Reviewerly/exHarmony huggingface dataset:
Interoperable OAuth 2.0 Framework

Internet of things (IoT) is becoming key paradigm in our everyday life. However, interoperability which is essential key to share services and resources in heterogenous IoT domains is difficult to be achieved since some technologies are often designed without considering interoperability. The open authorization (OAuth) 2.0 framework used in IoT as well as conventional web environment also did not consider the interoperability in spite of its wide usage. In other words, systems cannot interoperate together without other alternatives even though they implement same OAuth 2.0 framework standard. We therefore propose IOAF (interoperable OAuth 2.0 framework). IOAF provides an additional authorization layer to support common authorization server capabilities, and supports four extended authorization grant types used to obtain IAT (interoperable access token) which has global scope in multiple domains. This paper shows the main requirements and benefits of IOAF, and describes detailed flow of authorization grant types.
Addressing Imbalance in Weakly Supervised Multi-Label Learning

Multi-label learning has been widely used in many fields to solve the problem of assigning multiple related categories to an instance. Nevertheless, the label for each training example is assumed complete in most of the current multi-label learning methods. As a matter of fact, it is often hard to obtain training samples with complete labels, thus weakly supervised multi-label learning is demanded, which has become a hot topic in recent years. Moreover, the missing labels would further aggravate the class imbalance in multi-label learning. In this paper, the asymmetric stage-wise loss function is introduced to make positive class samples farther away from the classification boundary than negative class samples by adjusting ramp as well as the margin parameters. In addition, the general aggregate loss function is replaced with the average top-k aggregate loss so as to protect the non-typical distributed samples from being sacrificed in the aggregation process of the loss function, and therefore improve the identification accuracy of minority labels. The experiments on both standard and large-scale multi-label data sets demonstrate that the proposed algorithm can solve the problem of class imbalance by changing the sample distribution of training set, thus obtaining competitive performance.
Treatment of Leachate from Garbage using Electrocoagulation Type MP-P (MonoPolar-Paralel) Methode

Leachate water is a very dangerous liquid due to its organic and inorganic content that can pollute water body around the landfill, hence treatment is needed. Electrocoagulation is one of the methods that can be used. The research aims to determine a characteristic of leachate water at TPA Sukawinatan (COD, pH, BOD5, TSS, and Cd content), the effect to electrodes distance (aluminium anode and iron cathode) and optimum time of treatment Leachate Water using electrocoagulation type MP-P (MonoPolar-Parallel) method. The electrical potential was set at 12 volts and time variations of 30,60,75, 90 and 120 minutes and electrodes distances of 6, 7 and 8 cm. The result obtained shown that COD decreased to 529.20 mg/L, pH became 8.34, BOD5 decreased to 17.6 mg/L, TSS became 108 mg/L and <0.0015 mg/L of Cd content. The optimum electrodes distance to treat leachate water was found 8 cm in 120 minutes fo electrocoagulation type MP-P.
A green SLA constrained scheduling algorithm for parallel/scientific applications in heterogeneous cluster systems

Cloud services utilize Heterogeneous Clusters as the hardware infrastructure at the backend. Minimal energy consumption in Heterogeneous Cluster System (HCS) is an important but challenging job. It can bring numerous benefits such as drop in operational cost, enhanced system reliability and green environment. Scheduling on HCS, with the objectives of minimizing the energy consumption, is being addressed by the research community at large. This work proposes a scheduling algorithm with green Service Level Agreement (SLA) constrained execution of parallel applications. The green SLA assumes that the tasks are to be scheduled on Dynamic Voltage Frequency Scaling (DVFS) enabled HCS. The proposed algorithm acts in two phases: first, it minimizes the energy consumption for the slacks of the schedule using DVFS technique, and second, a green SLA is made between a customer and service provider to maintain a good energy-performance trade-off. With this strategy, makespan of the schedule is extended a bit but the energy consumption is minimized for the period of extended makespan. It is observed that the decrease in one level of voltage and frequency of high-speed Processing Element (PE) saves more energy compared to that of low-speed PE. To utilize this, jobs running on higher voltage and frequency level of the PE are prioritized first. The performance of the proposed algorithm is studied by simulation on several standard Directed Acyclic Graphs (DAGs) and few other well-known real-world scientific workflows. The comparative study exhibits the effectiveness of the proposed algorithm over state of the art on four real heterogeneous PEs.
Improving the understanding of navigational commands by adapting a robot’s directional perception based on the environment

Intelligent service robots are being developed for emerging areas of robotics applications. Human-friendly interactive features are preferred for these service robots since these robots are anticipated to be operated by non-experts. Humans prefer to use voice instructions for exchanging the ideas b etween peers. Such voice instructions often include distance and direction related language descriptors that are fuzzy in nature. Therefore, these service robots must be capable of interpreting the meaning of such fuzzy notions in language instructions in order to enhance the rapport between the robots and their users. This paper proposes a method to interpret the directional notions in motional and positional navigational commands by considering the fuzziness associated with linguistic notions. A fuzzy inference system has been developed in order to adapt a robot’s perception of fuzzy directional notions based on the environment. This adaptation is realized by weighting the output membership function with the distribution of free space around the robot or a reference object. Experiments have been conducted in an artificially created domestic environment with heterogeneous characteristics. According to the experimental results, the proposed system is capable of enhancing the understanding of navigational commands with fuzzy notions.
SQL: Superpixels via quaternary labeling

This paper formulates superpixel segmentation as a pixel labeling problem and proposes a quaternary labeling algorithm to generate superpixel lattice. It is achieved by seaming overlapped patches regularly placed on the image plane. Patch seaming is formulated as a pixel labeling problem, where each label indexes one patch. Once the optimal seaming is completed, all pixels covered by one retained patch constitute one superpixel. Further, four kinds of patches are distinguished and assembled into four layers correspondingly, and the patch indexes are mapped to the quaternary layer indexes. It significantly reduces the number of labels and greatly improves labelling efficiency. Furthermore, an objective function is developed to achieve optimal segmentation. Lattice structure is guaranteed by fixing patch centers to be superpixel centers, compact superpixels are assured by horizontal and vertical constraints enforced on the smooth terms, and coherent superpixels are achieved by iteratively refining the data terms. Extensive experiments on BSDS data set demonstrate that SQL algorithm significantly improves labeling efficiency, outperforms the other superpixel lattice methods, and is competitive with state-of-the-art methods without lattice guarantee. Superpixel lattice allows contextual relationships among superpixels to be easily modeled by either MRFs or CNN.
A Universal Generating Algorithm of the Polyhedral Discrete Grid Based on Unit Duplication

Based on the analysis of the problems in the generation algorithm of discrete grid systems domestically and abroad, a new universal algorithm for the unit duplication of a polyhedral discrete grid is proposed, and its core is “simple unit replication + effective region restriction”. First, the grid coordinate system and the corresponding spatial rectangular coordinate system are established to determine the rectangular coordinates of any grid cell node. Then, the type of the subdivision grid system to be calculated is determined to identify the three key factors affecting the grid types, which are the position of the starting point, the length of the starting edge, and the direction of the starting edge. On this basis, the effective boundary of a multiscale grid can be determined and the grid coordinates of a multiscale grid can be obtained. A one-to-one correspondence between the multiscale grids and subdivision types can be established. Through the appropriate rotation, translation and scaling of the multiscale grid, the node coordinates of a single triangular grid system are calculated, and the relationships between the nodes of different levels are established. Finally, this paper takes a hexagonal grid as an example to carry out the experiment verifications by converting a single triangular grid system (plane) directly to a single triangular grid with a positive icosahedral surface to generate a positive icosahedral surface grid. The experimental results show that the algorithm has good universality and can generate the multiscale grid of an arbitrary grid configuration by adjusting the corresponding starting transformation parameters.
An Ensemble of Deep Auto-Encoders for Healthcare Monitoring

Ambient Intelligence (AmI) is a new paradigm that redefines interaction between humans, sensors and flow of data and information. In AmI, environment is more sensitive and more responsive to the user who acts spontaneously in the foreground, while sensors, machines and intelligent methods act in background. Behind the AmI interfaces, a huge volume of data is collected and analysed to make decision in real time. In the field of health care, AmI solutions prevent the patients from emergency situations by using data mining techniques. This paper assess the performance of deep learning against traditional dimensionality reduction and classification algorithms for healthcare monitoring in a hospital environment. An ensemble method is proposed using three classifiers on a reduced version of the dataset, where the dimensionality reduction technique is based on deep learning. The evaluation based on different performance metrics like accuracy, precision, recall and f-measure illustrated reliable performance of the proposed ensemble method.
A Dynamic Pricing Mechanism Model Based on Sentiments Analysis

Nowadays we can see tides of sentiments flooding on social media on any topics you can think of. Companies from industries like Hospitality, Advertisement, Retail etc. To classify, aggregate and use these sentiments so as to predict sentimental analysis which can be used for the revenue growth. By keeping track of the changes in sentiments towards a particular product, companies that have dynamic pricing mechanisms which take advantage of the fluctuations in customers' interest and demand. This paper is based on [1] and focuses on economic aspects related to Big Data, which implements the proposed Dynamic Pricing Mechanism Model to take into account sentiment analysis as the major factor. Results show that there is a significant increase in revenue generation using the proposed dynamic pricing model comparing to the existing dynamic pricing model.
Eruptive Styles Recognition Using High Temporal Resolution Geostationary Infrared Satellite Data

The high temporal resolution of the Spinning Enhanced Visible and InfraRed Imager (SEVIRI) instrument aboard Meteosat Second Generation (MSG) provides the opportunity to investigate eruptive processes and discriminate different styles of volcanic activity. To this goal, a new detection method based on the wavelet transform of SEVIRI infrared data is proposed. A statistical analysis is performed on wavelet smoothed data derived from SEVIRI Mid-Infrared( MIR) radiances collected from 2011 to 2017 on Mt Etna (Italy) volcano. Time-series analysis of the kurtosis of the radiance distribution allows for reliable hot-spot detection and precise timing of the start and end of eruptive events. Combined kurtosis and gradient trends allow for discrimination of the different activity styles of the volcano, from effusive lava flow, through Strombolian explosions, to paroxysmal fountaining. The same data also allow for the prediction, at the onset of an eruption, of what will be its dominant eruptive style at later stages. The results obtained have been validated against ground-based and literature data.
Biomechatronic Simulator for Fencing Training Using Virtual Reality Technology

Fencing is a combat sport in which fencers compete in three weapons: foil, saber and epee. They differ from each other in blade type, valid target and scoring technique. Although the fundamental role of speed, coordination and sensory-motor skills in fencing is indisputable, merely rapid and correct accomplishment of a movement does not ensure success. Outright bout against an opponent requires high level of perceiving, information processing and accurate decision-making. Thus, in fencing, as a 'game of anticipation and deception', technical abilities are just a tool used to execute chosen strategy and tactics. The repeatability of joints' movements relevant to the lunge performance was measured. Shoulder, elbow and wrist joint of the upper arm, hip joint and knee leg movements were checked in order to match and determine the number of necessary recordings. The study covered 10 athletes, 6 saber and 4 epee fencers, members of Polish National Team. There is a repeatability in measured movements. The weak and negative correlation coefficient is observed just between several subjects. The strong correlation coefficient was noted across trials within a single subject. This result implies, that frequently repeated movements create stable movement patterns which is individual for each of the subjects. According to individual character of performed actions, animation database should contain possible highest number of recorded athletes. Just widening of database can provide expected level of diversity to fencing training and prevent the effect of 'learning' just one opponent.
Sports science: The correction of a sportsperson’s pose using a knowledge-based method

This study proposes a method that uses the Time-of-Flight (ToF) for an assisting device. A contactless Kinect device is used to obtain images of basketball players Field Goal Shooting (FGS) is used to detect posture. The method classifies the FGS postures of players into 3-stage routines for analy sis. The ToF first sets the posture correcting conditions using the data provided by professional players and analyzes the postures with the highest and lowest FGS percentage and the most and least data from the statistical results. This gives a stable FGS reference value for the basketball player. The proposed technique has a higher correction rate for the FGS posture (for 100 continuous shots) for a player and allows processing in real-time, with a delay of only 0.4μs.
Multi-Constrained Optimization Method of Line Segment Extraction Based on Multi-Scale Image Space

Image-based line segment extraction plays an important role in a wide range of applications. Traditional line segment extraction algorithms focus on the accuracy and efficiency, without considering the integrity. Serious line segmentation fracture problems caused by image quality will result in poor subsequent applications. To solve this problem, a multi-constrained line segment extraction method, based on multi-scale image space, is presented. Firstly, using Gaussian down-sampling with a classical line segment detection method, a multi-scale image space is constructed to extract line segments in each image scale and all line segments are projected onto the original image. Then, a new line segment optimization and purification strategy is proposed with the horizontal and vertical distances and angle geometric constraint relationships between line segments to merge fracture line segments and delete redundant line segments. Finally, line segments with adjacent positions are optimized using the grayscale constraint relationship, based on normalized cross-correlation similarity criterion for realizing the second optimization of fracture line segments. Compared with mainstream line segment detector and edge drawing lines methods, experimental results (i.e., indoor, outdoor, and aerial images) indicate the validity and superiority of our proposed methods which can extract longer and more complete line segments.
Resource Recommendation Algorithm Based on Text Semantics and Sentiment Analysis

Traditional recommendation systems rarely take the contextual semantics of the application scenarios into account when implementing the resources recommendation, which results in those algorithms having serious deficiencies in real-time, robustness, and quality in the actual learning circumstance. On the other hand, sentimental factors and individual preference also have great impacts on users' demands. The objective of this study was to determine a resource recommendation scheme based on the semantic similarity and sentiment analysis of review text. Extracting the semantic and sentiment information of the resources, filling user rating matrix, and calculating users' similarity with adjusted cosine measures, will obtain the personalized recommendation results. Experiment results demonstrate that the proposed algorithm can better characterize user preference by obtaining information-in-depth, and outperforms the state-of-the-art methods.
Safety for pedestrian recognition in sensor networks based on visual compressive sensing and adaptive prediction clustering

Aiming at the imbalance between energy use and tracking accuracy in multi-sensor target recognition, a pedestrian target recognition method based on visual compressed sensing and adaptive predictive clustering is proposed to track multiple pedestrians simultaneously. After acquiring the pedestrian target image, the scale invariant features of the pedestrian face in the image are extracted firstly, and the target is sparsely represented by the feature dictionary. Then adaptive prediction clustering is used to capture the change of pedestrian behavior attributes. Then, the sensor is selected by Region method, and the sensor contributing to the pedestrian area is activated to realize the pedestrian tracking. In the simulation scenario, 500 sensors are randomly deployed in a given square area. Because of fewer sensors and shorter computation time, the network lifetime has been significantly improved.
An Efficient and Resource-Aware Hashtag Recommendation Using Deep Neural Networks

The goal of this research is to design a system that can predict and recommend hashtags to users when new images are uploaded. The proposed hashtag recommendation system is called HAZEL (HAshtag ZEro-shot Learning). Selecting right hashtags can increase exposure and attract more fans on a social media platform. With the help of the state-of-the-art deep learning technologies such as Convolutional Neural Network (CNN), the recognition accuracy has improved significantly. However, hashtag prediction is still an open problem due to the large amount of media contents and hashtag categories. Using single machine learning method will not be sufficient. To address this issue, we combine image classification and semantic embedding models to achieve the expansion of recommended hashtags. In this research, we show that not all hashtags are equally meaningful, and some are not suitable in recommendation. In addition, by periodically updating semantic embedding model, we ensure that the hashtags being recommended follow the latest trends. Since the recommended hashtags have not received any training examples in the first place, it fulfills the concept of Zero-shot learning. We demonstrate that our system HAZEL can successfully recommend hashtags that are the most relevant to each image input by applying our design to a larger scale of image-hashtag pairs on Instagram.
Tail latency in node.js: energy efficient turbo boosting for long latency requests in event-driven web services

Cloud-based Web services are shifting to the event-driven, scripting language-based programming model to achieve productivity, flexibility, and scalability. Implementations of this model, however, generally suffer from long tail latencies, which we measure using Node.js as a case study. Unlike in traditional thread-based systems, reducing long tails is difficult in event-driven systems due to their inherent asynchronous programming model. We propose a framework to identify and optimize tail latency sources in scripted event-driven Web services. We introduce profiling that allows us to gain deep insights into not only how asynchronous event-driven execution impacts application tail latency but also how the managed runtime system overhead exacerbates the tail latency issue further. Using the profiling framework, we propose an event-driven execution runtime design that orchestrates the hardware's boosting capabilities to reduce tail latency. We achieve higher tail latency reductions with lower energy overhead than prior techniques that are unaware of the underlying event-driven program execution model. The lessons we derive from Node.js apply to other event-driven services based on scripting language frameworks.
2D Rotation-Angle Measurement Utilizing Least Iterative Region Segmentation

When geometric moments are used to measure the rotation-angle of plane workpieces, the same rotation angle would be obtained with dissimilar poses. Such a case would be shown as an error in an automatic sorting system. Here, we present an improved rotation-angle measurement method based on geometric moments, which is suitable for automatic sorting systems. The method can overcome this limitation to obtain accurate results. The accuracy, speed, and generality of this method are analyzed in detail. In addition, a rotation-angle measurement error model is established to study the effect of camera pose on the rotation-angle measurement accuracy. We find that a rotation-angle measurement error will occur with a non-ideal camera pose. Thus, a correction method is proposed to increase accuracy and reduce the measurement error caused by camera pose. Finally, an automatic sorting system is developed, and experiments are conducted to verify the effectiveness of our methods. The experimental results show that the rotation angles are accurately obtained and workpieces could be correctly placed by this system.
A Skipping FP-Tree for Incrementally Intersecting Closed Itemsets in On-Line Stream Mining

An on-line mining for a data stream consisting of large transactions is still quite difficult because of an explosion of frequent itemsets. In this paper, we propose a new data structure, called a skipping FP-tree, which enables us to effectively compress the set of closed itemsets in a stream. We show, through experimental evaluations, the skipping FP-tree achieves more than ten times faster computation for incremental intersections.