Reviewerly/exHarmony huggingface dataset:
Artificial Intelligence Algorithms and Techniques in the computation of Player-Adaptive Games

Abstract A game usually used as a synonym for entertainment also serves as an educational tool. Originally targeting the enjoyment, the meaning of the game has evolved to much greater conceptions and applications. Games require physical or mental or sometimes both the simulation. Many games help develop practical skills, serve as a form of exercise, or otherwise perform an educational, simulation, or psychological role. Having to be built on some key elements, which are goals, rules, challenge, and interaction. Various strategies and algorithms such as path finding and decision trees, have been developed to simulate those interactions between the human user and the computer in front of him. In video games, artificial intelligence (AI) is used to generate responsive, adaptive or intelligent behaviours primarily in non-player characters (NPCs) similar to human-like intelligence. This paper makes a short analysis of those preferred techniques and suggest from the study and the outcomes, the efficiency of each. The paper also focuses on the heuristic function, implementation platforms and design guidelines of the various searching algorithms used in adaptive games.
Learned Dynamic Guidance for Depth Image Reconstruction

The depth images acquired by consumer depth sensors (e.g., Kinect and ToF) usually are of low resolution and insufficient quality. One natural solution is to incorporate a high resolution RGB camera and exploit the statistical correlation of its data and depth. In recent years, both optimization-based and learning-based approaches have been proposed to deal with the guided depth reconstruction problems. In this paper, we introduce a weighted analysis sparse representation (WASR) model for guided depth image enhancement, which can be considered a generalized formulation of a wide range of previous optimization-based models. We unfold the optimization by the WASR model and conduct guided depth reconstruction with dynamically changed stage-wise operations. Such a guidance strategy enables us to dynamically adjust the stage-wise operations that update the depth image, thus improving the reconstruction quality and speed. To learn the stage-wise operations in a task-driven manner, we propose two parameterizations and their corresponding methods: dynamic guidance with Gaussian RBF nonlinearity parameterization (DG-RBF) and dynamic guidance with CNN nonlinearity parameterization (DG-CNN). The network structures of the proposed DG-RBF and DG-CNN methods are designed with the the objective function of our WASR model in mind and the optimal network parameters are learned from paired training data. Such optimization-inspired network architectures enable our models to leverage the previous expertise as well as take benefit from training data. The effectiveness is validated for guided depth image super-resolution and for realistic depth image reconstruction tasks using standard benchmarks. Our DG-RBF and DG-CNN methods achieve the best quantitative results (RMSE) and better visual quality than the state-of-the-art approaches at the time of writing. The code is available at https://github.com/ShuhangGu/GuidedDepthSR.
Automatic detection and characterisation of the first P- and S-wave pulse in rocks using ultrasonic transmission method

In this paper, we propose a new methodology for the automatic picking of P- and S- wave arrivals. The measurement of elastic wave velocities is crucial for characterisation of the elastic properties of rocks, as well as their physico-mechanical properties and durability. P-waves are easy to generate and acquire, whereas the acquisition and subsequent analysis of the S-waves present major difficulties in most investigations of rocks. In our proposed method the recorded signal is first pre-processed in the wavelet domain, removing low-frequency disturbances and filtering noise. The signal is then analysed in the time-domain to detect and characterise the first pulse and, subsequently, estimate the corresponding onset time. The automatic approach analyses all pulses detected in the output signal and selects the first pulse of the P or S wave relative to symmetry, amplitude and duration criteria. This triple check provides greater confidence in the obtained results. We record P- and S- waveforms through the transmission method for a broad range of sedimentary, igneous and metamorphic rocks used in buildings. Results are compared to manual picking, which is considered as a true or reference value. The recorded signals show that microstructural components of rocks have a strong influence on the output signal. Mineralogical composition, porosity and particle size affect the wave velocity, attenuation, wavelength and waveform, which in turn influences the manual picking of the onset time. The proposed methodology provides precise values of the onset time of P- and S- waves. The discrepancies between automatic and manual P- and S- wave picking are 0.7 and 4.4%, respectively. P-wave signals have a high signal-to-noise ratio and their arrival times are clear and easy to determine. However, the arrival time of S-waves presents significant problems, mainly for medium to coarse-grained rocks. In this case, the output signal is contaminated by P-waves and has a lower signal-to-noise ratio. Besides, propagation of the S-wave through the rock affects the frequency and waveform of the first pulse, which complicates manual picking of the onset time. The developed methodology distinguishes between the S-wave and the remaining P-waves contained in the transversal signal, independently of the skill and subjectivity of a human analyst.
Rich Visual and Language Representation with Complementary Semantics for Video Captioning

It is interesting and challenging to translate a video to natural description sentences based on the video content. In this work, an advanced framework is built to generate sentences with coherence and rich semantic expressions for video captioning. A long short term memory (LSTM) network with an improved factored way is first developed, which takes the inspiration of LSTM with a conventional factored way and a common practice to feed multi-modal features into LSTM at the first time step for visual description. Then, the incorporation of the LSTM network with the proposed improved factored way and un-factored way is exploited, and a voting strategy is utilized to predict candidate words. In addition, for robust and abstract visual and language representation, residuals are employed to enhance the gradient signals that are learned from the residual network (ResNet), and a deeper LSTM network is constructed. Furthermore, three convolutional neural network based features extracted from GoogLeNet, ResNet101, and ResNet152, are fused to catch more comprehensive and complementary visual information. Experiments are conducted on two benchmark datasets, including MSVD and MSR-VTT2016, and competitive performances are obtained by the proposed techniques as compared to other state-of-the-art methods.
A Telepresence Wheelchair with 360-Degree Vision Using WebRTC

This paper presents an innovative approach to develop an advanced 360-degree vision telepresence wheelchair for healthcare applications. The study aims at improving a wide field of view surrounding the wheelchair to provide safe wheelchair navigation and efficient assistance for wheelchair users. A dual-fisheye camera is mounted in front of the wheelchair to capture images which can be then streamed over the Internet. A web real-time communication (WebRTC) protocol was implemented to provide efficient video and data streaming. An estimation model based on artificial neural networks was developed to evaluate the quality of experience (QoE) of video streaming. Experimental results confirmed that the proposed telepresence wheelchair system was able to stream a 360-degree video surrounding the wheelchair smoothly in real-time. The average streaming rate of the entire 360-degree video was 25.83 frames per second (fps), and the average peak signal to noise ratio (PSNR) was 29.06 dB. Simulation results of the proposed QoE estimation scheme provided a prediction accuracy of 94%. Furthermore, the results showed that the designed system could be controlled remotely via the wireless Internet to follow the desired path with high accuracy. The overall results demonstrate the effectiveness of our proposed approach for the 360-degree vision telepresence wheelchair for assistive technology applications.
Privacy-Protected Deletable Blockchain

A deletable blockchain has been proposed recently to change the immutability of the traditional blockchain. However, the users' identities and transaction contents are all public in the scheme, and the public data may reveal the users' privacy. In order to protect the privacy of the users, we propose a privacy-protected deletable blockchain based on the proof-of-space consensus mechanism, which does not rely on complex cryptographic tools or any trusted party. In order to satisfy full transparency and accountability in an anonymous environment, we use a traceable ring signature or a Pedersen commitment scheme to disclose the users' real identities or the real transaction contents respectively according to different deletion reasons. During the deletion process, we propose a linkable multi-signature scheme, which allows multiple users to generate a valid signature by using their one-time addresses as pseudonyms to protect their identity privacy. Moreover, the proposed multi-signature scheme can link two sub-signatures if they are generated by the same malicious user. Finally, we simulate the generation and deletion process of a block under the proof-of-space consensus mechanism and give the time of generating and deleting a block. The experimental results prove the efficiency and feasibility of our proposed scheme.
Anomaly and Activity Recognition Using Machine Learning Approach for Video Based Surveillance

In the current era, the majority of public places such as supermarket, public garden, mall, university campus, etc. are under video surveillance. There is a need to provide essential security and monitor unusual anomaly activities at such places. The major drawback in the traditional approach, that there is a need to perform manual operation for 24 <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">*</sub> 7 and also there are possibilities of human errors. This paper focuses on anomaly detection and activity recognition of humans in the videos. The anomaly detection system uses principal component analysis network (PCANet) and Convolutional Neural Network (CNN) to solve the problems of manual operation such as the false alarms, missing of anomalous events and locating the position of an anomaly in the video. The frames wise abnormal event is detected using principal component analysis and Support Vector Machines (SVM) classifier. The location of the abnormality in a frame is detected using Convolutional Neural Network.
Towards Verification-Aware Knowledge Distillation for Neural-Network Controlled Systems: Invited Paper

Neural networks are widely used in many applications ranging from classification to control. While these networks are composed of simple arithmetic operations, they are challenging to formally verify for properties such as reachability due to the presence of nonlinear activation functions. In this paper, we make the observation that Lipschitz continuity of a neural network not only can play a major role in the construction of reachable sets for neural-network controlled systems but also can be systematically controlled during training of the neural network. We build on this observation to develop a novel verification-aware knowledge distillation framework that transfers the knowledge of a trained network to a new and easier-to-verify network. Experimental results show that our method can substantially improve reachability analysis of neural-network controlled systems for several state-of-the-art tools.
FPGA implementation of highly scalable AES algorithm using modified mix column with gate replacement technique for security application in TCP/IP

Field Programmable Gate Arrays (FPGA) offers a faster, increasingly adjustable arrangement. Earlier Data Encryption Standard (DES) algorithms have been developed, however it could not keep up with advancement in a technology and it is no longer appropriate for security. With this motivation, this work developed an efficient FPGA implementation of Advanced Encryption Standard (AES) targets to investigate a huge number of security processes followed in the TCP/IP protocol suite and to suggest a novel new architecture for the existing version. The first contribution of the studies turned into to provide the safety for packages of the utility layer protocols. The AES cryptographic encryption, decryption and key management set of rules to for the safety of transmission control protocol/internet protocol (TCP/IP) protocol suite turned into carried out. AES is one of the maximum famous cryptographic algorithms used for records safety. The cost and consumption of power in the AES can be decreased substantially by way of optimizing the structure of AES. This research article projects an implementation based on modification in Mix column in AES techniques which gives a compact structure with efficient mix column Boolean expression the usage of resource sharing architecture and gate replacement method. The ON-chip power utilization and area overhead of the proposed hardware implementation outperforms the preceding work performed in this area. The proposed architecture have been carried out on the most latest virtex 6 lower power Field programmable gate array (FPGA), whereas overhead and on-chip utilization of power are compared with the previous works and it is proved that proposed method has lower area utilization and ON-Chip utilization of power.
ki67 nuclei detection and ki67-index estimation: a novel automatic approach based on human vision modeling

The protein ki67 (pki67) is a marker of tumor aggressiveness, and its expression has been proven to be useful in the prognostic and predictive evaluation of several types of tumors. To numerically quantify the pki67 presence in cancerous tissue areas, pathologists generally analyze histochemical images to count the number of tumor nuclei marked for pki67. This allows estimating the ki67-index, that is the percentage of tumor nuclei positive for pki67 over all the tumor nuclei. Given the high image resolution and dimensions, its estimation by expert clinicians is particularly laborious and time consuming. Though automatic cell counting techniques have been presented so far, the problem is still open.In this paper we present a novel automatic approach for the estimations of the ki67-index. The method starts by exploiting the STRESS algorithm to produce a color enhanced image where all pixels belonging to nuclei are easily identified by thresholding, and then separated into positive (i.e. pixels belonging to nuclei marked for pki67) and negative by a binary classification tree. Next, positive and negative nuclei pixels are processed separately by two multiscale procedures identifying isolated nuclei and separating adjoining nuclei. The multiscale procedures exploit two Bayesian classification trees to recognize positive and negative nuclei-shaped regions.The evaluation of the computed results, both through experts' visual assessments and through the comparison of the computed indexes with those of experts, proved that the prototype is promising, so that experts believe in its potential as a tool to be exploited in the clinical practice as a valid aid for clinicians estimating the ki67-index. The MATLAB source code is open source for research purposes.
PERQ

A knowledge-based question-answering (KB-QA) system is one that answers natural-language questions by accessing information stored in a knowledge base (KB). Existing KB-QA systems generally register an accuracy of 70-80% for simple questions and less for more complex ones. We observe that certain questions are intrinsically difficult to answer correctly with existing systems. We propose the PERQ framework to address this issue. Given a question q, we perform three steps to boost answer accuracy: (1) (Prediction) We predict if q can be answered correctly by a KB-QA system S. (2) (Explanation) If S is predicted to fail q, we analyze them to determine the most likely reasons of the failure. (3) (Rectification) We use the prediction and explanation results to rectify the answer. We put forward tools to achieve the three steps and analyze their effectiveness. Our experiments show that the PERQ framework can significantly improve KB-QA systems' accuracies over simple questions.
Evaluation and Visualization of Driver Inattention Rating From Facial Features

In this paper, we present <monospace xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">AutoRate</monospace> , a system that leverages the front camera of a windshield-mounted smartphone to monitor driver’s attention by combining several features. We derive a driver attention rating by fusing spatio-temporal features based on the driver state and behavior such as head pose, eye gaze, eye closure, yawns, use of cellphones, etc. We perform extensive evaluation of <monospace xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">AutoRate</monospace> on real-world driving data and also data from controlled, static vehicle settings with 30 drivers in a large city. We compare <monospace xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">AutoRate</monospace> ’s automatically-generated rating with the scores given by 5 human annotators. We compute the agreement between <monospace xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">AutoRate</monospace> ’s rating and human annotator rating using kappa coefficient. <monospace xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">AutoRate</monospace> ’s automatically-generated rating has an overall agreement of 0.88 with the ratings provided by 5 human annotators. We also propose soft attention mechanism in <monospace xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">AutoRate</monospace> which improves <monospace xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">AutoRate</monospace> ’s accuracy by 10%. We use temporal and spatial attention to visualize the key frame and the key action which justify the model’s predicted rating. Further, we observe that personalization in <monospace xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">AutoRate</monospace> can improve driver specific results by a significant amount.
Integrated Path Following and Collision Avoidance Using a Composite Vector Field

Path following and collision avoidance are two important functionalities for mobile robots, but there are only a few approaches dealing with both. In this paper, we propose an integrated path following and collision avoidance method using a composite vector field. The vector field for path following is integrated with that for collision avoidance via bump functions, which reduce significantly the overlapping effect. Our method is general and flexible since the desired path and the contours of the obstacles, which are described by the zero-level sets of sufficiently smooth functions, are only required to be homeomorphic to a circle or the real line, and the derivation of the vector field does not involve specific geometric constraints. In addition, the collision avoidance behaviour is reactive; thus, real-time performance is possible. We show analytically the collision avoidance and path following capabilities, and use numerical simulations to illustrate the effectiveness of the theory.
Anomaly Detection using Convolutional Spatiotemporal Autoencoder

In this paper an efficient technique for detecting anomalies in videos is proposed. Most of the applications of Convolutional Neural Networks (CNNs) are based on object detection and recognition. Generally Convolutional layers are used in images, however they are supervised and need labels for learning which is cumbersome task. Proposed method includes spatio-temporal model for detection of anomalies in videos of crowded places like walkways, bus or railway stations etc. The model consists of two significant aspects, one for representation of spatial features and one for temporal progression of spatial features. Convolutional layers are used for spatial feature representation and Long Short Term Memory network (LSTM) is used for temporal progression representation. Anomaly score is introduced to detect the event is anomalous or normal. Experimental accuracy is comparable to state-of-the-art methods on standard databases like Avenue and UCSD.
Vector-Valued Graph Trend Filtering With Non-Convex Penalties

This work studies the denoising of piecewise smooth graph signals that exhibit inhomogeneous levels of smoothness over a graph, where the value at each node can be vector-valued. We extend the graph trend filtering framework to denoising vector-valued graph signals with a family of non-convex regularizers, which exhibit superior recovery performance over existing convex regularizers. Using an oracle inequality, we establish the statistical error rates of first-order stationary points of the proposed non-convex method for generic graphs. Furthermore, we present an ADMM-based algorithm to solve the proposed method and establish its convergence. Numerical experiments are conducted on both synthetic and real-world data for denoising, support recovery, event detection, and semi-supervised classification.
An Empirical Study of Detection-Based Video Instance Segmentation

Video instance segmentation (VIS) is a composite task that requires the joint detection, tracking, and segmentation of objects in a video. In this work, we introduce a complete framework for VIS, which integrates the strengths of instance segmentation and general object tracking in addressing the unique challenges of VIS. In developing the framework, we investigate effective ways of coordinating the two components for maximum benefits while thoroughly investigate their separate contributions. Our approach improves over the official baseline by an absolute 14.4% in mAP and achieves the second place in the 2019 YouTubeVIS challenge.
Blockchain Based Spectrum Sharing Algorithm

We propose a spectrum sharing algorithm based on blockchain and game theory. Unlike traditional spectrum sharing methods which use a centralized platform, our algorithm takes the decentralization and high-level trust advantages of blockchain for operators to share free spectrum and improve the utilization rate. Our system is built on consortium blockchain in which operators can trade spectrum directly. Consortium blockchain is used to authenticate members waiting to join, record information of all the transactions and ensure that it cannot be tampered by cryptography, consensus algorithm and other methods. In addition, the operators will use the game theory to specifically share spectrum between each other in our model. The operators will make their optimal sharing strategy based on game theory. Simulation results show that the proposed algorithm can effectively improve the spectrum utilization of operators and increase their revenue.