Reviewerly/exHarmony huggingface dataset:
Early Experience with Transformer-Based Similarity Analysis for DataRaceBench

DataRaceBench (DRB) is a dedicated benchmark suite to evaluate tools aimed to find data race bugs in OpenMP programs. Using microbenchmarks with or without data races, DRB is able to generate standard quality metrics and provide systematic and quantitative assessments of data race detection tools. However, as the number of microbenchmarks grows, it is challenging to manually identify similar code patterns for DRB, within the context of identifying duplicated kernels or guiding the additions of new kernels. In this paper, we experiment with a transformer-based, deep learning approach to similarity analysis. A state-of-the-art transformer model, CodeBERT, has been adapted to find similar OpenMP code regions. We explore the challenges and the solutions when applying transformer-based similarity analysis to new source codes which are unseen by pre-trained transformers. Using comparative experiments of different variants of similarity analysis, we comment on the strengths and limitations of the transformer-based approach and point out future research directions.
Phishing Website Detection Using LGBM Classifier With URL-Based Lexical Features

People have become increasingly reliant on the internet for their everyday activities, and the majority of internet users are uninformed of the phishing attack, which is caused by the careless or unaware nature of humans, resulting in financial loss as well identity theft. Phishing detection is not a new research area; multiple efforts from various perspectives have been conducted in this domain and machine learning-based approaches have gained popularity among existing works. Machine learning-based model used a large number of features including external service-based features for phishing website detection, but these features are time-consuming and not suitable for real-time detection. This research proposes a fast and lightweight phishing website detection model based on URL-based lexical features, as well as a novel dataset. The experimental result shows that the parameter-tuned light gradient boosting machine (LGBM) classifier outperforms the random forest classifier, and the Pearson correlation algorithm outranks the other feature selection methods. The proposed approach out performs most of the existing method interms of accuracy with newly created dataset as well as the other two benchmark datasets.
Design of robust Gaussian approximate filter and smoother with latency probability identification

The Kalman filter is a linear optimal estimator when it can accurately receive measurements and the noise obeys a Gaussian distribution. However, the measurement noise may have heavy-tailed characteristics because of outlier interference. Actually, real-world measurement information may have one-step randomly delayed measurements (ORDMs) with unknown latency probability (LP) due to exogenous disturbances. Moreover, systems are usually not linear. To overcome the influence of the state estimator of nonlinear systems from heavy-tailed measurement noise (HMN) and the unknown LP, this paper focuses on the robust Gaussian approximate (GA) filter and smoother for the above issues. First, the one-step predicted probability density function (PDF) is modeled as a Gaussian distribution, and the likelihood PDF is modeled as a Normal-Gamma-Beta (NGBM) distribution. Furthermore, novel robust Gaussian approximate filter and smoother are proposed based on the variational Bayesian technique. They provide general estimation frameworks for estimating state of nonlinear systems with HMN and ORDMs, and different nonlinear estimators can be implemented using different numerical techniques. In addition, the computational complexity of the proposed algorithms is calculated. Finally, taking a univariate nonstationary growth model, a passive ranging problem, and target tracking as examples, the effectiveness and superiority of the proposed robust algorithms contrasted with the existing algorithms are shown. The outcomes show that the proposed methods have higher estimation precision; however, the computational burden is marginally increased.
Experimental investigation of the tropospheric temperature gradient with Flightradar24

Sitting on the terrace on a warm summer evening and looking up at the sky, we often wonder where an airplane is going to fly. Based on the observed flight direction, we can make assumptions, but rarely check if they are really correct. The app Flightradar24 offers the possibility to confirm our assumptions. For this purpose, you just need to target the aircraft with your smartphone or tablet and then you will get augmented reality (AR) information for the flight displayed in the live image (Fig. 1): takeoff and destination, distance to the observer, airline, and type and photo of the aircraft. By tapping on the AR window, more detailed information on the flight times, the distance traveled, and the exact flight route is displayed on a map (Fig. 2). In this way, data of particular physical interest can also be retrieved, namely the aircraft’s current altitude, its speed, its position, the prevailing wind speed, and the outside temperature. This corresponds to a retrieval of measurement data, so that the app can also be used for physical quasi-experiments. At this point, the investigation of the temperature profile of the troposphere will be described. Many further quantitative analyses can be found in Ref. 2. This approach to modeling also provides an example of handling real data in the sense of data literacy.
Hybrid classifier model with tuned weights for human activity recognition

A wide variety of uses, such as video interpretation and surveillance, human-robot interaction, healthcare, and sport analysis, among others, make this technology extremely useful, human activity recognition has received a lot of attention in recent decades. human activity recognition from video frames or still images is a challenging procedure because of factors including viewpoint, partial occlusion, lighting, background clutter, scale differences, and look. Numerous applications, including human-computer interfaces, robotics for the analysis of human behavior, and video surveillance systems all require the activity recognition system. This work introduces the human activity recognition system, which includes 3 stages: preprocessing, feature extraction, and classification. The input video (image frames) are subjected for preprocessing stage which is processed with median filtering and background subtraction. Several features, including the Improved Bag of Visual Words, the local texton XOR pattern, and the Spider Local Picture Feature (SLIF) based features, are extracted from the pre-processed image. The next step involves classifying data using a hybrid classifier that blends Bidirectional Gated Recurrent (Bi-GRU) and Long Short Term Memory (LSTM). To boost the effectiveness of the suggested system, the weights of the Long Short Term Memory (LSTM) and Bidirectional Gated Recurrent (Bi-GRU) are both ideally determined using the Improved Aquila Optimization with City Block Distance Evaluation (IACBD) method. Finally, the effectiveness of the suggested approach is evaluated in comparison to other traditional models using various performance metrics.
Can Observing Others' Actions Help Prevent Polarization?: Results from Laboratory Experiments

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Quantum memory effects in atomic ensembles coupled to photonic cavities

This article explores the dynamics of many-body atomic systems symmetrically coupled to Lorentzian photonic cavity systems. Our study reveals interesting dynamical characteristics, including non-zero steady states, super-radiant decay, enhanced energy transfer, and the ability to modulate oscillations in the atomic system by tuning environmental degrees of freedom. We also analyze a configuration consisting of a three-atom chain embedded in a photonic cavity. Similarly, we find a strong enhancement of the energy transfer rate between the two ends of the chain and identified specific initial conditions that lead to significantly reduced dissipation between the two atoms at the end of the chain. Another configuration of interest consists of two symmetrical detuned reservoirs with respect to the atomic system. In the single atom case, we show that it is possible to enhance the decay rate of the system by modulating its reservoir detuning. In contrast, in the many-atom case, this results in dynamics akin to the on-resonant cavity. Finally, we examine the validity of the rotating wave approximation through a direct comparison against the numerically exact hierarchical equations of motion. We find good agreement in the weak coupling regime, while in the intermediate coupling regime, we identify qualitative similarities, but the rotating wave approximation becomes less reliable. In the moderate coupling regime, we find deviations of the steady states due to the formation of mixed photon-atom states.
An entangled mixture of variational autoencoders approach to deep clustering

We present a novel deep clustering algorithm that utilizes a variational autoencoder (VAE) framework with an entangled multi encoder-decoder neural architecture. Our model enforces a complementary structure that guides the learned latent representations towards a better space arrangement. It differs from previous VAE-based clustering algorithms by employing a new generative model that uses multiple encoder-decoders that are entangled to provide a joint clustering decision. The optimal clustering is found by optimizing a lower bound of the model likelihood function. Both the reconstruction component and the regularization component of the ELBO objective function are explicitly involved in the clustering procedure. We show that this modeling results in both better clustering capabilities and improved data generation. The proposed method is evaluated on standard datasets and is shown to significantly outperform state-of-the-art deep clustering methods.
RoRED: Bootstrapping labeling rule discovery for robust relation extraction

Labeling rules can be leveraged to produce training data by matching the sentences in the corpus. However, the robustness of the relation extraction is reduced by noisy labels generated from incorrectly matched and missing sentences. To address this problem, we propose the bootstrapping labeling rule discovery method for robust relation extraction (RoRED). Specifically, we first define PN-rules to filter incorrectly matched sentences based on positive (P) and negative (N) rules. Second, we design a semantic-matching mechanism to match missing sentences based on semantic associations between rules, words, and sentences. Moreover, we present a co-training-based rule verification approach to refine the labels of matched sentences and improve the overall quality of bootstrapped rule discovery. Experiments on a real-world dataset indicate that RoRED achieves at least a 20% gain in F1 score compared to state-of-the-art methods.
Chatbot Optimization using Sentiment Analysis and Timeline Navigation

A chatbot or conversational agent is a software that can interact or ``chat'' with a human user using a natural language, like English, for instance. Since the first chatbot developed, many have been created but most of their problems still persist, like providing the right answer to the user and user acceptance itself. Considering such facts, in this work, we present a chatbot-building framework that considers the use of sentiment analysis and tree timelines to provide a better chatbot answer. For instance, as presented in our experiments, the user can be addressed to a human attendant when its sentiment is very negative, or even try another branch of the tree timeline, as an alternative answer, whenever the user sentiment is less negative.
Personalized Federated Learning for In-Hospital Mortality Prediction of Multi-Center ICU

Federated learning (FL), as a paradigm for addressing challenges of machine learning (ML) to be applied in private distributed data provides a novel and promising scheme to promote ML in multiple independently distributed healthcare institutions. However, the non-IID and unbalanced nature of the data distribution can decrease its performance, even resulting in the institutions losing motivation to participate in its training. This paper explored the problem with an in-hospital mortality prediction task under an actual multi-center ICU electronic health record database that preserves the original non-IID and unbalanced data distribution. It first analyzed the reason for the performance degradation of baseline FL under this data scenario, and then proposed a personalized FL (PFL) approach named POLA to tackle the problem. POLA is a personalized one-shot and two-step FL method capable of generating high-performance personalized models for each independent participant. The proposed method, POLA was compared with two other PFL methods in experiments, and the results indicate that it not only effectively improves the prediction performance of FL but also significantly reduces the communication rounds. Moreover, its generality and extensibility also make it potential to be extended to other similar cross-silo FL application scenarios.
Bridging the Gap Between Cumbersome and Light Detectors via Layer-Calibration and Task-Disentangle Distillation in Remote Sensing Imagery

With urgent application requirements, such as satellite in-orbit processing and unmanned aerial vehicle tracking, knowledge distillation (KD) following the teacher–student teaching mechanism has shown great potential to obtain lightweight detectors. However, compact students have limited accuracy due to the interference of large-scale variations and blurred boundaries in remote sensing objects. Specifically, previous methods mostly force teacher–student responses from the layer of the same depth and scale to align. Stereotyped manual interlayer associations may cause discriminative features of multiscale objects to be incorrectly bundled. Furthermore, the regression branch follows the identical distillation paradigm as the classification branch, resulting in ambiguous object bounding box deviations. To solve the above two issues, we propose an effective KD framework called layer-calibration and task-disentangle distillation (LTD). First, the cross-layer calibration distillation (CCD) structure is innovatively proposed. It adaptively binds a student layer with several related target layers, rather than a fixed layer in the teacher model. Appropriate and clear knowledge of large and small objects is transmitted. Since the CCD structure requires explicit global inner product computation between multiple layers, the local implicit calibration (LIC) module is further proposed to reduce distilled convergence difficulty. Second, the task-aware spatial disentangle distillation (TASD) structure is devised to transfer task-decoupled semantics and localization knowledge in a divide-and-conquer manner, alleviating objects’ localization imprecision. Experiments demonstrate that our LTD achieves state-of-the-art performance on several datasets and is a plug-and-play approach to most detectors. The code will be available soon.
Idea generation and knowledge creation through maker practices in an artifact-mediated collaborative invention project

This investigation involved carrying out interventions that engaged teams of lower-secondary (13–14-year-old) Finnish students in using traditional and digital fabrication technologies to make materially embodied collaborative inventions. By relying on video data and ethnographic observations of the student teams' collaborative invention processes, the investigation focused on investigating 1) how the teams generated and developed their design ideas in their materially anchored making process and 2) what kinds of maker practices they relied on during open-ended invention projects. The study focused on a microanalytic study of three teams of students, and we utilized and developed visual data analysis methods. Our findings reveal the complex nature of the student teams' materially contextualized ideation and the knowledge creation activities that took place within their projects. The findings suggest that open-ended, materially mediated co-invention projects offer ample opportunities for creative cultural participation and practice-based knowledge creation in schools.
Attention Knowledge Network Combining Explicit and Implicit Information

The existing knowledge graph embedding (KGE) method has achieved good performance in recommendation systems. However, the relevancy degree among entities reduces gradually along the spread in the knowledge graph. Focusing on the explicit and implicit relationships among entities, this paper proposes an attention knowledge network combining explicit and implicit information (AKNEI) to effectively capture and exactly describe the correlation between entities in the knowledge graph. First, we design an information-sharing layer (ISL) to realize information sharing between projects and entities through implicit interaction. We innovatively propose a cross-feature fusion module to extract high-order feature information in the model. At the same time, this paper uses the attention mechanism to solve the problem of the decline of information relevance in the process of knowledge graph propagation. Finally, the features of KGE and cross feature fusion module are integrated into the end-to-end learning framework, the item information in the recommendation task and the knowledge graph entity information are interacted implicitly and explicitly, and the characteristics between them are automatically learned. We performed extensive experiments on multiple public datasets that include movies, music, and books. According to the experimental results, our model has a great improvement in performance compared with the latest baseline.
Code-line-level Bugginess Identification: How Far have We Come, and How Far have We Yet to Go?

Background. Code-line-level bugginess identification (CLBI) is a vital technique that can facilitate developers to identify buggy lines without expending a large amount of human effort. Most of the existing studies tried to mine the characteristics of source codes to train supervised prediction models, which have been reported to be able to discriminate buggy code lines amongst others in a target program. Problem. However, several simple and clear code characteristics, such as complexity of code lines, have been disregarded in the current literature. Such characteristics can be acquired and applied easily in an unsupervised way to conduct more accurate CLBI, which also can decrease the application cost of existing CLBI approaches by a large margin. Objective. We aim at investigating the status quo in the field of CLBI from the perspective of (1) how far we have really come in the literature, and (2) how far we have yet to go in the industry, by analyzing the performance of state-of-the-art (SOTA) CLBI approaches and tools, respectively. Method. We propose a simple heuristic baseline solution GLANCE (aimin G at contro L - AN d C ompl E x-statements) with three implementations (i.e., GLANCE-MD, GLANCE-EA, and GLANCE-LR). GLANCE is a two-stage CLBI framework: first, use a simple model to predict the potentially defective files; second, leverage simple code characteristics to identify buggy code lines in the predicted defective files. We use GLANCE as the baseline to investigate the effectiveness of the SOTA CLBI approaches, including natural language processing (NLP) based, model interpretation techniques (MIT) based, and popular static analysis tools (SAT). Result. Based on 19 open-source projects with 142 different releases, the experimental results show that GLANCE framework has a prediction performance comparable or even superior to the existing SOTA CLBI approaches and tools in terms of 8 different performance indicators. Conclusion. The results caution us that, if the identification performance is the goal, the real progress in CLBI is not being achieved as it might have been envisaged in the literature and there is still a long way to go to really promote the effectiveness of static analysis tools in industry. In addition, we suggest using GLANCE as a baseline in future studies to demonstrate the usefulness of any newly proposed CLBI approach.
Fuzzy Gain-Scheduling Based Fault Tolerant Visual Servo Control of Quadrotors

When military and civil missions such as transportation increase, fault tolerant control of unmanned aerial vehicles will be an obligation. Although onboard sensors provide information about the status of a quadrotor, the camera is not included in the list. In this study, visual servo control of quadrotors as a popular method for motion control is addressed. we address a visual servo control system for quadrotors as a popular method for motion control. The feature motions in the image plane are analyzed to reveal the relation between the actuator faults and these motions. Four AI fault approximators, a neural network, an extreme learning machine, a linear support vector machine, and a long short-term memory are used to approximate actuator faults of a quadrotor while using feature inputs. The results are convincing and the approximation results are used by a fuzzy logic unit to provide gain-scheduling based fault tolerant control. The proposed system shows sufficient results as a visual servo system for fixed and moving feature targets while providing fault tolerance.
Flexible Route Planning for Multiple Mobile Robots by Combining Q–Learning and Graph Search Algorithm

The use of multiple mobile robots has grown significantly over the past few years in logistics, manufacturing and public services. Conflict–free route planning is one of the major research challenges for such mobile robots. Optimization methods such as graph search algorithms are used extensively to solve route planning problems. Those methods can assure the quality of solutions, however, they are not flexible to deal with unexpected situations. In this article, we propose a flexible route planning method that combines the reinforcement learning algorithm and a graph search algorithm for conflict–free route planning problems for multiple robots. In the proposed method, Q–learning, a reinforcement algorithm, is applied to avoid collisions using off–line learning with a limited state space to reduce the total learning time. Each vehicle independently finds the shortest route using the A* algorithm, and Q–learning is used to avoid collisions. The effectiveness of the proposed method is examined by comparing it with conventional methods in terms of computation time and the quality of solutions. Computational results show that for dynamic transportation problems, the proposed method can generate the solutions with approximately 10% of the computation time compared to the conventional Q–learning approach. We found that the required computation time is linearly increased with respect to the number of vehicles and nodes in the problems.