Miracle-dz/UniFAR-FTUs huggingface dataset:
Synchronously Generating Two Languages with Interactive Decoding

In this paper, we introduce a novel interactive approach to translate a source language into two different languages simultaneously and interactively. Specifically, the generation of one language relies on not only previously generated outputs by itself, but also the outputs predicted in the other language. Experimental results on IWSLT and WMT datasets demonstrate that our method can obtain significant improvements over both conventional Neural Machine Translation (NMT) model and multilingual NMT model.
Micro fluidity and macro stability in inventor networks

From a macro perspective, inventor networks are characterized by rather stable structures. However, the high levels of fluidity of inventors and their ties found in reality contradicts this macro pattern. In order to explain these contradicting patterns, we zoom in on the intermediate group structures of co-patenting relationships found among inventors in German laser technology research over a period of 45 years. Our findings suggest that continuity of individual actors is not a key factor in maintaining structural stability of networks. Group level explorations indicate that the successor of an existing key player belonged to the exiting key player's ego-network, indicating that the group level provides a source of stability and functionality to the system.
A Framework for Synthesis of Musical Training Examples for Polyphonic Instrument Recognition

Music information retrieval (MIR), an interdisciplinary field involving the classifying or detection of structure in music, is essential for processing, indexing, querying and making recommendations from the vast amount of musical data available on the web and in audio library collections. Deep neural networks have yielded state-of-the-art results in several MIR tasks, but are often limited by their reliance on the availability of large amounts of annotated training data. Thus, applying deep learning in MIR may prove difficult when applied to music databases with limited amounts of labelling. This thesis addresses the question of whether algorithmically compositions generated from a specification of instruments and note events may serve as a viable alternative to real labeled music recordings for use as training data in MIR classification tasks. We propose a simple framework for generation of synthetic musical compositions for use as training data using the popular Musical Instrument Digital Interface (MIDI) protocol, which may be rendered as audio using commonly available synthesizers. In addition, we apply a variety of audio transformations to the generated audio samples for data augmentation purposes. We apply this music synthesis algorithm to the MIR tasks of polyphony estimation (number of instruments sounding) and instrument recognition (which instruments are playing) in polyphonic tracks where multiple instruments may sound simultaneously in each analysis frame, and evaluate our framework on publicly available annotated music datasets. We empirically demonstrate that pure synthesis of a musical training set without usage of a training set of music yields statistically significant improvements over a random or majority classifier. The main contribution of this thesis is to show that synthetic musical composition generation coupled with data augmentation has the potential to aid content-based MIR in music collections with limited amounts of annotation.
Experimental study of support vector machines and Naïve Bayes classifier on automated subject area classification

Subject area classification allow researchers to identify publications based on their discipline or research domain. When number of document is large, classification of publication documents become increasingly difficult. Besides, covering granularity of broad range of subject areas manually is a critical problem. In recent areas, machine learning has emerged as an effective way for automated classification in various domains such as text, images and videos. Problems with classifying large amount of publication papers can be solved with automating the process of subject area classification using supervised machine learning approaches. This paper represents an experimental study that used support vector machines and naive bayes for automated classification of subject areas. Text classification method is used to find the probability of a document to be in certain category based on co-words and their frequency in a document. The proposed experimentation is consisted of two phases. In first phase, a list of co-words was generated from a collection of document in each of selected subject areas using text pre-processing technique. In second phase, both Support Vector Machines(SVM) and Naive Bayes classifiers were used to conduct the experimentation and performance of each method was observed. It was found that SVM performs better than Naive Bayes classifier in multi-label classification.
Vehicle detection in aerial image based on deep learning

At present, urban traffic congestion is becoming more and more serious. Traditional vehicle detection methods generally have problems of low precision and low recognition rate. In order to solve these problems, a vehicle detection method in aerial image based on deep learning is proposed. The method uses the YOLOv3 algorithm and optimizes the algorithm network, increase the number of rasters of the final predicted output, improves the detection ability of the network for dense groups, and verifies the effect by training a new model. The experimental results show that the new training model has a good effect on vehicle detection, which solves the problem of low detection and recognition rate of urban traffic vehicle detection, and makes good predictions and judgments on urban traffic congestion.
Using ID3 Decision Tree Algorithm to the Student Grade Analysis and Prediction

Data mining techniques play an important role in data analysis. For the construction of a classification model which could predict performance of students, particularly for engineering branches, a decision tree algorithm associated with the data mining techniques have been used in the research. A number of factors may affect the performance of students. Data mining technology which can related to this student grade well and we also used classification algorithms prediction. In this paper, we used educational data mining to predict students' final grade based on their performance. We proposed student data classification using ID3(Iterative Dichotomiser 3) Decision Tree Algorithm.
A Review towards the Sentiment Analysis Techniques for the Analysis of Twitter Data

Any opinion of an individual through which the feelings, attitudes and thoughts can be expressed is known as sentiment. The kinds of data analysis which is attained from the news reports, user reviews, social media updates or microblogging sites is called sentiment analysis which is also known as opinion mining. The reviews of individuals towards certain events, brands, product or company can be known through sentiment analysis. The responses of general public are collected and improvised by researchers to perform evaluations. The popularity of sentiment analysis is growing today since the numbers of views being shared by people on the microblogging sites are also increasing. All the sentiments can be categorized into three different categories called positive, negative and neutral. Twitter, being the most popular microblogging site, is used to collect the data to perform analysis. Tweepy is used to extract the source data from Twitter. Python language is used in this research to implement the classification algorithm on the collected data. The features are extracted using N-gram modeling technique. The sentiments are categorized among positive, negative and neutral using a supervised machine learning algorithm known as K-Nearest Neighbor.
Microservices Monitoring with Event Logs and Black Box Execution Tracing

Monitoring is a core practice in any software system. Trends in microservices systems exacerbate the role of monitoring and pose novel challenges to data sources being used for monitoring, such as event logs. Current deployments create a distinct log per microservice; moreover, composing microservices by different vendors exacerbates format and semantic heterogeneity of logs. Understanding and traversing the logs from different microservices demands for substantial cognitive work by human experts. This paper proposes a novel approach to accompany microservices logs with black box tracing to help practitioners in making informed decisions for troubleshooting. Our approach is based on the passive tracing of request-response messages of the REpresentational State Transfer (REST) communication model. Differently from many existing tools for microservices, our tracing is application transparent and non-intrusive. We present an implementation called MetroFunnel and conduct an assessment in the context of two case studies: a Clearwater IP Multimedia Subsystem (IMS) setup consisting of Docker microservices and a Kubernetes orchestrator deployment hosting tens of microservices. MetroFunnel allows making useful attributions in traversing the logs; more important, it reduces the size of collected monitoring data at negligible performance overhead with respect to traditional logs.
Collecting Labels for Rare Anomalies via Direct Human Feedback - An Industrial Application Study

Many systems rely on the expertise from human operators, who have acquired their knowledge through practical experience over the course of many years. For the detection of anomalies in industrial settings, sensor units have been introduced to predict and classify such anomalous events, but these critically rely on annotated data for training. Lengthy data collection campaigns are needed, which tend to be combined with domain expert annotations of the data afterwards, resulting in costly and slow process. This work presents an alternative by studying live annotation of rare anomalous events in sensor streams in a real-world manufacturing setting by experienced human operators that can also observe the machinery itself. A prototype for visualization and in situ annotation of sensor signals is developed with embedded unsupervised anomaly detection algorithms to propose signals for annotation and which allows the operators to give feedback on the detection and classify anomalous events. This prototype allowed assembling a corpus of several weeks of sensor data measured in a real manufacturing surrounding and was annotated by domain experts as an evaluation basis for this study. The evaluation of live annotations reveals high user motivation after getting accustomed to the labeling prototype. After this initial period, clear anomalies with characteristic signal patterns are detected reliably in visualized envelope signals. More subtle signal deviations were less likely to be confirmed an anomaly due to either an insufficient visibility in envelope signals or the absence of characteristic signal patterns.
Technologie optique sans fil infrarouge et visible pour la connectivité de capteurs corporels

This thesis is focused on the field of indoor optical wireless communication for health monitoring based on body sensors. The state of the art of optical wireless in the infrared, visible and UV domains as well as the analysis of health related systems using this technology have been provided. This helped to define the objectives and orientations of this thesis. We have studied the use of infrared technology for data transmission between a sensor worn by a patient and receivers located at the corners of a central lighting panel at the ceiling of the environment. A link in visible was used for the transmission of data from the luminaire to the patient carrying a smartphone equipped with a decoder. The main challenges were the robustness of the infrared and visible links with regard to patient mobility and the impact of the user's body due to the location of the sensor. The channel simulations performed using the Ray-Tracing technique associated with the Monte-Carlo method allowed determining the channel gain, which is the main parameter representing the performance. Due to the patient mobility, the analysis was performed statistically and taking into account different locations of the sensor on the body, from the ankle to the shoulder. The optimal physical and geometrical parameters for transmitters and receivers to ensure the best performance have been deduced. It has been shown that it is essential to model the presence of the body for both uplink and downlink. The overall performance of the system has highlighted the potential of fully optical wireless transmissions for medical surveillance based on body sensors. This has been partly confirmed by experiments carried out from infrared communicating sensor prototypes and commercial products for the visible link.
SVDD Based Data-Driven Fault Detection

Conventional data driven process monitoring algorithms are limited to Gaussian process data for principal component analysis (PCA) algorithm and non-Gaussian process data for independent component analysis (ICA) algorithm. This paper provides a comparison study between the conventional data driven methods and support vector data description (SVDD) algorithm for fault detection (FD). Different from the traditional methods, SVDD algorithm has no Gausssian assumption. Thus the distribution of process data is not important for SVDD method. In order to compare their FD performances of the proposed methods from the application viewpoint, Tennessee Eastman (TE) benchmark process is utilized to compare the results of all the discussed methods. Simulation results on TE process show that ICA and SVDD methods perform better for false faults than the PCA method.
Why Treatment for Alcohol Dependence is Changing

The economic perspectives of treatment professionals and insurance payors have become widely divergent, and payors are insisting on low-cost (outpatient) treatment methods. This trend has forced treatment providers to modify the inpatient traditional Minnesota model of treatment, to outpatient care, thereby reducing the amount and intensity of the treatment exposure. This shift toward lower intensity levels of care is occurring at a time when the absolute numbher of patients in treatment is declining. The thrust toward cost-efficiency imposed by payors is therefore aimed at reducing both over-treatment and over-capacity. Nonetheless, outpatient, lower cost treatment is probably not feasible or workable for all patients and in many cases hospitalization is still appropriate. Treatment providers must update and modify their methodologies in line with current fiscal realities while preserving the efficacy of traditional closed-unit treatment.
An Indoor Localization Method of Image Matching Based on Deep Learning

To overcome the problems of low accuracy and poor stability brought by the complexity of scenarios, an indoor localization method of image matching based on Deep Learning is proposed. The method includes taking images of indoor surroundings with cameras of mobile devices, setting up a dataset of images containing information on position and direction, and training a Convolutional Neural Network (CNN) with the image data. Then use the trained CNN to match the current images taken by the cameras of mobile devices to estimate precise location. The results of experiments show that the accuracy rate of CNN can reach up to 99.2%, positioning accuracy rate is up to 90%, and positioning precision is within 2 metres of diameter. This algorithm can achieve sound robustness, and fairly excellent generalization capabilities.
Parameter Tuning Algorithms in Modeling And Simulation

In this study, parameter optimization studies in modeling and simulations performed in different areas are examined. The common feature of all works are the modeling of systems which are difficult to observe their results in real environment and the problem of setting up large parameter space. The goal is to reflect the real system of the generated model, and it is necessary to select the most suitable parameters in the large parameter space. This is an important issue and is beyond the limits of human problem solving. From the work done, different classifications were developed to categorize the methods of parameter adjustment and to evaluate the existing work. This helps model developers to find the algorithm that produce the optimal solution for the parameter tunning problem that will arise in future modeling studies.
Multi-layer Network Attack and Defense Model Based on Entropy Method to Measure Network Security

Network security is also facing major challenges. On the one hand, network security assessment can dynamically reflect the state of network security and reveal potential threats. On the other hand, network security assessment is the basis of network security management, it is also the prerequisite for building a reliable and secure network. This paper proposes a multi-level network security measurement model, which divides the network into a network environment layer and a network attack and defense layer. The network environment layer considers the network hardware security and the reliability of a network topology. The network attack and defense layer mainly examines the security status of the network from the perspective of network attack and defense. The model comprehensively evaluates and measures the network from different layers of the network, and conducts experiments. The result of experiment proves that the model can effectively assess the security of the network.
ACME: A Tool to Improve Configuration Memory Fault Injection in SRAM-Based FPGAs

Circuits in harsh environments, as space, tend to suffer severe problems caused by radiation. In this scenario, where the behavior of the system can be jeopardized, it is critical to produce fault tolerant circuits that can operate correctly. An important task in this scenario is to effectively test the new fault tolerant designs to guarantee their correct operation. There exist several and diverse methods to achieve this task, from actual test flights to the use of particle accelerators. Fault injection emulation is one of the most popular methods, due to its low cost, availability and convenience. There are a number of tools to perform fault injection using a field-programmable gate array (FPGA) as a supporting platform for this task. However, most of these tools are very dependent on the FPGA version and technology, with limited capability to control the injection process in a precise way. In this paper we present ACME (Automatic Configuration Memory Error-injection), a new tool able to pinpoint fault injections in specific areas of the design under test, with great control and precision of the process. In addition, the methodology to configure the tool and make it work with new FPGA families is also provided.
A Neural Citation Count Prediction Model based on Peer Review Text

Citation count prediction (CCP) has been an important research task for automatically estimating the future impact of a scholarly paper. Previous studies mainly focus on extracting or mining useful features from the paper itself or the associated authors. An important kind of data signals, peer review text, has not been utilized for the CCP task. In this paper, we take the initiative to utilize peer review data for the CCP task with a neural prediction model. Our focus is to learn a comprehensive semantic representation for peer review text for improving the prediction performance. To achieve this goal, we incorporate the abstract-review match mechanism and the cross-review match mechanism to learn deep features from peer review text. We also consider integrating hand-crafted features via a wide component. The deep and wide components jointly make the prediction. Extensive experiments have demonstrated the usefulness of the peer review data and the effectiveness of the proposed model. Our dataset has been released online.
Performance comparison of VVC, AV1 and EVC

This paper presents a study comparing the coding efficiency performance of three video codecs: (a) the Versatile Video Coding (VVC); (b) AV1 codec of the Alliance for Open Media (AOM); and (c) the MPEG-5 Essential Video Coding (EVC). Two approaches to coding were used: (i) constant quality (QP) for VVC, AV1, EVC; and (ii) target bit rate (VBR) for AV1. Constant quality encoding is performed with all the three codecs for an unbiased comparison of the core coding tools. Whereas, target bitrate coding is done with the AV1 codec to study the compression efficiency achieved with rate control, which can and does have a significant impact. Performance is tabulated for on two fronts: (1) objective performance based on PSNR’s and (2) informal subjective assessment. Our general conclusion derived from the assessment of objective metrics and subjective evaluation is that VVC appears to be superior to AV1 and EVC under both constant quality and target bitrate coding. However, relative to currently popular codecs such as AVC and HEVC, that difference is modest.
UTSP: User-Based Two-Step Recommendation With Popularity Normalization Towards Diversity and Novelty

Information technologies such as e-commerce and e-news bring overloaded information as well as convenience to users, cooperatives and companies. Recommender system is a significant technology in solving this information overload problem. Due to the outstanding accuracy performance in top- $N$ recommendation tasks, two-step recommendation algorithms are suitable to generate recommendations. However, their recommendation lists are biased towards popular items. In this paper, we propose a user based two-step recommendation algorithm with popularity normalization to improve recommendation diversity and novelty, as well as two evaluation metrics to measure diverse and novel performance. Experimental results demonstrate that our proposed approach significantly improves the diversity and novelty performance while still inheriting the advantage of two-step recommendation approaches on accuracy metrics.
Designing tangible tools to support collaboration in the co-design of healthcare services

As the challenges facing the Norwegian healthcare system are growing in scale and complexity, cross-disciplinary collaboration is needed between public and private sectors to rethink how we design services for care. Service design has emerged as a fruitful area of design practice and research that can support cross-disciplinary collaboration. It may do so by providing effective co-design communication tools that bridge the evidence-based culture of the medical world with working cultures, perspectives and languages of other fields. Despite the documented use of such tools, few examples in the literature describe the actual design process of developing three-dimensional communication tools and how designed attributes of tools may impact on their effectiveness and influence collaboration. This paper presents examples of tool prototyping as part of an ongoing PhD research. It includes three cases that are taken up to explore how to design context appropriate tools. This is done through the application of an analytical framework that draws on the use of metaphors and affordances in physical objects to offer an account of how tangible tools may be developed and implemented to support collaboration in the co-design of healthcare services.