Reviewerly/exHarmony huggingface dataset:
Proposal pyramid networks for fast face detection

Recent face detectors based on deep convolutional neural networks (DCNN) have substantially improved the detection performance in the wild. However, the detection speed is still the biggest bottleneck, which hinders the practical deployment of these face detectors on resource-limited platforms. To address this issue, the paper proposes a Proposal Pyramid Network (PPN) to generate face candidates extremely fast, which reduces the major computational complexity in cascaded DCNN face detectors. PPN is a lightweight fully convolutional network with multiple branches, which takes a single image as input and generates face proposals with different scales in terms of separate branches simultaneously. In this manner, it avoids the traditional image pyramid structure and thus achieves a very fast processing speed. To evaluate the effectiveness of our proposed method, a three-stage cascaded DCNN face detector is realized based on PPN. Extensive experimental results on several public benchmarks show that the proposed face detector achieves comparable accuracy to the-state-of-arts while the detection speed reaches 60 FPS with an i5 CPU, which significantly exceeds previous DCNN-based face detectors. (The source code is available at https://github.com/zhaofan0622/PPN).
"What's Happening at that Hip?"

We present two studies to discuss the design, usability analysis, and educational outcome resulting from our system Augmented Body in physiotherapy classroom. We build on prior user-centric design work that investigates existing teaching methods and discuss opportunities for intervention. We present the design and implementation of a hybrid system for physiotherapy education combining an on-body projection based virtual anatomy supplemented by pen-based tablets to create real-time annotations. We conducted a usability evaluation of this system, comparing with projection only and traditional teaching conditions. Finally, we focus on a comparative study to evaluate learning outcome among students in actual classroom settings. Our studies showed increased usage of visual representation techniques in students'11 note taking behavior and statistically significant improvement in some learning aspects. We discuss challenges for designing augmented reality systems for education, including minimizing attention split, addressing text-entry issues, and digital annotations on a moving physical body.
Brightness preserving optimized weighted bi‐histogram equalization algorithm and its application to MR brain image segmentation

Abstract Medical image segmentation is crucial for neuroscience research and computer‐aided diagnosis. However, intensity inhomogeneity and existence of noise in magnetic resonance images lead to incorrect segmentation. In this article, an effective method called enhanced fuzzy level set algorithm is presented to segment the white matter, gray matter, and cerebrospinal fluid automatically in contrast‐enhanced brain images. In this method, first, exposure threshold is computed to divide the input histogram into two sub‐histograms of different gray levels. The input histogram is clipped using a mean gray level to control the excessive enhancement rate. Then, these two sub‐histograms are modified and equalized independently to get a better contrast enhanced image. Finally, an enhanced fuzzy level set algorithm is employed to facilitate image segmentation. The extensive experimental results proved the outstanding performance of the proposed algorithm compared with other existing methods. The results conform its effectiveness for MR brain image segmentation.
The alchemy of computation: designing with the unknown

Modern computers allow a methodical search of possibly billions of experiments and the exploitation of interactions that are not known in advance. This enables a bottom-up process of design by assembling or configuring systems and testing the degree to which they fulfill the desired goal. We give two detailed examples of this process. One is referred to as Cartesian genetic programming and the other evolution-in-materio. In the former, evolutionary algorithms are used to exploit the interactions of software components representing mathematical, logical, or computational elements. In the latter, evolutionary algorithms are used to manipulate physical systems particularly at the electrical or electronic level. We compare and contrast both approaches and discuss possible new research directions by borrowing ideas from one and using them in the other.
Efficient Ensemble Classification for Multi-Label Data Streams with Concept Drift

Most existing multi-label data streams classification methods focus on extending single-label streams classification approaches to multi-label cases, without considering the special characteristics of multi-label stream data, such as label dependency, concept drift, and recurrent concepts. Motivated by these challenges, we devise an efficient ensemble paradigm for multi-label data streams classification. The algorithm deploys a novel change detection based on Jensen–Shannon divergence to identify different kinds of concept drift in data streams. Moreover, our method tries to consider label dependency by pruning away infrequent label combinations to enhance classification performance. Empirical results on both synthetic and real-world datasets have demonstrated its effectiveness.
Robust self-tuning spectral clustering

Clustering, as an effective data analysis technique, is widely used in industrial application and science research. In this paper, we have proposed a novel spectral clustering method to solve three problems of spectral clustering, i.e., cluster-initialization, cluster-specification, and noise-robustness. To do this, we first learn a high-quality affinity matrix, and then capture an inherent clustering pattern in real feature space and projected subspace, and finally extract connected components to conduct clustering. By comparing to five comparison methods in six real data sets, our proposed method has achieved a competitive clustering performance in terms of four evaluation metrics.
Reveal

Recording experiences and memories is an important role for digital photography, with smartphone cameras leading to individuals taking increasing numbers of pictures of everyday experiences. Increasingly, these are automatically stored in personal, cloud-backed, photo repositories. However, such experiences can be forgotten quickly, with images 'lost' within the user's library, loosing their role in supporting reminiscing. We investigate how users might be provoked to view these images and the benefits they bring through the development and evaluation of a proactive, location-based reminiscing tool, called Reveal. We outline how a location-based approach allowed participants to reflect more widely on their photo practice, and the potential of such reminiscing tools to support effective management and curation of individual's increasingly large personal photo collections.
Interactive Visual Analytics for Sensemaking with Big Text

Analysts face many steep challenges when performing sensemaking tasks on collections of textual information larger than can be reasonably analyzed without computational assistance. To scale up such sensemaking tasks, new methods are needed to interactively integrate human cognitive sensemaking activity with machine learning. Towards that goal, we offer a human-in-the-loop computational model that mirrors the human sensemaking process, and consists of foraging and synthesis sub-processes. We model the synthesis loop as an interactive spatial projection and the foraging loop as an interactive relevance ranking combined with topic modeling. We combine these two components of the sensemaking process using semantic interaction such that the human's spatial synthesis actions are transformed into automated foraging and synthesis of new relevant information. Ultimately, the model's ability to forage as a result of the analyst's synthesis activities makes interacting with big text data easier and more efficient, thereby facilitating analysts' sensemaking ability. We discuss the interaction design and theory behind our interactive sensemaking model. The model is embodied in a novel visual analytics prototype called Cosmos in which analysts synthesize structure within the larger corpus by directly interacting with a reduced-dimensionality space to express relationships on a subset of data. We then demonstrate how Cosmos supports sensemaking tasks with a realistic scenario that investigates the affect of natural disasters in Adelaide, Australia in September 2016 using a database of over 30,000 news articles.
Multi-class multimodal semantic segmentation with an improved 3D fully convolutional networks

Semantic segmentation is an important but challenging task in the field of medical image analysis. Automatic labeling for different anatomical structures can be useful for disease diagnosis, treatment planning and development/degeneration evaluation. However, due to the large shape and appearance variance among different subjects, accurate and reliable semantic segmentation is difficult for automatic labeling and time-consuming for manual labeling. In this paper, we propose a multi-class semantic segmentation algorithm based on the 3D fully convolutional networks, exploring the multimodal image blocks. The use of multimodal image blocks naturally allows an effective data augmentation. Besides, we also investigated the use of ensemble learning and conditional random fields (CRFs) as the post-processing step, which obtains the spatially consistent segmentation results. Our proposed method was evaluated on two public benchmarks: BRATS2017 and MNI-HiSUB25. For both datasets, the experimental results demonstrated that our method was able to establish an effective semantic segmentation framework with multi-modality data and achieve better performance compared to the conventional fully convolutional network.
Signet: A Deep Learning based Indian Sign Language Recognition System

Sign language is the primary mode of communication between hearing and vocally impaired population. The government of India has enacted the Rights of Persons with Disabilities Act 2016 (RPwD Act 2016). This act recognizes Indian Sign Language (ISL) as an important communication medium for communicating with hearing impaired people. This also insists the need for sign language interpreters in all Government organizations and public sector undertakings in order to abide RPwD Act 2016. This can avoid their isolation from the rest of society to a great extent. In this work, we propose a signer independent deep learning based methodology for building an Indian Sign Language (ISL) static alphabet recognition system. Here, we review various existing methods in sign language recognition and implement a Convolutional Neural Network (CNN) architecture for ISL static alphabet recognition from the binary silhouette of signer hand region. We also discuss in detail, the dataset used along with the training phase and testing phase of CNN. The proposed method was successfully implemented with an accuracy of 98.64% which is better than most of the currently existing methods.
ReCall

Although voice forums are widely used to enable marginalized communities to produce, consume, and share information, their financial sustainability is a key concern among HCI4D researchers and practitioners. We present ReCall, a crowdsourcing marketplace accessible via phone calls where low-income rural residents vocally transcribe audio files to gain free airtime to participate in voice forums as well as to earn money. We conducted a series of experimental and usability evaluations with 28 low-income people in rural India to examine the effect of phone types, channel types, and review modes on speech transcription performance. We then deployed ReCall for two weeks to 24 low-income rural residents who placed 5,879 phone calls, completed 29,000 micro tasks to yield transcriptions with 85% accuracy, and earned INR 20,500. Our mixed-methods analysis indicates that each minute of crowd work on ReCall gives users eight minutes of free airtime on another voice forum, and thus illustrates a way to address the financial sustainability of voice forums.
Explaining the Workings Principle of Cloud-based Multi-factor Authentication Architecture on Banking Sectors

Already in the initial years of the 21 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">st</sup> century, Cloud Computing has left an indelible mark as one of the key emergent technologies that already has begun revolutionizing the way we look at Information Technology. The banking sectors have also ventured into cloud-based systems, solutions, and services. Transferring banking applications to the cloud reduces bulk investments, time for initial set-up and also carbon footprint. However, if the security aspect of banking applications to the cloud is not managed comprehensively, it can lead to irreversible damage to the brand, loss of customer trust, and enormous liabilities. Our research objective is to offer cloud-based multi-factor authentication architecture to the financial institutions which would provide high levels of data protection. We propose a triple-stage verification process to establish identity and root out any malware attacks during access of banking applications by users from remote locations.
FastText-Based Intent Detection for Inflected Languages

Intent detection is one of the main tasks of a dialogue system. In this paper, we present our intent detection system that is based on fastText word embeddings and a neural network classifier. We find an improvement in fastText sentence vectorization, which, in some cases, shows a significant increase in intent detection accuracy. We evaluate the system on languages commonly spoken in Baltic countries—Estonian, Latvian, Lithuanian, English, and Russian. The results show that our intent detection system provides state-of-the-art results on three previously published datasets, outperforming many popular services. In addition to this, for Latvian, we explore how the accuracy of intent detection is affected if we normalize the text in advance.
Conversational Agents

In the last five years, work on software that interacts with people via typed or spoken natural language, called chatbots, intelligent assistants, social bots, virtual companions, non-human players, and so on, increased dramatically. Chatbots burst into prominence in 2016. Then came a wave of research, more development, and some use. The time is right to assess what we have learned from endeavouring to build conversational user interfaces that simulate quasi-human partners engaged in real conversations with real people. This workshop brings together people who developed or studied various conversational agents, to explore themes that include what works (and hasn't) in home, education, healthcare, and work settings, what we have learned from this about people and their activities, and social or ethical possibilities for good or risk.
Improving the Consistency and Usefulness of Architecture Descriptions: Guidelines for Architects

The need to support software architecture evolution has been well recognized, even more since the rise of agile methods. However, assuring the conformance between architecture descriptions and the implementation remains challenging. Inconsistencies emanate among multiple architecture descriptions, and between architecture descriptions and code. As a consequence, architecture descriptions are not always trusted and used to the extent that their authors wish for. In this paper, we present two surveys with 93 and 72 participants to examine architectural inconsistencies, with a focus on how they evolve over time and can be mitigated using practical guidelines. We identified the importance of capturing emerging elements to keep the architecture description consistent with the implementation, and consider the current-state and future-state architecture separately. Consequences of inconsistencies typically arise at later stages, especially if an architecture description concerns multiple teams. Our guidelines suggest to limit the upfront architecture to stable decisions, while paying attention to concerns that matter across team borders. In the ideal case, companies should aim to integrate architects into the teams to capture emerging aspects with time.
Process traceability in distributed manufacturing using blockchains

This paper introduces a novel Machine Sharing Economy (MSE) approach to support machine owners in sharing idle machine capacities by means of a marketplace. Manufacturing networks and supply chains are thus no longer static and driven by long-term agreements, but rather dynamic and time-bound. In such distributed manufacturing systems, it is important to keep track of the operations that have been performed, including relevant production information such as the actual machines performing a task, tools, and their parameters. We propose a tamper-proof blockchain-based framework to close the feedback cycle by emitting relevant events of the production process and storing process values in the blockchain. This paper describes the overall architecture of the system including a detailed discussion how the feedback cycle for distributed manufacturing systems has been realized using blockchains. In addition, we perform an empirical validation at a real-world digital factory and present our lessons learned.
Extrinsic Calibration between a Camera and a 2D Laser Rangefinder using a Photogrammetric Control Field

The combination of a camera and a 2D laser rangefinder (LRF) is widely used in robotics, mapping, and unmanned driving to simultaneously obtain the 3D geometry and color texture of a scene. However, data misregistration between the camera and the LRF frequently occurs due to the difficulty of precise installation and alignment between them. Extrinsic calibration between the camera and the LRF is necessary. In this study, a photogrammetric control field is used to perform a robust and accurate calibration between the camera and the LRF which requires only one shot. With the use of the photogrammetric control field as the common reference, extrinsic calibration between two sensors is decoupled by calibrating each sensor separately. First, using the room corner of the control field, the LRF is calibrated with respect to the control field by solving a simplified perspective-three-point problem. Then, a large number of control points are used to obtain the robust and accurate extrinsic parameters of the camera with respect to the control field. Experiments with simulated and real data were performed. The experimental results show that the proposed scheme is accurate, precise, and robust under different noise levels, and the calibration results can be utilized in indoor and outdoor applications.
A robust and secure image sharing scheme with personal identity information embedded

In a (t, n)-threshold secret image sharing (SIS) scheme, the secrecy of a secret image relies on check and balance among participants. Unfortunately, the decentralized management also brings an underlying security flaw, i.e., once an attacker impersonates an authorized participant to submit a fake shadow for reconstruction no information about the secret image can be revealed. In order to resist such an identity-based attack, a novel SIS scheme with identity-based authentication is proposed, where a natural image is encoded through compressive sensing and then the compressive image is divided into n shadows (i.e., shares) according to an innovate way in which the production of shadows is controlled by participants’ identification number. Only when any t or more authorized participants cooperate together can the original image be reconstructed. Besides, a simple but efficient non-uniform scalar quantization method and a group communication protocol are presented to perfect the proposed scheme. Performance and security analyses indicate that our scheme possesses many advantages, including no pixel expansion, flexible shadow size, progressive display capability, error-resilient capability, no necessity for secure channels, and high security against statistical attack, brute-force attack, and identity-based attack.
Towards automated network mitigation analysis

Penetration testing is a well-established practical concept for the identification of potentially exploitable security weaknesses and an important component of a security audit. Providing a holistic security assessment for networks consisting of several hundreds hosts is hardly feasible though without some sort of mechanization. Mitigation, prioritizing counter-measures subject to a given budget, currently lacks a solid theoretical understanding and is hence more art than science. In this work, we propose the first approach for conducting comprehensive what-if analyses in order to reason about mitigation in a conceptually well-founded manner. To evaluate and compare mitigation strategies, we use simulated penetration testing, i.e., automated attack-finding, based on a network model to which a subset of a given set of mitigation actions, e.g., changes to the network topology, system updates, configuration changes etc. is applied. Using Stackelberg planning, we determine optimal combinations that minimize the maximal attacker success (similar to a Stackelberg game), and thus provide a well-founded basis for a holistic mitigation strategy. We show that these Stackelberg planning models can largely be derived from network scan, public vulnerability databases and manual inspection with various degrees of automation and detail, and we simulate mitigation analysis on networks of different size and vulnerability.
Reversible data hiding using B‐tree triangular decomposition based prediction

A novel reversible data hiding (RDH) technique has been proposed in this study using a B-tree triangular decomposition-based prediction of image pixels. The superiority of a prediction error expansion-based RDH scheme depends on a good prediction strategy for image pixels. In the proposed scheme, the B-tree triangular decomposition is used to recursively decompose the image into a set of right-angled triangles. The vertices of such triangles serve as the reference pixels to predict (interpolate) the intensity values of other non-reference pixels within the triangle. Data bits are reversibly embedded into these non-reference pixels by expanding the prediction error. Moreover, according to the proposed scheme, the number of bits being embedded in these pixels is varied (either one or two bits) based on an estimated local complexity of the triangle. The local complexity of a triangle is computed from the intensity of vertices of the triangle. The superior performance of the proposed method is verified through extensive experiments.
Content-Based Image Retrieval using Local Patterns and Supervised Machine Learning Techniques

CBIR is a very important domain, especially in the last decade due to the increased need for image retrieval from the multimedia database. In general, we extract low level (color, texture, and shape) or high-level features (when we include machine learning techniques) from the images. In our work, we proposed a new CBIR system using Local Neighbor Pattern (LNP) with supervised machine learning techniques. We evaluated the performance of this system by comparing the system with Local Tetra Pattern (LTrP) using Corel 1k, Vistex and TDF face databases. We used three types of the database (i.e color, texture and face databases) to improve the effectiveness of our system. Performance analysis shows that LNP gives better performance regarding the average recall than LBP, LDP, and LTrP. To increase the accuracy of this system we used the LNP method with machine learning techniques and performance analysis shows that local pattern with machine learning techniques improves the average accuracy from 36.23% to 85.60% when we use LNP with cubic SVM on DB1 (Corel1K), and from 82.51% to 99.50 % when we use LNP with fine KNN on DB2 (Vistex DB), and from 56.63% to 95% when we use LNP with ensemble subspace discriminant on DB3 (face DB).