Reviewerly/exHarmony huggingface dataset:
Graph Signal Sampling with Deep Q-Learning

We formulate the graph signal sampling problem as a reinforcement learning process in discrete action space. A deep Q-network (DQN) is used for the agent to learn the sampling strategy. To adapt to the training environment, we redesigned the step and episode. In each episode, the agent learns how to select a node for the current state, and selects the best one at the final step of this episode. Besides, we redefined the action and reward. Based on the sampling sets, we use the same reconstruction method to test the performance. Through comparative experiments, the reconstruction accuracy of our method outperforms that of uniform random sampling and random walk sampling methods. Our method can quickly select the key nodes in the initial sampling stage. According to our investigation, it is the first time that deep Q-learning is successfully applied to the graph signal sampling field.
Lipschitzness of Minimal-Time Functions in Constrained Continuous-Time Systems with Applications to Reachability Analysis

The minimal-time function with respect to a closed set for a constrained continuous-time system provides the first time that a solution starting from a given initial condition reaches that set. In this paper, we propose infinitesimal necessary and sufficient conditions for the minimal-time function to be locally Lipschitz. As an application of our results, we show that, in constrained continuous-time systems, the Lipschitz continuity of the minimal-time function with respect to the boundary of the set where the solutions are defined plays a crucial role on the Lipschitz continuity of the reachable set.
Play culture across makerspaces: Connecting into a global makerspace online

This article is based on a school experiment of three local makerspaces that connected and turned into a global makerspace online. The three municipal schools were from Australia, United Kingdom and Denmark and participated in the Global Makerspace project, which was a part of the European Union project Makerspaces in the early years: Enhancing digital literacy and creativity. The Global Makerspace project was conducted over 4 days in the Autumn 2018. The schools’ participating teachers and pupils belonged to the first years of primary level. In this article, the Danish experiences from the project are explored. The data collection analysed draws on a micro-ethnographic study of the experiences from the Danish makerspace completed by the Danish facilitator from the project Makerspaces in the early years: Enhancing digital literacy and creativity. The article identifies the teachers’ and pupils’ maker activities out from a play cultural point of view and reckon making as connecting to describe how offline maker communities connected into one online maker community. The baseline of the school experiment was to explore and stress the potential of how communities like teachers and pupils across time and space in collaboration can transform and develop play cultures with different combinations of technologies by sharing local maker activities into global activities online.
Adaptive Learning Kalman Filter with Gaussian Process

This paper presents an adaptive Kalman filter for a linear dynamic system perturbed by an additive disturbance. The objective is to estimate both of the state and the unknown disturbance concurrently, while learning the disturbance as a stochastic process of the state vector. This is achieved by estimating the state according to the extended Kalman filtering applied to the marginal distribution of the state, and by estimating the disturbance from a backward smoothing technique. The corresponding pair of the estimated states and disturbances are fetched to a Gaussian process, which is constantly updated to resemble the disturbance process. The unique feature is that all of uncertainties in the estimated state and disturbance are accounted throughout the learning process. The efficacy of the proposed approach is illustrated by a numerical example.
Golay Layer: Limiting Peak-to-Average Power Ratio for OFDM-based Autoencoders

In this study, we propose a differentiable layer for OFDM-based autoencoders (OFDM-AEs) to avoid high instantaneous power without regularizing the cost function used during the training. The proposed approach relies on the manipulation of the parameters of a set of functions that yield complementary sequences (CSs) through a deep neural network (DNN). We guarantee the peak-to-average-power ratio (PAPR) of each OFDM-AE symbol to be less than or equal to 3 dB. We also show how to normalize the mean power by using the functions in addition to PAPR. The introduced layer admits auxiliary parameters that allow one to control the amplitude and phase deviations in the frequency domain. Numerical results show that DNNs at the transmitter and receiver can achieve reliable communications under this protection layer at the expense of complexity.
Product Recommendation System based on User Trustworthiness &amp; Sentiment Analysis

The current online product recommendation system based on reviews has many limitations due to randomness in the review patterns. The data which is used are the reviews and ratings from the e-commerce websites. This data might contain fake reviews that make the data uncertain. Due to this, the currently existing systems produce ambiguous results on this present data. Instead of this, the new system uses only genuine reviews, considering the trustworthiness of the user and generates the results in a more significant manner. The proposed system scrapes reviews from different online websites and performs opinion mining and sentiment analysis on it. Other factors like star ratings, the buyer’s profile and previous purchases and whether the review has been given after purchasing or not are included. Based on these factors &amp; user trustworthiness, the website from which the user should buy the product will be recommended.
HSCJN: A holistic semantic constraint joint network for diverse response generation

The sequence-to-sequence (Seq2Seq) model generates target words iteratively given the previously observed words during the decoding process, which results in the loss of the holistic semantics in the target response and the complete semantic relationship between responses and dialogue histories. In this paper, we propose a generic diversity-promoting joint network, called Holistic Semantic Constraint Joint Network (HSCJN), enhancing the global sentence information, and then regularizing the objective function with penalizing the low entropy output during the training stage. Our network introduces more target information to improve diversity and captures direct semantic information to better constrain relevance simultaneously. Moreover, the proposed method can be easily applied to any Seq2Seq structure. Extensive experiments on several dialogue corpuses show that our method effectively improves both semantic consistency and diversity of generated responses, and achieves better performance than other competitive methods.
Potential of Solar Energy in Residential Rooftop Surface Area in Semarang City, Indonesia

This study examines the potential of solar energy in the urban environment with a case study in Semarang, Indonesia by analyzing the intensity of solar radiation and the residential rooftop area.The study aims to obtain a quantitative description of the potential for electricity production from rooftop solar photovoltaic systems in residential areas and estimate the mitigation potential of CO2.The estimation method has adopted the hierarchies assessment: estimation of physical, geographic, and technical potential.This study shows the residential roof area spread over 16 districts in the city of Semarang is 412,987.50m 2 to 2,083,387 m 2 has the average potential to of solar energy every year of 44,051 -222,222 MWh/year.Total the low-carbon electricity is equivalent to 40.87% of the total electricity consumption in 2018 at Semarang City and reduce 1,394 tonCO2 in a year.Potential electricity production is proposed to set rules for the future empowerment of solar energy and analyze the potential at different time levels, such as monthly, weekly and daily.
Partial Occlusion Face Recognition Method Based on Acupoints locating through Infrared Thermal Imaging

As a new intelligent technology of identity recognition., face recognition is an important means of security authentication. However, in real life, the face may be blocked by hats, masks and other occlusions, resulting in the existing face recognition algorithms and methods cannot be applied in practical applications. In order to solve the above problems, this paper proposes a partial occlusion face recognition method based on infrared thermal imaging to locate acupoints. Firstly, the face image is collected and the face features are obtained by Principal component analysis (PCA) algorithm. Secondly, the infrared thermal imaging technology is introduced adaptively by judging the occlusion status of the face, and then the individual facial acupoints are located by BP neural network algorithm to assist in the effective recognition of partially occluded faces. The results of simulation show that this recognition method proposed can increase the accuracy of partial occlusion face recognition, which has practical value.
Preserving Statistical Privacy in Distributed Optimization

We present a distributed optimization protocol that preserves statistical privacy of agents' local cost functions against a passive adversary that corrupts some agents in the network. The protocol is a composition of a distributed “zero-sum” obfuscation protocol that obfuscates the agents' local cost functions, and a standard non-private distributed optimization method. We show that our protocol protects the statistical privacy of the agents' local cost functions against a passive adversary that corrupts up to t arbitrary agents as long as the communication network has (t+1 )-vertex connectivity. The “zero-sum” obfuscation protocol preserves the sum of the agents' local cost functions and therefore ensures accuracy of the computed solution.
How connectivity affects otherwise traditional toys? A functional analysis of Hello Barbie

The current smart toy research has not fully acknowledged the existing research on traditional toys. Instead, the focus has been on the digital layer of smart toys. This study contributes to bridging the gap between traditional and smart toy research by applying the analytical model known as the functional manipulation potential of toys (FMP) to identify if and how smart features such as connectivity affect otherwise traditional toys. Based on purposeful sampling of informant-rich cases, this study uses FMP to analytically compare the digitally enhanced Hello Barbie doll to a traditional Barbie. The data, analyzed using abductive methods, comprises the tangible dolls and the 8,000 lines of dialogue dialog that Hello Barbie can speak. The results show that connectivity can notably change the FMP of an otherwise traditional toy. These changes took place in two forms: additions and amplifications. Additions mean that Hello Barbie contains functional elements a traditional Barbie does not. Amplifications refer to situations where already existing elements were augmented by the connectivity. Implications for future research are discussed.
Adaptive Digital Image Watermarking Technique Through Wavelet Texture Features

Remarkable advancement in social media websites and chats, sharing photos, audios, and videos has become very easy and dangerous. Digital image watermarking technique has the potential to address the issue of privacy, ownership, and authenticity of the media shared using such medium. Invisible digital image watermarking techniques are necessary which are imperceptible within host image and robust to common signal and image processing attacks. In this paper, we present a watermarking technique for digital images through adaptive texturization, statistical parameters, and Bhattacharya distance. The primary idea is to segment the host image into four different regions based on frequency distribution using discrete wavelet transform. Subsequently important statistical parameters mostly applied in image processing techniques such as mean, standard deviation, skew, kurtosis, and variance are calculated from the wavelet transform coefficients of each region of host and watermark image. These statistical parameters of segmented regions of host and watermark image are then applied to obtain Bhattacharya distance. Wavelet transform coefficients of watermark image are embedded into wavelet transform coefficients of one of the regions of carrier image with minimum Bhattacharya distance through embedding factor. Performance of the technique was tested on multiple host and watermark images under common image processing attacks, which yield better results.
IoTalho: IoT Advancing Learning from High-tech Objects

This paper aims to present a platform to support the learning of high school students (without previous programming knowledge) for the development of technological solutions supported by the Internet of Things concept. This platform is designed from the integration of the following modules: A user-friendly visual programming environment for a specific microcontroller architecture; Web resource sharing tools assisting students in the tasks of communication and socialization of their projects (e.g., Wikis, Forums, Social Networks); A middleware, responsible for the communication between users (interfacing remotely with a teacher from a Web page) and sensors/actuators, present in an architecture remotely monitored by a teacher. With this proposal, it is expected that learners involved can be motivated and attracted by subjects related to the fields of Computer Science and (Electrical, Electronics, Mechatronics) Engineering. A scenario-based evaluation was performed to validate this platform.
The 1970 United States computer chess championship: The start of the longest-running experiment in computer science history

On August 31, 1970, an experiment began that continues to this day. The first chess tournament for computers was held as part of the Association for Computing Machinery’s (ACM’s) National Conference. The interest generated was tremendous, leading to ACM sponsoring an annual event until 1994. Chess competitions continue to this day, allowing for 50 years of data on the growth of artificial intelligence capabilities in this domain. During this period, program ratings have soared from roughly 1400 in 1970 to over 3500 today. The 1970 event was the first continuous competition in computer science history, and it represents the longest ongoing experiment in computing history.11 Some paragraphs of the text have been taken from Man vs. Machine: Challenging Human Supremacy at Chess by Karsten Müller and Jonathan Schaeffer [2018].
Optimal Path Tracking With Dubins’ Vehicles

In this article, patrolling with Dubins' vehicles is investigated. The vehicles have significant kinematic constraints such as minimum-turning radius, and are unable to move in a reverse direction, i.e., they can only track planar curvature-bounded paths. The problem is more challenging than the conventional patrolling problem because the Euclidean traveling salesmen problem (ETSP) solution provides poor estimates of the actual travel time and vehicle location in this case. An algorithm called the pulley algorithm (PA) is developed to convert the ETSP optimal solution to a kinematically feasible optimal Dubins path that can be tracked by Dubins' vehicles. The PA guarantees that its corresponding optimal path is suitable for patrolling, i.e., for repetitive tracks among the way points. In addition, an upper bound for the PA is presented to show the difference between it and the ETSP optimal solution. This article also introduces enhancements to some of the existing algorithms in the literature in terms of the solution approach. This is followed by practical implementation to control two-wheeled mobile robots using model predictive control to track the Euclidean and the Dubins paths obtained for the patrolling operation.
Ultrashort-Term Power Fluctuation Forecasting Based on the Prediction of the Shipborne Panel Tilt Angle

When a solar ship is navigating in the ocean, the swaying motion of a photovoltaic panel will affect the output power of the photovoltaic (PV) power generation system more frequently and violently. In addition to considering multiple climatic factors, this paper also adopts a ship swaying motion and radiation level of sunlight to establish a suitable calculation model for the output power of photovoltaic systems, which are rarely considered at the same time in previous studies, and also to make ultrashort-term power predictions. Furthermore, this paper proposes a multilayer heterogeneous particle swarm optimization (PSO) algorithm to design the weights and thresholds of a long short-term memory (LSTM) neural network to improve the accuracy of forecasting the changes of a photovoltaic panel’s angle, which is used for accurate power output prediction for the purpose of power planning. The case analysis shows the effectiveness of the algorithm, which provides a more reliable method for designing a power prediction system.
VEst: An Efficient Solution to the Camera Velocity Estimation from Minimal Feature Points

This paper presents a new method to determine camera velocity from image data. Velocity estimation has been underexplored, compared to estimating rotation and translation. Existing methods suffer from various drawbacks in terms of necessary conditions on the 3D structure of the scene, the types of camera velocities they can solve for, convergence of estimates, or necessary partial knowledge of the scene or velocity. Our approach uses optical flow vectors and delivers estimates of angular and linear velocities. Rigid body velocity equations are used to derive a polynomial system, which is then solved for the desired velocity terms as well as the current depths of feature points that root the optical flow vectors. Simulations are conducted to validate the correctness and accuracy of the proposed algorithm. The real-world KITTI dataset is used further to demonstrate the performance in practical problem.
Exploring software bug-proneness based on evolutionary clique modeling and analysis

Even if evolutionary coupling between files has been widely used for various studies, such as change impact analysis, defect prediction, and software design analysis etc., there has little work focusing on studying the linkage among evolutionary coupled files. In this paper, we propose a novel model, evolutionary clique (EClique), to characterize evolutionary coupled files as maintainable groups for bug fixes, analyze their bug-proneness and examine the possible causes of the bug-proneness. To identify ECliques from a project, we propose two history measures to reason about the evolutionary coupling between files, and create a novel clustering algorithm. Given the evolutionary coupling information, our clustering algorithm will automatically identify ECliques in a project. We conduct analyses on 33,099 commits of ten open source projects to evaluate the usefulness of our EClique modeling and analysis approach: (1) The results show that files involved in an EClique are more likely to share similar design characteristics and change together for resolving bugs; (2) The results also show that the identified ECliques significantly contribute to a project’s bug-proneness. Meanwhile, the majority of a project’s bug-proneness can be captured by just a few ECliques which only contain a small portion of files; (3) Finally, we qualitatively demonstrate that bug-prone ECliques often exhibit design problems that propagate changes among files and can potentially be the causes of bug-proneness. To reduce the bug-proneness of a software project, practitioners should pay attention to the identified ECliques, and resolve design problems embedded in these ECliques.
A Coupled Spring Forced Bat Searching Algorithm: Design, Analysis and Evaluation

Swarm intelligence based optimization algorithms mimic the cooperation and interaction behaviors from social or nature phenomena as an optimization mechanism to solve complex, non-convex and/or ill-conditioned general nonlinear problems with high efficiency. Recently, a novel and heuristic algorithm, Bat searching algorithm (BA) has been proposed. Moreover, numerical evaluation has already demonstrated the better performance of BA compared with other algorithms variations. In this paper, we propose a coupled spring forced BA (SFBA) algorithm by considering that each particle is a spring and is coupled with the optimal solution found so far as the second abstract spring. The synergistic integration of the coupled springs, the bat's behavior, and swarm intelligence governs and navigates the new algorithm in the searching process. Moreover, the convergence of the SFBA is studied via Jury's Test. Numerical evaluation is provided for the proposed SFBA algorithm by conducting comparison with other variations of BA in the literature, which indicates that the performance of SFBA surpasses all the listed variations of BA significantly. In summary, the proposed SFBA algorithm offers a new efficient approach to address complex, large-scale, nonconvex nonlinear optimization problems which are normally hard to solve using the conventional methods.
Fuzzy clustering-based microaggregation to achieve probabilistic k-anonymity for data with constraints

Microaggregation is an effective data-driven protection method that permits us to achieve a good trade-off between disclosure risk and information loss. In this work we propose a method for microaggregation based on fuzzy c-means, that is appropriate when there are constraints (linear constraints) on the variables that describe the data. Our method leads to results that satisfy these constraints even when the data to be masked do not satisfy them.
Co-occurrence graph based hierarchical neural networks for keyphrase generation

More and more attention has been paid to automatic keyphrase generation as it facilitates a wide variety of downstream AI applications, such as information retrieval, text summarization and opinion mining. Although sequence-to-sequence architecture with attention and copy mechanisms (CopyNet) to this task shows promising results, it still suffered from the following shortcomings: (i) it only encodes the keyphrase (usually consists of several words) in word level which can not adequately capture the overall meaning of keyphrase; (ii) it lacks a suitable way to model the correlation among different keyphrases which is very helpful for generating richer and more comprehensive candidate phrases. To overcome these challenges, a novel keyphrase generation model named Hierarchical CopyNet with graph attention networks (HCopy-GAT) is proposed. Firstly, the Hierarchical Recurrent Encode-Decoder neural network (HRED) is employed to learn the expressive embeddings of keyphrases in both word-level and phrase-level. Secondly, the graph attention neural networks (GAT) is applied to model the correlation among different keyphrases. Furthermore, we developed a new dataset named SOFTWARE, which can be taken as a new testbed for keyword generation tasks. With empirical experiments on several real datasets (including our newly built dataset), the proposed HCopy-GAT model outperforms state-of-the-art keyphrase generation models.