Reviewerly/exHarmony huggingface dataset:
Analysis and Recognition of Human Postures for Robotic Applications

The static and dynamic stability of the human body posture was investigated. Recordings of human movements were used for this purpose. The trajectories of the projection of the point mass of the body onto the support plane were compared with the ZMP trajectories. Representative movements were tested. Furthermore, research on recognizing human body posture was also presented. A posture discriminant was introduced and applied to the posture prediction results provided by the classifying neural network. It has been shown that the applied discriminant supports the process of distinguishing key postures even in the presence of additional, potentially ambiguous, or less specific postures.
On the Design of MoviWE.Q: An Omnidirectional Electric-Powered Wheelchair for Indoor Mobility

This paper presents the design of a novel electric-powered wheelchair for indoor use, named MovieWE.Q. The leading idea is that of exploiting a redundant set of actuation variables to achieve omnidirectional mobility through conventional wheels. The aim is overcoming the mobility limitations of conventional differential drive electric wheelchairs while avoiding the drawbacks of mecanum and omni wheels-based systems. The system is conceived to be commanded by the user seated on the wheelchair via a conventional joystick or other alternative control interfaces, by an assistive operator through the use of a sensorized handle mounted on the back of the chair, or by an autonomous navigation system. The study focuses on the functional design of the wheelchair and it proposes the executive design of the prototype of MoviWE.Q.
Dual-Arm Constrained Manipulation Planning Using Heuristic Search with Local Replanning

Lately, there has been an increasing interest in the robotics and automation industry to use robot systems equipped with dual arms to manipulate complicated or heavy objects that are difficult to handle with only a single arm. However, planning for bimanual manipulation is complex and challenging due to the additional constraints that need to be fulfilled and the much-reduced feasible solutions. In this paper, we are applying and adapting A* heuristic search algorithm for the purpose of pathfinding and planning for constrained dual-arm motion from a start position to a goal position. The weighted version of the algorithm is used, and a local replanning approach that delays collision check is also employed in the dual-arm manipulation planning to increase the search's speed performance and adapt to any obstacles that might be present in the environment.
A Visualization Data Analysis of Second-hand Houses in Zhuhai Based on Python and ECharts and Bootstrap

Abstract. In the era of huge data, because of the information explosion, it becomes extremely difficult for users to completely understand the source data information of second-hand houses. It is particularly necessary to use large data analysis technology and data visualization technology to meet the needs of buyers and renters. In view of the complicated information system of second-hand housing, buyers and renters need to obtain objective and comprehensive second-hand housing data in a short time, a visual analysis system for second-hand housing data is developed. Selenium+XPath crawler is used to climb the second-hand house data of Zhuhai in Shell second-hand housing network. Then, the data is stored into corresponding CSV files. The CSV files are converted into JSON files through the conversion website. Finally, the visual interface is built by Bootstrap and is displayed with the visual analysis chart of ECharts. Users can directly click the menu on the web-page, a detailed understanding of the second-hand housing source intuitive and appropriate visualization chart data, so as to better make the right decision to buy or rent. The system has a relatively powerful function, to meet the real needs of buyers and renters.
Fast Support Vector Classifier with Quantile

In the classification task, many improved algorithms have been developed based on Support Vector Machines (SVM). Since the recently proposed Fast Support Vector Classifier (FSVC) can handle large scale datasets, this paper improves FSVC by quantile, called QSVC, which uses quantile rather than average value of samples to represent all samples. The advantages of QSVC are as follows: 1) QSVC performs better on the skewed distribution; 2) and the robustness of quantile is better. Experiments show that our QSVC performs better in accuracy and speed than FSVC. For example,Table 2 shows that the average accuracy of QSVC is higher than that of FSVC and Liblinear, and it takes less time.
Fast Twin Support Vector Classification for Large Scale Problems

The twin support vector machines (TWSVM) is a milestone in multi-plane classification with state-of-the-art performance on many classification problems. However, on large scale datasets, the learning speed of TWSVM is expensive. In this paper, we propose a fast twin support vector machines (FTWSVM). In our FTWSVM, a pair of hyperplanes are computed directly from the training dataset without numerical iterations. Experiments on several benchmark datasets show that our method can exhibit good generalization performance and fast learning compared to the fast support vector classifier (FSVC), which specializes in big data problems, and TWSVM, which generalizes well speed.
Exploration of Blockchain Technology in Trusted atmospheric Environmental Data

In the process of atmospheric environment data monitoring and governance, due to the assessment and ranking of the main governance departments, environmental data are often affected by many objective and subjective factors, resulting in data fraud or loss. Previously, after finding such phenomena through manual comparison, the management department often took the worst value of the environmental data in the day as the environmental data on the assessed area, so as to assess and punish. Although this method can play a certain deterrent effect, it seriously affects the authenticity of data and the fairness of assessment, in the long run, it will bring unpredictable challenges to the authenticity of environmental data. Based on blockchain technology, this paper combines residual network, forward Euler method, and convolutional neural network to calculate the mapping relationship between different monitoring stations through historical environmental data. Based on the training set, the minimum deviation value of distorted data or lost data is obtained, so as to provide key technical support for the trusted management of atmospheric environmental data, realize data trusted management, and ensure the integrity, authenticity and traceability of atmospheric environmental data..
Blockchain Technology In Loyalty Program Applications

Loyalty programs provide income to the user as a result of their expenditures. It helps businesses if the customer ensures continuity. A loyalty program is based on the bilateral relationship between the customer and the business. It is important to be sure of the reliability of this bilateral relationship and to be able to follow it. Today, many companies are trying to incorporate Blockchain technology into their systems because they pay attention to reliability and transparency. In this study, a loyalty ecosystem based on Blockchain technology, which will include member businesses, change the shopping and reward experience, and encourage employees is explained. We propose an application named Decentralized Loyalty Token (DLOT) to be used by entities where the community is together and the spending potential is high such as restaurants, cafes, and e-commerce platforms that want to be included in this structure. Users will earn rewards while spending DLOT Tokens and can store and accumulate the rewards indefinitely or use them for any payment. In addition, users will be able to convert the loyalty Tokens into a digital value in other businesses included in Wallet. The details of the proposed Blockchain-based loyalty system are explained along with benefits to customers and entities.
MC Sharding: An Efficient Blockchain Sharding Based on Minimum Cut

The application of sharding is of significant help to improve the scalability of blockchain. The existing sharding system puts too much emphasis on the isolation of zones, which to a certain extent increases the isolation between zones and brings additional load. In this paper, we propose a sharding approach based on the Minimum Cut Method (MC sharding), which is a complete sharding. The number of connections inside each zone is essentially the same. The evaluation results show that MC sharding is more secure and scalable than the existing system, with 1.5 times higher transaction throughput and significantly lower stale block rate in a blockchain system with 4000 nodes and 6 shards. Most importantly, MC sharding can be applied in practice very quickly because it does not change the main procedure of Bitcoin and the block data structure.
Distribution Line Bird's Nest Detection Based on Improved Lightweight YOLOv5

In order to reduce the risk to the power system from bird nests in distribution line towers, the power grids often use modern monitoring devices for real-time detection of bird's nests. However, traditional object detection models are typically difficult to implement on the mobile side and frequently involve big arithmetic cases. And some lightweight models have poor reliability and accuracy. Therefore, this paper proposes a Shuffle-BiFPN-YOLOv5 model, which lightens the original YOLOv5 object detection model and fuses more features to perform accurate and quick bird's nest detection tasks on mobile. Firstly, we replace the backbone network with ShuffleNet v2 to reduce the network parameters and prompt the operation speed. Next, add the BiFPN structure to the Neck layer to further combine and exploit the features; Finally, apply CIoU loss to anticipate the bounding box's regression. In this paper, the proposed network is trained by the collected distribution line bird's nest images, and ablation experiments with corresponding evaluation indexes are set up for comparative analysis. The experimental results show that the proposed model in this paper has good performance in terms of detection accuracy and speed. The Params and FLOPs of the model are 6.14M and 2.6G, which are 12.41% and 83.54% smaller than the original YOLOv5. The P, R, [email protected], and [email protected]:0.95 are 96.9%, 97.3%, 97.5%, and 83.3%, and the final detection speed FPS is 31.74Hz, which can meet the task of detection in real time with high accuracy.
Two heads are better than one: Dual systems obtain better performance in facial comparison

Forensic facial image comparison based on recognition algorithms has been widely applied in forensic science. Previous researches have been concentrating on the cases of using single system during comparison, while how to use multiple systems has not yet been studied. In this paper, a dual-systems model (including SeetaFace and FaceNet) for facial comparison was constructed, and Bayesian networks were utilized as the basic frame. In order to prove its superiority, a large-scale experiment (on the dataset CelebA) has been carried on to evaluate the score-based likelihood ratio. We used three likelihood ratio evaluation tools (Empirical Cross-Entropy, Cost Likelihood Ratio, Limit Tippett Plots) to assess the performance of the model. The Wasserstein distance was also used to evaluate the detailed likelihood ratio performance. The experimental results show that the likelihood ratio performance of our dual-systems model is better than single system. Besides, our method of model building and evaluation can also be used in the condition of triple or more systems.
A Study on Secured Unmanned Aerial Vehicle-Based Fog Computing Networks

&lt;div&gt;With the recent advancement in technologies, researchers worldwide have a growing interest in unmanned aerial vehicles (UAVs). The last few years have been significant in terms of its global awareness, adoption, and applications across industries. In UAV-aided wireless networks, there are some limitations in terms of power consumption, data computation, data processing, endurance, and security. So, the idea of UAVs and Edge or Fog computing together deals with the limitations and provides intelligence at the network’s edge, which makes it more valuable to use in emergency applications. Fog computing distributes data in a decentralized way and blockchain also works on the principle of decentralization. Blockchain, as a decentralized database, uses cryptographic methods including hash functions and public key encryption to secure the user information. It is a prominent solution to secure the user’s information in blocks and maintain privacy. The ongoing development in collaborative UAVs and fog computing networks had put further a major challenge of security that need to be investigated. This article presents a study on security attacks and requirements needed to ensure security in UAV-based fog networks. We provide blockchain security in UAV-based fog computing to secure a large volume of data. The communicated data is stored in the form of blocks with hash functions, which guarantee security in the network. Thus, the aim of blockchain security is to guarantee that any hostile third party cannot corrupt or change the live missions or tasks allocated to actual UAV–Fog nodes.&lt;/div&gt;
Improved lower bound for the complexity of unique shortest vector problem

Abstract Unique shortest vector problem (uSVP) plays an important role in lattice based cryptography. Many cryptographic schemes based their security on it. For the cofidence of those applications, it is essential to clarify the complexity of uSVP with different parameters. However, proving the NP-hardness of uSVP appears quite hard. To the state of the art, we are even not able to prove the NP-hardness of uSVP with constant parameters. In this work, we gave a lower bound for the hardness of uSVP with constant parameters, i.e. we proved that uSVP is at least as hard as gap shortest vector problem (GapSVP) with gap of $$O(\sqrt{n/\log (n)})$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mi>O</mml:mi> <mml:mo>(</mml:mo> <mml:msqrt> <mml:mrow> <mml:mi>n</mml:mi> <mml:mo>/</mml:mo> <mml:mo>log</mml:mo> <mml:mo>(</mml:mo> <mml:mi>n</mml:mi> <mml:mo>)</mml:mo> </mml:mrow> </mml:msqrt> <mml:mo>)</mml:mo> </mml:mrow> </mml:math> , which is in $$NP \cap coAM$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mi>N</mml:mi> <mml:mi>P</mml:mi> <mml:mo>∩</mml:mo> <mml:mi>c</mml:mi> <mml:mi>o</mml:mi> <mml:mi>A</mml:mi> <mml:mi>M</mml:mi> </mml:mrow> </mml:math> . Unlike previous works, our reduction works for paramters in a bigger range, especially when the constant hidden by the big- O in GapSVP is smaller than 1. Graphical abstract
Analytic ray tracer on GPU for central receiver systems

An accurate ray tracer is essential to calculate the efficiency of central receiver systems. Furthermore, its runtime has a direct influence on the applicable optimization method for finding optimal layouts of heliostat fields. Developing a ray tracer that achieves both objectives is the main focus of this study. There exist several different ray tracing techniques, starting from the classical Monte Carlo method to analytical convolution methods. Within this work, we introduce a new analytical ray tracer with high accuracy at low runtime. This is achieved by integrating over the bivariate Gaussian distributions used in our convolution ray tracer. In such a way the raytracer is capable of including the effect of multiple solar rays without the need of simulating each ray. Besides the overall efficiency, the new ray tracer can also compute accurate flux maps with thousand times fewer rays than the Monte Carlo method. To further improve the overall model accuracy, a new equation to convolute the sun, slope and tracking error is derived. We show that it perfectly matches the results of a separate accounting for these errors. The newly integrated convolution ray tracer is developed on the same platform as a bidirectional Monte Carlo ray tracer and a convolution method. Besides a large cross-validation of the results, this allows for a reasonable direct run time comparison. With extensive case studies, the quality of the solution, the run time, and various other aspects are investigated. Furthermore, all ray tracers are also implemented on the GPU improving the run time compared to the CPU version by a factor of 50. We show that the integrated convolution ray tracer running on the GPU achieves an accuracy of 99.95% for an annual simulation in 0.7 s for the PS10 and in 1.8 s for the Gemasolar.
High capacity reversible data hiding with contiguous space in encrypted images

Reversible data hiding in encrypted images (RDHEI) is used to embed secret data into an encrypted image to ensure data security and image privacy. However, a considerable portion of the existing methods do not achieve a high data embedding rate (ER) with complete reconstruction of the original image. Considering the aforementioned limitation, this paper proposes a new RDHEI method with contiguous space. The proposed method pre-processes the original image by dividing it into non-overlapping blocks to determine a label map based on absolute prediction errors. Next, the label map is encoded, and bit-plane shifting is done to create contiguous space for embedding. Thus, the proposed method effectively exploits local and overall image spatial correlation and creates a larger space for embedding in a bit-plane-wise manner. Compared to the state-of-the-art methods, the ER of the proposed method is significantly increased while ensuring complete reversibility and lossless extraction of the hidden data.
Debugging Buildings with Mixed Reality

When "debugging" i.e., diagnosing which hardware is malfunctioning in a building, building managers usually have to inspect the target location in person, collect extra measurements with sensing devices, and compare the readings against the data from the Building Management System (BMS). Throughout this process, they have to overcome the inconvenience of finding the corresponding points in the BMS of the physical hardware/measurement of interest and they usually have no better option than text to describe the precise locality of each measurement taken. We explore utilizing mixed-reality (MR) techniques to make this process more intuitive: we create a digital twin of the building and building hardware connecting to the BMS in an MR scene and build a handheld sensing platform that reports both precise location and sensing data to this scene in real time. We show that building managers can find and inspect target hardware points through the MR scene easily and the portable sensing platform facilitates the recording of sensing data along with precise localization information.
Towards Benchmarking Privacy Risk for Differential Privacy: A Survey

While utilizing the Differential Privacy (DP) method, there are still many open issues regarding the method's effectiveness and reliability, including multiple difficulties in measuring the privacy level, which leads to implications in assessing risks while operating with personal data. Today, many experts and researchers emphasize the need and importance of utilizing DP for various kinds of data and tasks. However, to the best of our knowledge, the studies have yet to analyze the DP method regarding privacy risk assessment, thereby assessing the risks while utilizing DP and in the context of known types of privacy attacks. As a result, this study examines the existing privacy risks for various DP types in the context of present types of attacks. Also, the concept of privacy risk for DP will be analyzed, along with the corresponding metrics and their utility measurement. Our research method relies on a literature review; as a result, studies published from 2010 to 2023 were reviewed. Selected articles we examined based on the existing types of attacks, methods and metrics used to assess privacy risks. Based on this, we advanced the concept of privacy risk since none of the scientific studies clearly established the notation privacy risk for DP. Thus, we seek to explain the concept of privacy risk for DP in the context of existing types of attacks, thereby enabling DP utilization concerning the GDPR.
Zone-Level Anomaly Detection in VAV Terminal Units Using an Unsupervised Learning Approach

Effective operation of HVAC systems is crucial to minimize energy inefficiencies and occupant discomfort. However, these systems can experience various problems, including hardware and software-related anomalies. On the other hand, conventional fault detection and diagnosis techniques mostly depend on pre-established lists of fault alarms resulting in the potential for anomalous occurrences that defy these fault classifications. This study presents a novel unsupervised approach for detecting anomalous zones in variable air volume (VAV) air handling units (AHUs). The method utilizes autoencoders (AE) and principal component analysis (PCA), based on zone-level trend logs. To evaluate the effectiveness of the proposed approach, a case study was conducted using data collected from a 71-zone VAV AHU system of an educational building in Ottawa, Canada. The proposed PCA-AE method successfully identified four anomalous zones by considering four zone-level trend logs. The findings demonstrated great adaptability for detecting a wide range of zone anomalies in VAV AHUs giving operators valuable insights about the system and notify of potential faults at early stage.
Design of Tree-Mimicking Solar Photovoltaic System Achieving Both Power Generation and Acceptability

The design of solar photovoltaic (PV) systems is a crucial challenge for sustainable urban development. Conventional systems are composed of flat-mounted systems, which maximize the power generation per unit cell. However, they require vast lands with sufficient sunlight and have been limited to installations in mountainous areas or on building rooftops. Solar PV tree has been proposed as a solution to this problem. It retains solar PV modules three-dimensionally in a structure similar to a natural tree. It has the potential to effectively utilize small scattered urban spaces by reducing the ground contact area compared to conventional systems. In solar PV tree systems, there is a demand for a design that harmonizes spatially with the urban environment, in addition to pursuing the conventional goal of ensuring power generation. In this paper, we parametrically describe tree-like shapes, evaluate their electrical performance and acceptability in the urban environment through simulations and demonstrate the effectiveness of utilizing small urban spaces.
Heterogeneous Multi-Agent Reinforcement Learning for Grid-Interactive Communities

Homogeneous Multi-Agent Reinforcement Learning (MARL) is well studied in games, robots, and simulations. What has not been fully explored is the effectiveness of Heterogeneous MARL in the building space. Heterogeneous MARL has been proven to be more effective than Homogeneous MARL in terms of performance in games. Heterogeneous MARL also has the added benefit of being a more realistic simulation because no two buildings can be expected to react in the same way. Here, we implement the MARLlib library with the CityLearn environment to analyze the benefits of Heterogeneous MARL and compare them to homogeneous agents in a small scale proof of concept.