Reviewerly/exHarmony huggingface dataset:
Semantic Interoperability in IoT: A Systematic Mapping

There has been a growing interest in adopting ontologies to address semantic interoperability in the domain of Internet of Things (IoT). Recent research claims that the lack of semantic interoperability support in IoT infrastructures hinders the potential of IoT for offering large scale value-added services. This article explores this fundamental issue through a systematic mapping of literature. The mapping intends to answer a set of research questions and identifies research gaps and trends that will guide future research in IoT semantic interoperability modeling.
ASP-SIFT: Using Analog Signal Processing Architecture to Accelerate Keypoint Detection of SIFT Algorithm

The scale-invariant feature transform (SIFT) algorithm is still one of the most reliable image feature extraction methods. Despite its excellent robustness on various image transformations, SIFT's intensive computational burden has been severely preventing it from being used in real-time and energy-efficient embedded machine vision systems. To reduce processing time and energy cost while executing SIFT, an analog signal processing architecture, analog signal processing (ASP)SIFT, is proposed in this article. In ASP-SIFT, the Gaussian pyramid construction, difference-of-Gaussian (DoG) pyramid construction and keypoint locating, which are the primary steps of the keypoint detection part of the SIFT algorithm, are done directly with analog circuit networks. Thus, by completing keypoint detection in the analog domain, the total processing time is approximately equal to the settling time of the circuit network. Besides, by adopting a current-mode circuit network operating in the subthreshold region, the power dissipation would be very low. Simulation results show that the total processing speed for a typical video graphics array (VGA)-format (640 × 480) image is up to 2.3 kframes per second, which is at least 3.26× faster than the state-of-the-art digital hardware accelerators, while the system power is 94.5 mW and the energy consumption is only 40 μJ per frame.
Indonesian Sign Language (BISINDO) Translation System with ORB for Bilingual Language

Sign language refers to a language which is used by some society as a means for daily communication. Unfortunately there are still many general society who still do not understand about Sign language so that it limits the communication process. In Indonesia, there are two sorts of sign languages which have been used. They are SIBI and BISINDO. The development of BISINDO is more widely used because it is more naturally used by the deaf in Indonesia. This paper tried to propose a sign language recognition method by using computer vision which can be understood by general society with bilingual language. The data used is alphabet gesture 26 types from A to Z. For extracting features form gesture used ORB as detector-descriptor has strengths of computation process which is fast and also invariant with measurement and rotation, therefore it gives the possibility of the input process of image which was tested from video. The structure which was applied in building vocabulary in translation process using Binary Search Tree with the strengths of the fast computation process.
Image Based Diagnosis for Agile Coaching

In order to facilitate Agile coaching, the definition, monitoring and control of metrics can be very useful. There exist different software tools to support these tasks. This paper presents how the tool Actionable Agile can support Agile coaching by the interpretation of the values in the different charts it provides. These charts and metrics can be considered as objective inputs to find improvement opportunities. This kind of tools can be seen as an asset to facilitate constant delivery of value and to create team awareness.
NURBS Piecewise Interpolation Algorithm Based on Discrete S-Type Velocity Planning

In order to make the 6-axis robot capable of processing NURBS curves, this paper firstly analyzes the recursive expressions of the NURBS curve and the calculation method of interpolation points, and proposes a new discrete S-type speed planning algorithm. In addition, to achieve the contour accuracy of NURBS and overcome the shortcomings of the feed-speed oscillation and hysteresis in the adaptive algorithm based on chord error, a segmented discrete S-type velocity planning algorithm for NURBS curve is proposed. The algorithm segmented the NURBS curve at the point of extreme curvature, and discrete S-type velocity planning algorithm was applied to the segmented curve respectively, thus achieving a better control of the chord error under continuous acceleration. Finally, the NURBS Trident curve is used as an example to simulate and compare different interpolation algorithms in Matlab.
Augmented Reality Tracking Registration Based on Improved KCF Tracking and ORB Feature Detection

Target tracking and feature detection in 3D registration of augmented reality(AR) system are vulnerable to environment, time-consuming algorithm and low accuracy. An AR tracking registration method based on improved Kernerlized Correlation Filter(KCF) KCF tracking and ORB (Oriented FAST and Rotated BRIEF) feature detection is proposed. Firstly, KCF algorithm is employed to track the registered target position. By considering the strategy of all previous frames, the problem of real-time updating of the weight vector of the target model in KCF tracking is solved, which makes the tracking of the target position real-time and effective. ORB operator with high real-time performance is used to detect the target position. Then the feature points of the detected target position are matched, and the three-dimensional registration matrix is calculated according to the matched feature points, and the three-dimensional registration is carried out. The experimental results show that the improved method achieves better real-time performance, stability and robustness.
The art of good neighboring in Kampoeng Cyber: Community economic development through ICTs

In this exploratory paper, we examine how a community in Indonesia, Kampoeng Cyber, leverages Information & Communication Technologies (ICTs) to support its social and community development. We describe the various technology-related activities and their impact on the transformation of the community that led to improvements in the quality of life of the community members. We interviewed thirteen of the community members and made direct observations of the community, its website, and social media accounts used by the community members. Textual analysis of the data revealed that the ICTs play a significant role in helping the community members to develop themselves in terms of both economic development and social tie maintenance. Our results suggest that government policies dealing with urban development may need to have a more explicit focus related to ICT use and community empowerment. We contribute to an enhanced understanding of ICT-based initiatives that strengthen the local community through participation.
A Method for Coloring Low-resolution Black and White Old Movies through Object Understanding

Coloring black and white old movies through video coloring technology is one of the research hotspots in the field of computer vision. Early old movies generally have problems such as low resolution and obscure objects, resulting in unsatisfactory coloring effects. This paper proposes a method for coloring low-resolution black and white old movies through object understanding. The approach first employs Microsoft's COCO dataset to train Conditional GAN (CGAN). The trained CGAN is not only able to learn color, but also able to understand objects. First, the black and white old movie is framed, and then the framed image is sent to the CGAN for coloring, finally color frames generated by the network are synthesized into a movie. The whole experimental process includes object recognition, object understanding and auto coloring. This paper takes the "Waterloo Bridge" movie as an example for testing. The test results show the coloring effects are satisfactory. Therefore, this paper implements the automatic colorization of black and white old movies by recognizing and understanding color objects of low resolution. This research work not only has some theoretical value, but also has the application value.
Elo, I Love You Won’t You Tell Me Your K

Elo is a rating schema used for tracking player level in individual and, sometimes, team sports, most notably – in chess. Also, it has found use in the area of tracking learner proficiency. Similar to the 1PL IRT (Rasch), Elo rating schema could be extended to serve the most demanding needs of learner skill tracking. Elo's advantage is that it has fewer parameters. However, the computational efficiency side of the search for the best-fitting values of these parameters is rarely discussed. In this paper, we are focusing on questions of implementing Elo and a gradient-based approach to find optimal values of its parameters. Also, we compare several variants of Elo to learning modeling approaches like Bayesian Knowledge Tracing. Our results show that the use of analytical gradients results in computational and, sometimes, statistical fit improvements on small and large datasets alike.
Research on SDN-Based Mimic Server Defense Technology

Existing defense theory methods still have certain defects in the face of hidden uncertain threats. In today's "easy to attack and defensive" cyberspace security situation, static defense technology cannot be very deep in defense depth and breadth. Goodly meet the growing security needs. The "network space mimicry defense theory" proposed by Academician Wu Jiangxing and his team introduces dynamic, diversified and randomized from the perspective of soft and hard components, thus having stronger defense capabilities and more satisfying the characteristics of "face defense". By studying the background mimicry defense dynamic strategy method, the semantic modeling method of the background mimicry defense dynamic strategy and the semantic consistency analysis method of the background mimicry defense dynamic strategy evolution, we apply the mimicry defense idea with the SDN framework to the web server. In defense, a new type of SDN-based mimic server defense technology is proposed, which can effectively improve the active defense capability of common attacks on Web servers and achieve effective.
Optimal translational-rotational invariant dictionaries for images

We provide the construction of a set of square matrices whose translates and rotates provide a Parseval frame that is optimal for approximating a given dataset of images. Our approach is based on abstract harmonic analysis techniques. Optimality is considered with respect to the quadratic error of approximation of the images in the dataset with their projection onto a linear subspace that is invariant under translations and rotations. In addition, we provide an elementary and fully self-contained proof of optimality, and the numerical results from datasets of natural images.
Benchmarking Incremental Regressors in Traversal Cost Assessment

Motivated by the deployment of multi-legged walking robots in traversing various terrain types, we benchmark existing online and unsupervised incremental learning approaches in traversal cost prediction. The traversal cost is defined by the proprioceptive signal of the robot traversal stability that is combined with appearance and geometric properties of the traversed terrains to construct the traversal cost model incrementally. In the motivational deployment, such a model is instantaneously utilized to extrapolate the traversal cost for observed areas that have not yet been visited by the robot to avoid difficult terrains in motion planning. The examined approaches are Incremental Gaussian Mixture Network, Growing Neural Gas, Improved Self-Organizing Incremental Neural Network, Locally Weighted Projection Regression, and Bayesian Committee Machine with Gaussian Process Regressors. The performance is examined using a dataset of the various terrains traversed by a real hexapod walking robot. A part of the presented benchmarking is thus a description of the dataset and also a construction of the reference traversal cost model that is used for comparison of the evaluated regressors. The reference is designed as a compound Gaussian process-based model that is learned separately over the individual terrain types. Based on the evaluation results, the best performance among the examined regressors is provided by Incremental Gaussian Mixture Network, Improved Self-Organizing Incremental Neural Network, and Locally Weighted Projection Regression, while the latter two have the lower computational requirements.
Multi-Threshold Corner Detection and Region Matching Algorithm Based on Texture Classification

In order to address the unreasonable distributed corners in single threshold Harris detection and expensive computation cost incurred from image region matching performed by normalized cross correlation (NCC) algorithm, multi-threshold corner detection and region matching algorithm based on texture classification are proposed. Firstly, the input image is split into sub-blocks which are classified into four different categories based on the specific texture: flat, weak, middle texture and strong regions. Subsequently, an algorithm is suggested to decide threshold values for different texture type, and interval calculation for the sub-blocks is performed to improve operation efficiency in the algorithm implementation. Finally, based on different texture characteristics, Census, interval-sampled NCC, and complete NCC are employed to perform image matching. As demonstrated by the experimental results, corner detection based on texture classification is capable to obtain a reasonable corner number as well as a more uniform spatial distribution, when compared to the traditional Harris algorithm. If combined with the interval classification, speedup for texture classification is approximately 30%. In addition, the matching algorithm based on texture classification is capable to improve the speed of 26.9%~29.9% while maintaining the comparable accuracy of NCC. In general, for better splicing quality, the overall stitching speed is increased by 14.1%~18.4%. Alternatively, for faster speed consideration, the weak texture region which accounts for a large proportion of an image and provides less effective information can be ignored, for which 23.9%~28.4% speedup can be achieved at the cost of a 1.9%~3.9% reduction in corner points. Therefore, the proposed algorithm is made potentially suited to uniformly distributed corner point calculation and high computation efficiency requirement scenarios.
PolSAR Image Speckle Reduction Based on Classification of Similarity Features Between Coherency Matrices

A novel PolSAR image speckle reduction algorithm based on a new definition of similarity coefficient is proposed in this paper. Pixels in image are firstly classified into three types by threshold segmentation which is calculated with the similarity features. Then, weighted filtering is applied on the pixels selected according to their types, power features and similarity properties. Experimental results with measured data collected by NASA/JPL AIRSAR system show that the proposed method is more effective than Lee Filter not only in speckle suppression but also in polarimetric properties and structure feature preservation.
Robust Reflectance Estimation for Projection-Based Appearance Control in a Dynamic Light Environment

We present a novel method that robustly estimates the reflectance, even in an environment with dynamically changing light. To control the appearance of an object by using a projector-camera system, an appropriate estimate of the object's reflectance is vital to the creation of an appropriate projection image. Most conventional estimation methods assume static light conditions; however, in practice, the appearance is affected by both the reflectance and environmental light. In an environment with dynamically changing light, conventional reflectance estimation methods require calibration every time the conditions change. In contrast, our method requires no additional calibration because it simultaneously estimates both the reflectance and environmental light. Our method is based on the concept of creating two different light conditions by switching the projection at a rate higher than that perceived by the human eye and captures the images of a target object separately under each condition. The reflectance and environmental light are then simultaneously estimated by using the pair of images acquired under these two conditions. We implemented a projector-camera system that switches the projection on and off at 120 Hz. Experiments confirm the robustness of our method when changing the environmental light. Further, our method can robustly estimate the reflectance under practical indoor lighting conditions.
Vision Based Autonomous Landing of the Quadrotor Using Fuzzy Logic Control

This paper focuses on the vision based autonomous landing of quadrotor using fuzzy logic controller with low-cost USB camera as perceptive system. Visual algorithm adopts open source AprilTag detection method with robustness to illumination variation and high accuracy in long range of distance. In order to make the quadrotor flying in continuous and smooth status in position and velocity, we adopt fuzzy PID logic as the controller to calculate desired velocity in longitude, latitude and vertical axis. First, using visual position feedback and the fuzzy PID controller velocity output, the quadrotor aligns its center with the pad center as well as reduce the altitude until lands. The control method has been tested in simulation, and the whole system has been carried out in real outdoor environment.
Nurse care activity recognition challenge using a supervised methodology

Human activity recognition is one of the important research topics in machine learning. Different algorithms have been proposed for human activity recognition in several years. But nurse care activity recognition is a new section in machine learning which mainly focused on physical activity recognition. We are the members of the team "Data Digger" accepted a challenge named "Nurse Care Activity Recognition Challenge". We used Carecom nurse care activity dataset for that challenge [14]. We compare several machine learning algorithms for the dataset. The proposed method was trained and evaluated on Carecom nurse care activity recognition dataset and got 69% accuracy after splitting the dataset.
Deep Text Prior: Weakly Supervised Learning for Assertion Classification

The success of neural networks is typically attributed to their ability to closely mimic relationships between features and labels observed in the training dataset. This, however, is only part of the answer: in addition to being fit to data, neural networks have been shown to be useful priors on the conditional distribution of labels given features and can be used as such even in the absence of trustworthy training labels. This feature of neural networks can be harnessed to train high quality models on low quality training data in tasks for which large high-quality ground truth datasets don't exist. One of these problems is assertion classification in biomedical texts: discriminating between positive, negative and speculative statements about certain pathologies a patient may have. We present an assertion classification methodology based on recurrent neural networks, attention mechanism and two flavours of transfer learning (language modelling and heuristic annotation) that achieves state of the art results on MIMIC-CXR radiology reports.
Traversing semantically annotated queries for task-oriented query recommendation

As search systems gradually turn into intelligent personal assistants, users increasingly resort to a search engine to accomplish a complex task, such as planning a trip, renting an apartment, or investing in stocks. A key challenge for the search engine is to understand the user's underlying task given a sample query like "tickets to panama", "studios in los angeles", or "spotify stocks", and to suggest other queries to help the user complete the task. In this paper, we investigate several strategies for query recommendation by traversing a semantically annotated query log using a mixture of explicit and latent representations of entire queries and of query segments. Our results demonstrate the effectiveness of these strategies in terms of utility and diversity, as well as their complementarity, with significant improvements compared to state-of-the-art query recommendation baselines adapted for this task.
Software-Defined Radio Ionosonde for Diagnostics of Wideband HF Channels with the Use of USRP Platform

Currently, HF channel occupancy studies focus on employing wideband channels that occupy 4-16 contiguous narrowband channels. This requires to do research into the methods of sounding wideband channels. The paper is addressed to the development of an ionosonde receive terminal designed with the use of USRP (Universal Software Radio Peripheral) platform as well as presents the outcomes of the studies into the performance of SDR ionosonde with an analogue passive chirp ionosonde. Findings of the research allowed to conclude that the developed receive terminal ensures the adequate measurement of wideband channels parameters at different operating frequencies and gives an estimate of the narrowband channels availability.
Dynamic Projection for the Design of an Adaptive Museum Guide

Very often historical buildings used as museums are characterized by rooms completely covered with decorations (e.g. frescoes, inlay, etc.) rich in details and symbolic contents. Providing adequate information to the visitors to allow them to fully appreciate the artworks is of paramount importance in this context. X-reality technologies have the potential to provide an effective response to the need to combine the educational mission of museums with the ability to involve visitors emotionally, allowing the public to learn new knowledge in a playful way. This study introduces a Museum Guide System, based on Dynamic Projection, to increase the involvement of visitors to the Studiolo by Federico da Montefeltro at Urbino, minimizing technology intrusiveness by ensuring a complete fusion of digital contents with the physical environment. The system is able to track the visitors, to detect their profile and to offer information and multimedia contents tailored to the characteristics of the audience (e.g., children, adults).
Web Page Classification Method Based on Semantics and Structure

Network information has got explosive growth on because of the rapid development of Internet technology. Web page classification technology can solve the problem of information clutter to a large extent, which is widely used in search engines, digital libraries and information retrieval. Web page classification is different from text classification, because web page is a kind of semi-structured data that not only contains text semantic information, but also has rich structural information. This paper analyzes and utilizes the structural characteristics of web pages. Thus, we propose a web page classification method focused on semantics and structure joint features, and train on top of convolution neural networks model. The results show that using joint features of structure and semantics information plays an important role in classification accuracy, which has increased F1 value of web page classification by 4%-6%.