Reviewerly/exHarmony huggingface dataset:
The Internet of Thugs?

A recent cyberattack on an oil pipeline system brought us face-to-face with the real-world implications of insecure cyberinfrastructure. Today's often haphazard IoT rollouts seem to neglect many of the painfully learned lessons of the PC era—can we still avoid the obvious?
Basic human–robot interaction system running on an embedded platform

Robotics will be a dominant area in society throughout future generations. Its presence is currently increasing in most daily life settings, with devices and mechanisms that facilitate the accomplishment of diverse tasks, as well as in work scenarios, where machines perform more and more jobs. This increase in the presence of autonomous robotic systems in society is due to their great efficiency and security compared to human capacity, which is thanks mainly to the enormous precision of their sensor and actuator systems. Among these, vision sensors are of the utmost importance. Humans and many animals naturally enjoy powerful perception systems, but, in robotics, this constitutes a constant line of research. In addition to having a high capacity for reasoning and decision-making, these robots incorporate important advances in their perceptual systems, allowing them to interact effectively in the working environments of this new industrial revolution. Drawing on the most basic interaction between humans, looking at the face, an innovative system is presented in this paper, which was developed for an autonomous and DIY robot. This system is composed of three modules. First, the face detection component, which detects human faces in the current image. Second, the scene representation algorithm, which offers a wider field of view than that of the single camera used, mounted on a servo-pan unit. Third, the active memory component, which was designed and implemented according to two competing dynamics: life and salience. The algorithm intelligently moves the servo-pan unit with the aim of finding new faces, follow existing ones and forgetting those that no longer appear on the scene. The system was developed and validated using a low-cost platform based on a Raspberry Pi3 board.
THE IMMERSION CYCLE: UNDERSTANDING IMMERSIVE EXPERIENCES THROUGH A CYCLICAL MODEL

Abstract The present work introduces a cyclical model which showcases the process of immersion during Immersive Technological Experiences (ITEs) such as Virtual Reality, Augmented Reality and Mixed Reality. This model is based on the identified concepts around immersion and immersive environments across 30 years. The concepts' similarities were used to organize them on a cyclical model by acknowledging the user's presence at the beginning and end of immersive experiences. The proposed model's value relies on its cyclical approach based on a user-centred perspective and having a general overview of the immersion process. The Immersive cycle can serve as a mapping tool for developers and researchers, thanks to the inclusion of guidelines that complements the model. Both of these were used in three different examples of ITEs. Furthermore, the cyclical model could be used as a tool for ideation and conceptualization during the early stages of developing immersive experiences. Nevertheless, it is recognized that this is the first step in developing this model; therefore, it still needs to be validated and improved based on tests with developers, designers and researchers in the field.
High-Performance Interactive Scientific Visualization With Datoviz via the Vulkan Low-Level GPU API

We reported initial work towards a new fast and scalable scientific visualization technology that leverages the Vulkan API to achieve unprecedented performance through GPUs. This technology is implemented in a C/C++ library called Datoviz that offers an intermediate-level API for scientific visualization libraries and software. Datoviz provides a unified graphics stack for 2-D, 3-D, graphical user interfaces, and natively supports efficient interactions between rendering and general-purpose GPU computing. A major direction of development is to investigate the integration of Datoviz as a low-level backend of a future version of VisPy, a popular Python scientific plotting library.
Scheduling computing loads for improved utilization of solar energy

The rise in the penetration of the internet across the world has led to a rapid increase in the consumption of energy at the data centers established by leading cloud data service providers. High power consumption by these data centers [DCs] leads to high operational costs and high carbon emissions into the environment. From a sustainability point of view, the ultimate goal is to maximize the productivity and efficiency of these data centers while keeping greenhouse gas emissions to the minimum and maximize data center productivity. This goal can be achieved by better resource utilization and replacing carbon-intensive approaches of energy production with green sources of energy. Due to the limited intermittent availability of renewable sources of energy, the ideal ‘Green’ design for the DCs, should incorporate inter-operability with both renewable and non-renewable sources of energy. In this paper, we propose a ren-aware scheduler to schedule computational workload by prioritizing their execution within the duration of green energy availability on the basis of the predicted hourly green energy and workload data of DCs. Our results demonstrate that our ren-aware scheduler can increase the green energy consumption by 51% compared to the conventional randomized scheduler that distributes load without considering green energy and load. It can also reduce the total energy consumption by 25% by putting the DCs to sleep during their idle time, as it saves 4.5 times more idle energy than the randomized scheduler. Additionally, the results also demonstrate how the role of time zones of the DCs and the duration of green energy availability in them is pivotal in our ren-aware scheduler's performance.
PRODUCT DEVELOPMENT METHODS IN VIRTUAL REALITY

Abstract The aim of this paper is to show to how far VR technology can help in the implementation of methods in product development. First, an overview of the product development approach and VR technology is given. Then, an evaluation scheme is developed to examine product development methods for their VR suitability. Finally, the results are evaluated and recommendations for future research are given.
Enhancing Creative Learning Methods by Immersive Virtual Reality: A Pilot Study in Classroom Environment

Technology is increasingly essential in engaging millennials. Over the past few decades, education pedagogy has struggled to develop new methods and tools to create better and higher quality education. Virtual reality (VR) technology is one of the solutions for new education pedagogy that can solve the restriction of space, time, and the act of learning. The VR inclusion into learning pedagogy also facilitates self-learning and problem-solving as well as assist students to explore new ideas. This study aims to develop and trial lessons of art and design using VR technology instead of traditional art and design classes. The participants are 6th standard school students. Within traditional teaching of art and design, depth of learning is insufficient. To challenge the traditional environment, VR technology-based art and design lesson are applied in the core curricula of art education in this study. Two sets of tests are conducted with 6th graders, one is pre- and post-test with VR-based lesson, and the other is pre- and post-test with non-VR-based lessons. Overall, this study hypothesizes that VR will make for better learning and upskilling of students' knowledge of art and design. This study found that the teaching outcome with VR and conventional teaching outcomes has a number of significant differences. Besides, it shows the pathway of VR-based teaching methodology in classroom environment.
CTR Prediction Model Considering the Importance of Embedding Vector

Click-through rate (CTR) prediction plays an important role in industrial bidding advertising. Most of the deep CTR models focus on the interaction problem of capturing features after embedding layer, such as PNN, NFM, DeepFM and xDeepFM. Few models consider the importance of features before feature interaction. The ECA module proposed in CVPR2020 computer vision is an extremely lightweight plug and play module, which is used to improve the performance of various deep CNN architectures. This paper introduces the idea of ECANET to modify the model embedding layer to dynamically learn the importance of embedding features and construct a new deep CTR prediction model. ECANET and NFM model are combined to build a novel model ECANFM. The effectiveness of the model is verified by a comparative experiment on a public data set.
Improving information retrieval from electronic health records using dynamic and multi-collaborative filtering

Due to the rapid growth of information available about individual patients, most physicians suffer from information overload and inefficiencies when they review patient information in health information technology systems. In this paper, we present a novel hybrid dynamic and multi-collaborative filtering method to improve information retrieval from electronic health records. This method recommends relevant information from electronic health records to physicians during patient visits. It models information search dynamics using a Markov model. It also leverages the key idea of collaborative filtering, originating from Recommender Systems, for prioritizing information based on various similarities among physicians, patients and information items. We tested this new method using electronic health record data from the Indiana Network for Patient Care, a large, inter-organizational clinical data repository maintained by the Indiana Health Information Exchange. Our experimental results demonstrated that, for top-5 recommendations, our method was able to correctly predict the information in which physicians were interested in 46.7% of all test cases. For top-1 recommendations, the corresponding figure was 24.7%. In addition, the new method was 22.3% better than the conventional Markov model for top-1 recommendations.
On-The-Fly Key Generation Based VLSI Implementation of Advanced Encryption Standard

Data transmitted in a digital format is vulnerable to breaches. Here, the FPGA-based Advanced Encryption Standard (AES) architecture is proposed to safeguard high speed data. 128 and 256-bit keys are supported by this implementation. This execution is recursive and processes single block of data containing 128 bits. Giving access towards a series of round keys and a block, the encipher and decipher block handling data paths are isolated. This opens the door to toughen the core wire just to encrypt or decrypt the mechanism. This helps the synthesis/build toolkits to optimize the necessary functionalities which limits the size to around 50 percent. It has been inspected to ensure the complete removal of decryption, and the core is still functioning. This is an implementation that is extremely compact. The design considerations proposed here enable users to build a trade-off between speed, use of resources and consumption of power. The proposed design is evaluated on various FPGA devices and is compared with several existing AES implementations showing area and power optimized through this method.
A Python Software Library for Computing with Words and Perceptions

Computing with Words (CWW) methodology has been used to design intelligent systems which make decisions by manipulating the linguistic information, like human beings. Human beings naturally understand (and express) themselves linguistically, and hence can reason (and make decision) just with linguistic information without any numerical measure. Perceptual Computing makes use of type 2 fuzzy sets for modeling the words in the CWW paradigm. This use of type-2 fuzzy sets enables better representation of the inherent uncertainty in the fuzzy linguistic semantics on numerous problems. To realise the potential of Perceptual Computing, its MATLAB implementation has been made freely available to the end-users/ researchers, and MATLAB is a proprietary development environment. Therefore, this contribution aims at proposing a python implementation of the Perceptual Computing, or its main processing element the perceptual computer that consists of three components viz., encoder, CWW engine and decoder. Our python implementation provides the end user with a seamless blending amongst all three components, which does not exist yet, to the best of our knowledge.
Markerless camera-based vertical jump height measurement using OpenPose

Vertical jump height is an important tool to measure athletes’ lower body power in sports science and medicine. This work improves upon a previously published self-calibrating algorithm, which determines jump height using a single smartphone camera. The algorithm uses the parabolic fall trajectory obtained by tracking a single feature in a high-speed video. Instead of tracking an ArUco marker, which must be attached to the jumping subject, this work uses the OpenPose neural network for human pose estimation in order to calculate an approximation of the body center of mass. Jump heights obtained this way are compared to the reference heights from a motion capture system and to the results of the original work. The result is a trade-off between increased ease-of-use and slightly diminished accuracy of the jump height measurement.
Enhanced-XGB: An Online Service Resource Demand Forecasting Method for Colocation Data Centers

The technology of colocation, which can effectively improve resource utilization by colocating online services and offline tasks in the same data center, is being adopted by more and more large scale cloud data centers. Predication of resource demands of online services is at the core of colocation since the offline task scheduler relies on the information of future idle resources. Due to the complex running environment, such prediction is far from trivial. XGBoost should be the most popular model for resource demand prediction in recent years. However, our testing and observation show that XGBoost cannot predict values out of the range of training data. We call such problem “range drift”. By addressing this problem, we propose a data center scale online service resource demand forecasting method, named Enhanced-XGB, which consists of three major parts: XGBoost, Coefficient CNN, and Offset CNN. The weight of each tree in XGBoost is learnt dynamically based on the features extracted by the Coefficient CNN, and the offset value is data-dependent functions learnt through the Offset CNN. Finally, the prediction result is obtained by the weighted sum of trees plus the offset value. With the help of CNNs integrated with XGBoost, our model can handle resource demand series with range drift, which makes it more powerful and generic than the original XGBoost. The Enhanced-XGB model has been deployed in a production system at Tencent Games. The experiment results show that our model is superior to XGBoost and LSTM based models in terms of prediction accuracy.
Video Captioning Method Based on Multi-Modal Information Fusion

Video captioning is a challenging problem in the field of computer vision, which aims at using computer to automatically generate human-readable natural language sentences to describe video content.However, the traditional method only uses the single mode information of the video sequence to generate description sentences, and it is not enough to describe the whole content of the video. To describe the video content more accurately and detailed, this paper designs a multi-modal fusion based model to capture and integrate multiple cues for video captioning. The information of different modes contained in the video is fused through feature concatenation and attention mechanism to generate more detailed description sequences, which including the original RGB information, the optical flow information and the attribute tag information. Thus, the model extracts all of the appearance information, motion information and high-level semantic information to represent the video. In experiment, the proposed method achieves comparable and even better results on the public video captioning dataset.
Real-Life Applications of Soft Computing in Cyber-Physical System: A Compressive Review

Cyber-physical system (CPS) is one of the most prominent emerging systems that are used for connecting the physical and cyber space. Recent advances in wireless sensor networks (WSN) and network and embedded systems have paved way for development of CPS for varied domains. CPS application domain includes health care, smart cities, transportation, environmental monitoring, and manufacturing. CPS requires smart and intelligent sensors with computation, communication, and control capabilities. Cyber-physical systems are complex heterogeneous distributed system, which require robust, efficient, and cost-effective solutions. Soft computing is one of the techniques capable of dealing with complex system, as it does not require strict mathematical definitions. Soft computing is a collection of evolutionary, artificial neural network, fuzzy logic, and Bayesian network. The power of soft computing is its strong learning ability and tolerance to uncertainty and imprecision. This work presents a comprehensive review of soft computing-based approaches used in diverse CPS applications.
Semantic Neighborhood Ordering in Multi-objective Genetic Programming based on Decomposition

Semantic diversity in Genetic Programming has proved to be highly beneficial in evolutionary search. We have witnessed a surge in the number of scientific works in the area, starting first in discrete spaces and moving then to continuous spaces. The vast majority of these works, however, have focused their attention on single-objective genetic programming paradigms, with a few exceptions focusing on Evolutionary Multi-objective Optimization (EMO). The latter works have used well-known robust algorithms, including the Non-dominated Sorting Genetic Algorithm II and the Strength Pareto Evolutionary Algorithm, both heavily influenced by the notion of Pareto dominance. These inspiring works led us to make a step forward in EMO by considering Multi-objective Evolutionary Algorithms Based on Decomposition (MOEA/D). We show, for the first time, how we can naturally promote semantic diversity in MOEA/D in Genetic Programming.
Adaptive Intermediate Representations for Video Understanding

A common strategy to video understanding is to incorporate spatial and motion information by fusing features derived from RGB frames and optical flow. In this work, we introduce a new way to leverage semantic segmentation as an intermediate representation for video understanding and use it in a way that requires no additional labeling. Second, we propose a general framework which learns the intermediate representations (optical flow and semantic segmentation) jointly with the final video understanding task and allows the adaptation of the representations to the end goal. Despite the use of intermediate representations within the network, during inference, no additional data beyond RGB sequences is needed, enabling efficient recognition with a single network. Finally, we present a way to find the optimal learning configuration by searching the best loss weighting via evolution. We obtain more powerful visual representations for videos which lead to performance gains over the state-of-the-art.
Improving Named Entity Recognition of Chinese Legal Documents by Lexical Enhancement

The paper studies the named entity recognition based on Chinese legal texts. Named entity recognition (NER) plays a critical role in a series of natural language processing tasks, and has been studied for many years. Different from general domain texts, Chinese legal texts have their own particularities: 1) there are many professional terms: 2) there are often abbreviations and pronouns in entity names; 3) nested combinations of words lead to excessively long entity names. Moreover, there is no publicly available data set of named entity annotation in the judicial field, which limits the development of judicial named entity recognition. This paper discusses a simpler and more effective method to introduce the lexical information into the character-based NER system, that is, fine tuning the character representation layer and introducing explicit lexical boundary information. This method not only avoids the design of complex sequence model structure, but also has good portability for any neural network model. The experimental results show that this method can obtain the F value of 95.35 on our annotated judicial data set, and has achieved good performance in annotated corpus of People's Daily in January, 1998.
Connexive Variants of Modal Logics Over FDE

Various connexive FDE-based modal logics are studied. Some of these logics contain a conditional that is both connexive and strict, thereby highlighting that strictness and connexivity of a conditional do not exclude each other. In particular, the connexive modal logics cBK $$^{-}$$ , cKN4, scBK $$^{-}$$ , scKN4, cMBL, and scMBL are introduced semantically by means of classes of Kripke models. The logics cBK $$^{-}$$ and cKN4 are connexive variants of the FDE-based modal logics BK $$^{-}$$ and KN4 with a weak and a strong implication, respectively. The system cMBL is a connexive variant of the modal bilattice logic MBL. The latter is a modal extension of Arieli and Avron's logic of logical bilattices and is characterized by a class of Kripke models with a four-valued accessibility relation. In the systems scBK $$^{-}$$ , scKN4, and scMBL, the conditional is both connexive and strict. Sound and complete tableau calculi for all these logics are presented and used to show that the entailment relations of the systems under consideration are decidable for finite premise set. Moreover, the logics $$\mathbf {cBK}^-$$ and $$\mathbf {cMBL}$$ are shown to be algebraizable. The algebraizability of $$\mathbf {cMBL}$$ is derived from proving $$\mathbf {cMBL}$$ to be definitionally equivalent to $$\mathbf {MBL}$$ . All connexive modal logics studied in this paper are decidable, paraconsistent, and inconsistent but non-trivial logics.
Cooperative Chaotic Evolution

We introduce two new strategies into the conventional chaotic evolution (CE) to facilitate the exchange of information between individuals instead of searching independently, and propose a cooperative CE (CoCE) with stronger performance. The first strategy designs a new crossover operation to ensure that mutant individual's exchange genes with other better individuals. Specifically, a mutant individual does not cross with its parent individual, but with a randomly selected individual whose fitness is not worse than that of the parent individual. The second strategy introduces an additional attraction generated by the randomly selected individual acting on the mutant individual, which can allow the offspring individual to prefer potential areas and speed up the search process. To evaluate the performance of our proposed strategies, we configure CoCE and CE with the exact same parameter settings and run them on 28 benchmark functions from CEC 2013 test suites. Each benchmark function is run 30 times independently on three different dimensions (i.e., 2-D, 10-D, and 30-D), and we also apply the Friedman test and Holm's multiple comparison test to check significant differences between CE and its three variants at the termination condition. The experimental results confirmed that our proposed CoCE has a faster convergence speed and higher convergence accuracy, and the acceleration effect is more significant for high dimensional optimization problems.