Miracle-dz/UniFAR-FTUs huggingface dataset:
Evaluation Guidelines to Deal with Implicit Phenomena to Assess Factuality in Data-to-Text Generation

Data-to-text generation systems are trained on large datasets, such as WebNLG, Ro-toWire, E2E or DART. Beyond traditional token-overlap evaluation metrics (BLEU or METEOR), a key concern faced by recent generators is to control the factuality of the generated text with respect to the input data specification. We report on our experience when developing an automatic factuality evaluation system for data-to-text generation that we are testing on WebNLG and E2E data. We aim to prepare gold data annotated manually to identify cases where the text communicates more information than is warranted based on the in-put data (extra) or fails to communicate data that is part of the input (missing). While analyzing reference (data, text) samples, we encountered a range of systematic uncertainties that are related to cases on implicit phenomena in text, and the nature of non-linguistic knowledge we expect to be involved when assessing factuality. We derive from our experience a set of evaluation guidelines to reach high inter-annotator agreement on such cases.
Simultaneously Self-Attending to Text and Entities for Knowledge-Informed Text Representations

Pre-trained language models have emerged as highly successful methods for learning good text representations. However, the amount of structured knowledge retained in such models, and how (if at all) it can be extracted, remains an open question. In this work, we aim at directly learning text representations which leverage structured knowledge about entities mentioned in the text. This can be particularly beneficial for downstream tasks which are knowledge-intensive. Our approach utilizes self-attention between words in the text and knowledge graph (KG) entities mentioned in the text. While existing methods require entity-linked data for pre-training, we train using a mention-span masking objective and a candidate ranking objective – which doesn’t require any entity-links and only assumes access to an alias table for retrieving candidates, enabling large-scale pre-training. We show that the proposed model learns knowledge-informed text representations that yield improvements on the downstream tasks over existing methods.
Jibes & Delights: A Dataset of Targeted Insults and Compliments to Tackle Online Abuse

Online abuse and offensive language on social media have become widespread problems in today’s digital age. In this paper, we contribute a Reddit-based dataset, consisting of 68,159 insults and 51,102 compliments targeted at individuals instead of targeting a particular community or race. Secondly, we benchmark multiple existing state-of-the-art models for both classification and unsupervised style transfer on the dataset. Finally, we analyse the experimental results and conclude that the transfer task is challenging, requiring the models to understand the high degree of creativity exhibited in the data.
Turn-Level User Satisfaction Estimation in E-commerce Customer Service

User satisfaction estimation in the dialogue-based customer service is critical not only for helping developers find the system defects, but also making it possible to get timely human intervention for dissatisfied customers. In this paper, we investigate the problem of user satisfaction estimation in E-commerce customer service. In order to apply the estimator to online services for timely human intervention, we need to estimate the satisfaction score at each turn. However, in actual scenario we can only collect the satisfaction labels for the whole dialogue sessions via user feedback. To this end, we formalize the turn-level satisfaction estimation as a reinforcement learning problem, in which the model can be optimized with only session-level satisfaction labels. We conduct experiments on the dataset collected from a commercial customer service system, and compare our model with the supervised learning models. Extensive experiments show that the proposed method outperforms all the baseline models.
Evaluating Text Generation from Discourse Representation Structures

We present an end-to-end neural approach to generate English sentences from formal meaning representations, Discourse Representation Structures (DRSs). We use a rather standard bi-LSTM sequence-to-sequence model, work with a linearized DRS input representation, and evaluate character-level and word-level decoders. We obtain very encouraging results in terms of reference-based automatic metrics such as BLEU. But because such metrics only evaluate the surface level of generated output, we develop a new metric, ROSE, that targets specific semantic phenomena. We do this with five DRS generation challenge sets focusing on tense, grammatical number, polarity, named entities and quantities. The aim of these challenge sets is to assess the neural generator’s systematicity and generalization to unseen inputs.
Wooden pallet image segmentation based on Otsu and marker watershed

Pallet detection is the key step of cargo handling for warehouse robots. In the visual detection, pallet needs to be segmented from the image background. In order to increase the recognition rate and reduce the influence of the surrounding environment and the pattern of the pallet, Otsu algorithm and marker watershed algorithm are combined to realize the image segmentation of the pallet contour. Based on the difference of colour information between the pallet and the background, Otsu algorithm is used to segment the pallet for the first time, and marker watershed is used to complete the target recognition. The experimental results show that the method can effectively solve the problem of over segmentation of the pallet object, and improve the recognition rate, which has some reference value for the design of the visual detection system of the warehouse robot wooden pallet.
Accurate Aspect Ratio Estimation in Scale Adaptive Tracking

In visual object tracking, robust scale estimation is always a challenging problem. Because of the fast movement of the target, the background, the relative distance and relative azimuth angle between the target and the background usually change greatly. However, traditional correlation filtering tracking method cannot track the target accurately or even lose the target when the target scale and aspect ratio change. To address these problems, this paper adds two independent filters for the length and width scale estimation to complete the adaptive tracking on the basis of the translation filter. The experimental results in the public data set show that: compared with the original trackers, our method improves the tracking accuracy significantly, and meets the needs of real-time target tracking.
Single image dehazing based on fusion of sky region segmentation

In order to solve the problem of image sharpening with sky region, a single image dehazing method combining sky region segmentation was proposed. Firstly, a fusion sky segmentation method combining OTSU algorithm with Canny operator edge detection is proposed to divide foggy image into sky region and non-sky region. Secondly, a linear index was constructed for the sky region using the dark channel prior sky transmittance to modify the sky region transmittance. For the non-sky region, a fast guided filter was used to optimize the dark channel prior non-sky transmittance. The two were combined to obtain the final transmittance, and the atmospheric light value was optimized by means of average estimation. Finally, combined with the atmospheric scattering model, the image is reconstructed dehaze. The experimental results show that the proposed method can segment the sky more accurately, estimate the atmospheric light value and transmittance more accurately and efficiently, and has the advantages of high contrast, natural color and clear details.
An improved Simhash algorithm based malicious mirror website detection method

There are a large number of similar or even identical webpages on the Internet. These webpages will cause unnecessary loss of network resources, including waste of storage space, decreased web search speed, and decreased user experience. And some malicious mirror websites will become tools for criminals to carry out illegal activities such as phishing attacks. In this paper, the autours analyzed the mainstream text similarity detection algorithms and webpage deduplication algorithms, and proposed an improved webpage deduplication algorithm based on Simhash. The algorithm converts the text collection into Simhash fingerprints for storage through mapping, and calculates the similarity of the two fingerprints through Hamming distance, thereby obtaining the similarity of the webpage. Experiments show that the algorithm proposed in this paper has a higher accuracy rate and recall rate, and can be better applied to the identification and detection of malicious mirror websites.
Object detection and recognition system based on computer vision analysis

Artificial intelligence based on deep learning enables the machine to have the ability of understanding and cognition, but the application of artificial intelligence technology in supermarket shopping scene is limited. In the post-epidemic era, the contactless self-checkout of unmanned supermarket is more in line with the development needs of modern society. We build Pytorch environment, first to collect pictures of a large number of commodities and labeling information, and training model is obtained by YOLO neural network algorithm, finally through a call to model to realize the recognition of goods. Neural network algorithm is used to improve the recognition rate of goods step by step and achieve the detection and recognition of objects. We have tested our model on the real supermarket commodity data set and the public data set ImageNet, and the results show that our model can achieve a certain practical effect.
Do new bike lanes impact air pollution exposure for cyclists?—a case study from Berlin

Cities in the 21st century are dynamically changing in response to environmental and societal pressures, not least among which are climate change and air pollution. In some of these metropoles, such as Berlin, a transformation of mobility systems has already begun. Along a mid-sized street in Berlin, a measurement campaign was conducted in 2020 to accompany the construction of a bike lane and the implementation of a community space along one of the side-streets. Using the new technology of low-cost sensors, higher resolution measurements of local air quality were enabled. Stationary and mobile measurements were taken using EarthSense Zephyr sensor systems before and after the construction of the bike lane and during the timeframe when the community space was in place. It was found that the implementation of the bike lane led to a reduction in NO2 exposure for cyclists. During periods when the community space was in place, a reduction in NO2 concentrations was also measured. This study highlights not only the utility of low-cost sensors for the measurement of urban air quality, but also their value in a science-policy context. Measuring local air quality changes in response to traffic interventions will enhance understanding of the associated health benefits, especially in connection with measures promoting more sustainable modes of active travel. More research of this nature is needed to gain a clear understanding of the impacts of traffic interventions on local air quality for better protection of human health.
Logics execution in a multi-layers memristor array

Memristor stateful logic based on a two-dimensional (2D) memristor crossbar array (MCBA) has been proved to be an effective approach to break the von Neumann bottleneck. But the three-dimensional (3D) array which has an inherent high storage density is less investigated in logic computation field. In this work, we implemented the whole linearly separable binary Boolean logic functions completely by using the stateful logic circuits configured in a multi-layer memristor crossbar array. This circuit primitive can execute multi-gates parallelly inside layer or the interlayer of 3D MCBA. This work provided a significant reference for the achievement of real processing in memory (PiM) in a high-density array.
Visual detection of eggs based on deep learning for egg picking robot

The chicken farm is a typical labor-intensive production environment, and the mechanization of egg picking work is one of the development directions of the chicken industry. This article uses a camera as a sensor for visual detection. Given the limited computing resources of the robot, we improve the feature extraction part of Mask R-CNN network to reduce the memory loss of parameters and speed up the detection process. The experimental results show that compared with the classic method and the Mask R-CNN basic algorithm, the method in this paper has a higher recognition rate that can better support egg picking robots in egg recognition and pose estimation.
Deep Program Representation Learning Analysis for Program Security

As the scale of software and the complexity of programs continue to grow, it is hard to meet development of modern computer technology only by manually extracting program features. In recent years, deep learning has achieved rapid development in different fields. Program representation learning based on deep learning has been widely used in many works, such as software vulnerability analysis, program analysis and malware detection. And it has gradually become a hot research direction in information security. After the deep analysis of existing research work on automatic program security detection, we catalog deep program representation learning for program security based on data representation and provide a comprehensive overview of deep program representation learning for program security under different application scenes. Then, we propose a deep program representation learning framework for program security. Finally, we conduct comparative analysis and summarize the challenges in deep program representation learning for program security.
A survey of offensive security research on PLCs

Industrial Control Systems (ICSs) are widely deployed in critical infrastructures, such as electric power, water filtration and distribution. Programmable Logic Controllers (PLCs) work as the core components of an ICS. The offensive security technology of PLCs has attracted the popular interests of both attackers and researchers. In this paper, we firstly abstract a 4-layers model of PLC’s implementation, and then summarize and classify attacks against PLCs into 5 types. Further, we point out the features and trends of the related attack studies, which is helpful to promote the research in the ICS security community and provide vital suggestions for defenders.
User Behavior Audit System of Real-time Web Log by Spark

With the rapid development of information technology, single sign-on authentication systems have been adopted by organizations to protect their information management systems and other service platforms. With the single sign-on method, users just log in the authentication systems once to access all the information in systems, which are more convenience, but accompanied by user permissions vulnerabilities and information security risks. Users’ browsing behaviors reflect the users’ usage habits, purposes, and work contents. Risks of information leakage can be reduced by auditing the users’ browsing behaviors. After analyzing the web logs of several service systems, we designed a user behavior audit system based on the Spark computing engine, which takes the real-time web log file as the data set. A possible preprocessing method for real-time web log data stream is proposed to extract users’ web session data. we used the Spark computing engine and the corresponding analysis to mine users’ behavior patterns. The online real-time detection and offline detection of user behavior are realized, and users are divided into three levels: normal, abnormal, and risky. The system administrators or auditors can view the calculation results of each stage online, manage the parameter configuration of the algorithm online, and adjust the user’s access right in real-time.
Pedestrian Detection Method Based on Deep Convolution Neural Network

Compared with the traditional pedestrian detection technology, the pedestrian detection technology based on deep learning has achieved overwhelming advantages. However, due to the large scale of deep convolution network, the demand for dedicated processor limits the popularization of pedestrian detection system. To solve these problems, this paper proposes a deep convolution network model with moderate network scale, which improves the universality of the detection model on the premise of ensuring the detection accuracy. Based on the low dimensional shallow convolution neural network, the optimal network structure is found from three aspects of network layer number, sensor field size and characteristic graph, and the final network parameters are determined by guiding experimental evaluation. The pedestrian image to be detected is input into the above network model, and the pedestrian detection result is obtained. The pedestrian detection results on several popular pedestrian databases such as Daimler, MIT and INRIA show that the network structure designed in this paper not only has moderate network size, but also has good detection performance. Cross experiments also verify the robustness and generalization of the algorithm. The work of this article is funded by the undergraduate training program for innovation and entrepreneurship (202010373050, 202010373051, 202010373046).
Nodes detection in CDN with enhanced naive bayes tree

In recent years, Content Distribution Network (CDN) plays a critical and central part of Internet infrastructure. There are more and more studies on CDN. Nodes detection in CDN, as a key technology among them, has become a hot research topic. Current methods mainly focus on collecting one or some CDN vendors nodes by manually constructing features set. However, because CDN nodes detection is not restricted by specific CDN vendors, these methods are not applicable. In this paper, we proposed a novel machine learning algorithm, i.e. enhanced Naive Bayes Tree to identify CDN IP addresses. This algorithm makes full use of the advantages of Decision Tree and Naive Bayes algorithm, and further improves the performance through enhanced part. We build this classifier based on analysis of the characteristics of DNS resolutions, HTTP logs and WHOIS lookup. Experimental results show that our approach outperforms in terms of accuracy and amount of packages. Moreover, we separately tested the enhanced part and it performs well. We believe that this method could be applied to CDN IP addresses detection.
A denoising algorithm for linear frequency modulation signal based on window function and empirical mode decomposition

The linear frequency modulation signal is widely used in radar, sonar detection and communications as a typical non-stationary signal. In order to better analyse the linear frequency modulation signal received by the receiver, it is necessary to perform denoising processing on the received signal. As a new type of time-frequency analysis method, empirical mode decomposition algorithm is very suitable for processing non-stationary signals. However, due to the large bandwidth of the linear frequency modulation signal, more noise will be retained when the traditional empirical mode decomposition algorithm is used to denoise the linear frequency modulation signal. Under above background, this paper proposes a denoising method based on the window function and the empirical mode decomposition to achieve further denoising of the linear frequency modulation signal. According to the simulation results, when the signal-to-noise ratio is about 0dB, the method proposed in this paper improves the signal-to-noise ratio by about 1.5dB compared with the traditional empirical modal decomposition algorithm.
Blind sidewalk segmentation based on the lightweight semantic segmentation network

A lightweight semantic segmentation network is proposed to solve the problem of real-time segmentation of blind sidewalk. On the basis of U-Net’s encode-decoding structure, the inverse residual block composed of deep separable convolution is used to replace the ordinary convolution for feature extraction, and the number of convolution layer channels is compressed. Experiments show that the blind segmentation method used can effectively overcome the disadvantages of traditional methods that are affected by the environment easily. Compared with U-Net, the parameter amount is reduced by 25.2 times, and the reasoning speed is increased by 3.6 times, while the loss of precision is just less than 2%, which basically meet the requirement of real-time inference.
A New 2-Correspondences Monocular Vision Navigation Method Under Planar Motion

Vision navigation has non-contact, high-precision and low-cost characteristics compared with other navigation method. This paper proposes a vision navigation method for planar moving platform in known scene based on two 2D-3D correspondences. Taking account of the arbitrary direction of camera and the relative pose between the camera and the platform, we propose a linear solver with unique solution, whose minimal number of requirement correspondences is 2 and can cope with arbitrary number of correspondences bigger than 2 directly. Our method is evaluated on both synthetic data and real-world data. The experimental results demonstrate that our method is high-accuracy and fast for vision navigation of planar moving platform.
HREmo: A measurement system of students’ studying state in online group class based on rPPG technology

The CONVID-19 Pandemic has broken the traditional teaching method, but ignorance of students’ feedback made many teachers feel the teaching quality was degraded. Although some researchers have proposed that whether students are attentive or dozing off by analysing student expressions, head corners, and eye postures, But it is difficult to adapt to the situation where students frequently lower their heads to take notes in actual situation. Therefore, this paper proposes a heart rate acquisition and analysis system HREmo based on remote photoplethysmography technology(rPPG). The time and frequency domain characteristics of the heart rate variability signal are further analysed through the heartbeat interval obtained by the pulse waveform. It has multiple advantages such as bases on objective indicators, calculates in fast speed and low cost. The recognition error of heart rate is within 1%, and it has accurate trend prediction for students’ concentration and mental workload. Furthermore, this article takes a group class as an example to describe in detail how to put HREmo in the teacher panel to help teachers provide students with more personalized status reminders.