Miracle-dz/UniFAR-FTUs huggingface dataset:
CPS transformation with affine types for call-by-value implicit polymorphism

Transformation of programs into continuation-passing style (CPS) reveals the notion of continuations, enabling many applications such as control operators and intermediate representations in compilers. Although type preservation makes CPS transformation more beneficial, achieving type-preserving CPS transformation for implicit polymorphism with call-by-value (CBV) semantics is known to be challenging. We identify the difficulty in the problem that we call scope intrusion. To address this problem, we propose a new CPS target language Λopen that supports two additional constructs for polymorphism: one only binds and the other only generalizes type variables. Unfortunately, their unrestricted use makes Λopen unsafe due to undesired generalization of type variables. We thus equip Λopen with affine types to allow only the type-safe generalization. We then define a CPS transformation from Curry-style CBV System F to type-safe Λopen and prove that the transformation is meaning and type preserving. We also study parametricity of Λopen as it is a fundamental property of polymorphic languages and plays a key role in applications of CPS transformation. To establish parametricity, we construct a parametric, step-indexed Kripke logical relation for Λopen and prove that it satisfies the Fundamental Property as well as soundness with respect to contextual equivalence.
Energy Efficiency Analysis and the Design of Risk Early Warning System

With the continuous development of the world economy, the role of energy is becoming more and more prominent. China's dependence on foreign energy is more and more obvious, and there are many factors leading to energy, such as price, politics, economy, diplomacy, etc., which easily makes China's economy subject to other countries. In such a large international background, the level of energy efficiency plays a decisive role in China's future development. This paper analyzes the characteristics of China's energy efficiency combined with the actual situation of China's energy on this basis, the functional structure, data processing flow and design objectives of the risk early warning system are designed.
Rapid Transformation Estimation Using Deep Learning for Defect Detection

Defect detection is a crucial step in the manufacturing of vehicle parts such as the engine. One major method for defect detection is to use image registration and image difference to identify and segment the defects. The key technology of this approach is to extract the accurate transformation information between the template image and the testing images. In this paper, we propose a novel deep neural network (DNN) method to learn the transformations from the training dataset. Wavelet transformation is introduced to denoise the images and reduce the image size for fast image registration. The results show that the trained DNN models are able to effectively predict the transformation between the template image and the actual test image in real time with high accuracy.
What are the critical security flaws in my system?

Delivering secure software is a challenge that every software engineering team needs to face and solve. Methods based on static analysis can help programmers identify security risks in the software. Security checkers built using static analysis methods are a great help but they can overload the users with their findings. Today there is no security checker for Erlang that understands the severity of the found vulnerability and uses the information to prioritise the found vulnerabilities when presenting the results to the programmers. In this paper we discuss how to prioritise vulnerabilities in Erlang programs. We propose a static analysis that determines the severity of a vulnerability. Building on top of our previous work, we extend the trust zone analyser algorithm with the proposed analysis to return prioritised results to the programmers. Our early evaluation shows that the trust zone analyser is able to identify and prioritise the most critical security flaws in an Erlang system.
Would you like a quick peek? providing logging support to monitor data processing in big data applications

To analyze large-scale data efficiently, developers have created various big data processing frameworks (e.g., Apache Spark). These big data processing frameworks provide abstractions to developers so that they can focus on implementing the data analysis logic. In traditional software systems, developers leverage logging to monitor applications and record intermediate states to assist workload understanding and issue diagnosis. However, due to the abstraction and the peculiarity of big data frameworks, there is currently no effective monitoring approach for big data applications. In this paper, we first manually study 1,000 randomly sampled Spark-related questions on Stack Overflow to study their root causes and the type of information, if recorded, that can assist developers with motioning and diagnosis. Then, we design an approach, DPLOG, which assists developers with monitoring Spark applications. DPLOG leverages statistical sampling to minimize performance overhead and provides intermediate information and hint/warning messages for each data processing step of a chained method pipeline. We evaluate DPLOG on six benchmarking programs and find that DPLOG has a relatively small overhead (i.e., less than 10% increase in response time when processing 5GB data) compared to without using DPLOG, and reduce the overhead by over 500% compared to the baseline. Our user study with 20 developers shows that DPLOG can reduce the needed time to debug big data applications by 63% and the participants give DPLOG an average of 4.85/5 for its usefulness. The idea of DPLOG may be applied to other big data processing frameworks, and our study sheds light on future research opportunities in assisting developers with monitoring big data applications.
A Comparison of Learning Classifier Systems’ Rule Compaction Algorithms for Knowledge Visualization

Learning Classifier Systems (LCSs) are a paradigm of rule-based evolutionary computation (EC). LCSs excel in data-mining tasks regarding helping humans to understand the explored problem, often through visualizing the discovered patterns linking features to classes. Due to the stochastic nature of EC, LCSs unavoidably produce and keep redundant rules, which obscure the patterns. Thus, rule compaction methods are invoked to produce a better population by removing problematic rules. Previously, compaction methods have neither been tested on large-scale problems nor been assessed on the performance of capturing patterns. We review and test the most popular compaction algorithms, finding that across multiple LCSs’ populations for the same task, although the redundant rules can be different, the accurate rules are common. Furthermore, the patterns contained consistently refer to the nature of the explored domain, e.g., the data distribution or the importance of features for determining actions. This extends the [O] set hypothesis proposed by Butz et al. [1], in which an LCS is expected to evolve a minimal number of non-overlapped rules to represent an addressed domain. Two new compaction algorithms are introduced to search at the rule level and the population level by compacting multiple LCSs’ populations. Two visualization methods are employed for verifying the interpretability of these populations. Successful compaction is demonstrated on complex and real problems with clean datasets, e.g., the 11-bits Majority-On problem that requires 924 different interacting rules in the optimal solution to be uniquely identified to enable correct visualization. For the first time, the patterns contained in learned models for the large-scale 70-bits Multiplexer problem are visualized successfully.
A Vehicle Detection Method Based on YOLO­V4 Model

Vehicle detection is a basic problem in target detection and has significant application value in fields such as intelligent traffic supervision and automatic driving. In order to improve the vehicle detection accuracy and detection speed, a vehicle detection method based on YOLOv4 is used in this paper. First, manually label the Apollo Scape automatic driving data set to establish a vehicle detection database, and then the YOLOv4 deep learning model is used to extract vehicle features adaptively, which avoiding the artificial subjective selection of features and achieving high precision vehicle target detection. The experimental results show that this model has a significant improvement in detection accuracy. Specifically, the mean average precision (mAP) reaches 92.1%, which is higher than the YOLOv4Tiny, YOLOv3 and Faster RCNN models 3.2%, 6.9% and 3.1%, respectively.
A large-scale empirical study on Java library migrations: prevalence, trends, and rationales

With the rise of open-source software and package hosting platforms, reusing 3rd-party libraries has become a common practice. Due to various failures during software evolution, a project may remove a used library and replace it with another library, which we call library migration. Despite substantial research on dependency management, the understanding of how and why library migrations occur is still lacking. Achieving this understanding may help practitioners optimize their library selection criteria, develop automated approaches to monitor dependencies, and provide migration suggestions for their libraries or software projects. In this paper, through a fine-grained commit-level analysis of 19,652 Java GitHub projects, we extract the largest migration dataset to-date (1,194 migration rules, 3,163 migration commits). We show that 8,065 (41.04%) projects having at least one library removal, 1,564 (7.96%, lower-bound) to 5,004 (25.46%, upper-bound) projects have at least one migration, and a median project with migrations has 2 to 4 migrations in total. We discover that library migrations are dominated by several domains (logging, JSON, testing and web service) presenting a long tail distribution. Also, migrations are highly unidirectional in that libraries are either mostly abandoned or mostly chosen in our project corpus. A thematic analysis on related commit messages, issues, and pull requests identifies 14 frequently mentioned migration reasons (e.g., lack of maintenance, usability, integration, etc), 7 of which are not discussed in previous work. Our findings can be operationalized into actionable insights for package hosting platforms, project maintainers, and library developers. We provide a replication package at https://doi.org/10.5281/zenodo.4816752.
MaSEC: Discovering Anchorages and Co-movement Patterns on Streaming Vessel Trajectories

The massive-scale data generation of positioning (tracking) messages, collected by various surveillance means, has posed new challenges in the field of mobility data analytics in terms of extracting valuable knowledge out of this data. One of these challenges is online cluster analysis, where the goal is to unveil hidden patterns of collective behaviour from streaming trajectories, such as co-movement and co-stationary (aka anchorage) patterns. Towards this direction, in this paper, we demonstrate MaSEC (Moving and Stationary Evolving Clusters), a system that discovers valuable behavioural patterns as above. In particular, our system provides a unified solution that discovers both moving and stationary evolving clusters on streaming vessel position data in an online mode. The functionality of our system is evaluated over two real-world datasets from the maritime domain.
Research on closed-loop force compliance control method of two-arm robot based on self-tuning function

Two-arm coordinated operation is a special operation mode of twoarm robot. When both arms operate the same object at the same time, the robot arm and the operating object form a closed chain. Due to the influence of positioning accuracy, interference force and other factors, an endogenous force will form in the closed chain, which may damage the operating object, so it must be released as soon as possible. In this paper, based on the principle of impedance control, a closed-loop force compliance control method based on self-tuning function for the coordinated operation of dual-arm robots is proposed, and a comparative test is carried out using 7-dof dual-arm robots as the test platform to verify the effectiveness of the proposed method.
Research on Industry and Commerce Management System Based on Computer Big Data algorithm

The rapid development and popularization of computer technology and big data has promoted the progress of various industries and has advantageously promoted the development of e-government. The industry and commerce management system is an important participant, manager, and service provider of national economic construction and development and plays a pivotal role in the construction of informatization. This article studies the critical technologies needed to realize the industry and commerce management system: ASP.NET technology, object-oriented technology, and system development tools ASP.NET and back-end database management system SQL Server 2010. Design the system architecture and functional modules Structure, according to the basic theory of database, the process and results of big data modeling are given. The detailed design of essential business modules such as business registration, daily management, and case handling are discussed.
An exploratory study of autopilot software bugs in unmanned aerial vehicles

Unmanned aerial vehicles (UAVs) are becoming increasingly important and widely used in modern society. Software bugs in these systems can cause severe issues, such as system crashes, hangs, and undefined behaviors. Some bugs can also be exploited by hackers to launch security attacks, resulting in catastrophic consequences. Therefore, techniques that can help detect and fix software bugs in UAVs are highly desirable. However, although there are many existing studies on bugs in various types of software, the characteristics of UAV software bugs have never been systematically studied. This impedes the development of tools for assuring the dependability of UAVs. To bridge this gap, we conducted the first large-scale empirical study on two well-known open-source autopilot software platforms for UAVs, namely PX4 and Ardupilot, to characterize bugs in UAVs. Through analyzing 569 bugs from these two projects, we observed eight types of UAV-specific bugs (i.e., limit, math, inconsistency, priority, parameter, hardware support, correction, and initialization) and learned their root causes. Based on the bug taxonomy, we summarized common bug patterns and repairing strategies. We further identified five challenges associated with detecting and fixing such UAV-specific bugs. Our study can help researchers and practitioners to better understand the threats to the dependability of UAV systems and facilitate the future development of UAV bug diagnosis tools.
Research on Wave Period Level Detection Based on 3D Convolutional Network

Wave observation is very important in the construction of ocean engineering. However, current research only advocates the use of two-dimensional convolutional networks to extract the spatial information of ocean waves, without considering the benefits of the time information of ocean wave motion. Therefore, in this paper, we propose a new network structure model (Wave-3Dcnn) based on a three-dimensional convolutional network, which can simultaneously learn the spatial and time information of the waves, and realize the research on the detection of wave levels. The simulation experiment verifies that the model proposed in this paper can obtain the best detection with 96.8% classification accuracy, and has a great advantage in real-time with a small amount of calculation.
Power Control for Cell-free Massive MIMO Based on AFSA

With the development of information technology nowadays, Cell massive multiple-input multiple-output (MIMO) system has gradually become the main technology of 5G communication systems. In previous studies, in order to realize the target of max-min power control, most researchers used the bisection algorithm to obtain the optimal solution. Then, since the bisection algorithm focuses on users with the smallest rate, the rate of other users will inevitably be sacrificed. This article introduces the artificial fish school algo-rithm to realize power control. Through the flexible setting of the objective function, the mean rate of users can be improved while achieving the max-min goal.
The Impact of Management Based on Overconfidence and Cash Dividends to the Company's Overinvestment

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.
Positive and negative control of helicase recruitment at a bacterial chromosome origin

The mechanisms responsible for helicase loading during the initiation of chromosome replication in bacteria are unclear. Here we report both a positive and a negative mechanism for directing helicase recruitment in the model organism Bacillus subtilis. Systematic mutagenesis of the essential replication initiation gene dnaD and characterization of DnaD variants revealed protein interfaces required for interacting with the master initiator DnaA and with a specific single-stranded DNA (ssDNA) sequence located in the chromosome origin (DnaD Recognition Element, “DRE”). We propose that the location of the DRE within the replication origin orchestrates recruitment of helicase to achieve bidirectional DNA replication. We also report that the developmentally expressed repressor of DNA replication initiation, SirA, acts by blocking the interaction of DnaD with DnaA, thereby inhibiting helicase recruitment to the origin. These findings significantly advance our mechanistic understanding of helicase recruitment and regulation during bacterial DNA replication initiation. Because DnaD is essential for the viability of clinically relevant Gram-positive pathogens, DnaD is an attractive target for drug development.
SemiBin: Incorporating information from reference genomes with semi-supervised deep learning leads to better metagenomic assembled genomes (MAGs)

Metagenomic binning is the step in building metagenome-assembled genomes (MAGs) when sequences predicted to originate from the same genome are automatically grouped together. The most widely-used methods for binning are reference-independent, operating de novo and allow the recovery of genomes from previously unsampled clades. However, they do not leverage the knowledge in existing databases. Here, we propose SemiBin, an open source tool that uses neural networks to implement a semi-supervised approach, i.e. SemiBin exploits the information in reference genomes, while retaining the capability of binning genomes that are outside the reference dataset. SemiBin outperforms existing state-of-the-art binning methods in simulated and real microbiome datasets across three different environments (human gut, dog gut, and marine microbiomes). SemiBin returns more high-quality bins with larger taxonomic diversity, including more distinct genera and species. SemiBin is available as open source software at https://github.com/BigDataBiology/SemiBin/.
Faster Rates of Molecular Sequence Evolution in Reproduction-Related Genes and in Species with Hypodermic Sperm Morphologies

Sexual selection is expected to drive the evolution of many striking behaviours and morphologies, leaving signatures of selection at loci underlying these phenotypes. However, relatively few studies have contrasted molecular sequence evolution at such loci across lineages that differ in their sexual selection context. Our comparative genomics study of Macrostomum, a large genus of free-living simultaneously hermaphroditic flatworms, takes advantage of functional annotations from the model species, M. lignano, and transcriptome assemblies of 97 congeners. We compare molecular sequence evolution in species with contrasting sperm morphologies, which are strongly associated with multiple convergent shifts in the mating strategy and thus reflect the sexual selection context in Macrostomum. The sperm of most reciprocally mating species carry lateral bristles, likely functioning as anchoring mechanisms against post-copulatory sperm removal. Hypodermically mating species lack these bristles, potentially as adaptations to a different environment experienced by hypodermic sperm. We document faster molecular sequence evolution in reproduction-related, compared to ubiquitously-expressed, genes across all sperm morphologies, consistent with more intense selection acting on the former. Furthermore, we observed faster molecular sequence evolution in species with hypodermic sperm morphologies, in both reproduction-related and ubiquitously-expressed genes. These genome-wide patterns suggest that shifts to hypodermic mating reduce the efficiency of selection, possibly due to higher selfing rates in hypodermically mating species. Moreover, we find little evidence for convergent amino acid changes across species. We provide the first comprehensive comparative analysis of molecular sequence evolution in a group of simultaneously hermaphroditic animals, across well-replicated contrasts of lineages with divergent sperm morphologies.
Modelling Fractional Behaviours Without Fractional Models

This paper first warns about the confusion or rather the implicit link that exists in the literature between fractional behaviours (of physical, biological, thermal, etc. origin) and fractional models. The need in the field of dynamic systems modelling is for tools that can capture fractional behaviours that are ubiquitous. Fractional models are only one class of models among others that can capture fractional behaviours, but with associated drawbacks. Several other modelling tools are proposed in this paper, thus showing that a distinction is needed between fractional behaviours and fractional models.
Does Concentrated Exposure Treatment for Obsessive-Compulsive Disorder Improve Insomnia Symptoms? Results From a Randomized Controlled Trial

Insomnia is a substantial problem in patients with obsessive-compulsive disorder (OCD). There is, however, a lack of studies investigating changes in concurrent symptoms of insomnia in OCD after concentrated treatment. A recent randomized controlled trial randomized participants to the Bergen 4-day treatment (B4DT, n = 16), or 12 weeks of unguided self-help (SH, n = 16), or waitlist (WL, n = 16). Patients from the SH- and WL-group who wanted further treatment after the 12 weeks were then offered the B4DT (total of 42 patients treated with the B4DT). There were no significant differences in symptoms of insomnia between the conditions at post-treatment, but a significant moderate improvement at 3-month follow-up for patients who received the B4DT. Insomnia was not associated with OCD-treatment outcome, and change in symptoms of insomnia was mainly related to changes in depressive symptoms. The main conclusion is that concentrated exposure treatment is effective irrespective of comorbid insomnia, and that insomnia problems are moderately reduced following treatment.