Reviewerly/exHarmony huggingface dataset:
The Effect of a Cardiovascular and Pulmonary Mobile Application on Student Learning of Assessment Skills: A Pilot Study

Purpose/Hypothesis: Advances in technology have recently resulted in the development of mobile learning applications (apps) for college students. Despite a recent influx of mobile apps, there is a lack of empirical data about the effectiveness of these apps on learning in the area of cardiovascular and pulmonary physical therapy (PT). The purpose of this study was to evaluate the effect of a researcher-generated cardiopulmonary mobile application on student skills in assessing blood pressure (BP), heart rate (HR), and pedal pulses. Number of Subjects: Twenty first-year PT graduate students were recruited for the study. Participants’ names were randomly chosen out of an envelope to assign them into 1 of 3 groups: demonstration (N = 7), mobile app (N = 7), or demonstration/mobile app (N = 6). Materials/Methods: After providing informed consent, each group participated in their randomized intervention. The demonstration group received demonstrations of each skill from a scripted therapist, followed by laboratory practice; the mobile app group received a 5-minute tutorial of how to use the app, followed by laboratory practice; the demonstration/mobile app group received both demonstration and the mobile app tutorial, followed by laboratory practice. Independent of group type, all participants were allotted 45 minutes in the laboratory to practice performing and interpreting BP, HR, and pedal pulses, after demonstration, demonstration/mobile app, or mobile app instruction. After the laboratory practice, participants were individually tested on the skills learned during the laboratory by a short competency examination administered by 3 licensed physical therapists. These physical therapists were blinded to each group's identity. Results: Total competency was highest among the demonstration/mobile app group as compared with the other 2 groups. The total competency examination score was statistically significant for different teaching methods with the use of demonstration and the mobile application being more effective than only using the mobile application P = .032. No significant difference was noted between the mobile app group and the group using demonstration only, P = .24. No other group differences were noted. Conclusion: Findings of this study suggest PT students demonstrate improved total competency examination scores (total score of BP, HR, and pedal pulses) when provided a mobile application combined with traditional demonstration. Clinical Relevance: There is currently a paucity of literature in PT education pertaining to mobile learning. The results of the current study support the use of a mobile app in addition to traditional learning methods. Future research should include a multisite design and validation of a clinical competency instrument.
Real-time CNN-based object detection and classification for outdoor surveillance images: daytime and thermal

Real time object detection and classification is essential for outdoor surveillance. Current state of the art real time object detection CNNs are trained on natural image datasets. However, outdoor surveillance images have very different characteristics: objects tend to be small and difficult to distinguish (averaging only 3% of image size). In addition, images come in different modalities, for example, nighttime surveillance images are grayscale thermal images representing heat emission not light reflection. Our dataset of images acquired from surveillance videos is comprised of ˜ 640 Daytime (DAY) color images and ˜ 360 nighttime grayscale THERMAL images. The dataset included three object categories: animals, people and vehicles. Because of the lack of large datasets for these scenarios, we evaluated using the much larger VOC dataset to augment our datasets. We conducted a study to determine the best combination of images to include in the training dataset, and how different types of images (i.e. DAY, THERMAL and VOC) affect each-others performance. We examined state of the art object detection and classification CNN architectures, focusing on accuracy and real time performance. By combining different images types THERMAL, DAY and 1200 VOC images in one dataset, the best results were obtained using transfer learning on YOLO-V3 with SPP, achieving 89.5 mAP for DAY images, and 79.53 mAP for THERMAL images, running at 35 fps. This setup provides a robust solution for many surveillance scenarios: night and daytime; far, small objects, as well as zoomed-in, large objects.
Deep Learning for Muscle Pathology Image Analysis

Inflammatory myopathy (IM) Inflammatory myopathy is a kind of heterogeneous disease that relates to disorders of muscle functionalities. The identification of IM subtypes is critical to guide effective patient treatment since each subtype requires distinct therapy. Image analysisPathology image analysis of hematoxylin and eosin (H&E)-stained whole-slide specimens of muscle biopsies are considered as a gold standard for effective IM diagnosis. Accurate segmentation of perimysium plays an important role in early diagnosis of many muscle inflammation diseases. However, it remains as a challenging task due to the complex appearance of the perimysium morphology and its ambiguity to the background area. The muscle perimysium also exhibits strong structure spanned in the entire tissue, which makes it difficult for current local patch-basedPatch-based methods to capture this long-range context information. In this book chapter, we propose a novel spatial clockwork recurrent neural networkRecurrent Neural Network (RNN) (spatial CW-RNN) to address those issues. Besides perimysium segmentation, we also introduce a fully automatic whole-slide image analysisPathology image analysis framework for IM subtype classification using deepConvolutional Neural Networks (CNNs) convolutional neural networks (DCNNs).
Conic relaxation approaches for equal deployment problems

An important problem in the breeding of livestock, crops, and forest trees is the optimum selection of genotypes that maximize genetic gain. The key constraint in the optimal selection is a convex quadratic constraint that ensures genetic diversity, so that optimal selection can be cast as a second-order cone programming (SOCP) problem. Yamashita et al. (2015) exploit the structural sparsity of the quadratic constraints and reduce the computation time drastically while attaining the same optimal solution. This paper is concerned with a special case of equal deployment (ED), in which we solve the optimal selection problem with the constraint that contribution of genotypes must either be a fixed size or zero. This involves a form of combinatorial optimization, and the ED problem can be described as a mixed-integer SOCP problem. In this paper, we discuss conic relaxation approaches for the ED problem based on LP (linear programming), SOCP, and SDP (semidefinite programming). We then propose a steepest-ascent method that combines the solution obtained from the conic relaxation with a concept from discrete convex optimization, in order to acquire an approximate solution for the ED problem in a practical time. From numerical tests, we observed that among the LP, SOCP, and SDP relaxation problems, SOCP gave a suitable solution from the viewpoint of the optimality and computation time. The steepest-ascent method starting from the SOCP solution provides high-quality solutions much faster than other existing methods that have been widely used for optimal selection.
An Online Cost-Efficient Transmission Scheme for Information-Agnostic Traffic in Inter-Datacenter Networks

In the era of cloud computing, network services are deployed on geographically distributed cloud platforms, which results in a large amount of inter-datacenter traffic. Multi-tier pricing schemes are widely adopted by cloud service providers (CSPs) to charge cloud users for inter-datacenter transmission services. To avoid a severe penalty associated with missing a deadline, cloud users are prone to selecting a sufficiently high service level. However, they are usually unaware of the total traffic volume before accessing the network; hence, a high transmission cost is introduced. In this paper, we propose an online cost-efficient transmission scheme for cloud users with information-agnostic traffic. The basic idea is to split a long-term transmission request into a series of short-term ones. In this scheme, we take into account the CSP&#x2019;s countermeasures, and model the interactions between the cloud users and the CSP as a Stackelberg game. We show that the optimal number of short-term requests and the associated transmission service levels can be determined with an online algorithm based on Lyapunov optimization. The experimental results reveal that the CSP and the cloud users can achieve a win-win outcome, whereby the transmission cost of cloud users can be reduced by 59 percent.
The Compare of Solo Programming Strategies in a Scrum Team: A Multi-agent Simulation Tool for Scrum Team Dynamics

Scrum is an agile framework within which people can address complex problems, while productively and creatively delivering products of the highest possible value. A strategy is a way that people going to solve problems based on the existing situation. Team strategy research is different from team composition and how it affects the performance. Team composition can affect the performance is an existing knowledge, without doing simulation, we still can know how personality and capability can affect its performance. But strategy and task allocations methods are a further research go beyond that, particular in an environment, such as scrum, that has an aim for why the team needs to be composed. With the same team, the same task but different strategy can cause significant various outcome for each sprint. This is the way that agent-based modelling is more useful than just say that team composition can affect its work. And based on the current information, to do investigation on how team composition can affect performance will needs various teams, but how strategies can affect team performance will only need the same team to be compared. Scrum is the major motivation that why team needs a strategy to work, the purpose of this strategy is not to investigate how team composition can affect the work but more about how to use the existing affect from the team composition to get the Scrum success rate enhanced, as in real world, we could not change the team as the company only has the team to do the job, but we can change the way that they are doing the job.
Cryptographic Design of PriCloud, a Privacy-preserving Decentralized Storage with Remuneration

Over the last years, demand for file hosting has sky-rocketed due to cost reductions and availability of services. However, centralized providers have a negative impact on the privacy of their users, since they are able to read and collect various data about their users and even link it to their identity via their payments. On the other hand, decentralized storage solutions like GNUnet suffer from a lack of participation by providers, since there is no feasible business model. We propose PriCloud, a decentralized storage system which allows users to pay their storage providers without sacrificing their privacy by employing anonymous storage smart contracts and private payments on a blockchain. We are able to provide privacy to the users and storage providers, and unlinkability between users and files. Our system offers decentralized file storage including strong privacy guarantees and built-in remuneration for storage providers.
Improving Learning through Cloud-based Mobile Technologies and Virtual and Augmented Reality for Australian Higher Education

Latest technological innovations like Cloud-based mobile technologies and virtual and Augmented Reality (AR) have the ability to create great learning experiences. However, few researches exist regarding how AR can be utilized and integrated to complement learning. Currently, the existing research focuses on integrating AR in classes to aid students but few dwells on integrating AR in online learning platforms applications. The increased use of mobile devices has allowed the introduction of online learning management systems that allow people to access e-books, papers, and articles online. As technological inventions continue to emerge, there is a need to investigate and internalize how these new technologies can complement learning. In this research, we investigate how the integration of VR and AR and Cloud-based mobile technologies can improve learning in Australian Higher Education. We conduct a survey to determine how the integration of AR and artificial in the Moodle learning management system can impact the user experiences. We then discuss the current development in virtual and AR and Cloud-based mobile technologies in the education system through a literature review. Our preliminary analysis shows that integration of AR and artificial intelligence may lead to better learning experiences.
Nonlinear Feedback Shift Registers and Zech’s Logarithms

We construct feedback functions of Nonlinear Feedback Shift Registers from feedback functions of Linear Feedback Shift Registers using the cross-join pairs method and Zech's logarithms in finite fields. An algorithm to generate all de Bruijn sequences of given order is presented.
Technique for lossless compression of color images based on hierarchical prediction, inversion, and context adaptive coding

Among the variety of multimedia formats, color images play a prominent role. A technique for lossless compression of color images is introduced. The technique is composed of first transforming a red, green, and blue image into luminance and chrominance domain (Y<italic>C</italic><sub>u</sub><italic>C</italic><sub>v</sub>). Then, the luminance channel <italic>Y</italic> is compressed with a context-based, adaptive, lossless image coding technique (CALIC). After processing the chrominance channels with a hierarchical prediction technique that was introduced earlier, Burrows–Wheeler inversion coder or JPEG 2000 is used in compression of those <italic>C</italic><sub>u</sub> and <italic>C</italic><sub>v</sub> channels. It is demonstrated that, on a wide variety of images, particularly on medical images, the technique achieves substantial compression gains over other well-known compression schemes, such as CALIC, M-CALIC, Better Portable Graphics, JPEG-LS, JPEG 2000, and the previously proposed hierarchical prediction and context-adaptive coding technique LCIC.
User Interface Evaluation on Government Knowledge Management Portal Using Webqual 4.0

Currently, the society is very critical and demands valid information transparency. Information transparency is needed to maintain good governance and can also prevent corruption. However, information on the sustainability of development programs carried out by the government is much unknown to the public. In addition, the government must be burdened with public dissatisfaction due to fake news (hoax). The Ministry of Public Works has tried to build a website to disseminate its programs. However, measurement of the quality of the website is needed. This study uses questionnaires with a Likert scale to obtain user interface assessment data in accordance with the WebQual 4.0 method. This study shows that the easiness of the website to use has the highest correlation with how helpful the website is. Public works ministry websites need to more frequently update valid information and promote transparency. The society also wants online complaint platform to make it easier to solve problems.
UX-Tips

User Experience (UX) is a quality attribute that plays an important role in the quality of software applications, as it addresses users' feelings about a given product. Thus, evaluating the UX is essential to improve its quality. However, most UX evaluation techniques limit to indicate whether the UX was positive or negative, which makes it challenging to identify what must be corrected by the software development team. This paper presents UX-Tips (User eXperience for Interactive ProductS), a technique developed to deal with this limitation, whose main objective is to allow the identification of problems related to UX. We also present two empirical studies conducted to evaluate UX-Tips. In the first study, we assessed the feasibility of the technique concerning its primary goal. In the second study, we compared the performance of UX-Tips with a similar technique. The results showed that UX-Tips allowed the subjects to indicate whether the UX was negative, and the specific problems they detected. It also allowed more effective and efficient evaluations, in addition to being considered easier to use. In practice, UX-Tips enable the software development team to identify the problems that impair users' experience, which should be fixed in order to provide a good UX.
A Parametric Rely-Guarantee Reasoning Framework for Concurrent Reactive Systems

Reactive systems are composed of a well defined set of event handlers by which the system responds to environment stimulus. In concurrent environments, event handlers can interact with the execution of other handlers such as hardware interruptions in preemptive systems, or other instances of the reactive system in multicore architectures. The rely-guarantee technique is a suitable approach for the specification and verification of reactive systems. However, the languages in existing rely-guarantee implementations are designed only for "pure programs", simulating reactive systems makes the program and rely-guarantee conditions unnecessary complicated. In this paper, we decouple the system reactions and programs using a rely-guarantee interface, and develop PiCore, a parametric rely-guarantee framework for concurrent reactive systems. PiCore has a two-level inference system to reason on events and programs associated to events. The rely-guarantee interface between the two levels allows the reusability of existing languages and their rely-guarantee proof systems for programs. In this work we show how to integrate in PiCore two existing rely-guarantee proof systems. This work has been fully mechanized in Isabelle/HOL. As a case study, we have applied PiCore to the concurrent buddy memory allocation of a real-world OS, providing a verified low-level specification and revealing bugs in the C code.
The first co-drive experience prototype

In this paper, we report on a first experience prototype for the Co-Drive concept, which is a new service for traveling and socializing by car between a driver on an automated vehicle and a remote passenger connected via virtual reality from home. Pushing beyond driving and safety functionalities, Co-Drive envisions a new way of sharing the trip, which could be enabled by future technologies, providing a social context for automated cars actions and teleoperated driving. We also reflect on the concept of Driver-Car assemblage as a human and material object conjunction, and we suggest that Co-Drive enables a new kind of assemblage among the Driver-Remote Passenger-Automated Car.
PD-ML-Lite: Private Distributed Machine Learning from Lightweight Cryptography

Privacy arises to a major issue in distributed learning. Current approaches that do not use a trusted external authority either reduce the accuracy of the learning algorithm (e.g., by adding noise), or incur a high performance penalty. We propose a methodology for private distributed ML from light-weight cryptography (in short, PD-ML-Lite). We apply our methodology to two major ML algorithms, namely non-negative matrix factorization (NMF) and singular value decomposition (SVD). Our protocols are communication optimal, achieve the same accuracy as their non-private counterparts, and satisfy a notion of privacy—which we define—that is both intuitive and measurable. We use light cryptographic tools (multi-party secure sum and normed secure sum) to build learning algorithms rather than wrap complex learning algorithms in a heavy multi-party computation (MPC) framework. We showcase our algorithms’ utility and privacy for NMF on topic modeling and recommender systems, and for SVD on principal component regression, and low rank approximation.
Blockchain Retrieval Model Based on Elastic Bloom Filter

Blockchain as emerging technology is revolutionizing several industries, especially the education industry, which has high requirements for the authenticity of data. The proposed blockchain technology realizes decentralization and time-sequence chain storage of data blocks, ensuring that the stored data blocks are not tamperable and unforgeable, and satisfy the high trust of data authenticity. However, current League Chains (such as Hyperledger Fabric) generally have problems such as low throughput and lack of indexing technology, which leads to inefficient data retrieval problems. To this end, this paper proposes a new elastic Bloom filter model that combines smart contracts. This model provides an adaptive adjustment method for Bloom filters, it can effectively reduce the false positive probability under the condition of low memory consumption and improve the efficiency of data retrieval. The experimental results based on Hyperledger Fabric show that compared with the standard Bloom filter model, the proposed model guarantees a lower false positive probability and verifies its high efficiency under data retrieval.
Optimization of Convolutional Neural Networks on Resource Constrained Devices

Implementation of convolutional neural networks (CNNs) on resource constrained devices like FPGA (example: Zynq) etc. is important for intelligence in edge computing. This paper presents and discusses different hardware optimization methods that were employed to design a CNN model that is amenable to such devices, in general. Adaptive processing, exploitation of parallelism etc. are employed to show the superior performance of proposed methods over state of the art.
Improved Stacked Hourglass Network with Offset Learning for Robust Facial Landmark Detection

The state-of-the-art algorithms on facial landmark detection are recently dominated by the heatmap regression based methods using fully convolutional networks. Despite their excellent performance, heatmap regression based methods have a few inherent defects, such as symmetric topology, discrete output and discrepancy between training and testing. In this paper, we propose an improved stacked hourglass network with offset learning for robust facial landmark detection to cover these drawbacks based on three insights. First, one can replace the encoder of hourglass network with a more powerful backbone to strengthen its feature extraction ability. Second, a multi-scale feature aggregation module can be applied to utilize the contextual information efficiently. Third, the offset learning is able to refine the landmarks predicted by hourglass network through a custom sub-network, which combines both heatmap information and coordinate information in a simple yet effective fashion. Extensive experiments on benchmark datasets 300W and AFLW demonstrate the effectiveness of our proposed method.