Reviewerly/exHarmony huggingface dataset:
Leveling the playing field

From the beginning of the history of AI, there has been interest in games as a platform of research. As the field developed, human-level competence in complex games became a target researchers worked to reach. Only relatively recently has this target been finally met for traditional tabletop games such as Backgammon, Chess and Go. This prompted a shift in research focus towards electronic games, which provide unique new challenges. As is often the case with AI research, these results are liable to be exaggerated or mis-represented by either authors or third parties. The extent to which these game benchmarks constitute "fair" competition between human and AI is also a matter of debate. In this paper, we review statements made by reseachers and third parties in the general media and academic publications about these game benchmark results. We analyze what a fair competition would look like and suggest a taxonomy of dimensions to frame the debate of fairness in game contests between humans and machines. Eventually, we argue that there is no completely fair way to compare human and AI performance on a game.
Is Image Memorability Prediction Solved?

This paper deals with the prediction of the memorability of a given image. We start by proposing an algorithm that reaches human-level performance on the LaMem dataset - the only large scale benchmark for memorability prediction. The suggested algorithm is based on three observations we make regarding convolutional neural networks (CNNs) that affect memorability prediction. Having reached human-level performance we were humbled, and asked ourselves whether indeed we have resolved memorability prediction - and answered this question in the negative. We studied a few factors and made some recommendations that should be taken into account when designing the next benchmark.
The trails of Just Cause 2

Behavioral profiling of players in digital games is a key challenge in game analytics, representing a particular challenge in Open-World Games. These games are characterized by large virtual worlds and few restrictions on player affordances. In these games, incorporating the spatial and temporal dimensions of player behavior is necessary when profiling behavior, as these dimensions are important to the playing experience. We present analyses that apply cluster analysis and the DEDICOM decompositional model to profile the behavior of more than 5,000 players of the major commercial title Just Cause 2 integrating both spatio-temporal trails and behavioral metrics. The application of DEDICOM to profile the spatio-temporal behavior of players is demonstrated for the purpose of analysing the entire play history of Just Cause 2 players, but also for the more detailed analysis of a single mission. This showcases the applicability of spatio-temporal profiling to condense player behavior across large sample sizes, across different scales of investigation. The method presented here provides a means to build profiles of player activity in game environments with high degrees of freedom across different scales of analysis - from a small segment to the entire game.
Swimming style recognition and lap counting using a smartwatch and deep learning

Human activity recognition from raw sensor data has enabled modern wearable devices to track and analyze everyday activities. However, when used in real world conditions, the performance of off-the-shelf devices is often insufficient. This paper tackles the problem of swimming style recognition and lap counting using sensor data from a single smartwatch. In total 17 hours of this data was collected from 40 swimmers of diverse backgrounds. The data was then used to train a convolutional neural network to recognize the four main swimming styles, transition periods and lap turns. Our method achieves an F1 score of 97.4% for style recognition and 99.2% for counting laps. To the best of our knowledge, these results are the first to enable accurate automatic swimming recognition in a realistic and completely uncontrolled environment.
The Analysis of the ELECTREE II Algorithm in Determining the Doubts of the Community Doing Business Online

Abstract Online businesses are no stranger to the community where online businesses are increasingly in demand by the public because of the various supporting factors in which the public does not need to plunge into places to buy the desired items simply through online media but the lively online media in the community cannot be separated from various frauds. Other business people are still many who are reluctant to switch to online business that is inseparable from various factors or reasons they still stick with the old business method. It is very easy to play online recently in various circles of students and even housewives, can be used as additional capital. To find out what factors are the main reason people still hesitate to do business with online methods realized by the application of a decision support system determines the doubts of the online business community with the ELECTRE II method. It is hoped that research can find factors that cause public doubt in online business, so that later the output of this system can be a good evaluation material for the community in doing business. ELECTRE II method can be applied in the case of determining the failure factor of students in the main subject of community doubt in online business by considering several alternatives and criteria. Which alternatives are Cost Expenses (A1), Faced Risks (A2), and Difficult / Doubt to start the business to be run (A3). Data taken from the questionnaire respondents to STIKOM Tunas Bangsa Pematangsiantar students. From the results of this study, it was concluded that (A1) Cost Expenses with a value of 1.0238. For further research, criteria and alternatives can be added, so that the data entered is more varied. In addition, the ELECTRE II method can be compared with other methods. Research as a recommendation for business people who want to enter the world of online business.
A Contextual Recurrent Collaborative Filtering framework for modelling sequences of venue checkins

Context-Aware Venue Recommendation (CAVR) systems aim to effectively generate a ranked list of interesting venues users should visit based on their historical feedback (e.g. checkins) and context (e.g. the time of the day or the user's current location). Such systems are increasingly deployed by Location-based Social Networks (LBSNs) such as Foursquare and Yelp to enhance the satisfaction of the users. Matrix Factorisation (MF) is a popular Collaborative Filtering (CF) technique that can suggest relevant venues to users based on an assumption that similar users are likely to visit similar venues. In recent years, deep neural networks have been successfully applied to recommendation systems. Indeed, various approaches have been previously proposed in the literature to enhance the effectiveness of MF-based approaches by exploiting Recurrent Neural Networks (RNN) models to capture the sequential properties of observed checkins. Moreover, recently, several RNN architectures have been proposed to incorporate contextual information associated with the users' sequence of checkins (for instance, the time interval or the geographical distance between two successive checkins) to effectively capture such short-term preferences of users. In this work, we propose a Contextual Recurrent Collaborative Filtering framework (CRCF) that leverages the users' preferred context and the contextual information associated with the users' sequence of checkins in order to model the users' short-term preferences for CAVR. In particular, the CRCF framework is built upon two state-of-the-art approaches: namely Deep Recurrent Collaborative Filtering framework (DRCF) and Contextual Attention Recurrent Architecture (CARA). Thorough experiments on three large checkin and rating datasets from commercial LBSNs demonstrate the effectiveness and robustness of our proposed CRCF framework by significantly outperforming various state-of-the-art matrix factorisation approaches. In particular, the CRCF framework significantly improves [email protected] by 5–20% over the state-of-the-art DRCF framework (Manotumruksa, Macdonald, and Ounis, 2017a) and the CARA architecture (Manotumruksa, Macdonald, and Ounis, 2018) across the three datasets. Furthermore, the CRCF framework is less significantly risky than both the DRCF framework and the CARA architecture across the three datasets.
A hybrid regularization approach for random vector functional-link networks

Neural networks have been widely applied to expert and intelligent systems in the fields of business, fault diagnosis, and forecasting. Especially, random vector functional-link networks (RVFL), an important structure, have also been considerably studied and used in recent years. This paper addresses in the investigation of regularization model for RVFL, and proposes a hybrid regularization approach for RVFL by adding the ℓ2 and ℓ1 norm penalty terms simultaneously. A novel iterative learning algorithm is developed by using a fixed point contractive map. Further, some theoretical properties, including the convergence, sparsity, and stability of the proposed algorithm, are discussed and analyzed concretely under reasonable assumptions. The proposed method greatly improves the learner’s sparsity and stability, guaranteeing the feasibility and effectiveness of network training. Experimental results on some benchmarks as well as face recognition database collected from the expert and intelligent systems, particularly the use of the statistical analysis strategy, verify the effectiveness and superiority of the proposed method, i.e. this new algorithm systematically outperforms the original RVFL and its variants in terms of the accuracy, sparsity, and stability of the solution.
Analysis of Backpropagation Method with Sigmoid Bipolar and Linear Function in Prediction of Population Growth

Abstract Backpropagation method is an artificial neural network method that is often used for prediction. However, the use of activation functions and training functions greatly affects the accuracy of a prediction. In this study will discuss the backpropagation method by applying the activation function of Sigmoid bipolar and linear to predict population growth in Simalungun regency, Indonesia. The purpose of this paper is to look at the level of population growth in the district so that the government has a benchmark in determining policies so that a surge in population growth can be minimized and that the government pays more attention to the level of welfare of its population. As for academics, this research can be used as input if you want to do a prediction or forecasting with different cases. The data used in this paper is population density data in Indonesia’s Simalungun district, which is sourced from the Simalungun regency statistics center of Indonesia. This study uses 5 architectural models, namely 3-5-1, 3-10-1, 3-5-10-1, 3-5-15-1 and 3-10-15-1. Of these 5 models, the best architectural model is 3-5-10-1 with an accuracy of 97% and an MSE value of 0.00034833. Minimum Error 0,001-0,01 and learning rate 0,01.
Implementation of an efficient extreme learning machine for node localization in unmanned aerial vehicle assisted wireless sensor networks

Summary Accurate node localization in wireless sensor networks (WSNs) is an essential for many networking protocols like clustering, routing, and network map building. The classical localization techniques such as multilateration and optimization‐based least square localization (OLSL) techniques estimate position of unknown node (UN) from the distance measured between all anchor nodes (ANs) and UNs. On the other hand, node localization using fixed terrestrial ANs suffers from poor localization accuracy because the ground to ground (GG) channel link is not reliable. By contrast, the mobile anchor deployed in unmanned aerial vehicle (UAV) provides high localization accuracy through reliable air to ground (AG) channel link. Still, the nonlinear distortion introduced in the wireless channel makes the distance measurement noisy. This noisy distance measurement also limits localization accuracy of classical localization techniques. Hence, the highly nonlinear artificial neural network (ANN) models such as multilayer perceptron (MLP) models can be applied effectively for node localization in UAV‐assisted WSNs. However, the MLP suffers from slow training speed, which limits its usage in real‐time applications. So, the extreme learning machine (ELM) is found to be a better alternative because it works on empirical error minimization theory, and its learning process requires only a single iteration. The detailed simulation analysis supports the proposed ELM localization scheme in terms of both localization accuracy and computational complexity.
A Rule for Committee Selection with Soft Diversity Constraints

Committee selection with diversity or distributional constraints is a ubiquitous problem. However, many of the formal approaches proposed so far have certain drawbacks including (1) computational intractability in general, and (2) inability to suggest a solution for instances where the hard constraints cannot be met. We propose a cubic-time algorithm for diverse committee selection that satisfies natural axioms and draws on the idea of using soft bounds.
Application of C4.5 Algorithm for Cattle Disease Classification

Abstract Data mining is a series of process to gain values such as informations that is manually unknown from a database by mining patterns from the data (with the intention to manipulate data into more useful information that is obtained by extracting data and recognizing important pattern or pulling data from the database). The intend of this research is to process cattle disease cases, which so far no research has been done to produce useful knowledge/science for institutions (Food and Livestock Security Agency) using data mining technique (C4.5 Algorithm). In the future, this research will be used by Food and Livestock Security Agency to counsel rancher in some area. This research will result a decision tree which will also classify rate of cattle disease. Proven by using the C4.5 algorithm, it can speed up the process of classification disease which initially takes about 1-2 months, and after using this application the results can be obtained right away.
Addressing Temporal Variations in Qubit Quality Metrics for Parameterized Quantum Circuits

The public access to noisy intermediate-scale quantum (NISQ) computers facilitated by IBM, Rigetti, D - Wave, etc., has propelled the development of quantum applications that may offer quantum supremacy in the future large-scale quantum computers. Parameterized quantum circuits (P QC) have emerged as a major driver for the development of quantum routines that potentially improve the circuit's resilience to the noise. PQC's have been applied in both generative (e.g. generative adversarial network) and discriminative (e.g. quantum classifier) tasks in the field of quantum machine learning. PQC's have been also considered to realize high fidelity quantum gates with the available imperfect native gates of a target quantum hardware. Parameters of a P QC are determined through an iterative training process for a target noisy quantum hardware. However, temporal variations in qubit quality metrics affect the performance of a P QC. Therefore, the circuit that is trained without considering temporal variations exhibits poor fidelity over time. In this paper, we present training methodologies for P QC in a completely classical environment that can improve the fidelity of the trained P QC on a target NISQ hardware by as much as 21.91%.
Intelligent Control Strategy for Robotic Arm by Using Adaptive Inertia Weight and Acceleration Coefficients Particle Swarm Optimization

This paper proposes an intelligent control strategy for enabling a robotic arm to grasp and place water-filled bottles without spilling any of the water.First, the system architecture of a five-degreeof-freedom robotic arm and its mechanical design are introduced.Second, both the forward and inverse kinematics of the robotic arm are derived.The study conducted an experiment in which the designed and implemented robotic arm could grasp a bottle of water and move it to another place.However, if the acceleration or the orientation of the robotic arm were inappropriate, the water in the bottle may be spilled during the movement.Therefore, the proposed strategy applies an inertial measurement unit for obtaining relevant information.According to the obtained information, the velocity curves of each joint could be optimized by adaptive inertia weight and acceleration coefficients particle swarm optimization.Finally, the experimental results demonstrated the feasibility and effectiveness of the proposed method.
Targeted Addresses Identification for Bitcoin with Network Representation Learning

The anonymity and decentralization of Bitcoin make it widely accepted in illegal transactions, such as money laundering, drug and weapon trafficking, gambling, to name a few, which has already caused significant security risk all around the world. The obvious de-anonymity approach that matches transaction addresses and users is not possible in practice due to limited annotated data set. In this paper, we divide addresses into four types, exchange, gambling, service, and general, and propose targeted addresses identification algorithms with high fault tolerance which may be employed in a wide range of applications. We use network representation learning to extract features and train imbalanced multi-classifiers. Experimental results validated the effectiveness of the proposed method.
An Effective Data Privacy Protection Algorithm Based on Differential Privacy in Edge Computing

With the rapid development of information science and the Internet of Things (IoT), people have an unprecedented ability to collect and share data, especially the various sensors as the entrance to data collection. At the same time, edge computing has begun to grasp the public's attention because of the difficult challenges of massive equipment access and massive data. Although such large amount of data provides a huge opportunity for information discovery, the privacy leakage has also been concerned. When the data publisher publishes various statistics, the attacker can obtain the statistical rules in the data by simply using the query function without contacting the user or the data publisher. Therefore, how to protect the data privacy of statistical information has become the focus of attention. In this paper, we proposed a partitioned histogram data publishing algorithm based on wavelet transform. Firstly, a partitioning algorithm based on greedy algorithm is used to obtain a better partition structure. Then, we use wavelet transform to add noise. Finally, for the authenticity and usability of histogram, we get the reductive original histogram structure. On the one hand, our algorithm can reduce the complexity of wavelet tree constructed by wavelet transform. On the other hand, the query noise changes from linear growth to multiple logarithm growth. So the accuracy of histogram counting query is improved. Experiments show that our algorithm has the significant improvement in data availability.
Impact of Puzzle-Based Learning Technique for Programming Education in Nigeria Context

This paper investigates the impact of puzzle-based learning technique (PbLT) for teaching introductory programming in the context of Nigeria. The participants in the study were computer science students at Usmanu Danfodiyo University, Sokoto, Nigeria. The study adopted the quantitative research method. The study shows that PbLT has a positive impact on the students' understanding of introductory programming. In addition, the students confirmed that MobileEdu-Puzzle learning application was supportive and easy to use. Nonetheless, the result revealed that information technology infrastructure especially internet connectivity is a major challenge that may hinder the use of mobile learning technologies such as MobileEdu-puzzle. The overall outcome shows that PbLT holds a lot of promise towards enhancing students learning experiences.
Preservation of quantum correlation between nitrogen-vacancy-center ensembles by squeezed-reservoir engineering

We propose a theoretical scheme to investigate the dynamics of quantum correlation between two nitrogen-vacancy-center ensembles (NVEs) coupled to a common superconducting coplanar waveguide resonator, driven by a broadband microwave squeezed field working as a squeezed-vacuum reservoir. Based on the reduced master equation for NVEs by the adiabatical elimination method in the bad-resonator limit, our results reveal quantum correlation preservation due to the interplay between the nonclassical feature of the squeezing reservoirs and the original dissipation mechanisms. The required operations are very close to the capabilities of current superconducting circuit-QED techniques. Our work may open interesting perspectives for devising active decoherence-immune quantum information devices.
K-Nearest Neighbor Hardware Accelerator Using In-Memory Computing SRAM

The k-nearest neighbor (kNN) is one of the most popular algorithms in machine learning owing to its simplicity, versatility, and implementation viability without any assumptions about the data. However, for large-scale data, it incurs a large amount of memory access and computational complexity, resulting in long latency and high power consumption. In this paper, we present a kNN hardware accelerator in 65nm CMOS. This accelerator combines in-memory computing SRAM that is recently developed for binarized deep neural networks and digital hardware that performs top-k sorting. We designed and simulated the kNN accelerator, which performs up to 17.9 million query vectors per second while consuming 11.8 mW, demonstrating >4.8X energy improvement over prior works.
Machine-Learning and R in Plastic Surgery – Evaluation of Facial Attractiveness and Classification of Facial Emotions

Although facial attractiveness is data-driven and nondependent on a perceiver, traditional statistical methods cannot properly identify relationships between facial geometry and its visual impression. Similarly, classification of facial images into facial emotions is also challenging, since the classification should consider the fact that overall facial impression is always dependent on currently present facial emotion. To address the problems, both profile and portrait facial images of the patients ( $$ n = 42 $$ ) were preprocessed, landmarked, and analyzed via R language. Multivariate regression was carried out to detect indicators increasing facial attractiveness after going through rhinoplasty. Bayesian naive classifiers, decision trees (CART) and neural networks, respectively, were built to classify a new facial image into one of the facial emotions, defined using Ekman-Friesen FACS scale. Nasolabial and nasofrontal angles' enlargement within rhinoplasty increases facial attractiveness ( $$ p < 0.05 $$ ). Decision trees proved the geometry of a mouth, then eyebrows and finally eyes affect in this descending order an impact on classified emotion. Neural networks returned the highest accuracy of the classification. Performed machine-learning analyses pointed out which facial features affect facial attractiveness the most and should be therefore treated by plastics surgery procedures. The classification of facial images into emotions show possible associations between facial geometry and facial emotions.