Reviewerly/exHarmony huggingface dataset:
Be Active! Participatory Design of Accessible Movement-Based Games

Regular physical exercise is an essential factor for preventing chronic diseases. Activities to support physical education in schools have been increasingly used in recent years as a target to get young people interested in sports. However, for visually impaired students it is difficult to participate in traditional team sports which are widely played in physical education. To overcome this issue, we developed a design toolkit consisting of building blocks that enable visually impaired students to create and play their own movement-based games. To investigate different types of building blocks and their potential to create accessible movement-based games, we conducted two game design workshops with visually impaired students. The results show that our building blocks can successfully be used by visually impaired students to empower them to become creators of movement-based games that are both accessible and engaging. By making our design-process transparent, we further provide insights on how to implement a co-creation process in a school for visually impaired students.
Technology for Monitoring and Coordinating an After-School Program in Remote Areas of India

This paper describes an after-school education program in rural India. The program maintains 53 education centers in remote villages in 21 states of India. This paper discusses the technology to monitor and coordinate these centers that are distributed over a wide area. It also describes the education technology that is used in the after-school education centers.
A comparative analysis between active structured light and multi-view stereo vision technique for 3D reconstruction of face model surface

In some application fields, such as reverse engineering or ancient cultural protection, to collect surface information from objects to generate 3D data is necessary. In this work, active structured light and multi-view stereo vision approach are compared for 3D face reconstruction. The comparison is performed by projecting coded structured light and taking multi-view images for face model. The final point cloud from each sensor is compared in terms of measurements in the scanned objects, to determine which sensor is best suited depending on the environment. For a fair comparison, a preliminary analysis of the performances of the two techniques is produced. Our results can be used as a reference for further comparisons in the analysis of other 3D techniques and algorithms.
Seamless learning for oral presentations: designing for performance needs

This paper investigates English language learners’ oral presentation needs, alongside students’ and instructors’ perceptions towards mobile seamless language learning. The findings will be used to develop a mobile-based learning environment. Interviews with both instructors and students were used to help build a Likert questionnaire which was refined with input from experts. Results reveal that students have a need for oral presentation training in areas such as slide design, presentation-specific language, structure, and body language. Instructors and students are willing to work with peers on oral presentation projects despite lacking previous experience. Deeper exploration from interviews show students believe sentence level language issues are more important than the organization and design of a presentation. Furthermore, instructors expressed problems with incorporating technology and online collaborative learning into language courses suggesting they need more training with learning technology. Students also believed online collaboration would be convenient but difficult to manage, indicating a need for both online and face-to-face collaboration. These findings will help with the development of a mobile seamless language learning framework and app for oral presentations. Guidelines are suggested to help instructors to improve students’ oral presentation performance.
Memory and Interconnect Optimizations for Peta-Scale Deep Learning Systems

Hardware accelerators are a promising solution to the stringent computational requirements of Deep Neural Networks (DNNs). Ranging from low-power IP cores to server class systems, various accelerator architectures with high TOPS/W peak processing efficiencies and flexibility to execute different DNN topologies have been proposed. Prior efforts improve core utilization through better data-flows and computation sequencing, but little effort has thus far been devoted to systematically programming DNN accelerators to extract best possible system utilization, particularly for DNN training, which can be parallelized across peta-scale systems. In this work, we address the hitherto open challenge of systematically mapping computations onto Peta-scale accelerator systems, comprising many (thousands of) processing cores spanning many chips, while maximizing overall system performance. We achieve this by characterizing the design space of possible mapping configurations, building a detailed performance model that incorporates every computation and data-transfer involved in DNN training, and using a design space exploration tool called DEEPSPATIALMATRIX to identify the performance optimal configuration. We highlight 4 key optimizations built within DEEPSPATIALMATRIX - hybrid data-model parallelism, inter-layer memory reuse, time-step pipelining, and dynamic spatial minibatching - each of which improve system utilization by carefully managing the available memory capacity and interconnect bandwidth to balance the compute vs. communication costs. On a 8-peta-FLOP accelerator system, we demonstrate 1.36×-32× improvement in training performance through our design space exploration and optimizations across image recognition (VGG16, ResNet50) and machine translation (GNMT) DNN models.
A Processing Model Using Natural Language Processing (NLP) For Narrative Text Of Medical Record For Producing Symptoms Of Mental Disorders

Mental health is still a serious problem in Indonesia. Data on basic health research in 2013 showed the prevalence of serious mental disorders in Indonesia reached 1.7 permil. The ratio of professional psychiatrists with the number of sufferers is 0.47 per 100000 patients which means that they still do not meet the requirements of WHO. This has an impact that not all people with mental disorders can be handled by professional psychologists directly. Therefore, the development of a case-based reasoning system (CBR) to help diagnosing types of mental disorders can be an option to overcome these gaps. In most health care places, an initial examination of mental patients is carried out by medical professionals who are not experts. At a certain level, the patient cannot express the symptoms that are felt, so the examiner can make narrative texts about the patient's condition. On the other hand, text documents are not structured that are commonly processed in CBR systems, so they cannot be directly applied. Therefore a text processing model to become symptoms is required. In this paper, the text medical record processing model using Natural Language Processing (NLP) is discussed to produce symptoms of mental disorders. This model is a part of the development of a case-based reasoning computer system to help diagnosing the types of mental disorders and their management.
Introducing Trust in Service-Oriented Distributed Systems through Blockchain

Business process management is concerned with the design execution, improvement, and monitoring of business processes. Systems that support the enactment and execution of processes have extensively been used by companies to streamline and automate intra-organizational processes. However, today's business enterprises must deal with global competition, heterogeneity, and rapidly develop new services and products. To address these requirements, the services reuse-based approach allowed enterprises to reconsider and optimize the way they do business, and change their information systems and applications to support collaborative business processes. Service choreographies support the reuse-based service-oriented philosophy in that they represent a powerful and flexible approach to realize systems by (possibly) reusing services and composing them in a fully distributed way. Nevertheless, for inter-organizational processes, challenges of collaborative design and lack of mutual trust have hampered a broader uptake. In this paper, we show an early stage approach to address the problem of trust in services choreography by using Blockchain technologies, in order to support the decentralized and peer-to-peer collaboration in a trustworthy manner, even in a network without any mutual trust between nodes.
Six constructions of asymptotically optimal codebooks via the character sums

In this paper, using additive characters of finite field, we find a codebook which is equivalent to the measurement matrix in Mohades et al. (IEEE Signal Process Lett 21(7):839–843, 2014). The advantage of our construction is that it can be generalized naturally to construct the other five classes of codebooks using additive and multiplicative characters of finite field. We determine the maximum cross-correlation amplitude of these codebooks by the properties of characters and character sums. We prove that all the codebooks we constructed are asymptotically optimal with respect to the Welch bound. The parameters of these codebooks are new.
VIANA: Visual Interactive Annotation of Argumentation

Argumentation Mining addresses the challenging tasks of identifying boundaries of argumentative text fragments and extracting their relationships. Fully automated solutions do not reach satisfactory accuracy due to their insufficient incorporation of semantics and domain knowledge. Therefore, experts currently rely on time-consuming manual annotations. In this paper, we present a visual analytics system that augments the manual annotation process by automatically suggesting which text fragments to annotate next. The accuracy of those suggestions is improved over time by incorporating linguistic knowledge and language modeling to learn a measure of argument similarity from user interactions. Based on a long-term collaboration with domain experts, we identify and model five high-level analysis tasks. We enable close reading and note-taking, annotation of arguments, argument reconstruction, extraction of argument relations, and exploration of argument graphs. To avoid context switches, we transition between all views through seamless morphing, visually anchoring all text- and graph-based layers. We evaluate our system with a two-stage expert user study based on a corpus of presidential debates. The results show that experts prefer our system over existing solutions due to the speedup provided by the automatic suggestions and the tight integration between text and graph views.
EmbML Tool: Supporting the use of Supervised Learning Algorithms in Low-Cost Embedded Systems

Machine Learning (ML) is becoming a ubiquitous technology employed in many real-world applications. In some applications, sensors measure the environment while ML algorithms are responsible for interpreting the data. These systems often face three main restrictions: power consumption, cost, and lack of infrastructure. Therefore, we need highly-efficient classifiers suitable to execute in unresourceful hardware. However, this scenario conflicts to the state-of-practice of ML, in which classifiers are frequently implemented in high-level interpreted languages, make unrestricted use of floating-point operations and assume plenty of resources. In this paper, we present a software tool named EmbML that implements a pipeline to develop classifiers for low-powered embedded systems. It starts with learning a classifier using popular software packages or libraries. Then, EmbML converts the classifier into a carefully crafted C++ code with support for embedded hardware. Our experimental evaluation shows that EmbML classifiers present competitive results in terms of accuracy, time and memory cost.
Virtual Obstacles for Safe Mobile Robot Navigation

Safe mobile robot navigation is important in both indoor and outdoor environments. In static environments, path planning of mobile robots is mainly governed by factors like shortest paths selection. However, in dynamic scenarios, some paths may be blocked temporarily (for ex. for cleaning) while others may not suitable for robot navigation due to high traffic. In such scenarios, robots must avoid using such paths. This paper proposes a method to actively block such paths by using virtual obstacles. The virtual obstacles can be placed or removed by a user anywhere on the map permanently or for specific intervals. The robot's global path is planned by considering the influence of virtual obstacles. Experiments in real scenarios shows that the proposed method is able to alter the global paths of the robots for safe navigation.
Implementation of Sentiment Analysis on Twitter Using Naïve Bayes Algorithm to Know the People Responses to Debate of DKI Jakarta Governor Election

Developments in information technology are increasing impact on communication and information exchange. One of its development is Twitter. The widespread use of social media has resulted in the availability of a large textual information so that it appears the need of presenting data that will allow users to get accurate information. This research will be conducted sentiment analysis on Twitter against election debates governor Jakarta to know the public response to some public figures. Ahead of the elections, many of the people who responded to a public figure in the social media Twitter. Public figures referred to in the study was the candidate of the Jakarta governor which are Basuki Tjahaja Purnama, Anies Rasyid Baswedan, and Harimurti Agus Yudhoyono. Based on this research, it will use the keyword 'Basuki' or 'Ahok' 'Agus' 'Badwater' 'pilgub' 'election' and 'debat' on Twitter sentiment in Indonesian language. Tweet contains a sentiment that has been collected, will be seen its profile location so it can be considered whether the user was included in the province of Jakarta or outside of Jakarta. Each tweet sentiments of these two categories will then be preprocessing to make it easy to be processed and analyzed. After passing through the phase, it will be calculated for each category with Naive Bayes algorithm in order to get the value of the sentiment of the people residing in the provinces of Jakarta and outside Jakarta to the three candidates for the Jakarta governor.
A sketch-aided retrieval approach for incomplete 3D objects

With the growing amount of digital collections of visual CH data being available across different repositories, it becomes increasingly important to provide archaeologists with means to find relations and cross-correspondences between different digital records. In principle, existing shape- and image-based similarity search methods can aid such domain analysis tasks. However, in practice, visual object data are given in different modalities, and often only in incomplete or fragmented state, posing a particular challenge for conventional similarity search approaches. In this paper we introduce a methodology and system for cross-modal visual search in CH object data that addresses these challenges. Specifically, we propose a new query modality based on 3D views enhanced by user sketches (3D+sketch). This allows for adding new context to the search, which is useful e.g., for searching based on incomplete query objects, or for testing hypotheses on existence of certain shapes in a collection. We present an appropriately designed workflow for constructing query views from incomplete 3D objects enhanced by a user sketch, based on shape completion and texture inpainting. Visual cues additionally help users compare retrieved objects with the query. The proposed approach extends on a previously presented retrieval system by introducing improved retrieval methods, an extended evaluation including retrieval in a larger and richer data collection, and enhanced interactive search weight specification. We demonstrate the feasibility and potential of our approach to support analysis of domain experts in Archaeology and the field of CH in general.
Model-Based Reinforcement Learning via Proximal Policy Optimization

Proximal policy optimization (PPO) is the state-of the-art most effective model-free reinforcement learning algorithm. Its powerful policy search ability allows an agent to find the optimal policy by trial and error but leads to high computation and low data-efficiency. Model-based algorithms can make the most efficient use of data by learning a forward model from observation, but face the challenge of model error. In this paper, we combine the strengths of both algorithms and introduce a data-efficient model-based approach called PIPPO (probabilistic inference via PPO). It makes online probabilistic dynamic model inference based on Gaussian process regression and executes offline policy improvement using PPO on the inferred model. Empirical evaluation on the pendulum benchmark problem shows that the proposed PIPPO algorithm has comparable performance and less interaction with the environment compared with traditional PPO.
SPEC2: SPECtral SParsE CNN Accelerator on FPGAs

To accelerate inference of Convolutional Neural Networks (CNNs), various techniques have been proposed to reduce computation redundancy. Converting convolutional layers into frequency domain significantly reduces the computation complexity of the sliding window operations in space domain. On the other hand, weight pruning techniques address the redundancy in model parameters by converting dense convolutional kernels into sparse ones. To obtain high-throughput FPGA implementation, we propose spec - the first work to prune and accelerate spectral CNNs. First, we propose a systematic pruning algorithm based on Alternative Direction Method of Multipliers (ADMM). The offline pruning iteratively sets the majority of spectral weights to zero, without using any handcrafted heuristics. Then, we design an optimized pipeline architecture on FPGA that has efficient random access into the sparse kernels and exploits various dimensions of parallelism in convolutional layers. Overall, achieves high inference throughput with extremely low computation complexity and negligible accuracy degradation. We demonstrate by pruning and implementing LeNet and VGG16 on the Xilinx Virtex platform. After pruning 75% of the spectral weights, achieves 0% accuracy loss for LeNet, and <; 1% accuracy loss for VGG16. The resulting accelerators achieve up to 24× higher throughput, compared with the state-of-the-art FPGA implementations for VGG16.
Implementation of the augmented reality to electronic practice

Abstract This study focuses on the application of augmented reality (AR) to electronic practice and its impact on the success of student. The developed application in this study has a recognition‐based system structure. The Unity3D platform and the Vuforia AR software development kit are used to perform the application. Flowcharts have been prepared for each electronic experiment and the audio files and the texts to be displayed on the screen have been used to improve interaction with the user. To test the effect of AR application on student success, an experimental study was also conducted on two groups of students (control group [non‐using AR] and test group [using AR]). The data obtained from the experimental study have been analyzed using independent sample t test. According to t ‐test result at 95% confidence level, the probability value p = .002113432 &lt; .05 for the use of AR is found. These results show that the use of AR in electronic practice makes a significant difference in student success.
Emerging Trends in Machine Learning

The emerging trends are brought into limelight and the issues faced in our daily lives are solved using various machine learning (ML) models such as Artificial Neural Networks, K-Nearest Neighbors, Logistic Regression and Support Vector Machines. This paper focuses on the upcoming trends and the security paradigms in ML. Malware analysis and its detection capabilities by comparing the previous samples are discussed in the paper. The paper discusses about the upcoming trends and the applications of machine learning. The first application is the Medical Image and Video Analysis (MVIA) in which the detection of a rare disease is done using the machine learning models. The second application discusses about the issue of web traffic in the communication networks and how it can be resolved. Finally, the paper concludes after elaborating the emerging trends and discussing the various security paradigms in ML.
QMCM: Minimizing Vapnik’s bound on the VC dimension

The recently proposed Minimal Complexity Machine (MCM) learns a hyperplane classifier by minimizing a bound on the Vapnik-Chervonenkis (VC) dimension. Both the linear and kernel versions of the MCM solve a linear programming problem, in order to minimize a bound on the VC dimension. This paper proposes a new quadratic programming formulation, termed as the Quadratic MCM (QMCM), that minimizes a tighter bound on the VC dimension. We present two variants of the QMCM, that differ in the norm of the error vector being minimized. We also explore a scalable variant of the QMCM for large datasets using Stochastic Gradient Descent (SGD), and present the use of the QMCM as a viable featureselection method, in view of the the sparse nature of the models it learns. We compare the performance of the QMCM variants with LIBLinear, a linear Support Vector Machine (SVM) library; as well as against Pegasos and the linear MCM for large datasets, along with sequential feature selection methods and ReliefF. Our results validate the superiority of the QMCM in terms of statistically significant improvements on benchmark datasets from the UCI Machine Learning repository.
Real-time Traffic Light Detection and Recognition based on Deep RetinaNet for Self Driving Cars

Self-driving cars are getting more popularized nowadays due to its safe, convenient and congestion free transportability. Most of the real-time challenges for autonomous driving like recognizing traffic lights, traffic signs, pedestrians are being accurately addressed by the newer state-of-the-art algorithms based on Deep Learning. Recent technological advancements in cloud computing and the availability of high-end cloud-based Graphical Processing Units (GPU) accelerated the development of AI algorithms substantially. For real time detection and recognition of traffic lights, we propose RetinaNet (a deep neural network architecture) based model through transfer learning. The deep neural network RetinaNet was used as model and the system was implemented in Keras with TensorFlow backend in Google Colaboratory cloud platform. The RetinaNet model was trained and evaluated on Bosch Small Traffic Light Dataset containing traffic light images of resolution 1280 by 720 pixels, which falls under four type of classes. The model achieved improved accuracy of detection and classification than other deep learning methods for real-time operation.
Named Entity Recognition for Nepali Language

Named Entity Recognition (NER) has been studied for many languages like English, German, Spanish, and others but virtually no studies have focused on the Nepali language. One key reason is the lack of an appropriate, annotated dataset. In this paper, we describe a Nepali NER dataset that we created. We discuss and compare the performance of various machine learning models on this dataset. We also propose a novel NER scheme for Nepali and show that this scheme, based on grapheme-level representations, outperforms character-level representations when combined with BiLSTM models. Our best models obtain an overall F1 score of 86.89, which is a significant improvement on previously reported performance in literature.