Reviewerly/exHarmony huggingface dataset:
P2 Hybridization - Tailored Solutions

It remains an open question how the future develops with respect to speed and consequence in the way of electrifying powertrains. Besides the strong influences of local legislation and public credits, the advancement towards competitive key components for electrified vehicles will strongly affect market penetration.
Semantic image inpainting with boundary equilibrium GAN

Recently, due to the vigorous development of deep learning, many methods in the field of image inpainting have been proposed which are different from the traditional image inpainting methods. This paper uses the high-quality image generation technology of BEGAN to complete the image inpainting task. Firstly, the image generation model is obtained by pretraining the generator and discriminator of BEGAN. Then this paper redesigns the loss function and finds the generated image suitable for the image inpainting task via gradient descent algorithm. By using the information contained in the undamaged part of the original image to be repaired, the BEGAN model can generate an image that is closest to the original image. Finally, the generated image is used to fill the lost area of the original image to be repaired, and the image inpainting task is completed. This paper confirms the validity of the method through the experiments on the CelebA and LFW datasets.
LUI: A multimodal, intelligent interface for large displays

On large screen displays, using conventional keyboard and mouse input is difficult because small mouse movements often do not scale well with the size of the display and individual elements on screen. We propose LUI, or Large User Interface, which increases the range of dynamic surface area of interactions possible on such a display. Our model leverages real-time continuous feedback of free-handed gestures and voice to control extensible applications such as photos, videos, and 3D models. Utilizing a single stereo-camera and voice assistant, LUI does not require exhaustive calibration or a multitude of sensors to operate, and it can be easily installed and deployed on any large screen surfaces. In a user study, participants found LUI efficient and easily learnable with minimal instruction, and preferred it to more conventional interfaces. This multimodal interface can also be deployed in augmented or virtual reality spaces and autonomous vehicle displays.
Improved Earth Observation Data Retrieval through Hashing Algorithms

Throughout the years, a wide range of satellite mission enabled the creation of a huge amount of Earth Observation (EO) data carrying complex information, whose exploitation is left behind due to the lack of handling capabilities. Computational resources are hardly keeping up with content analysis and information retrieval. In order to increase the search speed through data warehouses for knowledge discovery, new indexing methods are required to handle both the size and the informational complexity of EO data. Feature extraction algorithms are able to describe the image content, yet, they require a very complex database. In this paper, we propose a methodology that combines feature extraction, hashing methods and optimized indexing to convert the images characteristics into hash codes in an effort to speed up the search process. For our experiments, we run our procedure on a data-set composed of several Sentinel-2 acquisitions form across Europe and we assess the query times.
Anomaly detection for deep-learning based license plate recognition in real time video

License plate recognition has a recognition rate of more than 98% in limited situations; however, the recognition rate sometimes falls to about 50% to 70% in unlimited situations for real time. The inability of filtering out anomaly data which is similar to a license plate results in the low recognition rate. This paper aims to suppress anomaly and improve the recognition rate. CNN consist of as few layers as possible for application with IoT edge technology. As a result of the experiment, the detection rate on real-time roads where situations are unlimited was 77% in normal models with the little filter performance. However, when applying the techniques of this paper, the detection rate was 88%.
The Feasibility of a Virtual Reality System for Attention Analysis

Attention is considered one of the most specific cognitive processes in human consciousness activities. The detection of attention depends on the well-designed experimental scenes in laboratories with single visual information which greatly limits the application in practice. Taking advantage of the virtual scenarios where features are able to be acquired, this study proposes a novel and effective virtual reality (VR) system to analyze the attention with multiple features. Ten participants were invited to the experiment and the real-time gaze data and operational information were recorded during the process. The Gaussian Mixture Model (GMM) and Dynamic Time Warping (DTW) were employed to analyze the gaze data and real-time operating information in the game to describe the attention preference and continuous attention. The experiment result presents that behaviors of distracted participants in these two experiments have a strong correlation. Besides, the proposed method demonstrates the feasibility of the VR system to analyze attention with multiple types of features.
Smart Cloud: A Self-organizing Cloud

The Self Organizing Cloud aims to provide efficiency i.e. robust and scalable solutions. Self organization can be provided through providing interactions among individuals. This paper proposes a self organizing architecture which predicts number of VMs using Kohonen algorithm and balances the load inheriting the convection process of molecules. The Kohonen algorithm is usually used for clustering. In this paper we apply Kohonen algorithm, an artificial neural network algorithm for prediction.
Micro-Baseline Structured Light

We propose Micro-baseline Structured Light (MSL), a novel 3D imaging approach designed for small form-factor devices such as cell-phones and miniature robots. MSL operates with small projector-camera baseline and low-cost projection hardware, and can recover scene depths with computationally lightweight algorithms. The main observation is that a small baseline leads to small disparities, enabling a first-order approximation of the non-linear SL image formation model. This leads to the key theoretical result of the paper: the MSL equation, a linearized version of SL image formation. MSL equation is under-constrained due to two unknowns (depth and albedo) at each pixel, but can be efficiently solved using a local least squares approach. We analyze the performance of MSL in terms of various system parameters such as projected pattern and baseline, and provide guidelines for optimizing performance. Armed with these insights, we build a prototype to experimentally examine the theory and its practicality.
Weakly-Supervised Action Recognition and Localization via Knowledge Transfer

Action recognition and localization has attracted much attention in the past decade. However, a challenging problem is that it typically requires large-scale temporal annotations of action instances for training models in untrimmed video scenarios, which is not practical in many real-world applications. To alleviate the problem, we propose a novel weakly-supervised action recognition framework for untrimmed videos to use only video-level annotations to transfer information from publicly available trimmed videos to assist in model learning, namely KTUntrimmedNet. A two-stage method is designed to guarantee an effective transfer strategy: Firstly, the trimmed and untrimmed videos are clustered to find similar classes between them, so as to avoid negative information transfer from trimmed data. Secondly, we design an invariant module to find common features between trimmed videos and untrimmed videos for improving the performance. Extensive experiments on the standard benchmark datasets, THUMOS14 and ActivityNet1.3, clearly demonstrate the efficacy of our proposed method when compared with the existing state-of-the-arts.
hTPM

Hardware-based TPM provides hardware-backed security solutions and a root of trust for various mission critical applications. However, hardware-based TPM has several intrinsic problems such as extremely low performance, off-chip security vulnerability, and a lack of incident response agility. In the upcoming Quantum computing era, it is critical to provide Quantum-Resistant (QR) cryptography functions without harming performance. Unfortunately, hardware-based TPM's rigid hardware and software architecture model makes it extremely difficult for hardware-based TPM to transition to accommodate future QR cryptographic systems. On the other hand, software-based TPMs (e.g., firmware-based TPM) provide a CPU-based, on-chip security solution. They utilize low-level on-chip primitives offered by chipsets such as ARM TrustZone or Intel Software Guard Extensions (SGX) to build a system with a high-level of trust computing environment. A software-based TPM solution provides higher performance, on-chip security, and incident response agility. However, it is lacking in hardware-backed protection and several vital features such as secure key storage, robustness against side-channel attacks, true random number generation, among others. In addition, its implementation is highly dependent on low-level primitives provided by each hardware vendor, which makes it difficult for it to be provided as a generalized solution. In this paper, we propose hybrid-TPM (hTPM), which fully utilizes the advantages of a hardware-based TPM and diminishes a hardware-based TPM's weaknesses through software-based TPM solutions inside a secure container, e.g., Virtualization-Based Security (VBS).
Research on Security for Wireless Body Area Network Data in Cloud Storage Based on Blockchain

Cloud computing enables users with limited resources to accomplish complex computation. Meanwhile, the massive human body sensing data generated by Wireless Body Area Network (WBAN) can be stored in cloud servers. However, cloud storage of human body sensing data is challenged by some issues. Users need to ensure that cloud servers can return the results and that they are correct. To address this problem, based on the argument system of commitment scheme, this paper designs a DMP verification algorithm. By compiling the hash of the header node into a constraint set and verifying the accuracy of the data of the intermediate node through a hash list, the algorithm reduces the compiling time of the intermediate node and improves the verification efficiency.
Research on Liquidity Risk Measurement Method of Financial Block Chain Based on Sand Box

To solve the problem of low accuracy of liquidity risk measurement of financial blockchain, this paper proposes a method based on sandbox to measure liquidity risk of financial blockchain. this paper proposes to collect the current flow information of financial block chain, divide the liquidity risk level of financial block chain according to the collected results, and optimize the risk measurement algorithm in combination with sandbox principle, thus realizing the quasi-measurement of liquidity risk of financial block chain. Finally, the experiment proves that the liquidity risk measurement method of financial block chain based on sandbox has higher accuracy and effectiveness.
Task-Nonspecific and Modality-Nonspecific AI

It is widely accepted in Artificial Intelligence (AI) that different tasks require different learning methods. The same is true for different sensory modalities. However, auto-programming for general purposes seems to require a learning engine that is task-independent and modality-independent. We provided the Developmental Network (DN) as such an engine to all contestants of the AI Machine Learning Contest 2016 for learning three well-recognized bottleneck problems in AI—vision, audition, and natural languages. For vision, the network learned abstract visual concepts and their hierarchy with invariant properties and autonomous attention. For audition, sparse and dense actions jointly serve as auditory contexts. For natural languages, the network acquires two natural languages, English and French, conjunctively in a bilingual environment (i.e., patterns of text as inputs). All the three sensory modalities used the same DN learning engine, but each had a different body (sensors and effectors). The contestants independently verified the DN’s base performance, and competed to add (hinted) autonomous attention for better performance. This seems to be the first task-independent and modality-independent learning engine, which was also verified by independent contestants. Much remains to be done in the learner-age related sophistication of learned tasks.
Evaluating Robustness of an Acting Framework over Temporally Uncertain Domains

The ultimate goal of automated planning is the execution of plans by an artificial agent in the environment. When interactions and collaboration with humans are considered, robust plan execution requires even more highly flexible and adaptable control capabilities in artificial agents. Therefore, plan-based controllers should effectively deal with exogenous events and environment dynamics in order to perform Planning and Acting in an efficient and effective way. The general approach pursued here conforms to the general idea that Acting is not merely executing plans but it entails a more complex process in which dynamic knowledge processing and plan adaptation are required. To this aim, this paper focuses on Human-Robot Collaboration (HRC) and the timeline-based approach which is known to be well suited to robustly deal with uncontrollable dynamics. This paper presents and discusses new interesting results obtained by leveraging the acting capabilities of a novel timeline-based Planning and Acting framework called PLATINUm in a realistic HRC scenario. On the one hand, results show how the variability of the environment can negatively impact the performance and reliability of Acting systems. On the other hand, they show how a proper management of temporal uncertainty strongly improve the Actin reliability.
Use of ladder snake media in improving student learning outcomes in mathematics learning in elementary school

Abstract This research is motivated by the low student learning outcomes in mathematics learning. This study aims to improve student learning outcomes in class V Kancana State Elementary School by using Snake Ladder learning media. The research method used by the researcher was a classroom action research which collaborated with the fifth-grade teachers of Kancana Elementary School, with a total of 21 students in class V consisting of 6 male students and 15 female students. This research takes place in 3 cycles, each cycle consists of 3 actions. The techniques used in data collection are observation, tests, interviews, documentation, and field notes. The results showed that the use of snakes and ladder learning media could improve student learning outcomes in mathematics learning in class V of Kancana Elementary School.
Research and Application of Community Population Information Association Model Based on IoT Multi-device Mining

With the rapid development of big data and Internet of Things technologies, the amount of population information data has exploded, resulting in a series of research and application of population information data. However, the population information data is large and complex, so traditional statistical methods are too time-consuming and labor-intensive to meet data collection. This paper proposes a community population information association method based on IoT data mining. The method diversifies the population information data collected by multiple devices and makes appropriate adjustments to classic FP-Growth algorithm, which constructs a community population information association model based on improved FP-Growth algorithm and finally mines the corresponding association rules. The experimental results show that the association model can support the operation of big data in speed and memory. Furthermore, the mined association rules have been verified to have certain reliability.
Navigation of inertial forces driven mini-robots using reinforcement learning

In this paper we propose a reinforcement learning (RL) framework for the autonomous navigation of a pair of mini-robots that are driven by inertial forces. The inertial forces are provided by two vibration motors on each mini-robot which are controlled by a simple and efficient low-level speed controller. The action of the RL agent is the direction of the velocity of each mini-robot, and it based on the position of each mini-robot, the distance between the mini-robots, and the sign of the distance gradient. Each mini-robot is considered as a moving obstacle to the other that must by avoided. We have introduced a suitable reward function that results into an efficient collaborative RL approach. A simulation environment is created using the ROS framework, that include the dynamic model of the mini-robot and of the vibration motors. Several application scenarios are simulated, and the presented results demonstrate the performance of the proposed framework.
Moving a large collection quickly: A study in chaos

The University of Louisville’s Ekstrom Library needed to remove 260,000 items from its shelves, and shift a further 95,000 within an extremely short deadline ahead of plans to redesign its third floor into an inviting, modern, student-friendly space for patrons to enjoy. The move faced three distinct challenges: space, item tracking, and sufficient personnel to complete the project. Although the project finished ahead of schedule, things did not go as smoothly as planned. OPAC issues and lack of sufficient storage space forced the Committee to readjust plans on the fly, resulting in issues that could possibly take years to fix.
Managing Interdisciplinary Links in the Educational Environment of Technical Colleges

The main methods of research are the analysis of scientific literature devoted to the problem of implementing interdisciplinary links of natural science, professional cycle disciplines, interdisciplinary courses in the institutions of secondary vocational education. To solve the integrated goal of implementing the requirements of the educational and professional standard of technical colleges we propose to use four types of interdisciplinary horizontal and vertical links, covering the subjects of natural science and general professional cycle, interdisciplinary courses, educational and industrial practice and professional standard requirements for education and training. It is proposed to distinguish four types of interdisciplinary links, aimed at solving the integrated goal of implementing the requirements of the educational and professional standard of technical colleges. In modern high-tech society there is a rapid dynamic growth of information on all branches of scientific knowledge, the emergence of new technologies and advanced forms and methods of work. These things make it difficult to identify the main and essential educational information, so the necessity to manage interdisciplinary links in the innovative educational environment of technical colleges is of primary consideration in developing modern professional Russian education.
Effect of the Illuminating Point Spread Function and Roughness on the Speckle Correlation Field

Under conditions differing from those subjected for central limit theorem, the spatial autocorrelation function of speckle pattern resulting from illuminated rough surface is investigated.Its dependence on different illuminating apertures and the average of the roughness heights is presented theoretically and experimentally.The experiments were carried out using a set of circular and square apertures having different sizes.The results indicate that, increasing the size of the illuminating aperture leads to a decrease in the width of the main lobe of the spatial autocorrelation function.
Phytoremediation of tofu wastewater using Eichhornia crassipes

Abstract The tofu industry in Indonesia is generally dominated by small industries so that the wastewater produced is not properly managed. The tofu industries generally produce wastewater from the washing, pressing, and molding process. Phytoremediation can be used as an alternative solution for tofu wastewater remediation process because of its advantages as a cost-effective, efficient, and eco-friendly technology. This research used parallel reactor with circulation flow. Eichhornia crassipes with 1500 gram/compartment was used as the growing medium. The variables used in this study is the phytoremediation treatment and phytoremediation time. Phytoremediation treatment variations used anaeration and aeration methods for 10 days. The results of this study showed that the effectiveness of the average decrease in the two treatments; (1) anaeration treatment could reduce BOD 59.84%, COD 58.95%, TSS 86.79% and ammonia 25.43%, (2) aeration treatment could reduce BOD 80.67%, COD amounting to 78.28%, TSS 65.79% and ammonia 49.79%. Percentage of absorption of pollutant levels (BOD, COD, TSS, ammonia) in tofu wastewater increases with increasing phytoremediation time and number of compartments. According to the effectiveness value, aeration treatment was the most effective treatment in reducing the levels of tofu wastewater pollutants.
Enabling by Voice: Voice Enabled Environmental Control (EC) Devices Using Interactive Smart Agents (ISA)

The following topics are dealt with: Internet of Things; medical computing; learning (artificial intelligence); biomechanics; patient monitoring; diseases; gait analysis; medical signal processing; handicapped aids; mechanoception.
Semantic Web-Linked Data and Libraries

The present society is considered an information society. A society where the creation, distribution, use, integration, and manipulation of digital information have become the most significant activity in all aspects. Information is producing from every sector of any society, which has resulted in an information explosion. Modern technologies are also having a huge impact. So managing this voluminous information is really a tough job. Again WWW has opened the door to connect anyone or anything within a fraction of a second. This study discussed the Semantic Web and linked data technologies and their effect and application to libraries for the handling of various types of resources.
A Dynamic Pricing Mechanism in IoT for DaaS: A Reinforcement Learning Approach

With the rapid development of the Internet of things, a large amount of data has been accumulated. However, how to make full use of these data has become a new problem. In this article, we will focus on how to develop data resources using the intelligent data pricing (SDP) approach. Establish a B2B data marketplace for integrating, storing, and analyzing business data. Simulate interactions between service providers and enterprises in the marketplace. Since the service provider has markov consciousness, q-learning algorithm is adopted to solve the model. Experimental results show that q-learning algorithm can make every participant in the market obtain the optimal profit.
Multi-head Attentive Social Recommendation

Recently social relationship among users has been exploited to improve the recommendation performance. The intuition behind most of these work is social homophily such that users are more similar to their neighbors. Attention mechanism or attention network from deep learning has been a popular component employed by recommendation models. However, how to attentively learn the influence between users remains pretty much open in the existing social recommendation models. In this paper, we propose a social recommendation model MAS, Multi-head Attentive Social Recommendation. The key to MAS is a multi-head attention network which can distinguish the impact of users' friends when predicting users' preference on different items. When compared to the state-of-the-art baseline methods on three real-world datasets, our method achieves the best performance.