Reviewerly/exHarmony huggingface dataset:
A systematic literature review on the impact of formatting elements on code legibility

Context: Software programs can be written in different but functionally equivalent ways. Even though previous research has compared specific formatting elements to find out which alternatives affect code legibility, seeing the bigger picture of what makes code more or less legible is challenging. Goal: We aim to find which formatting elements have been investigated in empirical studies and which alternatives were found to be more legible for human subjects. Method: We conducted a systematic literature review and identified 15 papers containing human-centric studies that directly compared alternative formatting elements. We analyzed and organized these formatting elements using a card-sorting method. Results: We identified 13 formatting elements (e.g., indentation) and 33 levels of formatting elements (e.g., two-space indentation), which are about formatting styles, spacing, block delimiters, long or complex code lines, and word boundary styles. While some levels were found to be statistically better than other equivalent ones in terms of code legibility, e.g., appropriate use of indentation with blocks, others were not, e.g., formatting layout. For identifier style, we found divergent results, where one study found a significant difference in favor of camel case, while another study found a positive result in favor of snake case. Conclusion: The number of identified papers, some of which are outdated, and the many null and contradictory results emphasize the relative lack of work in this area and underline the importance of more research. There is much to be understood about how formatting elements influence code legibility before the creation of guidelines and automated aids to help developers make their code more legible.
The Construction of Judgment Model for Cultivating Intelligent Accounting Professionals in Universities Based on Logarithmic Periodic Power Law Model

Abstract The talent training evaluation model not only helps to evaluate the talent itself but also provides feedback on the content of the talent training evaluation. Therefore, this paper establishes an efficient and intelligent talent training evaluation model for accounting professionals based on the logarithmic cycle power law model. The main content of talent training evaluation is set as general knowledge skills, professional thinking, and values. The log-periodic power-law model and the least squares method are combined to reduce the dimensionality of the nonlinear parameters of the judging content and to quantify the judging of intelligent accounting professional talent training in universities, which is convenient for the calculation of linear functions. With the help of log-periodic power-law oscillation to prove that talent training is changing in a cyclical pattern, the feasibility of its prediction is demonstrated. The study shows that the talent cultivation judgment model constructed based on the log-periodic power-law model is very accurate, especially in talent cultivation value judgment prediction. The model achieves zero error in the prediction of some data, and the maximum error between prediction and actual is only 6%. In the judgment of general knowledge and skill cultivation, the maximum error between the prediction and the actual score of the model is no more than 2 points. This shows that the talent development evaluation model based on the log-periodic power law model can make accurate predictions of talent development evaluation.
BARC: Breed-Augmented Regression Using Classification for 3D Dog Reconstruction from Images

Abstract The goal of this work is to reconstruct 3D dogs from monocular images. We take a model-based approach, where we estimate the shape and pose parameters of a 3D articulated shape model for dogs. We consider dogs as they constitute a challenging problem, given they are highly articulated and come in a variety of shapes and appearances. Recent work has considered a similar task using the multi-animal SMAL model, with additional limb scale parameters, obtaining reconstructions that are limited in terms of realism. Like previous work, we observe that the original SMAL model is not expressive enough to represent dogs of many different breeds. Moreover, we make the hypothesis that the supervision signal used to train the network, that is 2D keypoints and silhouettes, is not sufficient to learn a regressor that can distinguish between the large variety of dog breeds. We therefore go beyond previous work in two important ways. First, we modify the SMAL shape space to be more appropriate for representing dog shape. Second, we formulate novel losses that exploit information about dog breeds . In particular, we exploit the fact that dogs of the same breed have similar body shapes. We formulate a novel breed similarity loss , consisting of two parts: One term is a triplet loss, that encourages the shape of dogs from the same breed to be more similar than dogs of different breeds. The second one is a breed classification loss . With our approach we obtain 3D dogs that, compared to previous work, are quantitatively better in terms of 2D reconstruction, and significantly better according to subjective and quantitative 3D evaluations. Our work shows that a-priori side information about similarity of shape and appearance, as provided by breed labels, can help to compensate for the lack of 3D training data. This concept may be applicable to other animal species or groups of species. We call our method BARC (Breed-Augmented Regression using Classification). Our code is publicly available for research purposes at https://barc.is.tue.mpg.de/ .
Towards Automated Ethogramming: Cognitively-Inspired Event Segmentation for Streaming Wildlife Video Monitoring

Abstract Advances in visual perceptual tasks have been mainly driven by the amount, and types, of annotations of large-scale datasets. Researchers have focused on fully-supervised settings to train models using offline epoch-based schemes. Despite the evident advancements, limitations and cost of manually annotated datasets have hindered further development for event perceptual tasks, such as detection and localization of objects and events in videos. The problem is more apparent in zoological applications due to the scarcity of annotations and length of videos-most videos are at most ten minutes long. Inspired by cognitive theories, we present a self-supervised perceptual prediction framework to tackle the problem of temporal event segmentation by building a stable representation of event-related objects. The approach is simple but effective. We rely on LSTM predictions of high-level features computed by a standard deep learning backbone. For spatial segmentation, the stable representation of the object is used by an attention mechanism to filter the input features before the prediction step. The self-learned attention maps effectively localize the object as a side effect of perceptual prediction. We demonstrate our approach on long videos from continuous wildlife video monitoring, spanning multiple days at 25 FPS. We aim to facilitate automated ethogramming by detecting and localizing events without the need for labels. Our approach is trained in an online manner on streaming input and requires only a single pass through the video, with no separate training set. Given the lack of long and realistic (includes real-world challenges) datasets, we introduce a new wildlife video dataset–nest monitoring of the Kagu (a flightless bird from New Caledonia)–to benchmark our approach. Our dataset features a video from 10 days (over 23 million frames) of continuous monitoring of the Kagu in its natural habitat. We annotate every frame with bounding boxes and event labels. Additionally, each frame is annotated with time-of-day and illumination conditions. We will make the dataset, which is the first of its kind, and the code available to the research community. We find that the approach significantly outperforms other self-supervised, traditional (e.g., Optical Flow, Background Subtraction) and NN-based (e.g., PA-DPC, DINO, iBOT), baselines and performs on par with supervised boundary detection approaches (i.e., PC). At a recall rate of 80%, our best performing model detects one false positive activity every 50 min of training. On average, we at least double the performance of self-supervised approaches for spatial segmentation. Additionally, we show that our approach is robust to various environmental conditions (e.g., moving shadows). We also benchmark the framework on other datasets (i.e., Kinetics-GEBD, TAPOS) from different domains to demonstrate its generalizability. The data and code are available on our project page: https://aix.eng.usf.edu/research_automated_ethogramming.html
Dress code for infrared safe scattering in QED

We study the $S$-matrix and inclusive cross-section for general dressed states in quantum electrodynamics. We obtain an infrared factorization formula of the $S$-matrix elements for general dressed states. It enables us to study what dressed states lead to infrared-safe $S$-matrix elements. The condition for dressed states can be interpreted as the memory effect which is nothing but the conservation law of the asymptotic symmetry. We derive the generalized soft photon theorem for general dressed states. We also compute inclusive cross-section using general dressed states. It is necessary to use appropriate initial and final dressed states to evaluate interference effects, which cannot be computed correctly by using Fock states due to the infrared divergence.
Real Time Web-based System to Detect Military Aircraft Using RESNET-50 Algorithm

As target detection in remote sensing imaging depends on aircraft type recognition, it is essential in both civil and military applications. The job is made more difficult by the existence of fine-grained features, which can result in significant intra-class changes due to variations in size, posture, and angle, as well as modest inter class changes due to very similar subcategories. This kind of system can be helpful for military security as recognition of the type of aircraft is very critical to the decisions being made. There are several existing ways which uses methods like Radar System and Radio footprints, Speed etc., to detect type of Aircraft. Although these methods are massively costly and still cannot detect the type of Aircraft accurately. In this paper aircraft is detected using ResNet-50, Advance State of Art Object Detection Algorithm implementing in Anaconda tool with train accuracy is 98% &amp; validate accuracy is 75%. A crucial area of artificial intelligence is object detection, which enables computer systems to perceive their surroundings by identifying things in visual pictures or movies. In case of any dangerous Aircraft, the system will have capability to raise alarm and Alert using Audio Sirens. The software requirement for this project is python, 3.6/anaconda, or newer and necessary python modules.
Fine-grained similarity semantic preserving deep hashing for cross-modal retrieval

Cross-modal hashing methods have received wide attention in cross-modal retrieval owing to their advantages in computational efficiency and storage cost. However, most existing deep cross-modal hashing methods cannot employ both intra-modal and inter-modal similarities to guide the learning of hash codes and ignore the quantization loss of hash codes, simultaneously. To solve the above problems, we propose a fine-grained similarity semantic preserving deep hashing (FSSPDH) for cross-modal retrieval. Firstly, this proposed method learns different hash codes for different modalities to preserve the intrinsic property of each modality. Secondly, the fine-grained similarity matrix is constructed by using labels and data features, which not only maintains the similarity between and within modalities. In addition, quantization loss is used to learn hash codes and thus effectively reduce information loss caused during the quantization procedure. A large number of experiments on three public datasets demonstrate the advantage of the proposed FSSPDH method.
Integrating collective know-how for multicriteria decision support in agrifood chains—application to cheesemaking

Agrifood chain processes are based on a multitude of knowledge, know-how and experiences forged over time. This collective expertise must be shared to improve food quality. Here we test the hypothesis that it is possible to design and implement a comprehensive methodology to create a knowledge base integrating collective expertise, while also using it to recommend technical actions required to improve food quality. The method used to test this hypothesis consists firstly in listing the functional specifications that were defined in collaboration with several partners (technical centers, vocational training schools, producers) over the course of several projects carried out in recent years. Secondly, we propose an innovative core ontology that utilizes the international languages of the Semantic Web to effectively represent knowledge in the form of decision trees. These decision trees will depict potential causal relationships between situations of interest and provide recommendations for managing them through technological actions, as well as a collective assessment of the efficiency of those actions. We show how mind map files created using mind-mapping tools are automatically translated into an RDF knowledge base using the core ontological model. Thirdly, a model to aggregate individual assessments provided by technicians and associated with technical action recommendations is proposed and evaluated. Finally, a multicriteria decision-support system (MCDSS) using the knowledge base is presented. It consists of an explanatory view allowing navigation in a decision tree and an action view for multicriteria filtering and possible side effect identification. The different types of MCDSS-delivered answers to a query expressed in the action view are explained. The MCDSS graphical user interface is presented through a real-use case. Experimental assessments have been performed and confirm that tested hypothesis is relevant.
Controlling Media Player Using Hand Gestures

Computer usage is increasing rapidly day by day but the input devices are limited and to access them, we need to be near the screen. To overcome this problem and control the screen, we can use hand gestures. For every operation, we used different hand gestures. We proposed a python program to control the media player through hand gestures. In this method, we used libraries like OpenCV, Media Pipe, PyAuto GUI, and other libraries to capture the video, provide ready-to-use ML solutions and automate your GUI and programmatically control your keyboard and mouse. Hand gestures will be used as the input for providing natural interaction by reducing external hardware interaction. The whole process is divided into two steps. Firstly, gesture recognition through the camera is done by OpenCV and media Pipe helps to identify the gesture b its position, and the respective command is executed. Secondly, PyAuto GUI is used to automate the keyboard and controls the media player.
Unifying Splitting

AVATAR is an elegant and effective way to split clauses in a saturation prover using a SAT solver. But is it refutationally complete? And how does it relate to other splitting architectures? To answer these questions, we present a unifying framework that extends a saturation calculus (e.g., superposition) with splitting and that embeds the result in a prover guided by a SAT solver. The framework also allows us to study locking, a subsumption-like mechanism based on the current propositional model. Various architectures are instances of the framework, including AVATAR, labeled splitting, and SMT with quantifiers.
Human skeleton behavior recognition model based on multi-object pose estimation with spatiotemporal semantics

Abstract Multi-object pose estimation in surveillance scenes is challenging and inaccurate due to object motion blur and pose occlusion in video data. Targeting at the temporal dependence and coherence among video frames, this paper reconstructs a multi-object pose estimation model that integrates spatiotemporal semantics for different scales and poses of video multi-objects. The model firstly, with an end-to-end detection framework, detects multiple targets in the video. Secondly, it enhances the positioning of key points of human body using the temporal cues among video frames and designs modular components to enrich the pose information, effectively refining the pose estimation. Finally, the improved human skeleton behavior recognition model based on pose estimation is employed to recognize the classroom behaviors of students oriented to video streams. Comparison with multiple classifiers through experiments reveals that the human skeleton behavior recognition model for multi-object pose estimation combined with spatiotemporal semantics exhibits an effectively improved accuracy.
Cryptocurrency Tracker Using ReactJs

It is a web-application developed by using ReactJS, in this application we can see the cryptocurrency live stats and all the cryptocurrency market cap, We can also see the change of price in last 24 hours. This app can convert the change into graphical representation it will also tell the ranking of each cryptocurrency according to their price. The Cryptocurrency tracker is a cross platform application, which tracks the crypto currency. The app is developed using Vscode, ReactJs, Git, Coingecko, and News data.dev. firebase cryptocurrency flutter fire base authfigma fire store cryptocurrency-tracker coingecko-api. Cryptocurrency is a digital payment system that doesn't rely on banks to verify transactions. It’s a peer-to-peer system that can enable anyone anywhere to send and receive payments. Instead of being physical money carried around and exchanged in the real world, cryptocurrency payments exist purely as digital entries to an online database describing specific transactions. When you transfer cryptocurrency funds, the transactions are recorded in a public ledger. Cryptocurrency is stored in digital wallets. Cryptocurrency received its name because it uses encryption to verify transactions. This means advanced coding is involved in storing and transmitting cryptocurrency data between wallets and to public ledgers. The aim of encryption is to provide security and safety. The first cryptocurrency was Bitcoin, which was founded in 2009 and remains the best known today. Much of the interest in cryptocurrencies is to trade for profit, with speculators at times driving prices skyward
Target localization network based on optical flow for object tracking

Object tracking is a very challenging branch in the field of computer vision and plays an extremely important role in different fields. However, most algorithms still have no good robustness when the target is out of view, fast motion and low resolution. Therefore, this paper proposes a method based on Siamese architecture and optical flow to address these situations. The tracking framework is mainly divided into tracking network and target localization network based on optical flow. The tracking network uses SiamBAN and the target localization network uses LiteFlowNet3 to estimate the optical flow information of target. The proposed method in this paper expands the search area of SiamBAN, and uses the target localization network to reposition the target when tracking fails. The target localization network can be easily embedded into the existing Siamese network to provide more accurate location of target during the tracking process. Experiments results on VOT2016, VOT2018 show that this method has good robustness against extreme conditions.
Research and application of MetaEdit+ based modeling language for Hot working domain

A software modeling method for hot working domain process systems based on domain metamodeling is proposed to address the problems in the development of Hot working domain process systems. Combining metamodeling, domain modeling and MDA to realize model-based Hot working process system development, the abstract syntax, concrete syntax and semantics of the domain modeling language are designed using MetaEdit+, a metamodeling tool. And based on this, a heat treatment process model was established. The method effectively improves the efficiency and model reusability of process system development software in the hot working domain.
A multi-modal model based on transformers for medical visual question answering

Compared with the general Visual Question Answering (VQA), Medical VQA is more challenging. Medical images contain more complex information than general images. Aiming at this point, we propose the IIF module that can improve the model's ability to obtain visual feature. In addition, we design QAM to help the model analyze the question better. On the VQA-RAD dataset, the accuracy of our model improved to 66.4% on the opened-ended questions and 80.1% on the closed-ended questions, outperforming other relevant models. The results on the VQA-MED 2019 dataset also verify the effectiveness of our model.
A multitype software buffer overflow vulnerability prediction method based on a software graph structure and a self-attentive graph neural network

Buffer overflow vulnerabilities are one of the most common and dangerous software vulnerabilities; however, the complexity of software code makes predicting buffer overflow vulnerabilities in software challenging. To accurately predict multiple types of software buffer overflow vulnerabilities, this paper proposes a multitype software buffer overflow vulnerability prediction method called MSVAGraph that is based on the graph structure of software and a self-attentive graph neural network. First, by analyzing software buffer overflow type vulnerabilities, a vulnerability feature set GSVFset extraction method based on graph structure is proposed to act as the software's basic unit. Second, a self-attentive pooling mechanism is used to design a vulnerability feature update mechanism based on a self-attentive graph neural network to transform the graph structure of the vulnerability feature set GSVFset into a feature vector representation. Finally, based on the updated GSVFset feature vector, a time-recursive-based neural network is designed to construct a prediction method for multitype software buffer overflow vulnerabilities. The method proposed in this paper validates executable programs of four types of buffer overflow vulnerabilities in the Juliet dataset using precision, accuracy, recall and F1 value as evaluation metrics. The prediction results have higher values after introducing the self-attentive pooling mechanism. The proposed MSVAGraph achieves high precision, accuracy, recall and F1 value, and can better preserve the network topology and node content information of graphs in the software's graph structure.
Fast saliency prediction based on multi-channels activation optimization

The saliency prediction precision has improved rapidly with the development of deep learning technology, but the inference speed is slow due to the continuous deepening of networks. Hence, this paper proposes a fast saliency prediction model. Concretely, the siamese network backbone based on tailored EfficientNetV2 accelerates the inference speed while maintaining high performance. The shared parameters strategy further curbs parameter growth. Furthermore, we add multi-channel activation maps to optimize the fine features considering different channels and low-level visual features, which improves the interpretability of the model. Extensive experiments show that the proposed model achieves competitive performance on the standard benchmark datasets, and prove the effectiveness of our method in striking a balance between prediction accuracy and inference speed. Moreover, the small model size allows our method to be applied in edge devices. The code is available at: https://github.com/lscumt/fast-fixation-prediction.