Reviewerly/exHarmony huggingface dataset:
RefCrowd: Grounding the Target in Crowd with Referring Expressions

Crowd understanding has aroused the widespread interest in vision domain due to its important practical significance. Unfortunately, there is no effort to explore crowd understanding in multi-modal domain that bridges natural language and computer vision. Referring expression comprehension (REF) is such a representative multi-modal task. Current REF studies focus more on grounding the target object from multiple distinctive categories in general scenarios. It is difficult to applied to complex real-world crowd understanding. To fill this gap, we propose a new challenging dataset, called RefCrowd, which towards looking for the target person in crowd with referring expressions. It not only requires to sufficiently mine natural language information, but also requires to carefully focus on subtle differences between the target and a crowd of persons with similar appearance, so as to realize fine-grained mapping from language to vision. Furthermore, we propose a Fine-grained Multi-modal Attribute Contrastive Network (FMAC) to deal with REF in crowd understanding. It first decomposes the intricate visual and language features into attribute-aware multi-modal features, and then captures discriminative but robustness fine-grained attribute features to effectively distinguish these subtle differences between similar persons. The proposed method outperforms existing state-of-the-art (SoTA) methods on our RefCrowd dataset and existing REF datasets. In addition, we implement an end-to-end REF toolbox for the deeper research in multi-modal domain. Our dataset and code can be available at: https://qiuheqian.github.io/datasets/refcrowd/.
The Unexplored Terrain of Compiler Warnings

The authors' industry experiences suggest that compiler warnings, a lightweight version of program analysis, are valuable early bug detection tools. Significant costs are associated with patches and security bulletins for issues that could have been avoided if compiler warnings were addressed. Yet, the industry's attitude towards compiler warnings is mixed. Practices range from silencing all compiler warnings to having a zero-tolerance policy as to any warnings. Current published data indicates that addressing compiler warnings early is beneficial. However, support for this value theory stems from grey literature or is anecdotal. Additional focused research is needed to truly assess the cost-benefit of addressing warnings.
Mining confident supervision by prototypes discovering and annotation selection for weakly supervised semantic segmentation

• An effective pipeline for weakly supervised semantic segmentation. • A principal prototypical features discovering (PPFD) mechanism for object localization. • A boundary accuracy measurement based strategy for annotation selection. • Annotation refinement by discovering reliable discriminative regions. Weakly supervised semantic segmentation methods generate proxy annotations for images based on image-level labels, and further optimize the segmentation network on these annotations with various constraints. However, the performance of current segmentation methods is often constrained by the quality of proxy annotations, even if saliency maps can work as strong background priors. Therefore, how to improve the quality of proxy annotations by mining more confident supervision has become a problem worth exploring and challenging. In this paper, we investigate two novel mechanisms to produce proxy annotations with higher quality: (1) designing a principal prototypical features discovering (PPFD) strategy for object localization, which is capable of utilizing image-level labels and coarse pixel-level annotations jointly, and (2) designing an effective annotation selection and refinement (ASR) strategy for improving annotations’ quality. The proposed strategy can localize foreground pixels by means of extracting prototypical feature from activation features of a group of images. Subsequently, the resulting PPFD based localization maps are utilized to generate proxy annotations for weakly supervised semantic segmentation. In addition, ASR is employed to recognize and refine the low-quality proxy annotations by self-judgment based mask scoring and discriminative regions mining. Experimental results on the PASCAL VOC 2012 and COCO 2014 datasets demonstrate that the proposed algorithm performs better than the state-of-the-art methods on the segmentation benchmark.
Mining Idioms in the Wild

Existing code repositories contain numerous instances of code patterns that are idiomatic ways of accomplishing a particular programming task. Sometimes, the programming language in use supports specific operators or APIs that can express the same idiomatic imperative code much more succinctly. However, those code patterns linger in repositories because the developers may be unaware of the new APIs or have not gotten around to them. Detection of idiomatic code can also point to the need for new APIs. We share our experiences in mining imperative idiomatic patterns from the Hack repo at Facebook. We found that existing techniques either cannot identify meaningful patterns from syntax trees or require test-suite-based dynamic analysis to incorporate semantic properties to mine useful patterns. The key insight of the approach proposed in this paper – Jezero – is that semantic idioms from a large codebase can be learned from canonicalized dataflow trees. We propose a scalable, lightweight static analysis-based approach to construct such a tree that is well suited to mine semantic idioms using nonparametric Bayesian methods. Our experiments with Jezero on Hack code show a clear advantage of adding canonicalized dataflow information to ASTs: Jezero was significantly more effective in finding new refactoring opportunities from unannotated legacy code than a baseline that did not have the dataflow augmentation.
Data Analysis and Visualization Application Research on Popular Scenic Spots in China

With the continuous improvement of people’s living standards, tourism has gradually become one of the important ways for people to relax and entertain. The demand of tourists for attractions is constantly changing with the further development of tourism. In order to better show users with travel plans more intuitive information about scenic spots and make them easy to use, crawler technology based on the Scrapy [1] framework is adopted in this paper in order to crawl the information of attraction on Qunar tourism website and save it in MySQL database, then the relevant data obtained will be cleaned and analyzed and the data visualization will be realized by using Echarts [2] and Wordcloud methods. Finally, the combination of Flask, a lightweight web framework, and front-end will be used to complete the data visualization platforms [3]–[5], so as to promote users’ visual perception of data in an intuitive and efficient way.
Blockchain validation framework for COVID-19 testing kit

Throughout the current pandemic situation, the functioning of the Covid-19 testing kits is very critical owing to its necessity in the diagnosis of the disease. The fabrication and supply of these kits is taking place at a large scale, therefore there may be some malpractices in the supply process. The authenticity of the kits is primarily significant and to keep the globe from using the defective kits is a big challenge. Therefore before using the kit, it must have permitted by some authentic and authoritative medical body such as EUA, ICMR, CCMB, etc. So, the validation of these testing kits is vital for the fair conveyance from production houses to the various agencies.. In this paper, we will discuss the framework which assures that the testing kit is approved by different metrics so that any malpractice in the supply chain is detected. The parties which are involved in these supply chain such as Validation, Notary and Manufacturers. Along with the above mentioned parties, buyer also acting an essential part and can inspect whether the kits is approved or not through its batch number. For inspection purpose, the framework is developed using Blockchain technology.
Off road path planning based on hybrid artificial potential field and ant colony algorithm

Aiming at the problem of unmanned vehicle path planning in off-road environment, the terrain three-dimensional grid model is constructed according to the elevation information, and a new algorithm of hybrid artificial potential field and ant colony algorithm is proposed for the purpose of minimum energy consumption. The algorithm constructs the artificial potential field through the slope information, improves the pheromone initialization strategy, and takes the relative path length as the objective function. The simulation results show that the path fluctuation obtained by the algorithm in off-road terrain is small, the energy consumption is relatively low, and the practicability is strong. Compared with the traditional ant colony algorithm, the convergence speed is faster and the result is better.
Criminal Law of Global Digitality

This contribution serves as critique of current criminal law regulations of global digitality. First, it defines criminal law of global digitality as “cybercrime” and examines the history and shortcomings of the term “cybercrime”. Next follows an analysis of the particular global challenges which arise from cybercrime. In the next two sections, the analysis differentiates between legislative and policy approaches to cybercrime, before examining the specific regulations and policies implemented in past decades. It concludes with a discussion of the characteristics of global digitality criminal law and the weaknesses of current cybercrime law. I argue that individual liberties are threatened by cybercrime prohibitions (substantive criminal law). Technical developments and cybercrime regulations also increase the surveillance possibilities of law enforcement authorities ranging from satellite tracking to data mining. Furthermore, respect for the suspect’s procedural rights, privacy rights and rule-of-law values plays a minor role in cybercrime legislation (procedural criminal law). In addition, the global dimension of cybercrime provokes jurisdictional conflicts that must be addressed, for example, when it comes to determining where the crime was committed. However, these challenges raise different issues of principle that are widely debated among critics of cybercrime law.
A Software Impact Analysis Tool based on Change History Learning and its Evaluation

Software change impact analysis plays an important role in controlling software evolution in the maintenance of continuous software development. We developed a tool for change impact analysis, which machine-learns change histories and directly outputs candidates of the components to be modified for a change request. We applied the tool to real project data to evaluate it with two metrics: coverage range ratio and accuracy in the coverage range. The results show that it works well for software projects having many change histories for one source code base.
DcR3 in All Its Aspects

Decoy receptor 3 (DcR3) is a supermember of tumor necrosis receptor (TNF) factor.DcR3 acts as a binding partner in multiple apoptotic ligands that inhibit apoptosis.TNF-associated apoptosis-inducing ligand (TRAIL)-induced apoptosis has been demonstrated to be sensitized by DcR3.DcR3 has been found to be a ''pleiotropic'' soluble factor with ''decoy'' and ''non-decoy'' activities that control cell functions.The connection between Fas and FasL can be inhibited by recombinant DcR3 coupled with an IgG1 Fc domain.TNF superfamily FasL can inhibit apoptosis and increase angiogenesis through neutralizing members of LIGHT and TL1A.DcR3 serum level is almost undetectable in most normal individuals without inflammatory diseases and cancer.According to several research, the level of DcR3 in the blood is linked to cancer stage in cancer patients.High DcR3 levels in serum or tissues have been found to be associated with poor prognosis and/or resistance to therapy in some cancer patients.As a result, identifying the cut-off value of the DcR3 serum level will provide to able to forecast the severity of disease in the future.While inhibiting DcR3 expression may slow tumor growth, improving DcR3-mediated effector functions could be a viable strategy for reducing autoimmunity and promoting tissue healing.Thus, recombinant DcR3 is a promising immunotherapeutic agent; yet, in the malignant environment, turning off DcR3 expression may improve cancer therapy success.The purpose of this review is to provide clinical convenience by collecting the findings of studies on DcR3 so far.These discoveries could help with cancer diagnosis, differentiation, metastasis, and stage detection.Furthermore, these may provide new therapeutic approaches to target carcinomas in the future.
Power to the city: Assessing the rooftop solar photovoltaic potential in multiple cities of Ecuador

Abstract Solar energy plays a crucial role in helping cities to decentralize energy production and thus decarbonize the energy mix. Reliable resource assessments are needed to support the deployment of solar power systems, especially in cities of developing countries where large solar potential remains untapped. The aim of this work is to assess the potential of rooftop solar photovoltaic (PV) in three populated cities in Ecuador’s mainland (Quito, Guayaquil and Cuenca) and in the Galapagos Islands. The assessment involves (i) the estimation of the available rooftop area based on geographic information system data, (ii) the calculation of energy yield based on hourly satellite-derived irradiance and meteorological data, and (iii) the economic feasibility assessment in terms of levelized cost of electricity (LCOE) compared to representative electricity tariffs. In addition, a sensitivity analysis is carried out to assess the variability of the estimated technical and economic potential with respect to changes in the input parameters. The results reveal a total available rooftop area of about 144 km 2 , mainly concentrated in urban parishes. The estimated energy yield is 16.94 ± 3.38 TWh/a, which could cover almost twice the annual energy consumption in 2019 of the study areas. The economic assessment shows that the LCOE ranges between 7.65 and 21.12 USD cents/kWh. However, the comparison of LCOE against representative residential tariff suggests that rooftop PV technology is not cost-competitive under most of the financial scenarios. The findings from this study will be of interest for local authorities and other decision makers to design policies and financing strategies to increase the penetration of rooftop PV and thus exploiting the large potential assessed in the study areas. The described methodology can be used for assessing the potential in other regions of Ecuador and thereby support the diversification and decarbonization of the energy mix in the country.
Biometric Voting System using LABVIEW

There are various methods to avoid fraudulence in the voting system. The main aim of the biometric voting system using LABVIEW is to improve the Indian voting system. By implementing the Bio-metric process, we can make the polling process more secure and efficient than the existing one. Every resident of India has a right to cast a vote and to utilize this right the election process should happen fairly and efficient. The election commission of India has a prime responsibility to implement the most efficient methods for voting. The political race commission of India has various sorts of casting a ballot framework; for example, emit expressive dance paper technique, and presently Electronic Voting Machine ( EVM’s) and Voter Verified Paper Audit Trail (VVPAT) casting ballot machines. The ongoing democratic framework has EVMs which can be hacked without any problem. There are many methods adopted to avoid malpractices. Here we’re using Global System for Mobile communication (GSM) module for sending messages to the citizens who cast their vote, that “your vote cast successfully, thank you," and also send alert notifications to assigned police in charge near the polling booth if the voter tries to reattempt to cast a vote. The person at the station must put their finger on the digits so that the veteran can get the digit identification card. This data is sent to the LABVIEW Control Unit for verification. The LABVIEW control unit receives and compares data from the reader with data already collected during voting registration. Individuals will be allowed to vote if information related to fingerprint information is contained in the database. Individual votes are prohibited unless a warning message is displayed in the dialog box.
Code Reviewer Recommendation in Tencent: Practice, Challenge, and Direction*

Code review is essential for assuring system quality in software engineering. Over decades in practice, code review has evolved to be a lightweight tool-based process focusing on code change: the smallest unit of the development cycle, and we refer to it as Modern Code Review (MCR). MCR involves code contributors committing code changes and code reviewers reviewing the assigned code changes. Such a reviewer assigning process is challenged by efficiently finding appropriate reviewers. Recent studies propose automated code reviewer recommendation (CRR) approaches to resolve such challenges. These approaches are often evaluated on open-source projects and obtain promising performance. However, the code reviewer recommendation systems are not widely used on proprietary projects, and most current reviewer selecting practice is still manual or, at best, semi-manual. No previous work systematically evaluated these approaches&#x2019; effectiveness and compared each other on proprietary projects in practice. In this paper, we performed a quantitative analysis of typical recommendation approaches on proprietary projects in Tencent. The results show an imperfect performance of these approaches on proprietary projects and reveal practical challenges like the &#x201C;cold start problem&#x201D;. To better understand practical challenges, we interviewed practitioners about the expectations of applying reviewer recommendations to a production environment. The interview involves the current systems&#x2019; limitations, expected application scenario, and information requirements. Finally, we discuss the implications and the direction of practical code reviewer recommendation tools.
Trend analysis and forecasting of publication activities by Indian computer science researchers during the period of 2010–23

Abstract Huge collections of published research documents are available in various repositories in Indian universities and research organizations. The efficient retrieval, estimation of research trends, and identification of research gaps with the global trend in different research areas, can be a great guiding tool for regulating research in the appropriate direction. This research attempts to analyse the trend of research activities carried out by Indian researchers in the discipline of Computer Science from 2010 to 19 and forecast the upcoming research trend for the years 2020–23. A repository of the abstracts based on domains given in the Computer Science Ontology (CSO) published by the Indian researchers is developed from the Scopus database. Document Index Graph document representation model is used to store the repository, shared phrases across the documents are extracted and phrase‐based similarity is computed. Combining the Single‐term and Phrase‐based similarity, the hybrid similarity is generated and similar documents are clustered using the DBSCAN clustering technique. Topics are identified for each cluster using the Latent Dirichlet Allocation algorithm and are automatically labelled using CSO. For each topic, the trend analysis and forecasting have been done using the Auto‐Regressive Integrated Moving Average. For the assessment of the forecasting performance, the dataset from 2010 to 17 is used as a training dataset and 2018–19 as a testing dataset. The average forecasting for the year 2018 for all CSO domains belongs to the Good forecasting category with Mean Absolute Percentage Error (MAPE) 18.34, and 2019 shows reasonable forecasting with MAPE 30.20 as per the MAPE interpretation given by Lewis. For each topic the average forecasting for years 2018–19 shows either Highly accurate, Good or Reasonable forecasting. As a result, the top four domains for the years 2020–23 are also identified which can help initial researchers in the identification of a relevant topic for research and exploration.
Complementing Classroom Learning Experience by Leveraging Educational Apps: A Study of Selected Undergraduates in Ghana

Using technology in education has lately become a necessary complement if not a replacement for traditional learning.Learners tend to support, enhance, and personalize their learning experiences with educational technologies.Some tertiary institutions officially provide educational apps to all students.Meanwhile, in the absence of such provisions, these technologies may be adopted by students.In any case, it is relevant to ascertain whether undergraduates leverage educational apps to complement their learning experiences and also, find out the types of apps they rely on to augment their traditional style of learning.Hence, using cluster and simple random sampling techniques, this study sampled 800 undergraduate respondents from four public universities in Ghana with a population of about 82,825 undergraduates.A questionnaire made up of both open-ended and close-ended questions was used to collect data.Descriptive statistics based on frequencies and percentages, and content analysis were used in the analysis and interpretation.The study reveals that majority of students partially depend on educational apps, while a significant minority depends fully on these apps.Consequently, some discussions that may encourage and motivate undergraduates to complement traditional learning with educational software which can result in a better learning experience are revealed.
Study of Multidimensional and High-Precision Height Model of Youth Based on Multilayer Perceptron

Predicting the adult height of children accurately has great social value for the selection of outstanding athlete as well as early detection of children's growth disorders. Currently, the mainstream method used to predict adult height in China has three problems: its standards are not uniform; it is stale for current Chinese children; its accuracy is not satisfactory. This article uses the data collected by the Chinese Children and Adolescents' Physical Fitness and Growth Health Project in Zhejiang primary and secondary schools. We put forward a new multidimensional and high-precision youth growth curve prediction model, which is based on multilayer perceptron. First, this model uses multidimensional growth data of children as predictors and then utilizes multilayer perceptron to predict the children's adult height. Second, we find the Table of Height Standard Deviation of Chinese Children and fit the data of zero standard deviation to obtain the curve. This curve is regarded as Chinese children's mean growth curve. Third, we use the least-squares method and the mean curve to calculate the individual growth curve. Finally, the individual curve can be used to predict children's state height. Experimental results show that this adult height prediction model's accuracy (between 2 cm) of boys and girls reached 90.20% and 88.89% and the state height prediction accuracy reached 77.46% and 74.93%. Compared with Bayley-Pinneau, the adult height prediction is improved 19.61% for boys and 13.33% for girls. Compared with BoneXpert, the adult height prediction is improved 25.49% for boys and 6.67% for girls. Compared with the method based on the bone age growth map, the adult height prediction is improved 15.69% for boys and 24.45% for girls.