Reviewerly/exHarmony huggingface dataset:
Semantic Image Segmentation With Propagating Deep Aggregation

In this article, we propose a new architecture of feature aggregation, which is designed to deal with the problem that the information of each convolutional layer cannot be used reasonably and the shallow layer information is lost in the process of transmission. In this method, propagating deep aggregation (PDA) is transplanted into the DeepLab-ASPP model to generate a new model combining structural continuity with feature aggregation. This new model can be divided into several stages according to the output resolution. In these stages, more possible feature fusion combinations can be realized, and the result from aggregation propagated among stages can be optimized and updated to get the best results. We demonstrate the effectiveness of the proposed model on the PASCAL VOC 2012 data set and the PASCAL-Context data set. Our method achieves state-of-the-art performance on two public benchmarks and significantly outperforms the previous results, 78.8% (versus 62.4%) on the PASCAL VOC 2012 data set and 46.2% (versus 37.8%) on the PASCAL-Context data set.
Proxy re‐encryption with equality test for secure data sharing in Internet of Things‐based healthcare systems

Abstract The integration of the Internet of Things (IoT) and cloud computing brings tremendous convenience and efficiency to process and store growing sensor data in healthcare systems. For the security of sensor data collected by healthcare systems, encrypting data before uploading is necessary. Nevertheless, these encrypted form sensor data make it a research challenge to achieve efficient data search and data sharing on the cloud‐assisted IoT simultaneously. Encrypting data by public key encryption with equality test (PKE‐ET) scheme, identified as a viable solution, is capable to search data encrypted with different keys efficiently. However, how to efficiently share searched healthcare record on the cloud server is still an unsettled problem. To address this problem, we combine the concepts of proxy re‐encryption (PRE) and PKE‐ET to obtain proxy re‐encryption with equality test (PRE‐ET). Inheriting the advantages of PKE‐ET and PRE, the user can search the required healthcare record data from data encrypted under different public keys. Meanwhile, the searched healthcare record can be shared securely and flexibly without disclosing the secret key and plaintext. We prove the security of our PRE‐ET construction based on divisible computation Diffie‐Hellman assumption in the random oracle model. Eventually, the proposed scheme is proven useful according to extensive efficiency analysis and comparison.
Making professional development more social: A systematic review of librarians' professional development through social media

To maintain relevancy with the ever-increasing competencies required of them, librarians on the job have frequently turned to professional development (PD) opportunities. One outlet that could potentially address barriers librarians face when accessing PD is social media, which can help librarians communicate across distances, are used at low to no cost, and can be accessed asynchronously as needed. Drawing from 25 studies conducted in 21 countries, this systematic literature review explored the empirical themes evident in the current research on librarians' adoption of social media for PD purposes. This review found (1) there is considerable potential in adopting social media for PD across contexts; (2) studies focused on the affordances provided by social media in areas where there is limited infrastructure, support, and resources for PD; (3) in congruence with prior research pertaining to the Technology Acceptance Model, librarians who felt more tech-savvy were more likely to adopt social media for PD; and (4) social media was viewed as democratic in creating a platform where diverse voices could participate. The paper concludes with a discussion of the implications for practitioners and recommendations for future research directions.
Weakly Supervised Deep Learning for Objects Detection from Images

Training of a convolutional neural network for object detection requires a large number of images with pixel-level annotations. Weakly supervised learning uses image-level labels to circumvent the issue of lack of semantic examples, which remains an open challenging. This paper proposes a cascaded deep network architecture that leverages the class activation mapping with global average pooling. The first stage of this architecture learns to infer object localization maps based on the image-level annotations, which generates bounding boxes of objects in every image. These image patches are adhesion areas in the original image. In the second stage, the image patches are used to train the detection network. Experiments are conducted using the PASCAL VOC 2012 datasets. Our proposed method obtains a mean average precision of 87.2% and demonstrates a competitive performance of classification performance with respect to the state-of-the-art methods. In the evaluation of object localization, the recall of our method is improved by 9%.
Road Accident Detection and Severity Determination from CCTV Surveillance

Wide variety of roadVeni, S. types like intersections, highways poses a real challenge to the computer vision algorithms.Anand, R. Hence, there is a need of efficient algorithm to detect the accident on road and also evaluate the severity of the incident. This can be used to improve the emergency services response time.Santosh, B. The work demonstrated in this paper aims to develop such an algorithm by modifying existing CCTV surveillance system. In this work, the accident is detected by the dispersion in the motion field of the vehicles during collision. Motion field of the road is obtained from the optical flow of the video frames. The moving objects in the frames are segmented and tracked. The dispersion in the angle vector of the optical flow is derived for each of the moving object. The dispersion of angle vector for each object is monitored, and deviation of the same from the threshold is determined as an accident. The harshness of the accident can be found by the range of dispersion of the motion field. The algorithm developed here is capable of detecting accidents between any types of moving objects.
Integrating Design Thinking into Scrum Framework in the Context of Requirements Engineering Management

Scrum is one of the Agile software development processes broadly adopted by industry. Scrum promotes frequent customer involvement and incremental short releases. Despite its popular use, Scrum's requirements engineering stage is inadequately defined which can lead to increase development time and cost, along with low quality or failure for the end products. This paper proposes an integrated framework, where the Design Thinking model is integrated with the Scrum framework in the context of requirements engineering management. Design Thinking is an iterative process, human-centric, and structured problem-solving approach. This integration aims to improve the existing Scrum requirements engineering by overcoming its challenges in cases, such as providing a comprehensive understanding of the customer's needs, and addressing the effects of the challenges in other cases, such as frequent changes of requirements.
Adaptive Inattentional Framework for Video Object Detection With Reward-Conditional Training

Recent object detection studies have been focused on video sequences, mostly due to the increasing demand of industrial applications. Although single-image architectures achieve remarkable results in terms of accuracy, they do not take advantage of particular properties of the video sequences and usually require high parallel computational resources, such as desktop GPUs. In this work, an inattentional framework is proposed, where the object context in video frames is dynamically reused in order to reduce the computation overhead. The context features corresponding to keyframes are fused into a synthetic feature map, which is further refined using temporal aggregation with ConvLSTMs. Furthermore, an inattentional policy has been learned to adaptively balance the accuracy and the amount of context reused. The inattentional policy has been learned under the reinforcement learning paradigm, and using our novel reward-conditional training scheme, which allows for policy training over a whole distribution of reward functions and enables the selection of a unique reward function at inference time. Our framework shows outstanding results on platforms with reduced parallelization capabilities, such as CPUs, achieving an average latency reduction up to 2.09×, and obtaining FPS rates similar to their equivalent GPU platform, at the cost of a 1.11× mAP reduction.
Extracting Cause of Death From Verbal Autopsy With Deep Learning Interpretable Methods

The international standard to ascertain the cause of death is medical certification. However, in many low and middle-income countries, the majority of deaths occur outside of health facilities. In these cases, Verbal Autopsy (VA), the narrative provided by a family member or friend together with a questionnaire is designed by the World Health Organization as the main information source. Until now technology allowed us to automatically analyze the responses of the VA questionnaire with the narrative captured by the interviewer excluded. Our work addresses this gap by developing a set of models for automatic Cause of Death (CoD) ascertainment in VAs with a focus on the textual information. Empirical results show that the open response conveys valuable information towards the ascertainment of the Cause of Death, and the combination of the closed-ended questions and the open response lead to the best results. Model interpretation capabilities position the Deep Learning models as the most encouraging choice.
The Hub

Walking meetings are a promising means to improve healthy behavior at work. By providing a physically active way of working, walking meetings can reduce our sitting time. Several obstacles that limit the social acceptance and wider adoption of walking meeting practice have been highlighted in previous research. Amongst these, the difficulty to take notes or present files is a recurring concern for office workers. To address these barriers, we designed the Hub, a network of stand-up meeting stations that accommodate different work-related tasks during walking meetings. We report on two pilot user tests investigating users' experiences and ideas for improvement, and present future research steps. We discuss the usefulness and relevance of the Hub concept to overcome the obstacles associated with walking meetings.
Robust Arabic Text Categorization by Combining Convolutional and Recurrent Neural Networks

Text Categorization is an important task in the area of Natural Language Processing (NLP). Its goal is to learn a model that can accurately classify any textual document for a given language into one of a set of predefined categories. In the context of the Arabic language, several approaches have been proposed to tackle this problem, many of which are based on the bag-of-words assumption. Even though these methods usually produce good results for the classification task, they often fail to capture contextual dependencies from textual data. On the other hand, deep learning architectures that are usually based on Recurrent Neural Networks (RNNs) or Convolutional Neural Networks (CNNs) do not suffer from such a limitation and have recently shown very promising results in various NLP applications. In this work, we use deep learning models that combine RNN and CNN for the task of Arabic text categorization using static, dynamic, and fine-tuned word embeddings. The experimental results reported on the Open Source Arabic Corpora (OSAC) dataset have shown the effectiveness and high performance of our proposed models.
Canine Co-design

Multi-species interfaces, where users consist of both human and non-human animals, continue to emerge in many application areas, such as zoos shared spaces between humans and domesticated animals. Within this, new instances of Canine-Computer Interaction are rapidly being developed, including applications that support assistance dogs in helping their handlers with disabilities, and interfaces that support human and dog bonding. Regardless of the specific application, many canine interfaces rely on a pressure-activated interaction for the canine input modality. We report on a two-phase study that explored the current practices of assistance dogs using pressure-activated switches (buttons) and aimed to understand canine experiences using different types of buttons. We discuss not only the practical design implications for designers developing canine interfaces, but also methodological findings in regard to conducting research with both canine and human participants. We aim to continue to extend design practices to include non-human animals as co-designers.
Extended Autoencoder for Novelty Detection with Reconstruction along Projection Pathway

Recently, novelty detection with reconstruction along projection pathway (RaPP) has made progress toward leveraging hidden activation values. RaPP compares the input and its autoencoder reconstruction in hidden spaces to detect novelty samples. Nevertheless, traditional autoencoders have not yet begun to fully exploit this method. In this paper, we propose a new model, the Extended Autoencoder Model, that adds an adversarial component to the autoencoder to take full advantage of RaPP. The adversarial component matches the latent variables of the reconstructed input to the latent variables of the original input to detect novelty samples with high hidden reconstruction errors. The proposed model can be combined with variants of the autoencoder, such as a variational autoencoder or adversarial autoencoder. The effectiveness of the proposed model was evaluated across various novelty detection datasets. Our results demonstrated that extended autoencoders are capable of outperforming conventional autoencoders in detecting novelties using the RaPP method.
Lateralized learning for robustness against adversarial attacks in a visual classification system

Deep learning is an important field of machine learning. It is playing a critical role in a variety of applications ranging from self-driving cars to security and surveillance. However, deep networks have deep flaws. For example, they are highly vulnerable to adversarial attacks. One reason may be the homogeneous nature of their knowledge representation, which allows a single disruptive pattern to cause miss-classification. Biological intelligence has lateral asymmetry, which allows heterogeneous, modular learning at different levels of abstraction, enabling different representations of the same object.
Interactive Programming for Parametric CAD

Abstract Parametric computer‐aided design (CAD) enables description of a family of objects, wherein each valid combination of parameter values results in a different final form. Although Graphical User Interface (GUI)‐based CAD tools are significantly more popular, GUI operations do not carry a semantic description, and are therefore brittle with respect to changes in parameter values. Programmatic interfaces, on the other hand, are more robust due to an exact specification of how the operations are applied. However, programming is unintuitive and has a steep learning curve. In this work, we link the interactivity of GUI with the robustness of programming. Inspired by programme synthesis by example, our technique synthesizes code representative of selections made by users in a GUI interface. Through experiments, we demonstrate that our technique can synthesize relevant and robust sub‐programmes in a reasonable amount of time. A user study reveals that our interface offers significant improvements over a programming‐only interface.
A framework for building closed-domain chat dialogue systems

This paper presents HRIChat, a framework for developing closed-domain chat dialogue systems. Being able to engage in chat dialogues has been found effective for improving communication between humans and dialogue systems. This paper focuses on closed-domain systems because they would be useful when combined with task-oriented dialogue systems in the same domain. HRIChat enables domain-dependent language understanding so that it can deal well with domain-specific utterances. In addition, HRIChat makes it possible to integrate state transition network-based dialogue management and reaction-based dialogue management. FoodChatbot, which is an application in the food and restaurant domain, has been developed and evaluated through a user study. Its results suggest that reasonably good systems can be developed with HRIChat. This paper also reports lessons learned from the development and evaluation of FoodChatbot.
Arbitrary Shape Natural Scene Text Detection Method Based on Soft Attention Mechanism and Dilated Convolution

Natural scene text detection has attracted much attention in the research field of computer vision, and it has been widely used in many applications, i.e., unmanned driving, robot sensing. Some methods have been proposed for horizontal and oriented text detection, but detecting irregular shapes and highly varying orientated text is still a challenging problem. To tackle this problem, we propose a robust arbitrary shape text detection method called Soft Dilated network (SDnet). The proposed method has two essential steps: (1) feature extraction by backbone; (2) post-processing approach to generate elaborated polygons or boundaries. In particular, the backbone is based on soft attention mechanism and dilated convolution. The soft attention mechanism learns and obtains importance feature from each feature channel, and dilated convolution can effectively aggregate multi-scale contextual information without losing the resolution, and enhance the robust of the network model. The proposed method can accurately detect curve text and discriminate text and non-text areas in an efficient fashion. In addition, Jaccard coefficient is used as loss function to promote the post-processing capability of detecting sparse-arranged and arbitrary shape text. Based on the aforementioned technique, the proposed method an effectively handle the problem of sparse arranged arbitrary natural scene text detection. Experiments were conducted on three benchmark datasets: curved text dataset CTW1500, Total-Text and oriented dataset ICDAR2015, and the results show that when compared with the state-of-the-art text detection methods, the proposed method is more robust and it can find smaller text blocks in the image due to the Loss Function calculation with Jaccard coefficient. Furthermore, we performed multiple sets of ablation experiments, verify the effectiveness of the propose method.
Automated image captioning

The research of comprehension of machine learning algorithms and procedures suitable for both image processing and natural language processing. The accommodation with existing packages used in implementing machine learning algorithms. The implementation of an algorithm that takes an image and describes it in comprehensive sentences. After the analyzed requirements, a bibliographic study was conducted for familiarization with the domain and possible models. Next, a study of the available technologies which would help to build the application was made, and then the design, implementation, and validation of the solution began. This paper presents a composite model, consisting of a deep convolutional neural network for feature extraction that makes use of transfer learning, and a recurrent neural network for building the descriptions. Keras with TensorFlow backend is used for implementation. A trained model that describes images using natural language was obtained.
An evolutionary optimization algorithm for gradually saturating objective functions

Evolutionary algorithms have been actively studied for dynamic optimization problems in the last two decades, however the research is mainly focused on problems with large, periodical or abrupt changes during the optimization. In contrast, this paper concentrates on gradually changing environments with an additional imposition of a saturating objective function. This work is motivated by an evolutionary neural architecture search methodology where a population of Convolutional Neural Networks (CNNs) is evaluated and iteratively modified using genetic operators during the training process. The objective of the search, namely the prediction accuracy of a CNN, is a continuous and slow moving target, increasing with each training epoch and eventually saturating when the training is nearly complete. Population diversity is an important consideration in dynamic environments wherein a large diversity restricts the algorithm from converging to a small area of the search space while the environment is still transforming. Our proposed algorithm adaptively influences the population diversity, depending on the rate of change of the objective function, using disruptive crossovers and non-elitist population replacements. We compare the results of our algorithm with a traditional evolutionary algorithm and demonstrate that the proposed modifications improve the algorithm performance in gradually saturating dynamic environments.
Fictional, Interactive Narrative as a Foundation to Talk about Racism

As a means of exploring the design space for supporting individuals who have had personal experiences with racism, we developed a Participatory Design (PD) method, Foundational Fiction, that addresses some unique concerns with using traditional PD to explore deeply sensitive topics. Challenging the assumption that PD must begin with a solicitation of participants' real lived experiences, we instead created a fictional interactive narrative for participants to work from. In the preliminary analysis, we observe that the use of a shared fiction relieved participants of the requirement to disclose very personal experiences, but nonetheless established a group understanding of what makes racist experiences difficult and supported a generative conversation about how technology might ease these situations.
Satellite Telemetry Data Anomaly Detection Using Bi-LSTM Prediction Based Model

Satellite telemetry data is regularly received by the ground station, and then the ground staff determines the potential operating risk by checking real-time telemetry data. Among these data analysis tasks, anomaly detection is necessary to determine potential failure risk or early fault. Nowadays, the ground operator usually used the historical experience to set a fixed alarm threshold to judge the telemetry data. However, lots of complex abnormal data are difficult to capture in time and accurately by setting experiential alarm threshold. Thus, in this work, a telemetry time series data anomaly detection method is proposed based on bi-directional long short-term memory neural network (Bi-LSTM). The proposed method applies the strong temporal feature extraction capability to model and regress the satellite data. As a result, time series prediction using this model can implement the point data anomaly detection by evaluating the predictor and real value. To improve the suitability of the prediction based model, a dynamic threshold optimization method is also focused and integrated into the proposed framework. Experimental results with three satellite telemetry data sets prove the effectiveness of the proposed method. In addition, the satisfied performance is verified by comparing with RNN and basic LSTM model.