Reviewerly/exHarmony huggingface dataset:
Design and characterization of an EOG signal acquisition system based on the programming of saccadic movement routines

It is estimated that by 2030 the number of people with disabilities will rise, thereby increasing the demand for assistive devices. Eye tracking has served as an alternative for the development of assistive technologies. Electrooculography (EOG) is an eye tracking technique used to measure the eye’s electrical activity. In this work, an AC-coupled EOG acquisition system composed of an instrumentation amplifier, low-pass, high-pass, and notch filters was designed. A graphical user interface (GUI) with programmed routines was made in order to characterize the system by the performance of saccadic eye movements. The peaks of the EOG signals in the horizontal and vertical directions at different viewing angles were analyzed to establish a relation between peak magnitude and gaze angle. Due to eye blinking, amplifier saturation, and subject fatigue, the vertical EOG was discarded for system characterization. A linear relationship was obtained between the horizontal EOG magnitude and the horizontal gaze angles in a range from 9.4° to 39.7°, with a coefficient of determination of 0.9991. The proposed EOG system is linear in the horizontal direction; however, DC coupling of the system is sought in a future stage to improve the characterization of eye movements.
Predictive Disk Provisioning for Adjustable Cloud Storage Solutions

Cloud service providers such as AWS and Azure have recently begun to offer storage solutions that allow disk performance to be adjusted “on-the-fly”. Such offerings allow the user to make use of short-term predictions of storage requirements. For example, instead of provisioning a single storage solution that is never under-provisioned, but frequently over-provisioned, one can configure the storage system to support higher performance during peak times; and cheaper, lower performance during periods with less demand. This paper explores the possibility of using a prediction system that utilizes past storage demands to predict the storage requirements over the next hour. We sought a single predictor that could perform well for all types of demand. The predictors were developed using approximately 200 years of storage performance requirements collected from high-performance storage systems in hundreds of companies. We have found that over-provisioning can be greatly reduced, but only at the expense of under-provisioning with a non-zero probability. However, the probability of being under-provisioned can be as low as 0.01%, which is similar to the target service level of cloud vendors. In addition, we have developed novel methods to search for effective predictors that perform well both on average and for rare events.
Design of security assistant software for domestic IT innovation operating system

Driven by China’s demand of independent high-end software, IT (information technology) innovation gradually comes into people’s lives. In this area, operating system as an important part, its research is bound up with the information security of key sectors in China. Through investigation, Linux-based operating systems urgently need security assistant software to facilitate long-term Windows users. To address this problem, we propose the framework of this software, and introduce its implementation in detail in this paper. First of all, we investigate the needs of users to determine the overall framework of the software and functions’ design. Secondly, we develop the back-end modules to implement preset logical functions, and design the front-end interface to comprehend users’ demands. Finally, we perform the functions of the software on Ubuntu 20.04 to verify the stability and feasibility.
Concept of blockchain-based micro-service control strategy for a domestic water heater

This paper proposes a concept of a blockchain-based micro-service control strategy for a domestic water heater. The novelty of the proposed concept is in the employment of blockchain technology, which enables decentralized access to service providers, thereby improving the security of the system. The concept aims to increase security while preserving energy consumption and costs reductions benefits associated with water heating by utilizing advanced control strategies and algorithms. The proposed system consists of a smart device, a service provider, data storage, and a smart contract. The smart device collects and stores data on hot water consumption, which is used to generate accurate predictions of hot water draw profiles. The service provider provides advanced control strategies designed to enhance the performance of the smart device. The data storage is decentralized to ensure data privacy and security. Payments between providers and consumers of the services are executed by employing smart contract deployed on a blockchain network. Testing of the proposed concept was conducted in a hybrid simulation environment. The results of the tests showed that even in decentralized system an improvement in energy consumption efficiency and a reduction in operating costs due to the advance control algorithms is possible without a decrease in users’ comfort.
Crop Enclave Interface for SGX Programs

Existing memory attacks against SGX use the enclave interface, such as ECALLs and OCALLs, to inject malicious data into the enclave’s trusted memory to trigger memory corruption vulnerabilities therein. Therefore, enclave interface security becomes a key issue in defending against such attacks. However, a comprehensive static analysis of source SGX programs is currently lacking to obtain sufficient a priori knowledge to provide effective runtime interface protection for the enclave. In view of this, we identify 8 types of unsafe input data of enclave and design a new interface cropping method, SGXCrop. This method extracts critical interface information from source SGX programs, including ECALLs in use and unsafe input data, which are cropped at runtime of SGX programs. Tests in real SGX environment verify that the proposed method can effectively crop illegal ECALLs and unsafe input data.
Evaluation of Isolation and Communication Mechanisms for Real-Time Containers

The use of lightweight methods for virtualization has potential for control technology, such as increased update rates and an extension of control systems with value-added services. A programmable logic controller (PLC) is no longer a closed system but extensively offers interfaces to the outside. The trends towards decentralization, highly flexible systems, and a flattening of the automation pyramid create new requirements that classic control architectures need to meet. In this context, a high degree of integration between components with simultaneous temporal and spatial isolation is crucial. This paper addresses the conception of an isolation mechanism for the extension of the control functionality by value-added services. The basis for this is a combination of a partitioning hypervisor and software containers, which was chosen based on an evaluation of current isolation mechanisms for container solutions. Different messaging-based approaches with a common API for communication between components are developed and evaluated.
Learning Diverse and Efficient Goal-reaching Policies for Robot Motion Planning

In robot motion planning, providing multiple diverse trajectories to guide a robot to reach a given goal is important for the resilience of the robot to environmental changes. Recent advances in reinforcement learning (RL) have shown promising results in enabling the agents to learn diverse trajectories in motion planning. The majority of such works employed a two-part reward system, consisting of a goal-reaching reward and a diversity reward. For the former, distance rewards are normally adopted for their rich signals to guide the agents closer to the goal. The problem is that distance rewards change constantly as the agents progress towards the goal and, when combined with the diversity rewards, may interfere with the latter, causing poor learning results. In this paper, we propose to use binary rewards as the goal-reaching rewards. Binary rewards are static and predictable, allowing the diversity rewards to remain stable. The problem of sparsity of binary rewards is resolved with Hindsight Experience Replay (HER). Experimental results show that the proposed method enables the agents to learn efficient goal-reaching policies that are more diverse and robust to resist environmental changes compared to the prior approaches.
Gaze orientation to evaluate object interaction in human functional assessment

The paper goal is to identify what and where each person, in a RGB image, is looking at, and correctly handle the case where the gaze target is out-of-frame. Human-Gaze-Target (HGT) projects mainly focus on “Where the person is looking at” on video-surveillance RGB images, but fail to exploit “What the person is looking at” in such context. On this document, we present a video-processing workflow that takes that step, fusing an accurate HGT algorithm with an object detection one, and enhancing the resulting system for the application of interest. Concretely, we introduce an algorithm called “Looking at whose function is transforming the HGT output into a shape comparable to that of the object detection standard algorithms. We also propose the inclusion of a preprocessing layer into the object detection algorithm, in order to increase its confidence on detecting small items, typically used in functional assessment evaluation tasks (such as silverware), that is the application of interest of this proposal. Finally, we generate and use a novel dataset for validating the global workflow proposed, that has been recorded in a specially adapted kitchen for the assessment task, the EYEFUL dataset, obtaining promising quantitative and qualitative results.
Proposal and implementation of k-anonymization method for data insertion and deletion

Preserving the privacy of individuals while publishing their relevant data has been an important issue. K-anonymization, which is one of the most useful privacy models, is valid for static data; however, it does not support dynamic data updating. In this paper, we propose a method for inserting and deleting data into and from k-anonymous tabular data while maintaining k-anonymity. This method ensures k-anonymity after multiple publications by retaining the raw data and the latest published data. In the deletion process, the specified record is first deleted from the data table, and the records remaining in the q*-block to which the specified record belonged are inserted into the data table excluding the remaining records. This insertion of multiple records is performed by repeating the above addition process in sequence.
Tiny Federated Learning with Bayesian Classifiers

Tiny machine learning (TinyML) represents an emerging research direction that aims to realize machine learning on Internet of Things (IoT) devices. The current TinyML research seems to focus on supporting the deployment of deep learning models on microprocessors, while the models themselves are trained on high performance computers or clouds. However, in the resource/time constrained IoT contexts, it is more desirable to perform data analytics and learning tasks directly on edge devices for crucial benefits such as increased energy efficiency, reduced latency as well as lower communication cost.To address the above challenge, this paper proposes a tiny federated learning algorithm for enabling learning of Bayesian classifiers based on distributed tiny data storage, referred to as TFL-BC. In TFL-BC, Bayesian learning is executed in parallel across multiple edge devices using local (tiny) training data and subsequently the learning results from local devices are aggregated via a central node to obtain the final classification model. The results of experiments conducted on a set of benchmark datasets demonstrate that our algorithm can produce final aggregated models that outperform single tiny Bayesian classifiers and that the result of tiny federated learning (of Bayesian classifier) is independent of the number of data partitions used for generating the distributed local training data.
ANN-based forecasting of solar irradiation under data clustering: an approach for improved estimation of PV power production

The performance of PV power-related variable forecasting is gaining importance due to the growing needs for PV plants integration in electrical grids and to the pivotal role of forecasting in new energy management systems. Based on such a consideration, this paper proposes a method using non-supervised learning combined with a partially recurrent ANN to obtain an increased accuracy in day-ahead solar irradiation forecasting. The proposed hybrid method is applied to two case studies related to locations with different climatic characteristics and compared to different other methods, including the non-clustering-based solution. The proposed method allows reducing the forecasting normalized root mean squared error up to about 10% and significantly outperforms a baseline method.
Age Group Recognizer based on Human Face Supporting Smart Digital Advertising Platforms

Smart digital advertising platforms have been widely employed in public areas in big cities. An age group recognizer is indispensable to support these platforms in providing relevant advertisements for each audience. These platforms also demand a recognizer that can run properly at the minimum on a CPU device to degrade the budget during system procurement. This study proposes an age group recognizer based on human faces to predict the age group of the audience’s face using an efficient architecture containing a light backbone. This work offers a residual mini multi-level module integrating two grouped convolution layers with diverse frequency levels to extract exclusive facial features maintained by residual operation. In order to improve the feature map’s quality, a deep lite attention module is proposed, consisting of the deep channel and lite spatial attention part. The architecture generates few parameters with cheap operation and achieves competitive performance on the benchmark datasets. In addition, the architecture integrated with face detection, as a recognizer, can perform fast on a CPU configuration in real-time with 144 frames per second.
Analysing navigation paths in constrained graphs using Petri nets

In robotics applications, supporting navigation in dynamic environments (described though a graph) have been addressed multiple times relying on very different approaches. In this work, we are considering the navigation in a pre-defined map, considering different areas (rooms) connected by some pathways. The navigation is constrained by resource availability and specific conditions allowing the movement between adjacent areas. The overall goal is to conclude if, given a specific map and all associated constraints and resources, it is possible to reach a specific location in the map (the ultimate goal) without being trapped in some deadlock or livelock situation. The validation of the proposed approach uses procedurally generated games, where a map is automatically generated considering a set of rooms and doors allowing the movement from a starting room to the ultimate location (end of the game). The player may collect specific resources in the visited rooms, which will be used to allow navigation, namely to unlock passages. In this paper, a strategy based on the translation of the map to a Petri net model is proposed. From the Petri net model, relying on the building of the associated state space (reachability graph) it is possible to conclude about existence of deadlocks and loops (livelocks) and if the ultimate goal is reachable or not. A minimum set of translation rules is presented and validated using a small map and supported by the IOPT-Tools framework, which provides the tools for editing, simulating, generating the associated state space and a query mechanism to conclude about reachability of the ultimate goal, as well as deadlocks.
Design and Optimization of Visual Tracking Algorithm Based on Deep Learning

Visual tracking is a challenge in computer vision. Visual tracking has various practical application scenarios, and is used in intelligent video surveillance system, analysis and research of driver’s abnormal behavior, intelligent robot research and other fields. Visual tracking algorithm combines video processing, automatic system control, informatics and so on, so as to automatically identify the target from the video image and extract the tracked target position information. In this paper, the visual tracking algorithm is designed and optimized based on deep learning. The experimental data show that the algorithm used in this paper is more advantageous, and the experimental results are evaluated by accuracy and success rate. The figure shows the percentage of the tracked target in the tracking frame. The error between the center position of the tracked target and the real target is measured by different thresholds. Similarly, the success rate indicates the percentage of the successfully tracked target frame, and the proposed algorithm is superior to the traditional tracking algorithm, so it is feasible to optimize the visual tracking algorithm. The target detection algorithm based on deep learning can achieve better target detection effect, and the amount of calculation is small. Continuous real-time tracking can effectively solve the problem of target blocking and improve the accuracy of visual tracking.
Application of Virtual Reality Technology Based on Fast Filtering Algorithm in Switching Operation of Substation

At present, VR(Virtual Reality) technology is widely used in various fields, and its effective integration with power system can realize the application of the combination of 3D model roaming and real simulator patrol with the help of VR technology on the basis of 3D refined modeling of substation. In this paper, the application of VR technology based on fast filtering algorithm in substation switching operation is studied. The sequence control program of switching operation of virtual simulation system is established. Generally, the noise in an image is randomly distributed, and its structural features are not obvious, so it can’t be best represented by atoms in a dictionary. In this paper, a denoising algorithm of image frequency division is proposed in combination with fast filtering algorithm. By repeatedly using the fast filtering algorithm, the original fuzzy infrared monitoring image of substation switching operation is divided into spatial domain low-frequency image and spatial domain high-frequency image, which are denoised by different methods respectively, and the denoised images are fused. The results show that the PSNR value of the image frequency division denoising algorithm based on fast filtering proposed in this paper is obviously higher than that of other algorithms. RMSE value is obviously smaller than other algorithms. It can be seen that this algorithm is helpful to effectively denoise the infrared monitoring image of substation switching operation.
Research on Public Management Statistical Data Quality Management Based on Fuzzy Clustering Algorithm

Public statistical data is a general term for data and analysis reflecting economic and social phenomena collected, collated and used by government statistical departments through various statistical work. Management of public statistical data quality management is an indispensable part of the government’s public management. It is a general term for activities to ensure the quality of government statistical data and supervise and control according to law. Its contents mainly include the scheme design of statistical work, the collection and summary accounting of statistical data, the statistical analysis of integrated data, the release and supervision of final statistical information, control and modification, etc. In this paper, the fuzzy clustering algorithm is a tool to find knowledge quickly and effectively. Fuzzy clustering algorithm is a step-by-step iterative data clustering method based on objective function optimization. Therefore, using fuzzy clustering algorithm can improve the speed of information retrieval and the efficiency of information utilization.
Study on Data Security of Computerization under Centralized Accounting Mode

After the comprehensive implementation of the financial centralized accounting, the expenditure behavior of administrative institutions has been controlled to a great extent, and the financial accounting, financial management and supervision have been strengthened. From the root of the control of financial funds to be strengthened, so as to ensure that the income and expenditure of administrative institutions are legal and compliant. However, after the implementation of financial computerization, there are many data security problems in financial information. The main measures to solve this problem are strictly control the login authority; strengthen the security and confidentiality of computerized financial files.
Research on Image Multi-Feature Steganography Based on Deep Learning

Digital steganography is one of the important branches of information security. It hides information in common transmission carriers in life, such as digital images, digital video, digital audio signals, etc., hiding the existence of information. At present, digital image steganography has become one of the research hotspots in the field of information security. Steganalysis technology uses the change of information embedding to the statistical characteristics of the original carrier to determine the existence of hidden information, so as to detect whether information is embedded in the carrier. In this paper, based on DL (Deep Learning), the steganographic analysis of image multiple features is further studied. The image multiple features can be divided into gray image, color image and binary image according to the difference of color and value bits represented by them. Grayscale images only express the brightness value of image pixels, but not the color information of image pixels. In view of the particularity of image steganography analysis task, the general DL network is specially designed, and the accuracy and speed of analysis are studied by changing the network structure, network layers, activation function and adding filter layer convolution.
A Method of Manipulator Trajectory Control Based on Coordinate System

This paper will take the six degrees of freedom robot as the research object, and realize the three-dimensional motion simulation of the six degrees of freedom robot based on software. Basic analysis and modeling of kinematics of six degrees of freedom robot. The first is to analyze the basis of kinematics of the robot, determine the D-H parameters of the six degrees of freedom robot, and establish the six degrees of freedom robot model with the ‘modified’ D-H method. According to the size parameters of the six degrees of freedom robot, the forward and inverse kinematics formulas are derived, and a closed solution method of inverse kinematics with less computational complexity is obtained. Call the robot toolbox in MATLAB to simulate and verify the six degrees of freedom robot, and adjust the position of each joint of the robot to prove the correctness of robot kinematics derivation. This paper mainly adopts Cartesian space trajectory planning. Linear interpolation or circular interpolation is used to determine the starting point and ending point trajectories of the end effector of a six degrees of freedom robot, and then several points are interpolated on the trajectory to determine the coordinates of different intermediate points on the path. Finally, the coordinates of the robot’s intermediate point are calculated in inverse kinematics, and the variables of six joint angles are calculated. Finally, the feasibility of Cartesian space trajectory planning is verified by simulation demonstration in MATLAB.