Miracle-dz/UniFAR-FTUs huggingface dataset:
A Game Strategy of Snatching Ball Based on Area Division

A strategy based on area division is proposed in order to resolve a route-planning problem persistent in the part of ball-scrambling competition. The attribution of this program allows us to divide the whole water pool into three sections, such as defending, offending and pilfering area. After anticipating the potential position of the ball and actually observing the position actually, an according polished-up strategy can be implemented, which turns out to be of great efficiency and convenience. There is no mainstream algorithm about fishing out the ball, so I put forward a “circuitous push-pull” strategy on the fishing out. According to the different position of the robotic fish, it can “push-pull” the ball out of the gate via different routes, propping posture, the action of the head and tail and the controlling of its speed, thus to accomplish the failure in score of the opponents. The strategies are proved to be highly feasible in the International Under-Water Robot Competition serves as the corner-stone of goal scoring.
Characteristics and Model of Vehicle Flow Fluctuation Based on Molecular Dynamics

This paper is based on molecular dynamics and systematically analyzes the fluctuation characteristics of traffic flow. Vehicle following behavior is affected by the leading vehicle and road environment, its operating speed and acceleration present random fluctuations. Abstract the vehicle into interacting molecules, construct molecular following model based on molecular dynamics. Collect the traffic flow characteristic data of test section sample points, study vehicle speed and acceleration fluctuation characteristics, derive of acceleration wave sensitivity coefficient model from the molecular following-up model. Based on the sensitivity coefficient of acceleration fluctuation characteristic, analyze the relationship between acceleration fluctuation and vehicle flow status systematically, study the evolution law of traffic flow in free flow state, synchronous flow state and blocking flow state. Traffic flow fluctuation is the spontaneous behavior of driving vehicles to restore dynamic balance by the driving or stimulation of traffic events, it provides theoretical basis for dynamic characteristics of traffic flow under the background of intelligent network traffic.
Research on Logistics Distribution Routing Problem of Unmanned Vehicles Based on Genetic Simulated Annealing Algorithm

Nowadays, unmanned logistics distribution has become a necessity for development. This paper proposes a vehicle routing model for logistics scheduling and distribution intending to minimize the distribution cost by integrating intelligent scheduling and path planning systems. The genetic simulated annealing algorithm is designed combining with the genetic algorithm and the simulated annealing algorithm. In this scheme, electric unmanned vehicles are used for urban logistics distribution to improve efficiency and reduce costs. At the same time, factors such as vehicle load, customer time window, and vehicle mileage limit are taken into consideration. Finally, the effectiveness and superiority of the proposed algorithm are verified by applying it to solve a specific case.
Sodinokibi intrusion detection based on logs clustering and random forest

Cyber-attacks are becoming more common and their consequences more and more disastrous. Machine learning is revolutionizing cyber security by analyzing massive amounts of data automatically. In this paper, we test the unsupervised learning method of k-means to detect the intrusion of Sodinokibi ransomware in logs. The k-means highlighted a small cluster of anomalous logs that are revealed to be the entry points of the cyberattack. This positive result allows us to consider automating of k-means, as a solution to monitor logs in real time and report abnormal behavior.
A bounded symbolic-size model for symbolic execution

Symbolic execution is a powerful program analysis technique which allows executing programs with symbolic inputs. Modern symbolic execution tools use a concrete modeling of object sizes, that does not allow symbolic-size allocations. This leads to concretizations and enforces the user to set the size of the input ahead of time, thus potentially leading to loss of coverage during the analysis. We present a bounded symbolic-size model in which the size of an object can have a range of values limited by a user-specified bound. Unfortunately, this model amplifies the problem of path explosion, due to additional symbolic expressions representing sizes. To cope with this problem, we propose an approach based on state merging that reduces the forking by applying special treatment to symbolic-size dependent loops. In our evaluation on real-world benchmarks, we show that our approach can lead in many cases to substantial gains in terms of performance and coverage, and find previously unknown bugs.
Flame Temperature Detection and Estimation Model Based on Deep Learning and Ordinary RGB Images

To solve the problems of traditional flame temperature accurate measurement that requires complex equipment, high cost, and difficulty in popularization, it proposes a method of rapid flame temperature monitoring and estimation based on deep learning. To establish a data training set based on RGB estimation of flame temperature, firstly, ordinary RGB and high-speed infrared cameras were used to capture the combustion process at the same time to obtain RGB images and corresponding temperature fields. Secondly, an adjustable modular network structure is established, which includes a video interpolation network, a flame detection network, and a flame temperature estimation network, and the switch training method is used to train the network. To prevent overfitting, a network training algorithm based on genetic algorithm is proposed, so that the training of the flame temperature estimation network is completed efficiently. Finally, the reliability of the calculation model is verified by a typical high-temperature combustion test of the solid propellant. The results show that the error between the calculated value and the measured temperature is only ±5.73%.
Efficient module-level dynamic analysis for dynamic languages with module recontextualization

Dynamic program analysis is a long-standing technique for obtaining information about program execution. We present module recontextualization, a new dynamic analysis approach that targets modern dynamic languages such as JavaScript and Racket, enabled by the fact that they feature a module-import mechanism that loads code at runtime as a string. This approach uses lightweight load-time code transformations that operate on the string representation of the module, as well as the context to which it is about to be bound, to insert developer-provided, analysis-specific code into the module before it is loaded. This code implements the dynamic analysis, enabling this approach to capture all interactions around the module in unmodified production language runtime environments. We implement this approach in two systems targeting the JavaScript and Racket ecosystems. Our evaluation shows that this approach can deliver order-of-magnitude performance improvements over state-of-the-art dynamic analysis systems while supporting a range of analyses, implemented on average in about 100 lines of code.
NALMO: A Natural Language Interface for Moving Objects Databases

Moving objects databases (MODs) have been extensively studied due to their wide variety of applications including traffic management, tourist service and mobile commerce. However, queries in natural languages are still not supported in MODs. Since most users are not familiar with structured query languages, it is essentially important to bridge the gap between natural languages and the underlying MODs system commands. Motivated by this, we design a natural language interface for moving objects, named NALMO. In general, we use semantic parsing in combination with a location knowledge base and domain-specific rules to interpret natural language queries. We design a corpus of moving objects queries for model training, which is later used to determine the query type. Extracted entities from parsing are mapped through deterministic rules to perform query composition. NALMO is able to well translate moving objects queries into structured (executable) languages. We support four kinds of queries including time interval queries, range queries, nearest neighbor queries and trajectory similarity queries. We develop the system in a prototype system SECONDO and evaluate our approach using 240 natural language queries extracted from popular conference and journal papers in the domain of moving objects. Experimental results show that (i) NALMO achieves accuracy and precision 98.1 and 88.1, respectively, and (ii) the average time cost of translating a query is 1.47s.
Research on Traffic Sign Recognition Method Based on Transfer Learning

In order to solve the problems in existing traffic sign recognition algorithms, this paper proposes a traffic sign recognition algorithm based on migration learning. This algorithm proposes to use the NSLC-AE architecture model to reconstruct clean data from dirty data (source and target data) To learn low-dimensional features that are robust to known natural noise (source noise and target noise). That is, in the low-dimensional space, use local constraints and discriminant information to find semantically similar samples for each sample, and construct clean data for model training in the high-dimensional space. Because in the low-dimensional space, high-dimensional natural noise can usually be decoupled into one or several hidden variables, while the interference characteristics in the low-dimensional space can simulate the influence of multiple natural noises. Therefore, this paper further proposes the N-GAN architecture based on the adversarial generative model, and trains the denoiser and mapper in the low-dimensional space in reverse to learn features that are robust to various natural noises. The experimental results show The improved model proposed in this paper can greatly reduce the image blur and the probability of recognition errors in the process of traffic sign recognition.
Health Misinformation Detection in Web Content: A Structural-, Content-based, and Context-aware Approach based on Web2Vec

In recent years, we have witnessed the proliferation of large amounts of online content generated directly by users with virtually no form of external control, leading to the possible spread of misinformation. The search for effective solutions to this problem is still ongoing, and covers different areas of application, from opinion spam to fake news detection. A more recently investigated scenario, despite the serious risks that incurring disinformation could entail, is that of the online dissemination of health information. Early approaches in this area focused primarily on user-based studies applied to Web page content. More recently, automated approaches have been developed for both Web pages and social media content, particularly with the advent of the COVID-19 pandemic. These approaches are primarily based on handcrafted features extracted from online content in association with Machine Learning. In this scenario, we focus on Web page content, where there is still room for research to study structural-, content- and context-based features to assess the credibility of Web pages. Therefore, this work aims to study the effectiveness of such features in association with a deep learning model, starting from an embedded representation of Web pages that has been recently proposed in the context of phishing Web page detection, i.e., Web2Vec.
FTSD: a fissionable lock for multicores

Delegation is the highly efficient solution for parallel synchronization. However, the existing the-state-of-the-art delegation locks offer good performance at the cost of occupying computing cores under moderate contention, but exhibit sub-optimal single-thread performance and non-scalable performance under no and high contention, respectively. In this paper, we present a fissionable lock, called FTSD, which consists of two underlying locks: a TTS lock for lock stealing, which serves as a fast path, and a NUMA-aware delegation lock, which offers scalable performance under high contention. Our evaluation shows that FTSD delivers as good or better performance than other state-of-the-art locks.
Deep Neuro-Evolution: Evolving Neural Network for Character Locomotion Controller

Designing the controller to actuate the virtual character is challenging given the high dimensions of both character states and controls. Flexible user control is normally required in interactive applications (virtual and augmented reality), which further adds to constructing a motion controller for the virtual characters. Although deep neural networks showed their potential in tackling this problem, existing works based on neural networks normally require the manual design of the network architecture, which requires professional experience and a trial-and-error process. This paper proposes an automatic solution to optimize both the topology and connection weights of the deep neural network, used as a character locomotion controller. This method mimics the evolution process in nature, evaluates individual fitness, and generates the individuals for the next generation while maintaining the current generation's fitness level. We show that this method is convenient to construct the locomotion controller for virtual characters on a general basis.
Effective Traffic Forecasting with Multi-Resolution Learning

Traffic forecasting plays a vital role in traffic management systems. Recently, deep learning models have been applied to citywide traffic forecasting. However, the existing work models and predicts traffic at a single (dense) resolution, making it challenging to capture long-range spatial dependencies or high-level traffic dynamics. This shortcoming limits the accuracy of prediction and results in computationally expensive models. We propose a traffic forecasting model based on deep convolutional networks to improve the accuracy of citywide traffic forecasting. Our model uses a hierarchical architecture that captures traffic dynamics at multiple spatial resolutions. Based on this architecture, we apply a multi-task learning scheme, which trains the model to predict traffic at different resolutions. Our model helps provide a coherent understanding of traffic dynamics by capturing spatial dependencies between different regions of a city. Experimental results on multiple real datasets show that our model can achieve competitive results compared to complex state-of-the-art approaches while being more computationally efficient.
Persistent memory aware performance isolation with dicio

Cloud vendors increasingly support persistent memory (PM) in their cloud platform to lower the total cost of ownership (TCO). They run multiple instances on cloud nodes supporting large memory with DRAM and PM. While PM enables the low-cost in-memory computations, adding PM to a cloud raises new challenges of managing the performance interference between co-running applications. Since PM has its own unique performance characteristics, cloud vendors need to rethink the performance isolation problem beyond the conventional performance interference models on DRAM. This work observes interference effects between DRAM and PM (i.e. PM-by-PM, PM-by-DRAM, and DRAM-by-PM interference). Based on our observations, this paper provides a new model to minimize the performance impact of a latency-critical (LC) application by a best-effort (BE) application when they heavily use both DRAM and PM. Especially, this work presents Dicio, which carefully considers the internal bandwidth bloating by PM’s write amplification to control performance interference. To compare with the state-of-the-art that does not support PM, we modify a recent system, Caladan, to support PM and compare Dicio with it. This work significantly improves the tail latency of memcached-pmem (LC) and the throughput of a BE process.
The Path Analysis of the Influence of Anxiety, Job Burnout and Self-efficacy on Well-being——Take the Sample of College English Teachers under the Novel Coronavirus Pneumonia

NOTICE OF RETRACTION: While investigating potential publication-related misconduct in connection with the ICIMTech 2021 Conference Proceedings, serious concerns were raised that cast doubt on the integrity of the peer-review process and all papers published in the Proceedings of this Conference. The integrity of the entire Conference has been called into question. As a result, of its investigation, ACM has decided to retract the Entire Conference Proceedings and all related papers from the ACM Digital Library. None of the papers from this Proceeding should be cited in the literature because of the questionable integrity of the peer review process for this Conference.
Probabilistic Delta debugging

The delta debugging problem concerns how to reduce an object while preserving a certain property, and widely exists in many applications, such as compiler development, regression fault localization, and software debloating. Given the importance of delta debugging, multiple algorithms have been proposed to solve the delta debugging problem efficiently and effectively. However, the efficiency and effectiveness of the state-of-the-art algorithms are still not satisfactory. For example, the state-of-the-art delta debugging tool, CHISEL, may take up to 3 hours to reduce a single program with 14,092 lines of code, while the reduced program may be up to 2 times unnecessarily large. In this paper, we propose a probabilistic delta debugging algorithm (named ProbDD) to improve the efficiency and the effectiveness of delta debugging. Our key insight is, the ddmin algorithm, the basic algorithm upon which many existing approaches are built, follows a predefined sequence of attempts to remove elements from a sequence, and fails to utilize the information from existing test results. To address this problem, ProbDD builds a probabilistic model to estimate the probabilities of the elements to be kept in the produced result, selects a set of elements to maximize the gain of the next test based on the model, and improves the model based on the test results. We prove the correctness of ProbDD, and analyze the minimality of its result and the asymptotic number of tests under the worst case. The asymptotic number of tests in the worst case of ProbDD is O(n), which is smaller than that of ddmin, O(n2) worst-case asymptotic number of tests. Furthermore, we experimentally compared ProbDD with ddmin on 40 subjects in HDD and CHISEL, two approaches that wrap ddmin for reducing trees and C programs, respectively. The results show that, after replacing ddmin with ProbDD, HDD and CHISEL produce 59.48% and 11.51% smaller results and use 63.22% and 45.27% less time, respectively.
Innovative Development of Maonan Traditional Crafts in the Digital Media Era

NOTICE OF RETRACTION: While investigating potential publication-related misconduct in connection with the ICIMTech 2021 Conference Proceedings, serious concerns were raised that cast doubt on the integrity of the peer-review process and all papers published in the Proceedings of this Conference. The integrity of the entire Conference has been called into question. As a result, of its investigation, ACM has decided to retract the Entire Conference Proceedings and all related papers from the ACM Digital Library. None of the papers from this Proceeding should be cited in the literature because of the questionable integrity of the peer review process for this Conference.
Metro Maps on Flexible Base Grids

We present new generic methods to efficiently draw schematized metro maps for a wide variety of layouts, including octilinear, hexalinear, and orthoradial maps. The maps are drawn by mapping the input graph to a suitable grid graph. Previous work was restricted to regular octilinear grids. In this work, we investigate a variety of grids, including triangular grids and orthoradial grids. In particular, we also construct sparse grids where the local node density adapts to the input graph (e.g. octilinear Hanan grids, which we introduce in this work). For octilinear maps, this reduces the grid size by a factor of up to 5 compared to previous work, while still achieving close-to-optimal layouts. For many maps, this reduction also leads to up to 5 times faster solution times of the underlying optimization problem. We evaluate our approach on five maps. All octilinear maps can be computed in under 0.5 seconds, all hexalinear and orthoradial maps can be computed in under 2.5 seconds.
Boosting Personalized Food Image Classifier by Sharing Food Records

Food image recognition tasks are generally addressed by using a closed dataset. In a real-world setting, however, the dataset is updated as the new class of food appears, and it is impossible to train a model that distinguishes all kinds of food in advance. Also, inter-class similarity and intra-class diversity make food image recognition tasks more challenging. Previous works have shown that a personalized classifier using an individual user's food records can deal with some of these problems. On top of that, we propose a personalized classifier that learns from not only the accumulating food records of the individual user but also the growing records of the entire user. To conduct a realistic experiment, we build a new dataset of daily food images using a food-logging application called FoodLog Athl. As a result, our proposed method significantly outperforms prior personalized classification methods for food image recognition in a realistic setting.
Micro-CLK: returning to the asynchronicity with communication-less microkernel

Inter-process communication (IPC) has always been the "Achilles heel" of microkernels, determining their overall performance. The entire history of microkernel development is tightly coupled to the debates about IPC, its efficiency, and the bottleneck it creates. The microkernel research community has consistently viewed the idea of message passing as the fundamental foundation of the microkernel, which is considered linked to its design inextricably. We are revising the place of IPC in the microkernel-based multi-server operating system design. We argue that communication can and potentially should be moved out to the userspace making microkernel the first tier resource manager solely. We describe a novel approach to out-of-kernel asynchronous messaging in multi-server microkernel OS and provide preliminary evaluation results to show its benefits.
Design and implementation of K-means parallel algorithm based on Hadoop

Aiming at the problem of low efficiency and instability of K-means clustering in big data environment, a parallel k-means algorithm based on Hadoop is proposed. Determine the initial number of clusters of K-means clustering through the elbow method, and then determine the initial cluster center based on the ideas of density and proximity, and use the MapReduce framework of the Hadoop ecosystem to achieve parallelization. Experiments show that the algorithm can improve the efficiency and convergence of K-means clustering in the case of massive data.