Reviewerly/exHarmony huggingface dataset:
Research on high-performance English translation based on topic model

Retelling extraction is an important branch of Natural Language Processing (NLP), and high-quality retelling resources are very helpful to improve the performance of machine translation. However, traditional methods based on the bilingual parallel corpus often ignore the document background in the process of retelling acquisition and application. In order to solve this problem, we introduce topic model information into the translation mode and propose a topic-based statistical machine translation method to improve the translation performance. In this method, Probabilistic Latent Semantic Analysis (PLSA) is used to obtains the co-occurrence relationship between words and documents by the hybrid matrix decomposition. Then we design a decoder to simplify the decoding process. Experiments show that the proposed method can effectively improve the accuracy of translation.
Academic Venue Recommendation Based on Refined Cross Domain

To submit a scientific discovery, researchers are generally overloaded with the huge number of academic venues. The concrete challenge is how to select the right academic venue that corresponds to the research area and fits the researcher's current interests (the academic venue types, publisher, rank, location) without missing the submission deadline. This paper proposes a new approach to supply researchers with adapted and personalized academic venues that satisfy their requirements and fit with their preferences. The proposed solution combines several recommendation techniques that use different data sources, i.e., authors past publications as the target domain and authors, from the reference list, past publications as source domain as well as preferences provided by the authors (venue's type, publisher, rank, location), to suggest situable academic venues. It is able to deal with authors changing interests by filtering out irrelevant conferences that do not respond to the researcher's requirements. It is also useful for young researchers who have not publications yet. Experimental results demonstrate the efficiency of our proposed method.
Sensitivity of Machine Learning Approaches to Fake and Untrusted Data in Healthcare Domain

Machine Learning models are susceptible to attacks, such as noise, privacy invasion, replay, false data injection, and evasion attacks, which affect their reliability and trustworthiness. Evasion attacks, performed to probe and identify potential ML-trained models’ vulnerabilities, and poisoning attacks, performed to obtain skewed models whose behavior could be driven when specific inputs are submitted, represent a severe and open issue to face in order to assure security and reliability to critical domains and systems that rely on ML-based or other AI solutions, such as healthcare and justice, for example. In this study, we aimed to perform a comprehensive analysis of the sensitivity of Artificial Intelligence approaches to corrupted data in order to evaluate their reliability and resilience. These systems need to be able to understand what is wrong, figure out how to overcome the resulting problems, and then leverage what they have learned to overcome those challenges and improve their robustness. The main research goal pursued was the evaluation of the sensitivity and responsiveness of Artificial Intelligence algorithms to poisoned signals by comparing several models solicited with both trusted and corrupted data. A case study from the healthcare domain was provided to support the pursued analyses. The results achieved with the experimental campaign were evaluated in terms of accuracy, specificity, sensitivity, F1-score, and ROC area.
Detecting Duplicate Questions in Stack Overflow via Source Code Modeling

Stack Overflow is one of the most popular Question-Answering sites for programmers. However, it faces the problem of question duplication, where newly created questions are identical to previous questions. Existing works on duplicate question detection in Stack Overflow extract a set of textual features on the question pairs and use supervised learning approaches to classify duplicate question pairs. However, they do not consider the source code information in the questions. While in some cases, the intention of a question is mainly represented by the source code. In this paper, we aim to learn the semantics of a question by combining both text features and source code features. We use word embedding and convolutional neural networks to extract textual features from questions to overcome the lexical gap issue. We use tree-based convolutional neural networks to extract structural and semantic features from source code. In addition, we perform multi-task learning by combining the duplication question detection task with a question tag prediction side task. We conduct extensive experiments on the Stack Overflow dataset and show that our approach can detect duplicate questions with higher recall and MRR compared with baseline approaches on Python and Java programming languages.
BMCSA:Multi-feature spatial convolution semantic matching model based on BERT

This paper proposes a multi-feature spatial convolutional semantic matching model (BMCSA) based on BERT by enriching different feature spatial information of semantic features. BMCSA employs the BERT model to extract the semantic features of the text, then uses the two-dimensional convolutional network to extract different feature spatial information, and finally combines the Attention mechanism to capture the global feature spatial information. We use two different semantic matching data sets and a text inference data set to verify the effectiveness of the proposed model. Experimental results prove that BMCSA is better than the baseline model.
Super-resolution based bitrate adaptation for HTTP adaptive streaming for mobile devices

The advancement of hardware capabilities in recent years made it possible to apply deep neural network (DNN) based approaches on mobile devices. This paper introduces a lightweight super-resolution (SR) network, namely SR-ABR Net, deployed at mobile devices to upgrade low-resolution/low-quality videos and a novel adaptive bitrate (ABR) algorithm, namely WISH-SR, that leverages SR networks at the client to improve the video quality depending on the client's context. WISH-SR takes into account mobile device properties, video characteristics, and user preferences. Experimental results show that the proposed SR-ABR Net can improve the video quality compared to traditional SR approaches while running in real time. Moreover, the proposed WISH-SR can significantly boost the visual quality of the delivered content while reducing both bandwidth consumption and number of stalling events.
Fast whole-slide cartography in colon cancer histology using superpixels and CNN classification

Automatic outlining of different tissue types in digitized histological specimen provides a basis for follow-up analyses and can potentially guide subsequent medical decisions. The immense size of whole-slide-images (WSI), however, poses a challenge in terms of computation time. In this regard, the analysis of non-overlapping patches outperforms pixelwise segmentation approaches, but still leaves room for optimization. Furthermore, the division into patches, regardless of the biological structures they contain, is a drawback due to the loss of local dependencies. We propose to subdivide the WSI into coherent regions prior to classification by grouping visually similar adjacent pixels into superpixels. Afterwards, only a random subset of patches per superpixel is classified and patch labels are combined into a superpixel label. We propose a metric for identifying superpixels with an uncertain classification and evaluate two medical applications, namely tumor area and invasive margin estimation and tumor composition analysis. The algorithm has been developed on 159 hand-annotated WSIs of colon resections and its performance is compared to an analysis without prior segmentation. The algorithm shows an average speed-up of 41% and an increase in accuracy from 93.8% to 95.7%. By assigning a rejection label to uncertain superpixels, we further increase the accuracy by 0.4%. Whilst tumor area estimation shows high concordance to the annotated area, the analysis of tumor composition highlights limitations of our approach. By combining superpixel segmentation and patch classification, we designed a fast and accurate framework for whole-slide cartography that is AI-model agnostic and provides the basis for various medical endpoints.
Live video analytics as a service

Many private and public organizations deploy large numbers of cameras, which are used in application services for public safety, healthcare, and traffic control. Recent advances in deep learning have demonstrated remarkable accuracy on computer analytics tasks that are fundamental for these applications, such as object detection and action recognition. While deep learning opens the door for the automation of camera-based applications, deploying pipelines for live video analytics is still a complicated process that requires domain expertise in the fields of machine learning, computer vision, computer systems, and networks. The problem is further amplified when multiple pipelines need to be deployed on the same infrastructure to meet different users' diverse and yet dynamic needs. In this paper, we present a live-video-analytics-as-a-service vision, aiming to remove the complexity barrier and achieve flexibility, agility, and efficiency for applications based on live video analytics. We motivate our vision by identifying its requirements and the shortcomings of existing approaches. Based on our analysis, we present our envisioned system design and discuss the challenges that need to be addressed to make it a reality.
Are University Students Independent: Twitter Sentiment Analysis of Independent Learning in Independent Campus Using RoBERTa Base IndoLEM Sentiment Classifier Model

Higher education is one of the fields that is affected by the COVID-19 pandemic. One of the regulations in Indonesian higher education is Merdeka Learning - Independent Campus (MB-KM). In this research article we are aiming to do an analysis of the sentiment using Twitter as the dataset in order to find out the sentiment of university students towards the program. The analysis was being done using RoBERTa Base IndoLEM Sentiment Classifier Model. With the total number of more than 30 thousand positive sentiments and almost 20 thousand negative sentiments found. The result of the model shows that it achieved an accuracy of 91.73%, with a precision of 83.33%, recall 89.74%, and a harmonic mean of the two (F1 score) of 86.42%. Based on the analysis, it also shows the distribution of the sentiment is 63.0% of positive sentiment and 37.0% of negative sentiments. This paper shows that there are more positive sentiments than the negative one.
Clinical language search algorithm from free-text: facilitating appropriate imaging

The comprehensiveness and maintenance of the American College of Radiology (ACR) Appropriateness Criteria (AC) makes it a unique resource for evidence-based clinical imaging decision support, but it is underutilized by clinicians. To facilitate the use of imaging recommendations, we develop a natural language processing (NLP) search algorithm that automatically matches clinical indications that physicians write into imaging orders to appropriate AC imaging recommendations.We apply a hybrid model of semantic similarity from a sent2vec model trained on 223 million scientific sentences, combined with term frequency inverse document frequency features. AC documents are ranked based on their embeddings' cosine distance to query. For model testing, we compiled a dataset of simulated simple and complex indications for each AC document (n = 410) and another with clinical indications from randomly sampled radiology reports (n = 100). We compare our algorithm to a custom google search engine.On the simulated indications, our algorithm ranked ground truth documents as top 3 for 98% of simple queries and 85% of complex queries. Similarly, on the randomly sampled radiology report dataset, the algorithm ranked 86% of indications with a single match as top 3. Vague and distracting phrases present in the free-text indications were main sources of errors. Our algorithm provides more relevant results than a custom Google search engine, especially for complex queries.We have developed and evaluated an NLP algorithm that matches clinical indications to appropriate AC guidelines. This approach can be integrated into imaging ordering systems for automated access to guidelines.
Künstliche Intelligenz: Herausforderungen und Nutzen in der Intensivmedizin

The high workload in intensive care medicine arises from the exponential growth of medical knowledge, the flood of data generated by the permanent and intensive monitoring of intensive care patients, and the documentation burden. Artificial intelligence (AI) is predicted to have a great impact on ICU work in the near future as it will be applicable in many areas of critical care medicine. These applications include documentation through speech recognition, predictions for decision support, algorithms for parameter optimisation and the development of personalised intensive care medicine. AI-based decision support systems can augment human therapy decisions. Primarily through machine learning, a sub-discipline of AI, self-adaptive algorithms can learn to recognise patterns and make predictions. For actual use in clinical settings, the explainability of such systems is a prerequisite. Intensive care staff spends a large amount of their working hours on documentation, which has increased up to 50% of work time with the introduction of PDMS. Speech recognition has the potential to reduce this documentation burden. It is not yet precise enough to be usable in the clinic. The application of AI in medicine, with the help of large data sets, promises to identify diagnoses more quickly, develop individualised, precise treatments, support therapeutic decisions, use resources with maximum effectiveness and thus optimise the patient experience in the near future.
RfX: A Design Study for the Interactive Exploration of a Random Forest to Enhance Testing Procedures for Electrical Engines

Abstract Random Forests (RFs) are a machine learning (ML) technique widely used across industries. The interpretation of a given RF usually relies on the analysis of statistical values and is often only possible for data analytics experts. To make RFs accessible to experts with no data analytics background, we present RfX, a Visual Analytics (VA) system for the analysis of a RF's decision‐making process. RfX allows to interactively analyse the properties of a forest and to explore and compare multiple trees in a RF. Thus, its users can identify relationships within a RF's feature subspace and detect hidden patterns in the model's underlying data. We contribute a design study in collaboration with an automotive company. A formative evaluation of RFX was carried out with two domain experts and a summative evaluation in the form of a field study with five domain experts. In this context, new hidden patterns such as increased eccentricities in an engine's rotor by observing secondary excitations of its bearings were detected using analyses made with RfX. Rules derived from analyses with the system led to a change in the company's testing procedures for electrical engines, which resulted in 80% reduced testing time for over 30% of all components.
Three-Dimensional Facial Ethnicity Identification Based on Cylindrical Projection and Deep Learning

Various applications have been developed in facial research to aid the recognition of personal attributes, analysis of race, and personal authentication for the security industry and other research fields. Because of the wealth of information contained in 3D facial images, 3D face recognition has recently become an active research field. Nevertheless, because of its high dimension, 3D face recognition remains a difficult task. We propose a 3D facial ethnicity identification method on the basis of cylindrical projection and deep learning in this paper. In the proposed method, we first use cylindrical projection to convert a 3D facial image (scanned 3D data) to a 2D grayscale image. The depth information is represented by the grayscale. The transformed 2D image is then fed into a convolutional neural network for ethnicity recognition. The proposed method is alignment-free, and any existing convolutional neural networks developed for 2D image recognition can be used to identify ethnicity. The effectiveness of the proposed method was validated on our private 3D dataset and demonstrated that the proposed method can give promising identification performances between the Mainland Japanese and the Ryukyuan.
Watch Less and Uncover More: Could Navigation Tools Help Users Search and Explore Videos?

Prior research has shown how ‘content preview tools’ improve speed and accuracy of user relevance judgements across different information retrieval tasks. This paper describes a novel user interface tool, the Content Flow Bar, designed to allow users to quickly identify relevant fragments within informational videos to facilitate browsing, through a cognitively augmented form of navigation. It achieves this by providing semantic “snippets” that enable the user to rapidly scan through video content. The tool provides visually-appealing pop-ups that appear in a time series bar at the bottom of each video, allowing to see in advance and at a glance how topics evolve in the content. We conducted a user study to evaluate how the tool changes the users search experience in video retrieval, as well as how it supports exploration and information seeking. The user questionnaire revealed that participants found the Content Flow Bar helpful and enjoyable for finding relevant information in videos. The interaction logs of the user study, where participants interacted with the tool for completing two informational tasks, showed that it holds promise for enhancing discoverability of content both across and within videos. This discovered potential could leverage a new generation of navigation tools in search and information retrieval.
Image declipping: Saturation correction in single images

High dynamic range (HDR) images present fine details in a scene and are visually more appealing than low dynamic range (LDR) images, since they contain a greater dynamic range of color gamut. HDR compatible displays are currently high-cost, therefore tone-mapping algorithms have widely been used to obtain high quality images for LDR screens with a lower cost. However, tone-mapped images may contain clipped pixel regions, which should be corrected to retrieve the lost information, to acquire visually pleasing LDR images. In a single image, the recovery of color and texture information in clipped regions is challenging, yet an attractive research field in image processing. Although there are several algorithms present in literature, developing a general framework for different types of image content is hard to achieve. This study proposes a single image declipping method based on linear embeddings, difference of pixels and block-search. Experimental results carried out on a tone-mapped HDR image dataset and LDR images demonstrate that the proposed algorithm is able to successfully recover saturated pixels in various types of images. Detailed statistical and visual comparisons show that this approach produces superior results on average for both tone-mapped and LDR images when compared to existing techniques.
Survey on Cloud Computing Security and Scheduling

The purpose of this study is to look at Cloud Computing Security. The term &#x201C;cloud computing&#x201D; refers to a way of delivering services over the internet. As a network service, computing resources are made available. These services are cost-effective, scalable, and self-contained. Cloud computing is responsible for the IT industry&#x0027;s rapid growth. Despite its many benefits, it has a number of security concerns that must be addressed. In order to make Cloud Computing even more secure and trustworthy, some efforts must be made against the model&#x0027;s hazards. All of the risks linked with Cloud Computing, as well as potential cures, are evaluated in this paper. Various resources like as memory, software, CPU, and network services are offered on-demand via the internet in cloud computing. Because all resources such as hardware, software, and network are used without physically purchasing them, the company&#x0027;s primary investment in the cloud is retained. Many advances are being implemented in the cloud platform to deliver a better experience for cloud users.
Mobile Anonymization and Pseudonymization of Structured Health Data for Research

Healthcare Organizations need to share the health data of the patients with Research Centers in order to fulfill research purposes and improve the healthcare services provided to the patients. However, the information being processed by the Research Centers includes personal and/or sensitive data, which puts the privacy of the individuals at stake. To mitigate the risk of identity disclosure and privacy violation, a variety of privacy mechanisms, such as anonymization and pseudonymization, can be applied to the personal data of the data subjects. In this paper a mobile library is presented in order to either anonymize or pseudonymize the individuals’ personal information which follows the Fast Healthcare Interoperability Resources protocol. To evaluate the implementation and the functionalities of the library two case studies are described – one for each privacy mechanism.
Single-image depth estimation using relative depths

Depth estimation from a single RGB image is a challenging task. It is ill-posed since a single 2D image may correspond to various 3D scenes at different scales. On the other hand, estimating the relative depth relationship between two objects in a scene is easier and may yield more reliable results. Thus, in this paper, we propose a novel algorithm for monocular depth estimation using relative depths. First, using a convolutional neural network, we estimate two types of depths at multiple spatial resolutions: ordinary depth maps and relative depth tensors. Second, we restore a relative depth map from each relative depth tensor. A relative depth map is equivalent to an ordinary depth map with global scale information removed. For the restoration, sparse pairwise comparison matrices are constructed from available relative depths, and missing entries are filled in using the alternative least square (ALS) algorithm. Third, we decompose the ordinary and relative depth maps into components and recombine them to yield a final depth map. To reduce the computational complexity, relative depths at fine spatial resolutions are directly used to refine the final depth map. Extensive experimental results on the NYUv2 dataset demonstrate that the proposed algorithm provides state-of-the-art performance.
Survey on energy efficient scheduling techniques on cloud computing

With ever-growing technical advances, performance of complex scientific and engineering applications has arrived at petaflops and exaflops range. However, massive power drawn from the large scale computing infrastructure has caused commensurate rise in electricity consumption, escalating data center ownership costs besides leaving carbon footprints. Judicious scheduling of complex applications with an objective to reduce overall makespan and reduced energy consumption has become one of the biggest confront in the realm of computing architectures. This paper presents a survey on energy efficient scheduling algorithms based on dynamic voltage and frequency scaling (DVFS) and dynamic power management (DPM) techniques. The parameters considered are mainly the makespan, processor energy (dynamic and static) consumption, and network energy (communication) consumption, wherever appropriate during task scheduling.