Reviewerly/exHarmony huggingface dataset:
An Evolution Strategy with Progressive Episode Lengths for Playing Games

Recently, Evolution Strategies (ES) have been successfully applied to solve problems commonly addressed by reinforcement learning (RL). Due to the simplicity of ES approaches, their runtime is often dominated by the RL-task at hand (e.g., playing a game). In this work, we introduce Progressive Episode Lengths (PEL) as a new technique and incorporate it with ES. The main objective is to allow the agent to play short and easy tasks with limited lengths, and then use the gained knowledge to further solve long and hard tasks with progressive lengths. Hence allowing the agent to perform many function evaluations and find a good solution for short time horizons before adapting the strategy to tackle larger time horizons. We evaluated PEL on a subset of Atari games from OpenAI Gym, showing that it can substantially improve the optimization speed, stability and final score of canonical ES. Specifically, we show average improvements of 80% (32%) after 2 hours (10 hours) compared to canonical ES.
Modeling Noisy Hierarchical Types in Fine-Grained Entity Typing: A Content-Based Weighting Approach

Fine-grained entity typing (FET), which annotates the entities in a sentence with a set of finely specified type labels, often serves as the first and critical step towards many natural language processing tasks. Despite great processes have been made, current FET methods have difficulty to cope with the noisy labels which naturally come with the data acquisition processes. Existing FET approaches either pre-process to clean the noise or simply focus on one of the noisy labels, sidestepping the fact that those noises are related and content dependent. In this paper, we directly model the structured, noisy labels with a novel content-sensitive weighting schema. Coupled with a newly devised cost function and a hierarchical type embedding strategy, our method leverages a random walk process to effectively weight out noisy labels during training. Experiments on several benchmark datasets validate the effectiveness of the proposed framework and establish it as a new state of the art strategy for noisy entity typing problem.
Large-Scale Dynamic Social Network Directed Graph K-In&amp;Out-Degree Anonymity Algorithm for Protecting Community Structure

Social network data publishing is dynamic, and attackers can perform association attacks based on social network directed graph data at different times. The existing social network privacy protection technology has low performance in dealing with large-scale dynamic social network directed graph data, and anonymous data publishing does not meet the needs of community structure analysis. A Dynamic Social Network Directed Graph K-In&Out-Degree Anonymity (DSNDG-KIODA) method to protect community structure is proposed. The method is based on the dynamic grouping anonymity rule to anonymize the dynamic K-in&out-degree sequence, and the virtual node distribution is added in parallel to construct an anonymous graph. The node information is transmitted based on the GraphX, and the virtual node pairs are selected and deleted according to the change of the directed graph modularity to reduce information loss. The experimental results show that the DSNDG-KIODA method improves the efficiency of processing large-scale dynamic social network directed graph data, and ensures the availability of community structure analysis when data is released.
Teleportation of quantum Fisher information under decoherence channels with memory

In this paper, we have investigated the teleportation of quantum Fisher information (QFI) for a single qubit system using a class of X-states as resources influenced by decoherence channels with memory, including amplitude-damping, phase-damping and depolarizing channels. Using the QFI to quantify the quality of teleportation, we first derive the explicit analytical results of QFIs with respect to the weight parameter () and phase parameter () under these decoherence channels, and find that both and are strongly dependent on the initial state's parameters ci, noisy channel parameters D and , as well as the teleported state's parameters and . For comparison with the teleportation of the single qubit case, we have also addressed the teleportation of QFI for a two-qubit entanglement system. The results show that , for a single qubit state, is more robust against noisy channels than that for a two-qubit state. However, for a two-qubit state is more robust than that of a single qubit state against noisy channels.
Detection of Tampering by Image Resizing Using Local Tchebichef Moments

There are many image resizing techniques, which include scaling, scale-and-stretch, seam carving, and so on. They have their own advantages and are suitable for different application scenarios. Therefore, a universal detection of tampering by image resizing is more practical. By preliminary experiments, we found that no matter which image resizing technique is adopted, it will destroy local texture and spatial correlations among adjacent pixels to some extent. Due to the excellent performance of local Tchebichef moments (LTM) in texture classification, we are motivated to present a detection method of tampering by image resizing using LTM in this paper. The tampered images are obtained by removing the pixels from original images using image resizing (scaling, scale-and-stretch and seam carving). Firstly, the residual is obtained by image pre-processing. Then, the histogram features of LTM are extracted from the residual. Finally, an error-correcting output code strategy is adopted by ensemble learning, which turns a multi-class classification problem into binary classification sub-problems. Experimental results show that the proposed approach can obtain an acceptable detection accuracies for the three content-aware image re-targeting techniques.
Network Embedding with Dual Generation Tasks

We study the problem of Network Embedding (NE) for content-rich networks. NE models aim to learn efficient low-dimensional dense vectors for network vertices which are crucial to many network analysis tasks. The core problem of content-rich network embedding is to learn and integrate the semantic information conveyed by network structure and node content. In this paper, we propose a general end-to-end model, Dual GEnerative Network Embedding (DGENE), to leverage the complementary information of network structure and content. In this model, each vertex is regarded as an object with two modalities: node identity and textual content. Then we formulate two dual generation tasks. One is Node Identification (NI) which recognizes nodes’ identities given their contents. Inversely, the other one is Content Generation (CG) which generates textual contents given the nodes’ identities. We develop specific Content2Node and Node2Content models for the two tasks. Under the DGENE framework, the two dual models are learned by sharing and integrating intermediate layers, with which they mutually enhance each other. Extensive experimental results show that our model yields a significant performance gain compared to the state-of-the-art NE methods. Moreover, our model has an interesting and useful byproduct, that is, a component of our model can generate texts, which is potentially useful for many tasks.
ShapeSense3D

We present an implementation of a textile sensing sleeve with attached strain sensors to capture the shape of body parts. A shape reconstruction algorithm was developed to reconstruct a geometrical model of the deformed sleeve using the elongation measurements obtained from the sensors and an optimisation process. The current system achieves a 0.44 mm error when reconstructing the radius of a conical shape. We discuss future improvements required to form a more reliable 3D shape sensing device. After further development, this sleeve could be utilised for health care application such as muscle density measurements, movement tracking or to replace plaster or thermoplastic casting in the fabrication of orthoses and prostheses.
On Privacy Protection of Latent Dirichlet Allocation Model Training

Latent Dirichlet Allocation (LDA) is a popular topic modeling technique for discovery of hidden semantic architecture of text datasets, and plays a fundamental role in many machine learning applications. However, like many other machine learning algorithms, the process of training a LDA model may leak the sensitive information of the training datasets and bring significant privacy risks. To mitigate the privacy issues in LDA, we focus on studying privacy-preserving algorithms of LDA model training in this paper. In particular, we first develop a privacy monitoring algorithm to investigate the privacy guarantee obtained from the inherent randomness of the Collapsed Gibbs Sampling (CGS) process in a typical LDA training algorithm on centralized curated datasets. Then, we further propose a locally private LDA training algorithm on crowdsourced data to provide local differential privacy for individual data contributors. The experimental results on real-world datasets demonstrate the effectiveness of our proposed algorithms.
Fault Models and Test Approaches in Reversible Logic Circuits

The operations in reversible circuits are fully controllable and observable due to their bijective property which provides cursive testing. Testing can be categorized into two behavioral schemes: (i) Online testing, where the detection of faults within the circuit is carried out during its operation, (ii) Offline testing, where test vectors are applied after extracting the circuit out from its normal operation and the correct output values are known. Test data minimization for a specific kind of fault model such as stuck-at, bridging, missing gate, cross-point, and cell faults, is an important factor in this type of testing using meta-heuristic algorithms and circuit modification methodologies. Diverse varieties of fault families in reversible logic circuits and an exclusive study of testable design advances for these faults are portrayed in this chapter. Plentiful approaches were projected under two extensive classifications to meet the challenge. The methodologies are alleged to coat almost all the faults and their sub kind by exploiting the properties of reversible gates and circuits. The objective is to minimize testing overhead, which can be achieved by reducing the cost metrics utilized for testability.
A Hybrid Two-Phase Recommendation for Group-Buying E-commerce Applications

The last two decades have witnessed an explosive growth of e-commerce applications. Existing online recommendation systems for e-commerce applications, particularly group-buying applications, suffer from scalability and data sparsity problems when confronted with exponentially increasing large-scale data. This leads to a poor recommendation effect of traditional collaborative filtering (CF) methods in group-buying applications. In order to address this challenge, this paper proposes a hybrid two-phase recommendation (HTPR) method which consists of offline preparation and online recommendation, combining clustering and collaborative filtering techniques. The user-item category tendency matrix is constructed after clustering items, and then users are clustered to facilitate personalized recommendation where items are generated by collaborative filtering technology. In addition, a parallelized strategy was developed to optimize the recommendation process. Extensive experiments on a real-world dataset were conducted by comparing HTPR with other three recommendation methods: traditional CF, user-clustering based CF, and item-clustering based CF. The experimental results show that the proposed HTPR method is effective and can improve the accuracy of online recommendation systems for group-buying applications.
Localizing Unseen Activities in Video via Image Query

Action localization in untrimmed videos is an important topic in the field of video understanding. However, existing action localization methods are restricted to a pre-defined set of actions and cannot localize unseen activities. Thus, we consider a new task to localize unseen activities in videos via image queries, named Image-Based Activity Localization. This task faces three inherent challenges: (1) how to eliminate the influence of semantically inessential contents in image queries; (2) how to deal with the fuzzy localization of inaccurate image queries; (3) how to determine the precise boundaries of target segments. We then propose a novel self-attention interaction localizer to retrieve unseen activities in an end-to-end fashion. Specifically, we first devise a region self-attention method with relative position encoding to learn fine-grained image region representations. Then, we employ a local transformer encoder to build multi-step fusion and reasoning of image and video contents. We next adopt an order-sensitive localizer to directly retrieve the target segment. Furthermore, we construct a new dataset ActivityIBAL by reorganizing the ActivityNet dataset. The extensive experiments show the effectiveness of our method.
Projecting real world into CrowdIntell network: a methodology

Purpose The purpose of this paper is to build cyber-physical-psychological ternary fusion crowd intelligence network and realize comprehensive, real, correct and synchronous projection in cyber–physical–psychological ternary fusion system. Since the network of crowd intelligence is the future interconnected network system that takes on the features of large scale, openness and self-organization. The Digital-selfs in the network of crowd intelligence interact and cooperate with each other to finish transactions and achieve co-evolution eventually. Design/methodology/approach To realize comprehensive, real, correct and synchronous projection between cyber–physical–psychological ternary fusion system, the authors propose the rules and methods of projection from real world to the CrowdIntell Network. They build the mental model of the Digital-self including structure model and behavior model in four aspects: identity, provision, demand and connection, thus forming a theoretical mental model framework of Digital-self. Findings The mental model is excepted to lay a foundation for the theory of modeling and simulation in the research of crowd science and engineering. Originality/value This paper is the first one to propose the mental model framework and projection rules and methods of Digital-selfs in network of crowd intelligence, which lays a solid foundation for the theory of modeling, simulation, intelligent transactions, evolution and stability of CrowdIntell Network system, thus promoting the development of crowd science and engineering.
Clustered fractional Gabor transform

In this paper, a novel clustered fractional Gabor transform (CFrGT) is proposed by exploiting the continuity structure of the fractional Gabor spectrum. The fractional Gabor expansion is reformulated under a Bayesian framework with correlated priors. To encourage the nonzero or zero coefficients to cluster in a spatial consistent constraint, the Markov random field (MRF) model is incorporated as the prior for the support of the fractional Gabor spectrum. And then the variational Bayes expectation-maximization algorithm is used to approximate the posterior of the hidden variables and estimate the parameters of MRF model. The proposed algorithm achieves high time-frequency resolution and localization under noisy and low sampling scenarios. Both the synthetic and the real data experimental results verify the effectiveness and its superiority over the conventional fractional Gabor transform.
WhatsApp as a Tool for Sustainable Glocal Linguistic, Social and Cultural Interaction

As a popular social media application, WhatsApp has become an integral part of daily lives of many people on earth. Researchers have investigated the individual, social and pedagogical benefits of the tool. However, the possible contributions of WhatsApp as a platform for cultural interaction in the glocal sense has not been examined yet. To call attention to this dimension of the tool, this case study aims to investigate the role of WhatsApp in maintaining glocal linguistic, social and cultural interaction among a group of foreign learners of Turkish. The participants had a three-week summer school experience in Turkey where they formed a WhatsApp group for communication. After returning back to their home countries, these participants continued their interaction with their friends through the WhatsApp group. The data for this study were collected through open-ended questions. The results of the content analysis revealed that WhatsApp was an effective application to promote the maintenance of glocal cultural interaction among the participants with its personal, social and linguistic contributions. The participants could have the chance to exchange ideas and increase their perspectives and knowledge as global citizens while preserving their cultural and national identities as local citizens. Their interactional experiences through WhatsApp can be said to have helped the participants increase their awareness and expand their intercultural knowledge from the glocal stance.
A Survey of Distance Metrics in Clustering Data Mining Techniques

Lately, due to the increasing size of databases, several aspects have been studied in detail, such as grouping, searching for the closest neighbor and other identification methods. It has been found that in the multidimensional space, the concept of distance does not offer high performance. In this paper, we study the effect of different types of distances on the group to see the similarities between objects. Among these distances we mention two distances: the Euclidean distance and Manhattan distance, implemented in a system developed to identify the architectural styles of the buildings. The aim of this paper is using cluster analysis to identify distance metrics impact in detection of architectural styles using Data Mining techniques.
On Retrospecting Human Dynamics with Attention

Deep recurrent neural networks have achieved impressive success in forecasting human motion with a sequence to sequence architecture. However, forecasting in longer time horizons often leads to implausible human poses or converges to mean poses, because of error accumulation and difficulties in keeping track of longerterm information. To address these challenges, we propose to retrospect human dynamics with attention. A retrospection module is designed upon RNN to regularly retrospect past frames and correct mistakes in time. This significantly improves the memory of RNN and provides sufficient information for the decoder networks to generate longer term prediction. Moreover, we present a spatial attention module to explore and exploit cooperation among joints in performing a particular motion. Residual connections are also included to guarantee the performance of short term prediction. We evaluate the proposed algorithm on the largest and most challenging Human 3.6M dataset in the field. Experimental results demonstrate the necessity of investigating motion prediction in a self audit manner and the effectiveness of the proposed algorithm in both short term and long term predictions.
Evaluation of the photovoltaic potential in built environment using spatial data captured by unmanned aerial vehicles

Abstract In this paper, a spatial photovoltaic (PV) potential evaluation method based on the combination of aerial photogrammetry and Geographic Information System (GIS) is proposed for PV potential evaluation of built environment (BE). The method can be applied to PV potential evaluation of not only building roofs, but also built environments such as carpark. The point cloud model of the studied field is established by processing images captured by unmanned aerial vehicles (UAV), which are then imported into GIS to estimate the available area for PV installation based on sunlight analysis after offsetting the identified borders of the urban and ecological infrastructures. The PV potential is determined based on the installation configurations of PV modules. With the help of the PVSYST software simulation, the proposed method in this paper is used to evaluate the PV potential of a carpark and a rooftop of a building in Singapore.
Smart Coaching: Enhancing Weightlifting and Preventing Injuries

Getting injured is one of the most devastating and dangerous challenges that an athlete can go through and if it is a big injury it could end his/her athletic career.In this paper, we propose a system to automate the idea of coaching an athlete, by using an IR Camera (Microsoft Kinect Xbox 360) to detect the misplaced joints of the athlete while doing the lift, and alerting the athlete before an injury can occur.We are now able to detect if the lift was correct or wrong and to detect what kind of mistake has been done in the lift by the athlete by using the Fast Dynamic time warping (FastDTW) method.The FastDTW method has outperformed other classification methods and can achieve recognition with 100% accuracy for dependent user movements.
Human Behavior Analysis Based on Attention Mechanism and LSTM Neural Network

Aiming at the problem that traditional video surveillance algorithm can not take into account the influence of surrounding conditions and the relationship between human behavior, a human behavior analysis method based on LSTM neural network combined with attention mechanism is proposed. This method focuses on the use of key information in surveillance video and related features of human behavior to achieve visual analysis of human behavior. The information of the moving object is obtained by the core region optical flow method between the frames connected to each other. The feature extraction of useful information in different scenarios is achieved through the attention mechanism. The detected moving object information and the extracted features are input into the LSTM neural network to realize real-time analysis of human behavior. The experimental results show that compared with other human behavior analysis methods, this method is more specific in the study of human behavior correlation and has a higher utilization rate for monitoring the surrounding environment information. The rate of human behavior recognition has been significantly improved.
Playing Card-Based RTS Games with Deep Reinforcement Learning

Game AI is of great importance as games are simulations of reality. Recent research on game AI has shown much progress in various kinds of games, such as console games, board games and MOBA games. However, the exploration in RTS games remains a challenge for their huge state space, imperfect information, sparse rewards and various strategies. Besides, the typical card-based RTS games have complex card features and are still lacking solutions. We present a deep model SEAT (selection-attention) to play card-based RTS games. The SEAT model includes two parts, a selection part for card choice and an attention part for card usage, and it learns from scratch via deep reinforcement learning. Comprehensive experiments are performed on Clash Royale, a popular mobile card-based RTS game. Empirical results show that the SEAT model agent makes it to reach a high winning rate against rule-based agents and decision-tree-based agent.