detect and compensate for scanning error in WLI. The actual scanning step is calculated from the continuous moving fringes with an iterative algorithm, and the surface is reconstructed with the calculated scanning steps to make compensation. Both simulations and experiments indicate that the high-accuracy measurements can be implemented under disturbance over a wide frequency band. The proposed algorithm could relax the environment requirement of white-light interferometer application.
Chaotic neural network algorithm with competitive learning for global optimization

Neural network algorithm (NNA) is one of the newest proposed metaheuristic algorithms. NNA has strong global search ability due to the unique structure of artificial neural networks. Further, NNA is an algorithm without any effort for fine tuning initial parameters. Thus, it is very easy for NNA to solve different types of optimization problems. However, when used for solving complex optimization problems, slow convergence and premature convergence are its drawbacks. To overcome the two drawbacks, this work presents an improved NNA, namely chaotic neural network algorithm with competitive learning (CCLNNA), for global optimization. In CCLNNA, population is first divided into excellent subpopulation and common subpopulation according to the built competitive mechanism. Then, to balance exploration and exploitation of CCLNNA, excellent subpopulation is optimized by the designed transfer operator while common subpopulation is updated by the combination of the designed bias operator and transfer operator. Besides, chaos theory is introduced to increase the chance of CCLNNA to escape from the local optimum. To investigate the effectiveness of the improved strategies, CCLNNA is first used to solve the well-known CEC 2014 test suite with 30 benchmark functions. Then it is employed for solving three constrained real-world engineering design problems. Experimental results reveal that the improved strategies introduced to NNA can significantly improve the optimization performance of NNA and CCLNNA is a very powerful algorithm in solving complex optimization problems with multimodal properties by comparing with the other competitive algorithms.
Integrated generalized zero-shot learning for fine-grained classification

Embedding learning (EL) and feature synthesizing (FS) are two of the popular categories of fine-grained GZSL methods. EL or FS using global features cannot discriminate fine details in the absence of local features. On the other hand, EL or FS methods exploiting local features either neglect direct attribute guidance or global information. Consequently, neither method performs well. In this paper, we propose to explore global and direct attribute-supervised local visual features for both EL and FS categories in an integrated manner for fine-grained GZSL. The proposed integrated network has an EL sub-network and a FS sub-network. Consequently, the proposed integrated network can be tested in two ways. We propose a novel two-step dense attention mechanism to discover attribute-guided local visual features. We introduce new mutual learning between the sub-networks to exploit mutually beneficial information for optimization. Moreover, we propose to compute source-target class similarity based on mutual information and transfer-learn the target classes to reduce bias towards the source domain during testing. We demonstrate that our proposed method outperforms contemporary methods on benchmark datasets.
An efficient parallel algorithm for mining weighted clickstream patterns

In the Internet age, analyzing the behavior of online users can help webstore owners understand customers' interests. Insights from such analysis can be used to improve both user experience and website design. A prominent task for online behavior analysis is clickstream mining, which consists of identifying customer browsing patterns that reveal how users interact with websites. Recently, this task was extended to consider weights to find more impactful patterns. However, most algorithms for mining weighted clickstream patterns are serial algorithms, which are sequentially executed from the start to the end on one running thread. In real life, data is often very large, and serial algorithms can have long runtimes as they do not fully take advantage of the parallelism capabilities of modern multi-core CPUs. To address this limitation, this paper presents two parallel algorithms named DPCompact-SPADE (Depth load balancing Parallel Compact-SPADE) and APCompact-SPADE (Adaptive Parallel Compact-SPADE) for weighted clickstream pattern mining. Experiments on various datasets show that the proposed parallel algorithm is efficient, and outperforms state-of-the-art serial algorithms in terms of runtime, memory consumption, and scalability.
Shallow Optical Flow Three-Stream CNN For Macro- And Micro-Expression Spotting From Long Videos

Facial expressions vary from the visible to the subtle. In recent years, the analysis of micro-expressions— a natural occurrence resulting from the suppression of one’s true emotions, has drawn the attention of researchers with a broad range of potential applications. However, spotting micro-expressions in long videos becomes increasingly challenging when intertwined with normal or macro-expressions. In this paper, we propose a shallow optical flow three-stream CNN (SOFTNet) model to predict a score that captures the likelihood of a frame being in an expression interval. By fashioning the spotting task as a regression problem, we introduce pseudo-labeling to facilitate the learning process. We demonstrate the efficacy and efficiency of the proposed approach on the recent MEGC 2020 benchmark, where state-of-the-art performance is achieved on CAS(ME) <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> with equally promising results on SAMM Long Videos.
Improved Multiclass Adaboost For Image Classification: The Role Of Tree Optimization

Decision tree boosting is considered as an important and widely recognized method in image classification, despite dominance of the deep learning based approaches in this area. Provided with good image features, it can produce a powerful model with unique properties, such as strong predictive power, scalability, interpretability, etc. In this paper, we propose a novel tree boosting framework which capitalizes on the idea of using shallow, sparse and yet powerful oblique decision trees (trained with recently proposed Tree Alternating optimization algorithm) as the base learners. We empirically show that the resulting model achieves better or comparable performance (both in terms of accuracy and model size) against established boosting algorithms such as gradient boosting or AdaBoost in number of benchmarks. Further, we show that such trees can directly and efficiently handle multiclass problems without using one-vs-all strategy employed by most of the practical boosting implementations.
Exploring Graph-querying approaches in LifeGraph

The multi-modal and interrelated nature of lifelog data makes it well suited for graph-based representations. In this paper, we present the second iteration of LifeGraph, a Knowledge Graph for Lifelog Data, initially introduced during the 3rd Lifelog Search Challenge in 2020. This second iteration incorporates several lessons learned from the previous version. While the actual graph has undergone only small changes, the mechanisms by which it is traversed during querying as well as the underlying storage system which performs the traversal have been changed. The means for query formulation have also been slightly extended in capability and made more efficient and intuitive. All these changes have the aim of improving result quality and reducing query time.