Reviewerly/exHarmony huggingface dataset:
Stacked residual blocks based encoder–decoder framework for human motion prediction

Human motion prediction is an important and challenging task in computer vision with various applications. Recurrent neural networks (RNNs) and convolutional neural networks (CNNs) have been proposed to address this challenging task. However, RNNs exhibit their limitations on long-term temporal modelling and spatial modelling of motion signals. CNNs show their inflexible spatial and temporal modelling capability that mainly depends on a large convolutional kernel and the stride of convolutional operation. Moreover, those methods predict multiple future poses recursively, which easily suffer from noise accumulation. The authors present a new encoder–decoder framework based on the residual convolutional block with a small filter to predict future human poses, which can flexibly capture the hierarchical spatial and temporal representation of the human motion signals from the motion capture sensor. Specifically, the encoder is stacked by multiple residual convolutional blocks to hierarchically encode the spatio-temporal features of previous poses. The decoder is built with two fully connected layers to automatically reconstruct the spatial and temporal information of future poses in a non-recursive manner, which can avoid noise accumulation that differs from prior works. Experimental results show that the proposed method outperforms baselines on the Human3.6M dataset, which shows the effectiveness of the proposed method. The code is available at https://github.com/lily2lab/residual_prediction_network.
A graphical diagnostic for heavy tailed data

Abstract Graphical diagnostics are described for general heavy tailed data. This tool allows for a model free assessment of the tails of a univariate dataset a transform on the tails of the data. In addition, one can add to the basic plots comparisons of a dataset to multiple models. Multivariate extensions are described using an ordering based on distance from a center.
Texture Analysis of Knee Osteoarthritis Using Contrast Limited Adaptive Histogram Equalization Based Gray Level Co-occurrent Matrix

Osteoarthritis (OA) is a joint disease associated with degenerative damage to joint cartilage. WHO reports that 40% of the world's population will suffer from OA, in which 80% of them have limited motion. due to the number of osteoarthritis sufferers is increasing and in the detection of osteoarthritis orthopedic doctors use manual x-ray images, detection of osteoarthritis using x-ray image input is necessary. This study uses x-ray image processing using the CLAHE method which is used for image enhancement to analyze bone texture using GLCM. The results showed that the accuracy rate of KL-0 was 20%, KL-15%, KL-220%, KL-355%, and KL-4 5%. Based on these results, adding methods will give better results.
Binary Field Point Multiplication Implementation in FPGA Hardware

In 1980, Victor Miller and Neal Koblitz proposed Elliptic Curve Cryptography (ECC) that is popularly known as asymmetric key crypto algorithm which is rooted in the ECDLP on finite fields known as Elliptic Curve Discrete Logarithm Problem. The main advantage of ECC, over the other public key cryptography (like RSA), is that it gives equivalent security performance level but using the smaller key size length. In this paper, we proposed an ECC hardware implementation based on FPGA. Also, hardware implementation of Elliptic Curve Digital Signature Algorithm (ECDSA) in FPGA and its comparison with other implementation is presented. The ECC hardware implementations are categorized into two main implementing technologies, and they are FPGA and ASIC implementations. We first discuss elliptic curves standards, point multiplication over binary fields GF(\(2^m\)), and the use of prime fields on the hardware implementation. The proposed method evaluates the performance analysis of ECC implementation in an FPGA.
Text-Driven Talking Head Using Dynamic Viseme and DFFD for SIBI

This research aims to build an android application that is able to help a deaf person in learning the Indonesian Sign Language System (SIBI). The application is packaged in the form of a 3D animated talking head. This research, the development of a talking head uses the dynamic viseme method. Dynamic viseme has the advantage of creating a more natural animation movement because it uses the original human model in the building process. By using the Dirichlet Free-Form Deformation (DFFD) method as control, the concatenative method is able to produce animated movements that are easily understood by the deaf person. In its build, linear interpolation was also used so that the displacement in the speaker becomes more natural. To build animated movements, four different human models were used. Experimental testing was carried out using surveys and calculations were carried out using the Mean Opinion Score (MOS) method. The number of surveys carried out was five. The purpose of the survey is to determine a model that is easier to understand by mouth movements. The test results on the Indonesian sentence show a value of 4.25. These results indicate that the 3D talking head has the potential to be developed so that it can be understood by a deaf person.
DSM-based variable ordering heuristic for reduced computational effort of symbolic supervisor synthesis

We consider the influence that the variable order of Binary Decision Diagrams (BDDs) has on the computational effort that is required for symbolic supervisor synthesis. In recent research it has been shown that improving the variable order can result in a substantial decrease of synthesis effort. We propose the combined use of the Dependency Structure Matrices (DSMs) and the matrix reordering heuristics Cuthill-McKee and Sloan to minimize the Weighted Event Span (WES) of the variable order. This is done by placing variables that often appear together in transition relations near each other. By performing benchmark experiments, we measure the reduction in synthesis effort by utilizing a variable order with minimized WES. The experiments show that our approach is competitive in reducing computational effort compared to FORCE, a state of practice variable ordering heuristic. Moreover, the best improvements in effort reduction are shown for the most computationally demanding models tested.
IT Governance

Corporate Governance Encompasses the set of rules, processes, and methods aimed at defining and meeting the strategic objectives of an organization.1 These objectives normally have long-term repercussions and define the road map of the companies, considering the different entities directly affected by the strategic decisions taken, such as external stakeholders.
Naive Bayes Method for Classification of Student Interest Based on Website Accessed

Abstract Interest is a feeling of liking a thing or activity without any coercion. Students' interest in a certain subject will maintain students' learning abilities, thus they could master it and get good learning outcomes. Interest can be known from the website accessed. The aim of this study is to build a web-based application that could classify student interest using Naïve Bayes based on the website accessed. In this study, the data used are 17.265 student internet history data. The application was tested using Black Box and the method was tested using Confusion Matrix. The result from the application testing met the expectation, and the method (Naïve Bayes) reached 99,81% accuracy using 70:30 data percentage. The top five classes obtained are Social Networking, Educational Institution, Streaming Video, Search Engines, and Web-Based Mail. The “Develop” class was also found, thus the study group related to application development is recommended to be formed.
A Coordinated Multiagent Reinforcement Learning Method Using Chicken Game

Sparse interaction in multiagent tasks is an important approach to reduce the exponential computational cost for multiagent reinforcement learning (MARL) systems. How to select proper equilibrium solutions is the key to find the optimal policy and to improve the learning performance when collisions occur. We propose a new MARL algorithm, Efficient Coordination based MARL with Sparse Interactions (ECoSI), using the sparse interaction framework and an efficient coordination mechanism, where equilibrium solutions are selected via Nash equilibrium and Chicken game. ECoSI not only separates the Q-value updating rule in joint states from non-joint states with sparse interactions to achieve lower computation and storage complexity, but also takes advantage of efficient coordination with equilibrium solutions to find the optimal policy. Experimental results demonstrate the effectiveness and robustness of ECoSI compared to other state-of-the-art MARL algorithms.
Multi-View Spectral Clustering via ELM-AE Ensemble Features Representations Learning

Spectral cluster based on multi-view data has proven effective for clustering multi-source real-world data because consensus and complementary information of multi-view data ensure the result of clustering. Feature learning is the vital step in spectral clustering, and excellent feature representations can effectively improve spectral clustering performance. In this article, we propose Multi-View Spectral Clustering via ELM-Autoencoder Ensemble Features Representations Learning (MvSC-EF-ELM). First, Extreme Learning Machine as an Autoencoder(ELM-AE) learns feature representations by adopting a singular value decomposition, which reconstructs the inputs using the embedding feature space. Second, the single view and multi-view spectral clustering algorithm are applied embedding feature representations space into the eigenspace and cluster's them, respectively. Experiments on benchmark datasets demonstrate that our approach empirically validates the power of ELM-AE for feature learning from raw data and may effectively facilitate multi-view spectral clustering and induce superior clustering results.
Stochastic Neighbor Embedding Algorithm Based on Quantum Genetic Algorithm with Gaussian Parameters

Stochastic neighbor embedding algorithm is an important nonlinear dimension reduction manifold learning algorithm in the field of big data and machine learning. In the stochastic neighborhood embedding algorithm, it changes the idea of constant distance based on the medium in and, while mapping high-dimensional to low-dimensional, trying to ensure that the distribution probability of each other is consistent. The gradient descent method is often used to solve the problem of minimum divergence, but because the gradient descent method has the disadvantage of easily falling into local optimal values, this article combines SNE with a quantum genetic algorithm and uses the strong uncertainty of the quantum genetic algorithm and high convergence to solve the problem.
Digital Assistance in the Maintenance of Offshore Wind Parks

Abstract Harsh environmental conditions, time pressure due to tightly calculated maintenance windows, complex technical systems and high quality standards determine the working environment for the maintenance and repair of offshore wind farms. Despite good qualifications and diverse training courses, maintenance technicians are reliant on information from manuals and checklists when performing their tasks and must document their work results in protocols. Both the supply of information and the data acquisition show a multitude of media disruptions. Hence, the processes on site and in the postprocessing are carried out slowly and uncertainly. The assistance system presented is intended to contribute to a consistently digital information flow and to support the technicians in a task-specific manner. Augmented Reality (AR) offers a way of presenting information and interacting with the assistance system during work execution. Using the example of the maintenance of crane systems used in a variety of ways in offshore wind farms, this article shows which potential can be tapped through digital assistance systems and AR applications.
Assessment and Correction of Solar Radiation Measurements with Simple Neural Networks

Solar radiation received at the Earth’s surface provides the energy driving all micro-meteorological phenomena. Local solar radiation measurements are used to estimate energy mediated processes such as evapotranspiration (ET); this information is important in managing natural resources. However, the technical requirements to reliably measure solar radiation limits more extensive adoption of data-driven management. High-quality radiation sensors are expensive, delicate, and require skill to maintain. In contrast, low-cost sensors are widely available, but may lack long-term reliability and intra-sensor repeatability. As weather stations measure solar radiation and other parameters simultaneously, machine learning can be used to integrate various types of environmental data, identify periods of erroneous measurements, and estimate corrected values. We demonstrate two case studies in which we use neural networks (NN) to augment direct radiation measurements with data from co-located sensors, and generate radiation estimates with comparable accuracy to the data typically available from agro-meteorology networks. NN models that incorporated radiometer data reproduced measured radiation with an R2 of 0.9–0.98, and RMSE less than 100 Wm−2, while models using only weather parameters obtained R2 less than 0.75 and RMSE greater than 140 Wm−2. These cases show that a simple NN implementation can complement standard procedures for estimating solar radiation, create opportunities to measure radiation at low-cost, and foster adoption of data-driven management.
Guaranteeing Type Consistency in Collective Adaptive Systems

Collective adaptive systems whose entities are loosely coupled by their exchange of complex data structures became a very common architecture for distributed web-based systems. As HTTP-based APIs transfer data as plain text, this exchange is very error prone: API changes and malicious data modifications may remain unnoticed. GraphQL addresses this concern at the server side with strong typing but leaves the clients untouched. In this paper we present an approach to align the type schemas provided by GraphQL and type definitions at the client side on three levels during the systems’ life cycles: At generation time by verifying queries against the GraphQL schema, at compile time by leveraging TypeScript’s type system, and at run time by using decoders to validate payloads. Key to our solution are a functional, type-safe domain-specific language for the definition of GraphQL queries and a corresponding generator implementation providing the GraphQL queries and TypeScript artefacts. Together they ensure that clients become aware of and are able to react to changes of the (evolving) GraphQL schema, a precondition for maintaining the consistency of the overall collective adaptive system. We will illustrate our approach along a popular GitHub-based, collaborative development scenario.
REAM: Resource Efficient Adaptive Monitoring of Community Spaces at the Edge Using Reinforcement Learning

An increasing number of community spaces are being instrumented with heterogeneous IoT sensors and actuators that enable continuous monitoring of the surrounding environments. Data streams generated from the devices are analyzed using a range of analytics operators and transformed into meaningful information for community monitoring applications. To ensure high quality results, timely monitoring, and application reliability, we argue that these operators must be hosted at edge servers located in close proximity to the community space. In this paper, we present a Resource Efficient Adaptive Monitoring (REAM) framework at the edge that adaptively selects workflows of devices and operators to maintain adequate quality of information for the application at hand while judiciously consuming the limited resources available on edge servers. IoT deployments in community spaces are in a state of continuous flux that are dictated by the nature of activities and events within the space. Since these spaces are complex and change dynamically, and events can take place under different environmental contexts, developing a one-size-fits-all model that works for all types of spaces is infeasible. The REAM framework utilizes deep reinforcement learning agents that learn by interacting with each individual community spaces and take decisions based on the state of the environment in each space and other contextual information. We evaluate our framework on two real-world testbeds in Orange County, USA and NTHU, Taiwan. The evaluation results show that community spaces using REAM can achieve > 90% monitoring accuracy while incurring ~ 50% less resource consumption costs compared to existing static monitoring and Machine Learning driven approaches.
Using FireEye Endpoint Security for educational purposes

In this paper, it is presented how a commercial cybersecurity tool “FireEye Endpoint Security” can be used in the hybrid installation as a part of practical student exercises for cybersecurity training in the Network Forensics and Malware Forensics classes. The purpose of the student exercise in such scenario is to understand the capabilities of enterprise-class cybersecurity tools while keeping costs minimal for the university. The working environment is the installation of remote virtual security appliances and university laboratory equipment for independent students' work. This is a scaled-down virtual environment available for independent student work where FireEye Endpoint Security is used in demo and proof-of-concept mode, which requires minimal local installation while providing a full feature set of the tool.
Reconstruction of Missing Markers in Motion Capture Based on Deep Learning

With the great success of the movie Avatar, optical motion capture systems have been widely used in the fields of virtual reality, movie animation and robotics. However, the optical motion capture system is prone to noise due to the occlusion of the markers during the capture process. In order to remove the noise in the data, commercial methods are to provide manual repair for noisy data, and use interpolation to fill in the missing data, which is time-consuming and labour-intensive to process and the repaired data is not smooth enough to be jittery. In this paper, we propose a denoising method that can intelligently detect noise in the data and reconstruct missing markers data without manual intervention. This method uses a deep learning method, based on the temporal and spatial relationship of motion sequences, to learn the logical relationship between the data, to quickly find the lost data and reconstruct it. Simulation proves that our method is efficient in reconstruct missing markers.
A recommender system for WordPress themes using item-based collaborative filtering technique

Recommender systems allow making personalized recommendations for items or products while browsing online, using numerous algorithms and techniques to predict and recommend potentially useful items to a specific user. This paper presents a system built for the purpose of filtering out WordPress themes trough the ratings or reviews of other similar users using item-based collaborative filtering technique and provide user-specific recommendations for the largest online marketplace of web templates called Themeforest, part of Envato marketplaces.
Voks: Digital instruments for chironomic control of voice samples

This paper presents Voks, a new family of digital instruments that allow for real-time control and modification of pre-recorded voice signal samples. An instrument based on Voks is made of Voks itself, the synthesis software and a given set of chironomic (hand-driven) interfaces. Rhythm can be accurately controlled thanks to a new methodology, based on syllabic control points. Timing can also be controlled with other methods, including scrubbing and playback speed variation. Pitch, vocal effort, voice tension, apparent vocal tract size, voicing ratio, aperiodicity ratio of the voice samples can be modified thanks to a real-time high-quality vocoder. Different forms of chironomic control of the vocal parameters are proposed. Pitch is controlled by continuous hand motions using a stylus on a surface (C-Voks) or a theremin (T-Voks). Other interfaces can be used as well. Syllabic rhythm is controlled using a biphasic button. Scrubbing, playback speed and timbre related parameters can be controlled using the theremin, control surfaces or continuous controllers like faders. In addition to realistic imitation of speaking or singing voices, other playing modes yield new interesting sounds. Voks participated in comparative perceptual evaluation of singing synthesis systems. It has been demonstrated in a live musical settings, using different control interfaces. In addition to musical or poetic performances, applications of performative vocal synthesis to language learning and speech reeducation are foreseen.
Hand Gesture Recognition Based on Semg Signal and Improved SVM Voting Method

To explore how to use as few features as possible to recognize as many gesture types as possible, a surface electromyographic (sEMG) gesture recognition algorithm based on an improved support vector machine (SVM) voting method is proposed. Compared with the traditional voting method, there are two main differences. First, all SVMs can be in different sub-training sets, where each channel corresponds to a sub-training set, and the sub-training set where each SVM is located is determined by a class distance-based separability criterion. Secondly, a separability enhancement method between two types of samples is proposed. Before training, each SVM is subjected to separability enhancement processing. Experimental results show that the dimensionality of the feature vector using the improved SVM voting method is only three-dimensional, and it can recognize twelve gestures with 84% accuracy. The recognition effect is better than several other methods, and the effectiveness is verified.
Devil is Virtual: Reversing Virtual Inheritance in C++ Binaries

The complexities that arise from the implementation of object-oriented concepts in C++ such as virtual dispatch and dynamic type casting have attracted the attention of attackers and defenders alike. Binary-level defenses are dependent on full and precise recovery of class inheritance tree of a given program. While current solutions focus on recovering single and multiple inheritances from the binary, they are oblivious of virtual inheritance. The conventional wisdom among binary-level defenses is that virtual inheritance is uncommon and/or support for single and multiple inheritances provides implicit support for virtual inheritance. In this paper, we show neither to be true. Specifically, (1) we present an efficient technique to detect virtual inheritance in C++ binaries and show through a study that virtual inheritance can be found in non-negligible number (more than 10% on Linux and 12.5% on Windows) of real-world C++ programs including Mysql and Libstdc++. (2) We show that failure to handle virtual inheritance introduces both false positives and false negatives in the hierarchy tree. These falses either introduce attack surface when the hierarchy recovered is used to enforce CFI policies, or make the hierarchy difficult to understand when it is needed for program understanding (e.g., during decompilation). (3) We present a solution to recover virtual inheritance from COTS binaries. We recover a maximum of 95% and 95.5% (GCC -O0) and a minimum of 77.5% and 73.8% (Clang -O2) of virtual and intermediate bases respectively in the virtual inheritance tree.