Reviewerly/exHarmony huggingface dataset:
Argumentation Graph Based Web Service Fault Diagnosis

Along with the standardization of Web services composition language and the widespread acceptance of composition technologies, Web services composition is becoming an efficient and cost-effective way to develop modern business applications. As Web services are defined independently from any execution context. A key challenge of (Web) service compositions is how to ensure reliable execution. Due to their inherent autonomy and heterogeneity, it is difficult to reason about the behavior of service compositions especially in case of failures. Therefore, there is a growing interest for verification techniques which help to identifying incorrect activities in service process executions. In this paper, we present a diagnosis method based on the Service Argumentation Graph (SAG) model and the service execution test cases for handling service faults. By analyzing the dependence relations of activities in SAG model, our diagnosis method identifies the incorrect activities, based on the differences between successful and failed execution test cases, which the execution test cases are partial executions and re-executions of composite service. Experimental results show that our model is effective in diagnosing faults in web service composition of various scales.
Characterizations for $$\text {XPath}^{}_{{\mathcal R}}(\downarrow ){}$$

Over the semantic universe of trees augmented with arbitrary sets of relations between nodes, we study model-theoretic properties of the extension \(\text {XPath}^{}_{{\mathcal R}}(\downarrow ){}\) of the downward fragment of XPath, equipped with a finite set \({\mathcal R}\) of relation symbols. We introduce an adequate notion of bisimulation, dependant on the set of relations \({\mathcal R}\) in consideration, and show a characterization result in the style of Hennessy-Milner’s, relating bisimulation and logical equivalence and showing that both coincide over finitely branching \({\mathcal R}\text {-trees}\). Furthermore, we also give a van Benthem-like theorem characterizing each \(\text {XPath}^{}_{{\mathcal R}}(\downarrow ){}\) as the fragment of first-order logic (over an adequate signature) with one free variable that is \({\mathcal R}\)-bisimulation-invariant. Finally, we show that our results are also valid when applied to universes of trees with some fixed semantics for the symbols of \({\mathcal R}\). This contains in particular the case of \(\text {XPath} _{=} (\downarrow )\) over data trees.
Imitating What You Need: An Adaptive Framework for Detector Distillation

Object detection models with favorable performances usually suffer from high computational costs. Knowledge distillation, a simple model compression method, aims at training a light-weight student network by transferring knowledge from a cumbersome teacher model. In this paper, we investigate different components of typical two-stage and single-stage detector in details, and propose a detector distillation framework that adaptively transfers knowledge from teacher to student according to task specific priors. The knowledge is transferred adaptively at three levels, i.e., feature backbone, classification head, and bounding box regression head, according to which model performs more reasonably. Furthermore, considering that it would introduce optimization dilemma when minimizing distillation loss and detection loss simultaneously, we propose a distillation decay strategy to help improve model generalization via gradually reducing the distillation penalty. Experiments on widely used detection benchmarks demonstrate the effectiveness of our method. Particularly, taking Faster R-CNN as an example, we achieve an accuracy of \(39.4\%\) with Resnet-50 on MS COCO 2017 dataset, which surpasses its baseline \(37.5\%\) by \(1.9\%\) points, and even better than the teacher model with \(39.3\%\) mAP.
Detecting Adversarial Samples with Neuron Coverage

Deep learning technologies have shown impressive performance in many areas. However, deep learning systems can be deceived by using intentionally crafted data, says, adversarial samples. This inherent vulnerability limits its application in safety-critical domains such as automatic driving, military applications and so on. As a kind of defense measures, various approaches have been proposed to detect adversarial samples, among which their efficiency should be further improved to accomplish practical application requirements. In this paper, we proposed a neuron coverage-based approach which detect adversarial samples by distinguishing the activated neurons' distribution features in classifier layer. The analysis and experiments showed that this approach achieves high accuracy while having relatively low computation and storage cost.
The Ratio of the Desired Parameters of Deep Neural Networks

There have been some researchers that investigate the accuracy of the approximation to a function that shows a generating pattern of data by a deep neural network. However, they have confirmed only whether at least one function close to the function showing a generating pattern exists in function classes of deep neural networks whose parameter values are changing. Therefore, we propose a new criterion to infer the approximation accuracy. Our new criterion shows the existence ratio of functions close to the function showing a generating pattern in the function classes. Moreover, we show a deep neural network with a larger number of layers approximates the function showing a generating pattern more accurately than one with a smaller number of layers under the proposed criterion, with numerical simulations.
Managing the quality of engineering training in the digital transformation era

The article deals with the transformation of the system of higher engineering education in the context of rapid reform of teaching in the context of digitalization. The familiar model of higher education is being transformed into a complex, popular, promising, innovative digital system. Distance education and training is an integral part of this digital system. The use of distance digital learning replaces the usual models of classical learning. Modern trends and needs to improve the quality of engineering education pose the task of determining the sources and directions of reforming engineering education. The role of modeling in the cognitive activity of the future specialist is shown. The author of the article gives an idea of the implementation of the principles of the organization of the educational process in the context of distance digital learning and suggests using the means of transmitting educational information in the form of cognitive-model tools.
LSTIF:Long-short Temporal Information Fusion Architecture for Video-based Person Re-identification

Person re-identification is a major application of computer vision in reality. Since the data obtained by monitoring in real life is often in video format, and the walking poses of pedestrians are different, in addition to the appearance of pedestrians, how to obtain the motion features of pedestrians, is extremely important for video-based person re-identification. Therefore, for the temporal information of the video, we propose a Long-short Temporal Information Fusion (LSTIF) network. We aggregate temporal information from two perspectives, short-term features containing detailed information and long-term features containing global information. Simultaneously, in order to reduce the amount of calculation, this network also uses non-local blocks, and extend the outpu feature map to the same size as the input, which is convenient for calculation. This paper verifies the effectiveness of our method on two commonly used datasets iLIDS-VID and DukeMTMC-VideoReID.
Common Pitfalls When Explaining AI and Why Mechanistic Explanation Is a Hard Problem

Recently researchers have started using explainability techniques for several different applications—to help foresee how a model might operate in the field, to persuade others to trust a model, and to assist with debugging errors. A large number of explainability techniques have been published with very little empirical testing to see how useful they actually are for each of these use cases. We discuss several pitfalls one can encounter when trying to utilize explainability techniques. We then discuss how recent work on the double descent phenomena and non-robust features indicate that mechanistic explanation of deep neural networks will be very challenging for most real-world applications. In some cases, one may be able to use an easily interpretable model, but for many applications deep neural networks will be more accurate. In light of this, we suggest more focus should be given to implementing out-of-distribution detection methods to detect when a model is extrapolating and thus is likely to fail. These methods can be used in lieu of explainability techniques for increasing trust and debugging errors.
A Causal-Regularity and Deep Learning-Based Conjoint Inference

Deep learning is based on the characteristics of the metadata information from input data to train and test data. It deals with high-dimensional and raw data. Through layer-by-layer stacking transformation, deep neural networks are trained, new rules being learnt. Therefore, even if the new data is given, the networks can automatically transform the data to an accurate knowledge representation based the new rules. The text classification process of deep learning, however, is subject to requirements from specific environments. If the premises cannot be satisfied, the robustness and accuracy of classification will be greatly weakened. Meanwhile, deep learning corresponds to a process of black-box training, and it is impossible to describe feature information artificially or explain the training results in many scenarios. If deep learning is to have a highly intelligent learning ability akin to that of human beings, it is necessary to add to it a high-level reasoning and judgment process similar to the human cognitive process. Motivated by causal regularity, we proposed a conjoint reasoning framework, named Causal Regularity Inference Framework (CRIF), which is based on the combination of causal regularity and deep learning to achieve text classification. Causal inference meets the requirements of high-level information reasoning. It can determine the correlations and causalities between the inputs and find out confounding feature words at the same time. Causal inference makes the training of deep learning more reasonable and explicable. Therefore, effective feature extraction can be carried out in the text processing to remove invalid confounding words, and reduce the data redundancy in training process and improve the accuracy of the text classification.
FreeLauncher: Lossless Failure Recovery of Parameter Servers with Ultralight Replication

Modern distributed machine learning (ML) systems leverage large-scale computing infrastructures to achieve fast model training. For many servers jointly training a model, failure recovery becomes an important challenge when a training task could be accomplished in minutes rather than days. The state-of-the-art checkpointing mechanism cannot meet the need of efficient recovery for large-scale ML, because its high cost prevents timely checkpointing and a server failure will likely cause a substantial loss of intermediate results when the checkpointing intervals are comparable to the entire training times. This paper proposes FreeLauncher (FLR), a lossless recovery mechanism for large-scale ML which performs ultralight replication (instead of checkpointing) to guarantee all intermediate training results (parameters) to be timely replicated. Our key insight is that in the parameter-server (PS) architecture there already exist multiple copies for each intermediate result not only in the server but also in the workers, most of which are qualified for failure recovery. FLR addresses the challenges of parameter sparsity (e.g., when training LDA) and staleness (e.g., when adopting relaxed consistency) by selectively replicating the latest copies of the sparse/stale parameters to ensure at least k up-to-date copies to be existent, which can handle any k-1 failures by re-launching the failed servers with recovered parameters from workers. We implement FLR on Tensorflow. Evaluation results show that FLR achieves lossless failure recovery (almost requiring no recomputation) at little cost.
Intelligent Document Analysis in the Insurance Market

We address the problem of classifying documents into categories for the insurance market. One of the most arduous tasks in any business is to sort through piles of documents. Often this is done using a large amount of the time of employees. The most basic, and time consuming task is the visual inspection of each document in order to discover its type. Our work aimed to address the problem of examining each document submitted by a customer to discover whether it is the one that was requested. We addressed this problem with supervised machine learning methods. In particular we have followed two approaches, in the first approach the documents are handled as images and we use convolutional neural networks (CNN). In the second approach we extract the text from images, and then we apply classification.
Research of Smart Sensors in Unmanned Warehouse System

The most important part of the unmanned warehouse system is maintaining correspondence between items and orders in the whole process of items being transported. This paper proposes three solutions for the deployment of unmanned warehouses, Automated Guided Vehicle (AGV), conveyor belt + RFID (with sorting activities deployed at front end), and conveyor belt + RFID (with sorting activities deployed at rear end). The simulation result of these three solutions using FlexSim has shown that replacing AGVs with a combination of RFID sensors and conveyor belts in high-throughput unmanned warehouses those do not require high order response speed can effectively reduce deployment costs and technical difficulties, and greatly improve order processing capabilities.
Adversarial Context-Aware Representation Learning of Multiword Expressions

Word composition is a promising method to learn the representations of long text. Unfortunately, the representations of non-compositional multiword expressions (e.g., go banana) can not be inferred by word composition. Most current methods regard a multiword expression as a single word, and learn its representation in the same way as word representations. However, many multiword expressions are ambiguous, that they express distinct meanings (literal or idiomatic) in different contexts. To resolve this problem, this paper proposes an adversarial context-aware representation learning method for multiword expressions, which generates representations based on the contexts of their occurrences. An adversarial training framework is introduced for further enhancing the representation learning method. The experimental results verify the beneficial of sense disambiguation of multiword expression for representations learning, and the proposed method achieved competitive performances on both the idiom token classification and compositionality prediction tasks.
Substituting Phrases with Idioms: A Sequence-to-Sequence Learning Approach

In this paper, a sequence-to-sequence model is proposed for translating sentences without idiom to sentence with the idiom. The problem is challenging in two ways, predicting the correct idiom based on context and generating the correct sentence using the idiom due to complex semantic and syntactic rules of language. Sequence-to-sequence learning has gained popularity in the past few years due to their surprising results on machine translation task. This work is based on sequence-to-sequence learning of word sequences along with their part-of-speech tags to predict sentences with correct idiomatic phrases. Results have shown that models have achieved higher BLEU score on using part-of-speech tags as the input sequences. These observations show the prominence of part-of-speech tags in identifying the hidden writing patterns in the language.
Research on Data Communication Based on Microservice Architecture and Knowledge Graph

In the process of communication network management, with the increase of user's demand for application network, the management architecture gradually generates various management modules, and each module independently completes the task. This process is relatively simple and easy to implement and saves a part of the physical space, but the data inside the system is chaotic and scattered, and the functions between data blocks are mostly repeated, which poses a serious threat to the maintenance and upgrade of the 5G communication network management system. For the above reason we try to use micro service architecture for 5 G communication network management system of each module for unified control, ensure that in the process of frequent data communication implements the function of optimization. At the same time, predicting the application of knowledge map in data relations also helps us to better the hierarchy between each data block. It contributes to the normal operation of the network management system. The experimental results on the benchmark data show that the method has excellent performance and good stability.
Tracing segmentation for satellite partial components under low-light environment

The lack of lighting in the space environment results in low segmentation accuracy and target lost. To solve this problem, a satellite component tracking method based on Few-Shot learning is proposed in this paper. First, we design a convolutional neural network, which inputs the first frame of mask information, and outputs the true label and important weight parameters. The Few-Shot learning incorporates the real labels, important weight parameters and the first frame feature information to generate target model parameters. Subsequent frames combine target model parameters with feature extraction, and finally output target mask after encoding and decoding. Our algorithm is evaluated on a new satellite partial component data set, and the simulation results show that the proposed method improves the segmentation accuracy and reduces the target loss rate compared to SiamMask under low-light environment.
A Sufficiency Agriculture Learning Platform for the Development of New Agriculturists

The agricultural industry has the problem of price reduction of products and market oversupply. The root cause comes from Thai farmers practicing monoculture, plant and cultivation of the same types of products at the identical time in a nearby area. To address this problem our team developed a mobile application “Kueakul”. This application serves a sufficiency agriculture platform for a new agriculturist to strengthen a cooperative society of agriculturists. The application is accessible through iOS and Android platforms. It provides eight major features: (1) login; (2) agriculturist profile; (3) preference of users; (4) advising services for basic agriculture; (5) allocation of land and plant model; (6) knowledge for mixed crops (7) crop nourishment (watering); (8) and a discussion forum. The applications “Kueakul” can help Thai agriculturists have better understanding of correct information to best-manage their resources. The application was successfully implemented and evaluated by 2 groups—target users and an expert. The results and evaluation found three most useful features: land allocation, watering scheduling and discussion forum with significant potential for sufficiency agriculture.
Truncated phase-based quantum arithmetic: Error propagation and resource reduction

There are two important, and potentially interconnecting, avenues to the realization of large-scale quantum algorithms: improvement of the hardware, and reduction of resource requirements demanded by algorithm components. In focusing on the latter, one crucial subroutine to many sought-after applications is the quantum adder. A variety of different implementations exist with idiosyncratic pros and cons. One of these, the Draper quantum Fourier adder, offers the lowest qubit count of any adder, but requires a substantial number of gates as well as extremely fine rotations. In this work we present a modification of the Draper adder which eliminates small-angle rotations to highly coarse levels, matched with some strategic corrections. This reduces hardware requirements without sacrificing the qubit saving. We show that the inherited loss of fidelity is directly given by the rate of carry and borrow bits in the computation. We derive formulas to predict this, complemented by complete gate-level matrix product state simulations of the circuit. Moreover, we analytically describe the effects of possible stochastic control error. We present an in-depth analysis of this approach in the context of Shor's algorithm, focusing on the factoring of RSA-2048. Surprisingly, we find that each of the $7\ifmmode\times\else\texttimes\fi{}{10}^{7}$ quantum Fourier transforms may be truncated down to $\ensuremath{\pi}/64$, with additive rotations left only slightly finer. This result is much more efficient than previously realized. We quantify savings in terms of both logical resources and raw magic states, demonstrating that phase adders can be competitive with Toffoli-based constructions.
Design and Research of Visual Data Analysis Technology in the Study Abroad Career Information System

Big data analysis mainly involves two different fields. One is how to store a large amount of data, and the other is how to process a large number of different types of data in a short time, that is, to solve the problems of big data storage and big data processing. Career planning for international students in China requires in-depth exploration of the value of big data, content analysis and calculation of big data, deep learning and knowledge calculation are the basis of big data analysis, visualization is both the key technology of data analysis and the presentation of key technology data analysis results. Based on the research of big data analysis technology, this paper puts forward the countermeasures for the career planning of international students in China, and proposes to improve the career planning channels through the information system and improve the informatization level of career planning.
Micro-expression Detection Using Main Directional Maximal Differential Analysis (MDMD) Method

Facial micro-expressions are fast, subtle, and involuntary facial movement which reveals a person’s underlying emotions. These expressions last for only a fraction of a second, so it’s difficult to deceit such expressions. The subtleness of these expressions poses a significant challenge to the naked eye; hence, a lot of work and researches has been made to detect and recognize these facial micro-expressions. One of the challenges for the detection of micro-expressions is the scarcity of a well-defined dataset. For now, there are mainly three datasets that are used for their detection, i.e., SAMM, SMIC, and CASMEII. In recent years, many types of research have been done, and different machine learning and deep learning algorithms have been introduced for the detection of these micro-expressions. Many efforts have been made in the past years. First, researchers extracted temporal features from a video for recognition. The optical flow method was also used with neural networks for their detection. Nowadays, apex frame within a video frame is being used for the spatial-temporal credit, and the 3DCNN model is also being used. This chapter explains the Main Directional Maximal Differential Analysis (MDMD) technique. The MDMD method uses significant differences in the main direction of the flow of light to detect facial motion. Instead of a single frame, three frames were used in the MDMD technique. Between these sets of frames, the optical flow method was applied. The F1 score obtained for micro-expression detection was 0.0376 for CAS(ME)2 and 0.04445 for SAMM.