Reviewerly/exHarmony huggingface dataset:
CNN-LSTM Combined Network for IoT Enabled Fall Detection Applications

Abstract An accidental fall could do a great damage to the health of elderly. Failure to provide timely assistance after a fall may cause injury or even death. In this paper, a fall detection algorithm based on Convolutional Neural Network (CNN)-Long Short Term Memory (LSTM) combined network is proposed, which makes full use of the powerful feature extraction ability of CNN and the excellent time series processing ability of LSTM. Data required by the algorithm is only the resultant acceleration from a low cost three-axis acceleration sensor. The experimental results show that compared with the algorithms based on Support Vector Machine (SVM) and CNN, the proposed algorithm has higher detection accuracy with a small data volume, which is very suitable for Internet of Things (IoT) enabled fall detection applications.
Using Search Queries to Understand Health Information Needs in Africa

The lack of comprehensive, high-quality health data in developing nations creates a roadblock for combating the impacts of disease. One key challenge is understanding health information needs of people. Without understanding people’s everyday concerns, health organizations and policymakers are less able to effectively target education and programming efforts. In this paper, we propose a bottom-up approach that uses search data to uncover and gain insight into health information needs of individuals in Africa. We analyze Bing searches related to HIV/AIDS, malaria, and tuberculosis from all 54 African nations. For each disease, we automatically derive a set of common topics, revealing a widespread interest in various types of information, including disease symptoms, drugs, concerns about breastfeeding, as well as stigma, beliefs in natural cures, and other topics that may be hard to uncover through traditional surveys. We expose the different patterns that emerge in health information needs by demographic groups (age and gender) and country. Using finergrained data, we also uncover discrepancies in the quality of content returned by search engines to users by topic and highlight differences in user behavior and satisfaction. Combined, our results suggest that search data can help illuminate health information needs in Africa and inform discussions on health policy and targeted education efforts both on- and off-line.
Video Summarization based on Motion Detection for Surveillance Systems

In this paper a video summarization method based on motion detection has been proposed. Sensor noise (noise of acquisition and digitization) and the illumination changes in the scene are the most limitations of the background subtraction approaches. In order to handle these problems, this paper present an approach based on the combining of the background subtraction and the Structure-Texture-Noise Decomposition. Firstly, each gray-level image of the sequence will be decomposed on three components, Structure, Texture and Noise. The Structure and Texture components of each image of the sequence are taken to generate the background model. The absolute difference used to subtract the background before compute the binary image of moving objects. We, also, propose a video summarization based on the background subtraction results. The generated background model is used to compute the change during all time of the sequence. The experimental results demonstrate that our approach is effective and accurate for moving objects detection and yields a good summarization of the video sequence.
Annif: DIY automated subject indexing using multiple algorithms

Manually indexing documents for subject-based access is a labour-intensive process. We propose using metadata gathered from bibliographic databases to train algorithms that assist librarians in that work. We have developed Annif, an open source tool and microservice for automated subject indexing. After training it with a subject vocabulary and existing metadata, Annif can be used to assign subject headings for new documents. We have tested Annif with different document collections including scientific papers, old scanned books and contemporary e-books, Q&amp;A pairs from an “ask a librarian” service, Finnish Wikipedia, and the archives of a local newspaper. The results of analysing scientific papers and current books have been reassuring, while other types of documents have proved to be more challenging. The current version is based on a combination of existing natural language processing and machine learning tools. By combining multiple approaches and existing open source algorithms, Annif can build on the strengths of individual algorithms and adapt to different settings. With Annif, we expect to improve subject indexing and classification processes especially for electronic documents as well as collections that otherwise would not be indexed at all.
NTIRE 2019 Challenge on Image Colorization: Report

This paper reviews the NTIRE challenge on image colorization (estimating color information from the corresponding gray image) with focus on proposed solutions and results. It is the first challenge of its kind. The challenge had 2 tracks. Track 1 takes a single gray image as input. In Track 2, in addition to the gray input image, some color seeds (randomly samples from the latent color image) are also provided for guiding the colorization process. The operators were learnable through provided pairs of gray and color training images. The tracks had 188 registered participants, and 8 teams competed in the final testing phase.
Selective Ensemble Based on Extreme Learning Machine for Sensor-Based Human Activity Recognition

Sensor-based human activity recognition (HAR) has attracted interest both in academic and applied fields, and can be utilized in health-related areas, fitness, sports training, etc. With a view to improving the performance of sensor-based HAR and optimizing the generalizability and diversity of the base classifier of the ensemble system, a novel HAR approach (pairwise diversity measure and glowworm swarm optimization-based selective ensemble learning, DMGSOSEN) that utilizes ensemble learning with differentiated extreme learning machines (ELMs) is proposed in this paper. Firstly, the bootstrap sampling method is utilized to independently train multiple base ELMs which make up the initial base classifier pool. Secondly, the initial pool is pre-pruned by calculating the pairwise diversity measure of each base ELM, which can eliminate similar base ELMs and enhance the performance of HAR system by balancing diversity and accuracy. Then, glowworm swarm optimization (GSO) is utilized to search for the optimal sub-ensemble from the base ELMs after pre-pruning. Finally, majority voting is utilized to combine the results of the selected base ELMs. For the evaluation of our proposed method, we collected a dataset from different locations on the body, including chest, waist, left wrist, left ankle and right arm. The experimental results show that, compared with traditional ensemble algorithms such as Bagging, Adaboost, and other state-of-the-art pruning algorithms, the proposed approach is able to achieve better performance (96.7% accuracy and F1 from wrist) with fewer base classifiers.
Semantic-Based Representation Binary Clone Detection for Cross-Architectures in the Internet of Things

Code reuse is widespread in software development as well as internet of things (IoT) devices. However, code reuse introduces many problems, e.g., software plagiarism and known vulnerabilities. Solving these problems requires extensive manual reverse analysis. Fortunately, binary clone detection can help analysts mitigate manual work by matching reusable code and known parts. However, many binary clone detection methods are not robust to various compiler optimization options and different architectures. While some clone detection methods can be applied across different architectures, they rely on manual features based on human prior knowledge to generate feature vectors for assembly functions and fail to consider the internal associations between features from a semantic perspective. To address this problem, we propose and implement a prototype GeneDiff, a semantic-based representation binary clone detection approach for cross-architectures. GeneDiff utilizes a representation model based on natural language processing (NLP) to generate high-dimensional numeric vectors for each function based on the Valgrind intermediate representation (VEX) representation. This is the first work that translates assembly instructions into an intermediate representation and uses a semantic representation model to implement clone detection for cross-architectures. GeneDiff is robust to various compiler optimization options and different architectures. Compared to approaches using symbolic execution, GeneDiff is significantly more efficient and accurate. The area under the curve (AUC) of the receiver operating characteristic (ROC) of GeneDiff reaches 92.35%, which is considerably higher than the approaches that use symbolic execution. Extensive experiments indicate that GeneDiff can detect similarity with high accuracy even when the code has been compiled with different optimization options and targeted to different architectures. We also use real-world IoT firmware across different architectures as targets, therein proving the practicality of GeneDiff in being able to detect known vulnerabilities.
A Novel Genetic Programming Algorithm with Knowledge Transfer for Uncertain Capacitated Arc Routing Problem

Uncertain Capacitated Arc Routing Problem (UCARP) is a challenging optimization problem. Genetic Programming (GP) has been successfully applied to train routing policies (heuristics to make decisions in real time rather than a fixed solution) to respond to uncertain environments effectively. However, the effectiveness of routing policy is scenario dependent, and it takes time to train a new routing policy for each scenario. In this paper, we investigate GP with knowledge transfer to improve the training efficiency by reusing useful knowledge from previously solved related scenarios. We propose a novel knowledge transfer approach which our experimental results show that it obtained significantly higher training efficiency than the existing GP knowledge transfer methods, and the vanilla training process without knowledge transfer.
Decoupled Box Proposal and Featurization with Ultrafine-Grained Semantic Labels Improve Image Captioning and Visual Question Answering

Object detection plays an important role in current solutions to vision and language tasks like image captioning and visual question answering.However, popular models like Faster R-CNN rely on a costly process of annotating ground-truths for both the bounding boxes and their corresponding semantic labels, making it less amenable as a primitive task for transfer learning.In this paper, we examine the effect of decoupling box proposal and featurization for down-stream tasks.The key insight is that this allows us to leverage a large amount of labeled annotations that were previously unavailable for standard object detection benchmarks.Empirically, we demonstrate that this leads to effective transfer learning and improved image captioning and visual question answering models, as measured on publiclyavailable benchmarks.
Is the Red Square Big? MALeViC: Modeling Adjectives Leveraging Visual Contexts

This work aims at modeling how the meaning of gradable adjectives of size ('big', 'small') can be learned from visually-grounded contexts.Inspired by cognitive and linguistic evidence showing that the use of these expressions relies on setting a threshold that is dependent on a specific context, we investigate the ability of multi-modal models in assessing whether an object is 'big' or 'small' in a given visual scene.In contrast with the standard computational approach that simplistically treats gradable adjectives as 'fixed' attributes, we pose the problem as relational: to be successful, a model has to consider the full visual context.By means of four main tasks, we show that state-of-the-art models (but not a relatively strong baseline) can learn the function subtending the meaning of size adjectives, though their performance is found to decrease while moving from simple to more complex tasks.Crucially, models fail in developing abstract representations of gradable adjectives that can be used compositionally.
On Robustness to Adversarial Examples and Polynomial Optimization

We study the design of computationally efficient algorithms with provable guarantees, that are robust to adversarial (test time) perturbations. While there has been an explosion of recent work on this topic due to its connections to test time robustness of deep networks, there is limited theoretical understanding of several basic questions like (i) when and how can one design provably robust learning algorithms? (ii) what is the price of achieving robustness to adversarial examples in a computationally efficient manner? The main contribution of this work is to exhibit a strong connection between achieving robustness to adversarial examples, and a rich class of polynomial optimization problems, thereby making progress on the above questions. In particular, we leverage this connection to (a) design computationally efficient robust algorithms with provable guarantees for a large class of hypothesis, namely linear classifiers and degree-2 polynomial threshold functions~(PTFs), (b) give a precise characterization of the price of achieving robustness in a computationally efficient manner for these classes, (c) design efficient algorithms to certify robustness and generate adversarial attacks in a principled manner for 2-layer neural networks. We empirically demonstrate the effectiveness of these attacks on real data.
Translated vs Non-Translated Method for Multilingual Hate Speech Identification in Twitter

Nowadays social media is often misused to spread hate speech. Spreading hate speech is an act that needs to be handled in a special way because it can undermine or discriminate other people and cause conflict that leading to both material and immaterial losses. There are several challenges in building a hate speech identification system; one of them is identifying hate speech in multilingual scope. In this paper, we adapt and compare two methods in multilingual text classification which are translated (with and without language identification) and non-translated method for multilingual hate speech identification (including Hindi, English, and Indonesian language) using machine learning approach. We use some classification algorithms (classifiers) namely Support Vector Machine (SVM), Naive Bayes (NB), and Random Forest Decision Tree (RFDT) with word n-grams and char n-grams (character n-grams) as feature extraction. Our experiment result shows that the non-translated method gives the best result. However, the use of non-translated method needs to be reconsidered because this method needs more cost for data collection and annotation. Meanwhile, translated without language identification method give a poor result. To address this problem, we combine translated method with monolingual hate speech identification, and the experiment result shows that this approach can increase the multilingual hate speech identification performance compared to translate without language identification. This paper discusses the advantages and disadvantages for all method and the future works to enhance the performance in multilingual hate speech identification.
Event Causality Recognition Exploiting Multiple Annotators’ Judgments and Background Knowledge

We propose new BERT-based methods for recognizing event causality such as "smoke cigarettes" → "die of lung cancer" written in web texts.In our methods, we grasp each annotator's policy by training multiple classifiers, each of which predicts the labels given by a single annotator, and combine the resulting classifiers' outputs to predict the final labels determined by majority vote.Furthermore, we investigate the effect of supplying background knowledge to our classifiers.Since BERT models are pre-trained with a large corpus, some sort of background knowledge for event causality may be learned during pre-training.Our experiments with a Japanese dataset suggest that this is actually the case: Performance improved when we pretrained the BERT models with web texts containing a large number of event causalities instead of Wikipedia articles or randomly sampled web texts.However, this effect was limited.Therefore, we further improved performance by simply adding texts related to an input causality candidate as background knowledge to the input of the BERT models.We believe these findings indicate a promising future research direction.
Integration of Dubbing Constraints into Machine Translation

Translation systems aim to perform a meaningpreserving conversion of linguistic material (typically text but also speech) from a source to a target language (and, to a lesser degree, the corresponding socio-cultural contexts).Dubbing, i. e., the lip-synchronous translation and revoicing of speech adds to this constraints about the close matching of phonetic and resulting visemic synchrony characteristics of source and target material.There is an inherent conflict between a translation's meaning preservation and its 'dubbability' and the resulting trade-off can be controlled by weighing the synchrony constraints.We introduce our work, which to the best of our knowledge is the first of its kind, on integrating synchrony constraints into the machine translation paradigm.We present first results for the integration of synchrony constraints into encoder decoder-based neural machine translation and show that considerably more 'dubbable' translations can be achieved with only a small impact on BLEU score, and dubbability improves more steeply than BLEU degrades.
Classification with costly features as a sequential decision-making problem

This work focuses on a specific classification problem, where the information about a sample is not readily available, but has to be acquired for a cost, and there is a per-sample budget. Inspired by real-world use-cases, we analyze average and hard variations of a directly specified budget. We postulate the problem in its explicit formulation and then convert it into an equivalent MDP, that can be solved with deep reinforcement learning. Also, we evaluate a real-world inspired setting with sparse training datasets with missing features. The presented method performs robustly well in all settings across several distinct datasets, outperforming other prior-art algorithms. The method is flexible, as showcased with all mentioned modifications and can be improved with any domain independent advancement in RL.
Image Classification Using Convolutional Neural Networks

Convolutional neural networks (CNNs), consist of multiple processing layers to learn the representations of data with multiple abstract levels.They are the most successful machine learning models in recent years.Within this paper, we will know the usage of a trained deep convolutional neural network model to extract the features of the images, and then classify the images.We will study image processing and understand image classification.It has good application prospects.
Consumption Behavior Analysis of Over the Top Services: Incremental Learning or Traditional Methods?

Network monitoring and analysis of consumption behavior are important aspects for network operators.The information obtained about consumption trends allows to offer new data plans aimed at specific users and obtain an adequate perspective of the network.Over The Top applications are known by their large consumption of network resources.Service degradation is a common mechanism that applies limits to the amount of information that can be transferred and it is usually applied in a generalized way, affecting the performance of applications consumed by users while leaving aside their behavior and preferences.With this in mind, a proposal of personalizing service degradation policies applied to users has been considered through data mining and traditional machine learning.However, such approach is incapable of considering the swift changes a user can present in their consumption behavior over time.In order to observe which approach is capable of a continuous model adaptation while maintaining their usefulness over time, this paper introduces a performance comparison of traditional and incremental machine learning algorithms applied to information about users' Over The Top consumption behavior.Two datasets are implemented for the tests: the first one is built through a real network experiment holding 1,581 instances, and the second one holds 150,000 instances generated in a synthetic way.After analyzing the obtained results, the best algorithm from the traditional approach was a Support Vector Machine while the best classifier from the incremental approach was an ensemble method composed by Oza Bagging and the K-Nearest Neighbor algorithm.
On the Agile Mindset of an Effective Team – An Industrial Opinion Survey

In this paper we present the results of an opinion survey among 52 agile practitioners who evaluated the importance of 26 selected elements of the agile mindset to the effectiveness of an agile team.In total, we have identified 70 unique agile mindset elements based on 11 literature sources and 5 interviews with industry experts.7 elements belonged to the "support for business goals" category, 20 to the "relationships within the team" category, 24 to the "individual features" category, and 19 to the "organization of work" category.Our survey shows the relative importance of the selected 26 agile mindset elements according to our respondents which is not fully consistent with the principles behind the Agile Manifesto. I. INTRODUCTIONgile Manifesto [1] together with the principles behind the Agile Manifesto [2] founded a set of driving values and key principles for the agile software development.Agile practitioners emphasize that effective performance of an agile team requires not only a given set of procedures, techniques and rituals, but, above all, a particular attitude, way of thinking and behavior of both the individuals and the entire team -a so called 'agile mindset' [3,4].Working in agile teams requires many non-technical and social competencies related to communication, organization, business, improvement and many more [5].These are not the typical strong competencies among software engineers [6], which is why they require support of Scrum Masters, mentors and coaches to develop deep understanding of the fundamentals of Agile.Agile mindset, by addressing all of these competence areas and by suggesting important factors to the effective teamwork, supports practitioners in mastering Agile in their projects [4].Altogether, developing the proper agile mindset contributes to the increasing success of agile software projects [7].The principles behind the Agile Manifesto themselves [2] recommend such attitudes and behaviors as focus on customer satisfaction, openness to change, face-to-face communication, sustainable development, simplicity, selforganization and improvement by frequent reflection.