Reviewerly/exHarmony huggingface dataset:
Improvement of the Educational Process by the Creation of Centers for Intellectual Development and Scientific and Technical Creativity

On the basis of the analysis of international experience, ways of solving problems concerning the design of the learning environment and the construction of variable models of subjects in the natural sciences and mathematical cycle with the use of functional modules of the computer-based learning system (CBLE) are proposed. Improvement of the education system in Ukraine is possible providing the revealing of the relationships and a pedagogically balanced combination of the traditional system of learning with the individual components of CBLE through the creation of centers for intellectual development and scientific and technical creativity. In order to improve variable educational programs in Ukraine, including programs for additional education of children and students, specific mechanisms within the framework of established centers of intellectual development and scientific and technical creativity “Clever” based on educational institutions are suggested to implement. The research proposes a methodological basis for CBLE with a thorough analysis of functional modules. The use of CBLE provides a concentration of learning resources; the diversity of learning trajectories and the results of the formation of the necessary competences; accessibility and equality of opportunities in learning for students; multifunctionality of the interaction of subjects in the educational process; the orientation of the content, forms and technologies of preparing students for the educational, scientific, research, and production integration in the educational process.
Data induced masking representation learning for face data analysis

Representation learning models, such as the sparse representation and low-rank representation, have shown pleasing efficacy in exploring the intrinsic data structures for patternrecognition tasks. However, conventional methods ignore the local geometric and similarity information among samples, and the performance is restricted. To address this issue, this paper proposes a novel Data Induced Masking Representation (DIMR) learning model by imposing explicit regularization and low-rank constraint. Specifically, DIMR is formulated for shrinking the representations of inter-class and non-neighbor samples. An extra representation regularization term is deployed with a data induced mask matrix, which can incorporate label and locality priors to guide the learning of affinity representation matrix. The affinity graph derived from DIMR is with low-rank, locality preservation (sparsity) and label guiding, such that it can better characterize the adjacent relationship between samples. Extensive experiments on benchmark face datasets demonstrate the superiority of DIMR for both semi-supervised classification and semi-supervised subspace learning tasks.
Attention-Based Abnormal-Aware Fusion Network for Radiology Report Generation

Radiology report writing is error-prone, time-consuming and tedious for radiologists. Medical reports are usually dominated by a large number of normal findings, and the abnormal findings are few but more important. Current report generation methods often fail to depict these prominent abnormal findings. In this paper, we propose a model named Attention-based Abnormal-Aware Fusion Network (A3FN). We break down sentence generation into abnormal and normal sentence generation through a high level gate module. We also adopt a topic guide attention mechanism for better capturing visual details and develop a context-aware topic vector for model cross-sentence topic coherence. Experiments on real radiology image datasets demonstrate the effectiveness of our proposed method.
Radiometric identification using variational mode decomposition

Radiometric Identification (RAI) is the identification of wireless devices through their Radio Frequency (RF) emissions. In recent years, the research community has investigated it applying different methods and sets of statistical features extracted from the digitized RF emissions. In this paper, the authors investigate the application of Variational Mode Decomposition (VMD), recently introduced as an improvement to Empirical Mode Decomposition (EMD). VMD is applied to two sets of RF emissions from: wireless devices supporting Dedicated Short Range Communications (DSRC) at 5.9 GHz and Internet of Things wireless devices transmitting in the Industrial, Scientific and Medical (ISM) band at 2.4 GHz. Various machine learning algorithms have been used for classification and results are compared. Performances of VMD are evaluated against other approaches used in literature in Line of Sight (LOS) conditions, with Additive White Gaussian Noise (AWGN) and fading effects. Results show that VMD significantly outperforms other approaches.
A very deep two-stream network for crowd type recognition

Crowd type identification is a crucial task in the emergency alert. In this paper, to solve accurate identification of crowd type, the crowd type description triad C-BMO < Behavior, Mood, Organized > and a novel crowd type recognition network (CTRN): very deep two-stream network architecture are proposed, respectively. The very deep two-stream network architecture is based on the static map and motion map in the video. To early warn the emergency, the reasoning rules of the emergency alert are proposed based on joining the crowd type and the crowd characteristics. To verify the proposed method, the crowd type dataset is collected, and we experiment with the proposed plan on the crowd type dataset. The experimental results demonstrate that the proposed model is competitive compared with the state-of-the-art techniques.
A bug finder refined by a large set of open-source projects

Static bug detection techniques are commonly used to automatically detect software bugs. The biggest obstacle to the wider adoption of static bug detection tools is false positives, i.e., reported bugs that developers do not have to act on. The objective of this study is to reduce false positives resulting from static bug detection tools and to detect new bugs by exploring the effectiveness of a feedback-based bug detection rule design. We explored a large number of software projects and applied an iterative feedback-based process to design bug detection rules. The outcome of the process is a set of ten bug detection rules, which we used to build a feedback-based bug finder, FeeFin. Specifically, we manually examined 1622 patches to identify bugs and fix patterns, and implement bug detection rules. Then, we refined the rules by repeatedly using feedback from a large number of software projects. We applied FeeFin to the latest versions of the 1880 projects on GitHub to detect previously unknown bugs. FeeFin detected 98 new bugs, 63 of which have been reviewed by developers: 57 were confirmed as true bugs, and 9 were confirmed as false positives. In addition, we investigated the benefits of our FeeFin process in terms of new and improved bug patterns. We verified our bug patterns with four existing tools, namely PMD, FindBugs, Facebook Infer, and Google Error Prone, and found that our FeeFin process has the potential to identify new bug patterns and also to improve existing bug patterns. Based on the results, we suggest that static bug detection tool designers identify new bug patterns by mining real-world patches from a large number of software projects. In addition, the FeeFin process is helpful in mitigating false positives generated from existing tools by refining their bug detection rules.
Sparse Bayesian approach for metric learning in latent space

This paper presents a new and efficient approach for metric learning in latent space. Our method discovers an optimal mapping from the feature space to a latent space that shrinks the distance between similar data items and also increases the distance between dissimilar ones. The proposed approach is based on a Bayesian variational framework which iteratively finds the optimal posterior distribution of parameters and hyperparameters of the model. Advantages of the proposed method to similar work are 1) Learning the noise of the latent variables on the low-dimensional manifold to find a more effective transformation. 2) Automatically finding the dimension of latent space and sparsification of the solution which prevents the overfitting problem. 3) Unlike Mahalanobis metric learning, the proposed algorithm roughly scales linearly to the dimension of data. Also, the present work is extended for learning in the feature space induced by an RKHS kernel. The proposed method is evaluated on small and large datasets coming from real applications such as network intrusion detection, face recognition, handwritten digits, letter recognition, and hyperspectral image classification. The results show that our method outperforms related representative and state-of-the-art methods in many small and large datasets.
A novel resolution independent gradient edge predictor for lossless compression of medical image sequences

Digital visualization of human body in terms of medical images with high resolution and bit depth generates tremendous amount of data. In the field of medical diagnosis, lossless compression technique is preferred that facilitates efficient archiving and transmission of medical images avoiding false diagnosis. Among various approaches to lossless compression of medical images, predictive coding techniques have high coding efficiency and low complexity. Gradient Edge Detector (GED) used in predictive coding is based on threshold value for prediction and choice of threshold is very important for efficient prediction. However, no specific method is adopted in the literature for threshold value selection. This paper presents an efficient prediction solution targeted at lossless compression of 8 bits and higher bit depth volumetric medical images up to 16 bits. Novelty of the proposed technique is developing Resolution Independent Gradient Edge Predictor (RIGED) algorithm to support 8- and 16-bit depth medical images. Percentage improvement of the proposed model is 30.39% over state-of-the-art Median Edge Detector (MED) and 0.92% over Gradient Adaptive Predictor (GAP) in terms of entropy for medical image dataset of different modalities having different resolutions and bit depths.
Interactive Immersive Public Displays as Facilitators for Deeper Participation in Urban Planning

Citizen participation is an important part of urban planning but few people participate, and most frequently they can do so only passively. This can result in excluding many people and to urban designs not reflecting people’s wishes. In this paper, we explore the use of interactive, immersive public displays as facilitators for deeper participation in urban planning. We propose a novel approach that combines panoramic videos of locations with overlays depicting planned buildings. We evaluated it in a lab-based user study (N = 21), where participants used a simple mobile client for interacting with a prototypical implementation to vote/comment on urban planning proposals. Usefulness, ease of use, ease of learning and user satisfaction were all rated highly by the participants. The pros and cons of the approach were further identified based on the analysis of the interview data. Overall, the results provide initial evidence that the approach succeeded in facilitating deeper participation.
Teaching Research Data Management for Students

Sound skills in managing research data are a fundamental requirement in any discipline of research. Therefore, research data management should be included in academic education of students as early as possible. We have been teaching an interdisciplinary full semester's course on research data management for six years. We report how we established the course. We describe our competency-based approach to teaching research data management and the curriculum of topics that we consider essential. We evaluate our approach by a survey done among the participants of the course and summarize the lessons we learned in teaching the course.
EduCHI 2019 Symposium

At CHI 2018, a workshop on developing a community of practice to support global HCI education was held, building on six years of research and collaboration in the area of HCI education. Many themes emerged from the workshop activities and discussions. Two particularly stood out: creating channels for discussions related to HCI education and providing a platform for sharing HCI curricula and teaching experiences. To that end, we are organizing a CHI 2019 symposium dedicated exclusively to HCI education: EduCHI 2019: Global Perspectives on HCI Education. The symposium will focus on the canons of HCI education in 2019 and beyond. It will offer a venue for HCI educators across disciplinary and geographical borders to discuss, dissect, and debate HCI teaching and learning. Through keynote addresses, paper presentations, and a panel discussion, we aim to discuss current and future HCI education trends, curricula, pedagogies, teaching practices, and diverse and inclusive HCI education. Post-symposium initiatives will aim to document and publish the discussions from the symposium.
A classifier task based on Neural Turing Machine and particle swarm algorithm

Deep learning in artificial intelligence looks for a general-purpose computational machine to execute complex algorithms similar to humans’ brain. Neural Turing Machine (NTM) as a tool to realize deep learning approach brings together Turing machine that is a general-purpose machine equipped to a long-term memory, and a neural network as a controller. NTM applies simple controllers to execute several simple/complex tasks such as copy, sort, N-gram, etc.; however, complex tasks such as classifications are neglected, and there is no control over improving the weights of NTM, either. This paper presents a framework called PSONTM that improves the accuracy of NTM using the LSTM deep neural network as the controller, and implements a complex classification task along with available tasks. Particle Swarm Optimization (PSO) algorithm is also applied in order to control the weights. The classifier task is compared with the basic SVM, KNN, Naïve Bayesian, and Decision Tree classification methods on MNIST, ORL, letter recognition, and ionosphere datasets. The accuracy of the proposed classification tasks is 99.73%, 97.9%, 99.02%, and 97.1%, respectively. That means the NTM classification task improved Naïve Bayesian 43.57%, Decision Tree 15.6%, and KNN 19.22% on average. In addition, the presented framework improves the available NTM tasks as well.
Blockchain-based secure firmware management system in IoT environment

Following the recent increase in smart factories and smart cities and the fourth industrial revolution, the number of IoT devices has been increasing and accordingly, the security of IoT devices has also become an important issue. However, due to reasons such as the low performance of IoT devices, the currently existing security solutions can be hardly applied and even if safe firmware is supplied, security problems may arise due to attacks such as man-in-the-middle attacks and roll-back attacks. To resolve such problems, we propose a new firmware management architecture using blockchains and IPFS (The InterPlanetary File System). Using IPFS can ensure the integrity of the firmware. However, IoT device version management and IPFS URL integrity cannot be guaranteed. Therefore, IoT devices are enabled to be provided with firmware information and updated through blockchian networks to ensure the integrity of the IPFS URL, and an IoT devices management function is provided. The proposed system enables safe firmware distribution and update, and IoT devices' security level is expected to be higher. Through the proposed system, safe firmware distribution and update will become possible and IoT device security levels are expected to be enhanced.
Live and global consistency checking in a collaborative engineering environment

During software and systems engineering, engineers have to rely on different engineering tools in order to capture different kinds of artifacts, such as requirement specifications, design models or code. Even though the artifacts that engineers capture with these tools are interdependent, the tools have limited abilities to detect inconsistencies among them. Today no approach exists that is able to provide live inconsistency feedback of engineering artifacts --- captured and maintained in different engineering tools --- without disrupting the engineers' workflow. The work presented in this paper introduces a novel approach for live, multi-tool, consistency checking where engineers continue to use their respective tools and receive inconsistency feedback across their tools' artifacts in a live manner. The approach uses a cloud-based engineering platform to replicate the tool's artifacts and to detect inconsistencies there. Within the cloud, engineers may link these artifacts and define cross-tool consistency rules. The approach was validated through an empirical study and two industrial case studies to demonstrate usefulness, correctness and scalability.
Study of linguistic features incorporated in a literary book recommender system

As reading for pleasure is declining, particularly among the young, we are motivated to investigate the textual elements that may play a role in generating high-quality book recommendations. Two recommendation algorithms were trained on more than a hundred features learned from the full text of the books. Both algorithms generate more accurate lists of suggested books than many competitive recommender systems. Exploration of the key textual elements is conducted together with qualitative analysis.
Modified double-notched specimen for ultra-high temperatures shear-strength testing of carbon/carbon composites

In this paper, a modified double-notched specimen (MDNS) is proposed to investigate shear strength of carbon/carbon composites at ultra-high temperatures. The effects of surplus notch length on distribution of shear stress in the gauge area are studied using finite element method. Both standard Iosipescu method and the MDNS method are used to test shear strength of 3D needled, 3D woven, and 4D woven C/C composites at room temperature. The results are in good agreement with each other. Uniform shear strain is observed using digital image correlation (DIC) technology to confirm the proposed method. Additionally, the shear strength of these three types C/C composites are measured at 2000 °C, 2400 °C, and 2800 °C using the designed testing system. The temperature field at 2000 °C is measured using a thermal imaging system to demonstrate uniform distribution of temperature. The failure mechanisms at ultra-high temperatures are also characterized via optical microscopy.
Examining users' partial query modification patterns in voice search

This article investigates how to improve the effectiveness of voice search systems. Earlier research found that participants employed voice search much less frequently than keyboard search. The main reasons that participants disliked voice search are system mistakes and the inability to modify queries. In keyboard search, query reformulation is facilitated by partial query modification, which is not supported by most of the current voice search systems. Consequently, users need to speak the complete query in voice search even with only minor changes. This article focuses on examining partial query modification during voice search through a Wizard of Oz user experiment. It examines if users would prefer partial query modification and how they perform it in voice search. Thirty‐two participants participated in the experiment. Results indicated that when given the opportunity, the users performed more partial query modifications than complete queries. Common partial query modification strategies and patterns emerged from the experiment. The results can be used to improve the voice search system design and benefit the research community in general. System implications and future work were discussed.
GroupCast: Preference-Aware Cooperative Video Streaming With Scalable Video Coding

In this paper, we propose a preference-aware cooperative video streaming system for videos encoded using scalable video coding (SVC). In the proposed system, the collaborating users are interested in watching a video together on a shared screen. However, the willingness of each user to cooperate is subject to her own constraints such as user data plans. Using SVC, videos are segmented into chunks and each chunk is encoded using layers, where each layer can be fetched through any of the collaborating users. We formulate the problem of finding the optimal quality decisions and fetching policy of the SVC layers of video chunks subject to the available bandwidth, chunk deadlines, and cooperation willingness of the different users as an optimization problem. The objective is to optimize a QoE metric that maintains a trade-off between maximizing the playback rate of every chunk and ensuring fairness among all chunks to achieve the minimum skip (stall) duration without violating any of the imposed constraints. We propose an offline algorithm to solve the non-convex optimization problem when the bandwidth prediction is non-causally known. This algorithm has a run-time complexity that is polynomial in the video length and the number of collaborating users. Furthermore, we propose an online version of the algorithm for practical scenarios, where erroneous bandwidth prediction for a short window is used. Real implementation with android devices using SVC encoded video and a public dataset of bandwidth traces reveals the robustness and performance of the proposed algorithm and shows that the algorithm significantly outperforms round robin-based mechanisms in terms of avoiding skips/stalls and fetching video chunks at their highest quality possible.
Triptech: A Method for Evaluating Early Design Concepts

Measuring user experience (UX) is an important part of the design process, yet there are few methods to evaluate UX in the early phases of product development. We introduce Triptech, a method used to quickly explore novel product ideas. We present how it was used to gauge the frequency and importance of user needs, to assess the desirability and perceived usefulness of design concepts, and to draft UX requirements for Now Playing-an on-device music recognition system for the Pixel 2. We discuss the merits and limitations of the Triptech method and its applicability to tech-driven innovation practices.
With an Eye to the Future

ArabHCI initiative was inaugurated in a CHI17 SIG Meeting that brought together 45+ HCI Arab/non-Arab researchers/practitioners who are conducting/interested in HCI within Arab communities. The meeting started an ongoing dialogue that recognizes the fact that HCI is still in its infancy in the Arab world and explores challenges and opportunities for shaping the future of HCI in the region. The meeting was further followed by three successful meetings in SIGCHI sponsored events that included general discussions about the state of HCI research in Arab countries, and a thematic discussion on exploring participatory design practices with Arab communities. In this workshop, we build on the momentum generated by our previous meetings, and attempt to draw a roadmap for HCI research and practice in Arab world. Our goal is to bring together researchers and practitioners to discuss case studies from their own work, share experiences and lessons learned, and envision the future of the field in this area. We plan to share the results of our discussions and research agenda with the wider CHI community through various social and scholarly channels.