Reviewerly/exHarmony huggingface dataset:
eknows: Platform for Multi-Language Reverse Engineering and Documentation Generation

Software documentation is an asset for many activities in maintenance and evolution of software. To alleviate the problem of outdated or lost documentation and to improve program understanding, reverse engineering tools and documentation generators have been developed and used during development, maintenance, and evolution of software systems. In this paper, we report on the development of six tools that address different programming languages (e.g. COBOL, Java, and C), problem domains (e.g. banking, insurance, and engineering), and SE activities (e.g. maintenance and migration). While the development of these tools was driven by domain-specific requirements, an architecture that supports reuse of components for the analysis and visualization of software from different programming languages was pursued from the beginning. This resulted in a polyglot software platform for building reverse engineering tools and documentation generators. The software platform provides a modular set of programming language frontends, static program analysis components and document/diagram generators. To evaluate effectiveness of the platform for tool creation, we conducted an industrial case study and discuss results on reuse potential, adoption of new languages and usage of a generic intermediate representation.
Dynamic video mix-up for cross-domain action recognition

In recent years, action recognition has been extensively studied. For some general action datasets, such as UCF101 [1], the recognition accuracy in a specific domain can reach 95%. However, due to the existence of the domain-wise discrepancy, the performance of the model will be significantly reduced when deployed to realistic scenes. Therefore, to support the generalization of the action recognition model in practical scenes, the cross-domain problem should be addressed urgently. In this paper, we propose a cross-domain video data fusion mechanism to reduce the difference between domains. Our method is different from existing methods in two points: (1) Instead of performing mix-up at the feature-level, we propose to execute the mix-up directly at the input-level, which introduces more original information beyond the middle features. In addition, a progressive learning method is introduced for adaptive cross-domain fusion. (2) To make full use of the action class knowledge from the source domain, we also propose pseudo-label guided mix-up data learning. Note that only top-ranking confident pseudo labels are selected to ensure the stable similarity between the source and target domains. We evaluate the proposed method on two widely used cross-domain datasets, including the UCF101-HMDB51full and UCF-Olympic. Extensive experimental results have shown that the proposed method is effective and achieves the state-of-the-art performance. In the HMDB51(source domain)→ UCF101(target domain) direction, the accuracy of our method can reach 98.60%, which is 9.54% improvement over the existing state-of-the-art method.
A Provable Secure and Lightweight Smart Healthcare Cyber-Physical System With Public Verifiability

The recent spades of cyber-attacks have compromised patients’ data security and privacy in healthcare cyber-physical systems (CPS) in the era of health 4.0 that integrates the Bio-Medical sensors, Internet of Things (IoT), and cloud technologies. The current research problem with a secure healthcare system for resource-constrained environment is the cost at which a secure healthcare system is achieved. Most existing systems either preserve security and privacy but could not be suitable for a limited resource environment or achieve efficiency but are later found insecure. To solve this issue, this article oversees the equilibrium between the security and efficiency for IoT-enabled secure healthcare platforms. We cryptanalysis Kumar and Chand's system and find their system is insecure due to which the system loses mutual authentication. In this article, we proposed a secure and lightweight smart healthcare CPS system. While preserving the privacy of the system, integrity of patient data on the cloud, and public verifiability of data in the least communication and computation. Additionally, we prove that the proposed solution is secure against indistinguishability under chosen-ciphertext attack and forgeability under chosen message attack.
Classification of fluorescent R-Band metaphase chromosomes using a convolutional neural network is precise and fast in generating karyograms of hematologic neoplastic cells

Karyotype analysis has a great impact on the diagnosis, treatment and prognosis in hematologic neoplasms. The identification and characterization of chromosomes is a challenging process and needs experienced personal. Artificial intelligence provides novel support tools. However, their safe and reliable application in diagnostics needs to be evaluated. Here, we present a novel laboratory approach to identify chromosomes in cancer cells using a convolutional neural network (CNN). The CNN identified the correct chromosome class for 98.8% of chromosomes, which led to a time saving of 42% for the karyotyping workflow. These results demonstrate that the CNN has potential application value in chromosome classification of hematologic neoplasms. This study contributes to the development of an automatic karyotyping platform.
Strategic oscillation for the balanced minimum sum-of-squares clustering problem

In the age of connectivity, every person is constantly producing large amounts of data every minute: social networks, information about trips, work connections, etc. These data will only become useful information if we are able to analyze and extract the most relevant features from it, which depends on the field of analysis. This task is usually performed by clustering data into similar groups with the aim of finding similarities and differences among them. However, the vast amount of data available makes traditional analysis obsolete for real-life datasets. This paper addresses the problem of dividing a set of elements into a predefined number of equally-sized clusters. In order to do so, we propose a Strategic Oscillation approach combined with a Greedy Randomized Adaptive Search Procedure. The computational experiments section firstly tunes the parameters of the algorithm and studies the influence of the proposed strategies. Then, the best variant is compared with the current state-of-the-art method over the same set of instances. The obtained results show the superiority of the proposal using two different clustering metrics: MSE (Mean Square Error) and Davies-Bouldin index.
Augmented and personalized digital narratives for Cultural Heritage under a tangible interface

Digital storytelling is widely used to enhance the process of integrating images, music, narrative and voice along with traditional storytelling methods. In recent years, newer visualization technologies such as Augmented and Virtual Reality allow more vivid representations and further influence the way museums present their cultural heritage through interactive narratives. Nowadays, many cultural institutions aim towards integrating such technologies to provide a more engaging experience, which is also tailored to the user by exploiting personalization and context awareness. In this work, the authors present the CHATS platform, a system for personalized digital storytelling in cultural heritage sites, enhanced with state-of-the-art visualization techniques. Technologies of AR and Smart Glasses are used to enhance visitors' experience, while also context-aware and personalization methods are used to provide cultural information based on user's profiles and interests.
APAN: Across-Scale Progressive Attention Network for Single Image Deraining

Recent single image deraining works have achieved significant improvement using convolutional neural networks. However, the rain streaks in the rain image share similar patterns with its multi-scale versions, which are not fully exploited in recent works. In this paper, we propose an <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">A</u> cross-scale <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">P</u> rogressive <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">A</u> ttention <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">N</u> etwork ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i.e.,</i> APAN) to explore the multi-scale collaborative representation for single image deraining. Specifically, we represent each rainy image via a multi-scale module. An across-scale attention module is then used to capture long-range feature correspondences from multi-scale features, which can model the rain streaks at an enlarging feature dimension. Afterwards, we construct a pyramid structure and further predict the rain streak progressively, which also guides the across-scale attention module to refine the feature representation from coarse to fine. The proposed model exploits self-similarity of features via an across-scale attention between different scales, which can well model the rain streak with long-range information. Experiments on several datasets show that our model achieves significant improvement compared with most state-of-the-art deraining models.
AutoScore-Survival: Developing interpretable machine learning-based time-to-event scores with right-censored survival data

Scoring systems are highly interpretable and widely used to evaluate time-to-event outcomes in healthcare research. However, existing time-to-event scores are predominantly created ad-hoc using a few manually selected variables based on clinician's knowledge, suggesting an unmet need for a robust and efficient generic score-generating method.AutoScore was previously developed as an interpretable machine learning score generator, integrating both machine learning and point-based scores in the strong discriminability and accessibility. We have further extended it to the time-to-event outcomes and developed AutoScore-Survival, for generating time-to-event scores with right-censored survival data. Random survival forest provided an efficient solution for selecting variables, and Cox regression was used for score weighting. We implemented our proposed method as an R package. We illustrated our method in a study of 90-day survival prediction for patients in intensive care units and compared its performance with other survival models, the random survival forest, and two traditional clinical scores.The AutoScore-Survival-derived scoring system was more parsimonious than survival models built using traditional variable selection methods (e.g., penalized likelihood approach and stepwise variable selection), and its performance was comparable to survival models using the same set of variables. Although AutoScore-Survival achieved a comparable integrated area under the curve of 0.782 (95% CI: 0.767-0.794), the integer-valued time-to-event scores generated are favorable in clinical applications because they are easier to compute and interpret.Our proposed AutoScore-Survival provides a robust and easy-to-use machine learning-based clinical score generator to studies of time-to-event outcomes. It gives a systematic guideline to facilitate the future development of time-to-event scores for clinical applications.
LMGP: Lifted Multicut Meets Geometry Projections for Multi-Camera Multi-Object Tracking

Multi-Camera Multi-Object Tracking is currently drawing attention in the computer vision field due to its superior performance in real-world applications such as video surveillance with crowded scenes or in wide spaces. In this work, we propose a mathematically elegant multi-camera multiple object tracking approach based on a spatial-temporal lifted multicut formulation. Our model utilizes state-of-the-art tracklets produced by single-camera trackers as proposals. As these tracklets may contain ID-Switch errors, we refine them through a novel pre-clustering obtained from 3D geometry projections. As a result, we derive a better tracking graph without ID switches and more precise affinity costs for the data association phase. Tracklets are then matched to multi-camera trajectories by solving a global lifted multicut formulation that incorporates short and long-range temporal interactions on tracklets located in the same camera as well as inter-camera ones. Experimental results on the WildTrack dataset yield near-perfect performance, outperforming state-of-the-art trackers on Campus while being on par on the PETS-09 dataset. We will release our implementations at this link https://github.com/nhmduy/LMGP.
Einstein-Podolsky-Rosen steering based on semisupervised machine learning

Einstein-Podolsky-Rosen (EPR) steering is a kind of powerful nonlocal quantum resource in quantum information processing such as quantum cryptography and quantum communication. Many criteria have been proposed in the past few years to detect the steerability both analytically and numerically. Supervised machine learning such as support vector machines and neural networks have also been trained to detect the EPR steerability. To implement supervised machine learning, one needs a lot of labeled quantum states by using the semidefinite programming, which is very time consuming. We present a semisupervised support vector machine method which only uses a small portion of labeled quantum states in detecting quantum steering. We show that our approach can significantly improve the accuracies by detailed examples.
A Novel Method for Quantitative Analysis of C-Reactive Protein Lateral Flow Immunoassays Images via CMOS Sensor and Recurrent Neural Networks

Objective: To design and implement an easy-to-use, Point-of-Care (PoC) lateral flow immunoassays (LFA) reader and data analysis system, which provides a more in-depth quantitative analysis for LFA images than conventional approaches thereby supporting efficient decision making for potential early risk assessment of cardiovascular disease (CVD). Methods and procedures: A novel end-to-end system was developed including a portable device with CMOS camera integrated with optimized illumination and optics to capture the LFA images produced using high-sensitivity C-Reactive Protein (hsCRP) (concentration level < 5 mg/L). The images were transmitted via WiFi to a back-end server system for image analysis and classification. Unlike common image classification approaches which are based on averaging image intensity from a region-of-interest (ROI), a novel approach was developed which considered the signal along the sample's flow direction as a time series and, consequently, no need for ROI detection. Long Short-Term Memory (LSTM) networks were deployed for multilevel classification. The features based on Dynamic Time Warping (DTW) and histogram bin counts (HBC) were explored for classification. Results: For the classification of hsCRP, the LSTM outperformed the traditional machine learning classifiers with or without DTW and HBC features performed the best (with mean accuracy of 94%) compared to other features. Application of the proposed method to human plasma also suggests that HBC features from LFA time series performed better than the mean from ROI and raw LFA data. Conclusion: As a proof of concept, the results demonstrate the capability of the proposed framework for quantitative analysis of LFA images and suggest the potential for early risk assessment of CVD. Clinical impact: The hsCRP levels < 5 mg/L were aligned with clinically actionable categories for early risk assessment of CVD. The outcomes demonstrated the real-world applicability of the proposed system for quantitative analysis of LFA images, which is potentially useful for more LFA applications beyond presented in this study.
RotoWrist: Continuous Infrared Wrist Angle Tracking using a Wristband

We introduce RotoWrist, an infrared (IR) light based solution for continuously and reliably tracking 2-degree-of-freedom (DoF) relative angle of the wrist with respect to the forearm using a wristband. The tracking system consists of eight time-of-flight (ToF) IR light modules distributed around a wristband. We developed a computationally simple tracking approach to reconstruct the orientation of the wrist without any runtime training, ensuring user independence. An evaluation study demonstrated that RotoWrist achieves a cross-user median tracking error of 5.9° in flexion/extension and 6.8° in radial and ulnar deviation with no calibration required as measured with optical ground truth. We further demonstrate the performance of RotoWrist for a pointing task and compare it against ground truth tracking.
There is more than one way to zen your Python

The popularity of Python can be at least partially attributed to the concept of pythonicity, loosely defined as a combination of good practices accepted within the community. Despite the popularity of both Python itself and the pythonicity of code written in it, this concept has not been studied that well, and the first attempts to define it formally are rather recent. In this paper, we take the next steps in exploring this topic by conducting an independent literature review in order to create a catalogue of pythonic idioms, reproduce the results of a recent paper on the usage of pythonic idioms, perform an external direct replication of it by reusing the same open source toolset and dataset, and extend the body of knowledge by also analysing how the use of pythonic idioms evolve over time in open source codebases.
Domain-adaptive active learning for cost-effective virtual metrology modeling

Virtual metrology (VM) is a promising solution for wafer-to-wafer quality monitoring in the semiconductor manufacturing process. VM alternates physical metrology with a prediction model trained using previous metrology data. Active learning can be used to build a VM model for new equipment efficiently with reduced metrology costs. However, conventional active learning is limited by a low prediction accuracy at its initial stage, which is referred to as the cold-start problem. In this study, we propose a domain-adaptive active learning method to address this issue. Using existing equipment as the source domain, the proposed method initializes the VM model through unsupervised domain adaptation from the source domain to the target domain. Active learning is then performed to iteratively update the VM model toward improving the prediction accuracy. Thus, the metrology cost required to obtain a VM model that satisfies the desired prediction accuracy for the target metrology task can be reduced. The effectiveness of the proposed method is demonstrated experimentally using real-world data from a semiconductor manufacturer.
A General Approach for Traffic Classification in Wireless Networks Using Deep Learning

Traffic Classification (TC) systems allow inferring the application that is generating the traffic being analyzed. State-of-the-art TC algorithms are based on Deep Learning (DL) and have outperformed traditional methods in complex and modern scenarios, even if traffic is encrypted. Most of the works on TC assume the traffic flows on a wired network under the same network management domain. This assumption limits the capabilities of TC systems in wireless networks since users’ traffic on one network domain can be negatively impacted by undetected users’ traffic from other network domains or detected ones but with no traffic context in a shared spectrum. To solve this problem, we introduce a novel framework to achieve TC at any layer on the radio network stack. We propose a spectrum-based procedure that uses a DL-based classifier to realize this framework. We design two DL-based classifiers, a novel Convolutional Neural Network (CNN) spectrum-based TC and a Recurrent Neural Networks (RNN) as baseline architecture, and benchmark their performance on three TC tasks at different radio stack layers. The datasets were generated by combining packet traces from real transmissions with an 802.11 standard-compliant waveform generator. Performance evaluations show that the best model can achieve an accuracy above 92% in the most demanding TC task, a drop of only 4.37% in accuracy compared to a byte-based DL approach, with micro-second per-packet prediction time, which is very promising for delivering real-time spectrum-based traffic analyzers.
Variational contrast-saturation enhancement model for effective single image dehazing

• We propose an adaptive variational model based on saturation and con- trast enhancement for single image dehazing. • An adaptive function is introduced to balance the enhancement between saturation and contrast to prevent excessive artifacts. • A split Bregman algorithm is employed to accomplish an efficient numer- ical implementation for the proposed variational model. • Experimental comparisons demonstrate that the proposed method is com- parable to some popular dehazing methods and even outperforms some of them both qualitatively and quantitatively. Haze removal, also known as image dehazing, plays an important role in image/video processing and computer vision applications. Its main purpose is to eliminate haze to improve the visibility and quality of the image. However, due to the lack of scene information, including depth, transmission, and atmospheric light, haze removal essentially becomes a very challenging inverse problem. In this paper, we propose a novel and highly effective variational model for single image haze removal. The proposed method mainly utilizes a new idea of joint contrast and saturation enhancement. Incorporated with both adaptivity and physical cues, the proposed model is able to produce clear and visually satisfactory dehazed results and simultaneously prevent excessive artifacts caused by over-enhancement. The split Bregman algorithm is employed to accomplish an efficient numerical implementation for the proposed variational model. A number of numerical experiments and comparisons with some representative methods are conducted to demonstrate the good performance of the newly proposed model.
On pencils of cubics on the projective line over finite fields of characteristic &gt;3

In this paper we study combinatorial invariants of the equivalence classes of pencils of cubics on PG(1,q), for q odd and q not divisible by 3. These equivalence classes are considered as orbits of lines in PG(3,q), under the action of the subgroup G≅PGL(2,q) of PGL(4,q) which preserves the twisted cubic C in PG(3,q). In particular we determine the point orbit distributions and plane orbit distributions of all G-orbits of lines which are contained in an osculating plane of C, have non-empty intersection with C, or are imaginary chords or imaginary axes of C.