Miracle-dz/UniFAR-FTUs huggingface dataset:
A Novel Graph Based Sequence Forecasting Model for Electric Load of Campus

In this study, a new power prediction system named Pattern Similarity Graph based Model (PSGM) based on time sequential prediction algorithm and graphical neural network (GNN) structure is proposed. The proposed GNN architecture extracts the deep information of each power node and is used to predict the short-term power by using the sequential convolutional network (TCN) based on serial model. In the study, the electric junction is the high-tech campus. The number of enterprises in the electric campus, the total area of the campus, the time of operation of the campus and the daily average temperature data were selected as the inherent attributes of the electric campus. After constructing the graph model by taking the similarity of the power sequence of the campus as the correlation between the campuses, the feature vector of depth information of each node is combined with the feature vector of its sequence for the final prediction of short-term power consumption. The experiment compares the results of adding temperature conditions and obtains the influence degree of temperature on electricity consumption. The results are compared with baseline regression algorithm, LSTM, and ARIMA. The experimental results show that this method is superior to the traditional regression algorithm, and reveals the effective results with its competitive performance.
StateFormer: fine-grained type recovery from binaries using generative state modeling

Binary type inference is a critical reverse engineering task supporting many security applications, including vulnerability analysis, binary hardening, forensics, and decompilation. It is a difficult task because source-level type information is often stripped during compilation, leaving only binaries with untyped memory and register accesses. Existing approaches rely on hand-coded type inference rules defined by domain experts, which are brittle and require nontrivial effort to maintain and update. Even though machine learning approaches have shown promise at automatically learning the inference rules, their accuracy is still low, especially for optimized binaries. We present StateFormer, a new neural architecture that is adept at accurate and robust type inference. StateFormer follows a two-step transfer learning paradigm. In the pretraining step, the model is trained with Generative State Modeling (GSM), a novel task that we design to teach the model to statically approximate execution effects of assembly instructions in both forward and backward directions. In the finetuning step, the pretrained model learns to use its knowledge of operational semantics to infer types. We evaluate StateFormer's performance on a corpus of 33 popular open-source software projects containing over 1.67 billion variables of different types. The programs are compiled with GCC and LLVM over 4 optimization levels O0-O3, and 3 obfuscation passes based on LLVM. Our model significantly outperforms state-of-the-art ML-based tools by 14.6% in recovering types for both function arguments and variables. Our ablation studies show that GSM improves type inference accuracy by 33%.
Remote Monitoring System of Suction Flange Welding Platform Based on Power Line Carrier Communication

Social development enables remote monitoring system application to increasingly more fields. This paper takes automatic welding platform of corporate suction flange as the research object. To facilitate centralized management of multiple welding platforms, realize automatic acquisition of sys-tem operating status and process as well as real-time status display during the welding platform operation, and give alarm information against operation faults in time, a remote monitoring system is designed based on power line carrier. The system consists of PLC as the lower computer and development board as the upper computer with Sam-sung S3C2440 as the core.
Design and Application of 3S-based Resettlement Project Information Platform

In the resettlement project, due to the inability to fully understand the information, there are many problems in the decision-making work. Therefore, it is necessary to establish a resettlement project information platform to provide support for mastering project information. This paper analyzes the design of the 3S-based immigration engineering information platform, and then analyzes the application of the platform.
Intent-based policy optimization in SD-WAN

To optimize bandwidth utilization in wide area networks, a controller typically maintains policies at edge routers. In this context, our demonstration presents a versatile policy optimization model that carefully selects the set of overlay links for each application based on its requirements and the overall intent of the operator. The optimization of policies is realized using an SLA prediction model for several intents. We demonstrate, for instance, that latency is improved by 40% when the high-quality intent is selected.
An empirical study of GUI widget detection for industrial mobile games

With the widespread adoption of smartphones in our daily life, mobile games experienced increasing demand over the past years. Meanwhile, the quality of mobile games has been continuously drawing more and more attention, which can greatly affect the player experience. For better quality assurance, general-purpose testing has been extensively studied for mobile apps. However, due to the unique characteristic of mobile games, existing mobile testing techniques may not be directly suitable and applicable. To better understand the challenges in mobile game testing, in this paper, we first initiate an early step to conduct an empirical study towards understanding the challenges and pain points of mobile game testing process at our industrial partner NetEase Games. Specifically, we first conduct a survey from the mobile test development team at NetEase Games via both scrum interviews and questionnaires. We found that accurate and effective GUI widget detection for mobile games could be the pillar to boost the automation of mobile game testing and other downstream analysis tasks in practice. We then continue to perform comparative studies to investigate the effectiveness of state-of-the-art general-purpose mobile app GUI widget detection methods in the context of mobile games. To this end, we also develop a technique to automatically collect GUI widgets region information of industrial mobile games, which is equipped with a heuristic-based data cleaning method for quality refinement of the labeling results. Our evaluation shows that: (1) Existing GUI widget detection methods for general-purpose mobile apps cannot perform well on industrial mobile games. (2) Mobile game exhibits obvious difference from other general-purpose mobile apps in the perspective GUI widgets. Our further in-depth analysis reveals high diversity and density characteristics of mobile game GUI widgets could be the major reasons that post the challenges for existing methods, which calls for new research methods and better industry practices. To enable further research along this line, we construct the very first GUI widget detection benchmark, specially designed for mobile games, incorporating both our collected dataset and the state-of-the-art widget detection methods for mobile apps, which could also be the basis for further study of many downstream quality assurance tasks (e.g., testing and analysis) for mobile games.
Multi-view semi-supervised classification overview

With the continuous exploration of data collection technology, the multi-view data, obtained from different ways or levels for the same object, and the multi-view algorithm, used for modeling and solving problems from multiple perspectives, have attracted extensive attention. In addition, if the traditional supervised learning method is used in multi-view learning, a great amount of labeled data are required to ensure the accuracy of the training model, but it is difficult to obtain the labeled samples. Thus, combining semi-supervised learning with multi-view method can effectively solve the difficulties of labelling. In this paper, we will describe the related multi-view semi-supervised learning algorithms and development status in the aspect of classification, and then introduce the latest real world applications. Finally, we will summarize and point out the possible research directions in the future.
TaintStream: fine-grained taint tracking for big data platforms through dynamic code translation

Big data has become valuable property for enterprises and enabled various intelligent applications. Today, it is common to host data in big data platforms (e.g., Spark), where developers can submit scripts to process the original and intermediate data tables. Meanwhile, it is highly desirable to manage the data to comply with various privacy requirements. To enable flexible and automated privacy policy enforcement, we propose TaintStream, a fine-grained taint tracking framework for Spark-like big data platforms. TaintStream works by automatically injecting taint tracking logic into the data processing scripts, and the injected scripts are dynamically translated to maintain a taint tag for each cell during execution. The dynamic translation rules are carefully designed to guarantee non-interference in the original data operation. By defining different semantics of taint tags, TaintStream can enable various data management applications such as access control, data retention, and user data erasure. Our experiments on a self-crafted benchmarksuite show that TaintStream is able to achieve accurate cell-level taint tracking with a precision of 93.0% and less than 15% overhead. We also demonstrate the usefulness of TaintStream through several real-world use cases of privacy policy enforcement.
Preserving and conserving culture: first steps towards a knowledge extractor and cataloguer for multilingual and multi-alphabetic heritages

Managing and sharing cultural heritages also in supranational and multi-literate contexts is a very hot research topic. In this paper we discuss the research we are conducting in the DigitalMaktaba project, presenting the first steps for designing an innovative workflow and tool for the automatic extraction of knowledge from documents written in multiple non-Latin languages (Arabic, Persian and Azerbaijani languages). The tool leverages different OCR, text processing techniques and linguistic corpora in order to provide both a highly accurate extracted text and a rich metadata content, overcoming typical limitations of current state-of-the-art systems; this will enable in the near future the development of an automatic cataloguer which we hope will ultimately help in better preserving and conserving culture in such a demanding scenario.
Wayfinder: towards automatically deriving optimal OS configurations

Tuning operating systems configuration in order to obtain the maximum application performance is a hard problem. This is due to the extremely large size of the configuration space offered by modern OSes, and to the fact that it is generally explored manually. To address that issue, we propose to bring automation to the OS configuration space exploration process, in order to derive effortlessly and as quickly as possible optimal OS configurations for a given use case. We present Wayfinder, a generic OS performance evaluation platform. Wayfinder is fully automated and ensures both the accuracy and reproducibility of results, all the while speeding up how fast tests are run on a system. Wayfinder is easily extensible and offers convenient APIs to (1) implement custom configuration space exploration techniques, (2) add new benchmarks and (3) support additional OS projects. We demonstrate Wayfinder’s capacity to automatically and efficiently explore a LibOS’ networking configuration space; as well as its ability to efficiently isolate parallel experiments to avoid noisy neighbors.
Application of Situation Awareness and Bow-tie Method in Flight Safety Event Reasoning

Flight safety event reasoning is an important task both in QAR (quick access recorder) data exceedence and statistical analyses. Situation awareness is booming recently as a very useful tool. It perceives, understands environments both inside and outside pilot's compartment, and then helps pilot think ahead about potential threats, risks toward targets (like operation during taking off or landing, etc.) and how to overcome them. QAR monitors the whole situation awareness process for subsequent analysis. Through QAR big data digging, we re-appear this process and evaluate pilot's performance. Risk situations/scenarios cause events for certain reasons. Correction methods for managing risk situations are obtained from opinion leaders and also included into our simulation system. This is a requisite step to introduce Artificial intelligence and machine learning algorithms later to fully automatically classify pilot behaviors into classes which will be studied later. We incorporate QAR as the situational awareness into Bowtie model to study the sequence of the event reasoning. Hard landing and long flare distance are studied through multiple flights QAR data and the achieve the Bowtie reasoning model.
A Malware Detection Method Based on Machine Learning and Ensemble of Regression Trees

In the context of the current large number of malicious codes, the detection and protection of malicious codes is particularly important. In recent years, a method of using deep learning to detect malicious code has emerged. Thus, in this paper, we propose a new detection method that converts binary files of malicious code into decimal arrays and use 1-D CNN to perform classification and recognition. Aiming at the imbalance in the number of code families, we choose xgboost, which performs well in the classification prediction competition. We conduct experiments on 9,458 malware samples from 25 different malware families in the Vision Research Lab. The experimental results show that our classification prediction reaches 97% accuracy.
A comprehensive study of deep learning compiler bugs

There are increasing uses of deep learning (DL) compilers to generate optimized code, boosting the runtime performance of DL models on specific hardware. Like their traditional counterparts, DL compilers can generate incorrect code, resulting in unexpected model behaviors that may cause catastrophic consequences in mission-critical systems. On the other hand, the DL models processed by DL compilers differ fundamentally from imperative programs in that the program logic in DL models is implicit. As such, various characteristics of the bugs arising from traditional compilers need to be revisited in the context of DL compilers. In this paper, we present the first systematic study of DL compiler bugs by analyzing 603 bugs arising in three popular DL compilers (i.e., TVM from Apache, Glow from Facebook, and nGraph from Intel). We analyzed these bugs according to their root causes, symptoms, and the stages where they occur during compilation. We obtain 12 findings, and provide a series of valuable guidelines for future work on DL compiler bug detection and debugging. For example, a large portion (nearly 20%) of DL compiler bugs are related to types, especially tensor types. The analysis of these bugs helps design new mutation operators (e.g., adding type cast for a tensor to promote implicit type conversion in subsequent tensor computations) to facilitate type-related bug detection. Further, we developed TVMfuzz as a proof-of-concept application of our findings to test the TVM DL compiler. It generates new tests based on TVM's original test suite. They expose 8 TVM bugs that are missed by the original test suite. The result demonstrates the usefulness of our findings.
Safe mutation with algebraic effects

It can be difficult to write safe concurrent programs which use shared mutable state. Subtle mistakes can lead to data races that manifest as unexpected program behaviour. The prevailing approaches to solving this dilemma are to either eschew mutable state altogether, or design bespoke languages that prevent data races by design. This article introduces a third approach by showing how safe mutation can be integrated into a mainstream functional programming language with algebraic effects. This article produces a framework that tracks the use of mutable state and guarantees data race freedom at compile time.
An empirical investigation of practical log anomaly detection for online service systems

Log data is an essential and valuable resource of online service systems, which records detailed information of system running status and user behavior. Log anomaly detection is vital for service reliability engineering, which has been extensively studied. However, we find that existing approaches suffer from several limitations when deploying them into practice, including 1) inability to deal with various logs and complex log abnormal patterns; 2) poor interpretability; 3) lack of domain knowledge. To help understand these practical challenges and investigate the practical performance of existing work quantitatively, we conduct the first empirical study and an experimental study based on large-scale real-world data. We find that logs with rich information indeed exhibit diverse abnormal patterns (e.g., keywords, template count, template sequence, variable value, and variable distribution). However, existing approaches fail to tackle such complex abnormal patterns, producing unsatisfactory performance. Motivated by obtained findings, we propose a generic log anomaly detection system named LogAD based on ensemble learning, which integrates multiple anomaly detection approaches and domain knowledge, so as to handle complex situations in practice. About the effectiveness of LogAD, the average F1-score achieves 0.83, outperforming all baselines. Besides, we also share some success cases and lessons learned during our study. To our best knowledge, we are the first to investigate practical log anomaly detection in the real world deeply. Our work is helpful for practitioners and researchers to apply log anomaly detection to practice to enhance service reliability.
Of JavaScript AOT compilation performance

The fastest JavaScript production implementations use just-in-time (JIT) compilation and the vast majority of academic publications about implementations of dynamic languages published during the last two decades focus on JIT compilation. This does not imply that static compilers (AoT) cannot be competitive; as comparatively little effort has been spent creating fast AoT JavaScript compilers, a scientific comparison is lacking. This paper presents the design and implementation of an AoT JavaScript compiler, focusing on a performance analysis. The paper reports on two experiments, one based on standard JavaScript benchmark suites and one based on new benchmarks chosen for their diversity of styles, authors, sizes, provenance, and coverage of the language. The first experiment shows an advantage to JIT compilers, which is expected after the decades of effort that these compilers have paid to these very tests. The second shows more balanced results, as the AoT compiler generates programs that reach competitive speeds and that consume significantly less memory. The paper presents and evaluates techniques that we have either invented or adapted from other systems, to improve AoT JavaScript compilation.
Bidirectional typing for Erlang

Erlang is a strict, dynamically typed functional programming language popular for its use in distributed and fault-tolerant applications. The absence of static type checking allows ill-typed programs to cause type errors at run time. The benefits of catching these type errors at compile time are the primary motivation for introducing a static type system for Erlang. The greatest challenge is to find a balance between keeping the type checking sound while retaining the flexibility and philosophy of the Erlang. However, since Erlang allows higher-rank polymorphism, it is unavoidable to require type annotations for some functions to ensure decidability. In this paper, we propose a static type system for Erlang based on bidirectional type checking. In bidirectional type checking, terms are either used to infer a type, or they are checked against a given type. With the bidirectional type checking, we are trying to keep type checking sound without limiting the language’s philosophy. In addition, type annotations are only required when inference fails, which only occurs at predictable places, such as usage of higher-ranked polymorphism.
Typical trajectory extraction method for ships based on AIS data and trajectory clustering

Ship trajectory data is an important spatio-temporal data, which contains important semantic information and behavior pattern information. Typical ship trajectory is a representative trajectory model in ship shipping process, which describes similar behavior patterns of ship groups and is of great significance in characterizing massive trajectory features, analyzing group motion behavior and detecting abnormal ship behavior. In this paper, we propose a typical trajectory extraction method based on improved DBSCAN clustering algorithm, firstly, in the aspect of AIS data preprocessing, we propose a multi-feature dimensional trajectory segmentation algorithm to segment the ship trajectory to avoid ignoring the local important information because of studying the ship trajectory as a whole, and propose an improved DP trajectory compression algorithm with multi-feature point extraction to compress the ship trajectory to improve the data In terms of trajectory segment similarity, the Hausdorff distance algorithm is adopted and improved. In terms of trajectory clustering, the improved adaptive parameter fast DBSCAN clustering algorithm is used for trajectory clustering, and finally, the typical motion trajectory of the ship is obtained by the center-of-mass vector extraction method. Experiments with AIS data collected in the inland river basin of the Yangtze River in Wuhan show that this paper can extract typical motion trajectories in large-scale trajectory data, and improve the accuracy and efficiency of the algorithm to a certain extent, laying the foundation for further ship abnormal behavior detection.
Sound and efficient concurrency bug prediction

Concurrency bugs are extremely difficult to detect. Recently, several dynamic techniques achieve sound analysis. M2 is even complete for two threads. It is designed to decide whether two events can occur consecutively. However, real-world concurrency bugs can involve more events and threads. Some can occur when the order of two or more events can be exchanged even if they occur not consecutively. We propose a new technique SeqCheck to soundly decide whether a sequence of events can occur in a specified order. The ordered sequence represents a potential concurrency bug. And several known forms of concurrency bugs can be easily encoded into event sequences where each represents a way that the bug can occur. To achieve it, SeqCheck explicitly analyzes branch events and includes a set of efficient algorithms. We show that SeqCheck is sound; and it is also complete on traces of two threads. We have implemented SeqCheck to detect three types of concurrency bugs and evaluated it on 51 Java benchmarks producing up to billions of events. Compared with M2 and other three recent sound race detectors, SeqCheck detected 333 races in ~30 minutes; while others detected from 130 to 285 races in ~6 to ~12 hours. SeqCheck detected 20 deadlocks in ~6 seconds. This is only one less than Dirk; but Dirk spent more than one hour. SeqCheck also detected 30 atomicity violations in ~20 minutes. The evaluation shows SeqCheck can significantly outperform existing concurrency bug detectors.