Reviewerly/exHarmony huggingface dataset:
Adaptive Online Learning for Video Object Segmentation

In this work, we address the problem of video object segmentation (VOS), namely segmenting specific objects throughout a video sequence when given only an annotated first frame. Previous VOS methods based on deep neural networks often solves this problem by fine-tuning the segmentation model in the first frame of the test video sequence, which is time-consuming and can not be well adapted to the current target video. In this paper, we proposed the adaptive online learning for video object segmentation (AOL-VOS), which adaptively optimizes the network parameters and hyperparameters of segmentation model for better predicting the segmentation results. Specifically, we first pre-train the segmentation model with the static video frames and then learn the effective adaptation strategy on the training set by optimizing both network parameters and hyperparameters. In the testing process, we learn how to online adapt the learned segmentation model to the specific testing video sequence and the corresponding future video frames, where the confidence patterns is employed to constrain/guide the implementation of adaptive learning process by fusing both object appearance and motion cue information. Comprehensive evaluations on Davis 16 and SegTrack V2 datasets well demonstrate the significant superiority of our proposed AOL-VOS over other state-of-the-arts for video object segmentation task.
Guided Compositional Generative Adversarial Networks

In this paper, we propose to synthesize natural images from a set of input objects. The proposed technique generates a scene which has high correlation with the provided set of input objects while also maintaining the natural placement of objects within the scene. The technique constitutes of a generative adversarial network trained on a large corpus of objects and natural scenes. This is in contrast with earlier works where the objective was to generate a natural scene from a noise vector or conditioning the network over a variable. However, such methods have limitations in their ability to control the objects within the generated images. On the contrary, we show that by training a Generative Adversarial Network with raw image pixels as input, we can generate scenes which constitute the objects as well as generate the surrounding environment suitable for the combination of the input objects. We provide qualitative and quantitative results on challenging MS-COCO dataset to show the effectiveness of the proposed technique.
Towards a Roadmap for Cloud TV Services in the Internet of Things Era

Cloud TV will play an important role in future pay-TV services and is quickly becoming the next arena for TV content providers. This emphasizes the need for a technology roadmap to address several key issues that may affect the deployment of future Cloud TV services. Taking into account an important blend of social, economic and technological factors, three alternative technologies, Internet Protocol TV, Over the Top and Smart TV have been investigated and ranked using the Analytical Hierarchy Process. The results reveal that OTT seems to take the precedence and security, privacy, accessibility, costs saving and time-to-market are crucial aspects, need to be taken into account.
The regression model for the procedure of correction of photos damaged by backlighting

In the paper, we propose an approach for selection a correction parameter for images damaged by backlighting. We consider the photos containing underexposed areas due to backlit conditions. Such areas are dark and have poorly discernible details. The correction parameter controls the level of amplification of local contrast in shadow tones. Besides, the correction parameter can be considered as a quality estimation factor for such photos. For an automatic selection of the correction parameter, we apply regression by supervised machine learning. We propose new features calculated from the co-occurrence matrix for the training of the regression model. We compare the performance of the following techniques: the least square method, support vector machine, random forest, CART, random forest, two shallow neural networks as well as blending and staking of several models. We apply two-stage approach for the collection of a big dataset for training: initial model is trained on a manually labeled dataset containing about two hundred of photos, after that we use the initial model for searching for photos damaged by backlit in social networks having public API. Such approach allowed to collect about 1000 photos in conjunction with their preliminary quality assessments that were corrected by experts if it was necessary. In addition, we investigate an application of several well-known blind quality metrics for the estimation of photos affected by backlit.
Design Concepts, Principles and Patterns in the Curriculum of the New Computing Education Era

Ubiquitous Computing, Mobile Computing and the Internet of Things (collectively referred to as UMI herein) involve recent advances in technology areas such as low-cost and miniaturized processing and sensing technologies, high-bandwidth wireless networking and so on. UMI technologies can also support the recent attempts to reform computing education. Yet, to accomplish this potential, relevant UMI learning scenarios are needed. Creating such scenarios can be challenging, since this particular field of computing education is still in its infancy. This paper discusses learning design knowledge which can orientate the future design of UMI learning scenarios. Content analysis was applied on ten quality UMI-oriented learning scenarios. The scenarios were freely accessible in online platforms, and they were designed for middle school education and for the UMI domain. Two different methodological approaches were employed: the first one involved mapping the scenarios to existing predefined learning design elements (i.e. design concepts, design principles, and design patterns). The learning design elements were previously defined in an online database and they involved ubiquitous tools. The second method involved mapping the scenarios to the parameters of a UMI learning ecology. The performed analysis revealed design concepts, principles, patterns and key characteristics underpinning the selected UMI scenarios: they cater for students’ active learning and engage them in interdisciplinary projects in which students are learning across contexts in groups and solve meaningful problems that exploit the functionalities of the UMI technologies. Several recommendations concerning the creation of quality UMI learning scenarios are suggested, such as: striking a balance between conceptual understanding and 21st century lifelong learning skills, highlighting how students’ collaboration is expected to happen in a UMI scenario, providing many opportunities for instructional scaffolding and explicitly mentioning spatiotemporal aspects of the UMI scenarios. These findings could be of interest to computing education researchers, tutors, and curriculum designers who wish to design UMI oriented educational scenarios.
A Convolutional Neural Networks Accelerator Based on Parallel Memory

Convolutional Neural Networks (CNNs) is one of the core algorithms for implementing artificial intelligence (AI), which has the characteristics of high parallelism and large amount of computations. With the rapid development of AI applications, general purpose processors such as CPU/GPU can’t meet the requirements for performance, power consumption and real-time performance of CNN. However, ASIC can fully exploit the parallelism of CNN and improve resource utilization to meet its requirements This paper has designed and implemented a new CNN accelerator based on parallel memory technology, which can support multiple parallelisms. A super processing unit (SPU) with kernel buffer and output buffer is proposed to make computation and data fetching more streamline then ensure the performance of accelerator. In addition, a two-dimensional buffer which can provide conflict-free non-aligned block access with different steps and aligned continuous access to meet the data requirements of varies parallelisms. The synthesis results show it can work at 1 GHz frequency with area overhead of 4.51 mm2 and on-chip buffer cost of 192 KB. We evaluated our design with varies CNN workloads, the efficiency of our design over 90% in most cases. Compared with the state-of-the-art accelerator architectures, the hardware cost of our design is smaller under the same performance.
Nearest Convex Hull Classifier with Simplified Proximity Measurement

This text proposes a new classifier of nearest convex hulls characterized by a simplified measurement of convex hull proximity to the test point. It is called a light nearest convex hull classifier (LNCH). The concept of penetration depth is used for measurement of test point proximity to the class convex hull if the test point is within the convex hull. The proximity is defined based on analysis of extremum points projected onto a direction vector from a given point to the class centroid. For the LNCH classifier, a decision rule for multi-class problems is derived, which uses the proposed proximity measurement estimation method. A comparison of LNCH and classifications using nearest neighbour algorithms (kNN) is performed and results of experimental studies based on synthesized numerical data and actual data of breast cancer diagnostic task are presented. Results have shown the high recognition precision of LNCH classifiers in comparison with other types of classifiers.
Facial Expression Recognition Based on Group Domain Random Frame Extraction

Modeling the dynamic variation of facial expression from a sequence of images is a key issue in facial expression recognition. However, the analysis of complete sequence temporal information requires significantly computational power. To improve the efficiency, a dynamic frame sequence convolutional network (DFSCN) is proposed in this study. In the proposed DFSCN, an expression sequence simplification method is first proposed to reduce the sequence length and takes the reduced new sequence as the input of DFSCN. An adaptive weighted feature fusion method for spatiotemporal feature learning is then put forward in DFSCN. A still frame convolutional network (SFCN) is introduced for complementing the still appearance information and the fine-tuning of DFSCN. Finally, these two models are combined together by weighted fusion to enhance the performance. Two public-available databases, CK+ and Oulu-CASIA, are used to evaluate the performance of the proposed approach. Experimental results show that the proposed method can effectively capture the dynamic process of expression sequence and the recognition performance is superior to other state-of-the-art methods.
Towards Deterministic Diverse Subset Sampling

Determinantal point processes (DPPs) are well known models for diverse subset selection problems, including recommendation tasks, document summarization and image search. In this paper, we discuss a greedy deterministic adaptation of DPPs. Deterministic algorithms are interesting for many applications, as they provide interpretability to the user by having no failure probability and always returning the same results. First, the ability of the method to yield low-rank approximations of kernel matrices is evaluated by comparing the accuracy of the Nyström approximation on multiple datasets. Afterwards, we demonstrate the usefulness of the model on an image search task.
Rainfall and Wave Height Prediction in Semarang City Using Vector Autoregressive Neural Network (VAR-NN) Methods

Abstract In the past few months in Semarang City, high rainfall and high waves give negative impact because the activities of fishermen to be stopped due to the waves of water. Another disadvantage is ROB flood. This is quite detrimental in addition to disrupting transportation and economic activities, but also causing damage to both government-owned assets and personal assets. This damage is caused by flood water which is mixed with sea water containing salt, so that it can cause corrosion. Vector of Autoregressive Neural Network (VAR-NN) is a development of the Autoregressive Vector (VAR) method using the Neural Network (NN) algorithm. The VAR method does not need to distinguish between endogenous and exogenous variables, its means that all variables used in the VAR model are used as endogenous variables. The VAR model can be used to explain and make predictions from endogenous variables from past data on these variables. VAR is developed using the Neural Network (NN) algorithm, where the basic principle of NN itself is an information processing algorithm that resembles the workings of the human brain, which uses a number of neurons to perform simple tasks. VAR-NN is often used in the economic, financial fields and believed to be able to make predictions of high rainfall and wave height. In this study using VAR-NN with FeedForward algorithm or called Backpropagatian. The results of rainfall prediction and wave height in Semarang city using this method have a Mean Square Error of 12,325 with the VAR-NN model (3, 2, 4).
Effect of Catadioptric Component Postposition on Lens Focal Length and Imaging Surface in a Mirror Binocular System

The binocular vision system is widely used in three-dimensional measurement, drone navigation, and many other fields. However, due to the high cost, large volume, and inconvenient operation of the two-camera system, it is difficult to meet the weight and load requirements of the UAV system. Therefore, the study of mirror binocular with single camera was carried out. Existing mirror binocular systems place the catadioptric components in front of the lens, which makes the volume of measurement system still large. In this paper, a catadioptric postposition system is designed, which places the prism behind the lens to achieve mirror binocular imaging. The influence of the post prism on the focal length and imaging surface of the optical system is analyzed. The feasibility of post-mirror binocular imaging are verified by experiments, and it is reasonable to compensate the focal length change by changing the back focal plane position. This research laid the foundation for the subsequent research on the 3D reconstruction of the novel mirror binocular system.
An Improved Active Contour Model in Medical Image Segmentation

Abstract Aiming at the problem that medical image segmentation has low precision because of restriction of image principle, this paper proposes an improved active contour model based on vector field convolution, and combining both the balloon force and gradient directional information. This method improves the energy function of model, has more chances to approximate the complex boundary. The experimental results show that the new model not only inherits the advantages of the previous models, but also show fast speed, limits the influence of false edge and noise disturbance.
Reducing Data Center Power Losses through UPS Serial Consolidation

Reducing data center (DC) power consumption is one of green Information and Communication Technology (ICT) challenges, which can be realised through improvement of DC energy-efficiency. To accomplish this improvement, each component of DC including those of power supply system such as uninterruptible power supplies (UPSs), has to operate at its maximum power efficiency. Since the broadly accepted concept based on server consolidation approach demonstrates significant DC power saving at the level of server racks, the contribution of constantly active UPSs to the overall DC power inefficiency become non-negligible. It is shown that a possible technique for reducing UPS power inefficiency in DCs can be based on dynamic merging of UPS activity state with server consolidation, where the neighbouring connectivity between one UPSs and two neighbour server racks have been established on the backup power supply paths. In this paper enhancement of neighbouring connectivity concept based on a new approach for establishing connectivity between UPSs and server racks on DC backup power delivery paths are introduced. More specifically, the algorithm which enables serial connectivity of active UPSs with DC server racks during merged UPS and server consolidation process is proposed. Through the extensive simulations of real DC having a large number of rack servers and UPSs, the proposed concept based on serial connectivity has been tested. Results show that proposed concept improves the energy-efficiency of DC when compared with neighbouring connectivity approach or existing legacy DC power supply approach lacking any UPS consolidation. Proposed concept can be used in practice as a solution which can reduce the impact of power consumption inefficiency of UPSs as main DC power supply elements.
Mixed Reality Medical First Aid Training System Based on Body Identification

Effective first aid training is helpful to improve the survival rate of the wounded in the face of natural disasters, emergencies and wars. While, traditional first aid training relies on the explanation and demonstration of experts, which has certain limitations. In this paper, we propose a novel type of first aid training system. The implementation of the system mainly includes three steps. First, medical body model images are collected to construct data sets. Second, key parts of the human body are identified and located based on a designed lightweight YOLO_v2 network. Finally, according to the results of the human identification, virtual guidance will be merged in the real environment by HoloLens glasses, and be uploaded to the server. With the Mixed Reality technology, we superimpose the corresponding virtual emergency instructions of cardiopulmonary resuscitation and artificial respiration in key parts of the body, and pass back to HoloLens glasses to realize first aid training. Experimental results show that the proposed medical first aid training system can make learners have a real touch and improve learning efficiency.
Decision Support System Assessing Feasibility Taekwondo Athlete (UPN “VETERAN” Yogyakarta)

Indonesian taekwondo athlete's achievements have improved because he managed to win several medals in international championships. Being a taekwondo athlete requires a willingness, discipline, good physical condition, experience, mental endurance, unyielding spirit, and proper posture. The assessment process is still done manually by asking taekwondo for their availability in the match; therefore, it requires a decision support system to measurement of participants (taekwondoin). Decision support is a way to provide problem-solving skills and be able to support the work of a coach in solving problems by giving suggestions towards certain decisions. The purpose of Simple Additive Weighting is to determine the feasibility of taekwondoin selection to become an athlete on the championship team. Dynamic criteria allow adding the criteria needed for coaches and managers. Coaches and managers also can refine taekwondo by adding the required criteria to the agreed additional criteria. The result of this paper is application can give the decision of athletes who deserve to enter the match team. To facilitate managers and coaches in screening the best athletes. The validation test results obtained 85% compatibility between the selection results in the application and manual calculations.
Dual Loop Theory: Eidetic Feedback Control and Predictive Feedback Control

We have been studying on human information processing and finding out two types of feedback loop, positive and negative which are used when people understand a sentence. Former one is eidetic feedback control by visual sensory organs with encoding short-term memory (STM). Latter one is predictive feedback control by phonological imagery and schema, which help recall and reconstruction or reformation of concepts concerning with long term memory (LTM). Moreover, those strategies might be related to their behavior or attitudes. We have hypotheses that there are individual differences depending on strategies how two loops are used. Those findings must lead coordinating transformation and learning control for AI doctor or care assistive robots, which are required to interact with various types of people so that they can predict their behavior and attitudes through feedforward control.
Capturing and using context in a mobile annotation application

We present an approach to integrating extemporary annotations on topics of interest to a user with information on the context in which the annotation is being taken. Context is here defined in terms of the set of surrounding devices (Bluetooth Low Energy enabled smartphones, WiFi hotspots) and the current calendar event. Contextify is a context-aware Android application which detects the current context and organizes the notes taken within context, allowing a form of context-based retrieval. Thus, the detected context represents location, other people present (referring to their BLE equipped smartphones as user proxies), and events. Users can easily retrieve notes when they return to the context where they created them. A context similarity algorithm derived from the Jaro-Winkler string similarity algorithm is used to compare contexts. Each note is tagged by the user and the system suggests the most appropriate tag, among the already used ones, at annotation creation time: the suggestion is based on the similarity of the current context with the contexts associated with previously tagged notes.
longSil: an Evaluation Metric to Assess Quality of Clustering Longitudinal Clinical Data

Longitudinal disease subtyping is an important problem within the broader scope of computational phenotyping. In this article, we discuss several data-driven unsupervised disease subtyping methods to obtain disease subtypes from longitudinal clinical data. The methods are analyzed in the context of chronic kidney disease, one of the leading health problems, both in the USA and worldwide. To provide a quantitative comparison of the different methods, we propose a novel evaluation metric that measures the cluster tightness and degree of separation between the various clusters produced by each method. Comparative results for two significantly large clinical datasets are provided, along with key insights that are possible due to the proposed evaluation metric.
Wearable assistive device for human motion and health monitoring

With the growing population, Healthcare services and Monitoring of people are considered as the major needs in the recent years. There are many health monitoring systems which are either used for multi purpose or for a specific needs. On the other hand Assistive Technology (AT) is the one which would help the people to do their regular work without any manual support to meet their Activities of Daily Living (ADLs). The proposed system is a wearable device which could serve the purpose of health and motion monitoring along with an assistive technology. By the data from the accelerometer, microphone and sensors the activity of the person is determined. When there is any variation in the real time values from the default values an alert message will be sent to their close concern. On the other hand an alarm will be given on set time intervals for their regular activities like taking pills, insulin etc.