Reviewerly/exHarmony huggingface dataset:
Contract as an algorithm: Introductory considerations

Legal norms contained in text-driven contracts (as well as in statutes and bylaws), which are written in natural language, can be subject of algorithmic conversion in certain phases of the contract circle (implementation, monitoring, control, interpretation). The application of the blockchain concept as a structural pattern opens the possibility of creating a code-driven contract with automated execution: a "smart" contract. Algorithmization is understood as a process which enables the text of the contract to be translated into a format that is understandable to software developers. To this end, the use of the following methodologies is proposed: design of a pseudocode, application of formal logic symbols and the use of flowcharts. Successful conversion of legal prose into a code calls for cooperation between lawyers and programmers. The framework of that cooperation is the establishment of the so-called "Legal Expert System" (LES). Originally conceived by lawyers, LES is a program which allows the algorithm to solve the problem of contract execution. Contract algorithmization should convert contracts from prose to a code, while preserving contract validity and efficiency. For the time being, smart contracts cannot regulate commercially complex scenarios; thus, the de lege lata application of smart contracts as a complete replacement for traditional (analogous) contracts is excluded. A potential object of algorithmization are the primary instructions aimed at executing the characteristic performance of the contract. Contract algorithmization is an ongoing process, which is here to stay. The significance of this process is indisputable but its scope, dynamics and assumptions are still only partially defined and tested. The necessary condition (but hardly a sufficient one) is to legitimize the conception of a contract as an algorithm in the process of defining contractual provisions. Further development of this concept will depend on the functioning of other elements in the environment where code-driven contracts would be used and, above all, on the commercial response to the entire process of contract algorithmization. In effect, in order to be widely applied, contract algorithmization must become a commercially viable activity.
Using Interactive E-books to Improve Students’ Academic Achievement in Mathematics

The study explored the effectiveness of e-books in teaching Mathematics to improve students’ academic achievement. Kotobee author and kotobee reader were used in the conduct of the study in one of the secondary schools in the Division of Cagayan de Oro City. The study used a one-group pretest-posttest design, involving quantitative and qualitative analyses. The kotobee author and reader, achievement test, and interviews were employed. Findings indicated that students achieved a satisfactory level when exposed to the interactive e-book. There was a statistically significant difference in how well students did on their tests before and after. Students are motivated to learn when using an interactive e-book to enhance their knowledge. Students had a remarkable learning experience with the interactive e-book.
Policy Gradient and Actor–Critic Learning in Continuous Time and Space: Theory and Algorithms

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Augmented Quarantine - An experiment in online teaching using augmented reality for customized design interventions

This paper presents experimental research about using Augmented Reality (AR) for interactive design processes, exploring a spatial "live" design method taking place in an overlay of real space and digital models.It discusses the processes and outcomes of a seminar undertaken at Graz University of Technology in winter term 2020/2021.Due to the Covid-19 pandemic, the course was taught online, and conceptualized to allow students the biggest possible learning experience during the lockdown.Ensuring accessibility to all participants, the seminar was based on the use of ubiquitous devices.The implementation of newly developed software, such as "Fologram", enabled the students to use AR systems at home with their personal computers and smartphones.The task of the course was to design customized interventions for the students' own domestic spaces, reacting to changing conditions and needs during the lockdown.The employed workflow was driven by an instant connection between 3D-modeling (Rhinoceros3D), parametric design (Grasshopper) and holographic immersion (Fologram).
TetraPackNet: Four-Corner-Based Object Detection in Logistics Use-Cases

While common image object detection tasks focus on bounding boxes or segmentation masks as object representations, we consider the problem of finding objects based on four arbitrary vertices. We propose a novel model, named TetraPackNet, to tackle this problem. TetraPackNet is based on CornerNet and uses similar algorithms and ideas. It is designated for applications requiring high-accuracy detection of regularly shaped objects, which is the case in the logistics use-case of packaging structure recognition. We evaluate our model on our specific real-world dataset for this use-case. Baselined against a previous solution, consisting of an instance segmentation model and adequate post-processing, TetraPackNet achieves superior results (9% higher in accuracy) in the sub-task of four-corner based transport unit side detection.
Sampling-Free Variational Inference for Neural Networks with Multiplicative Activation Noise

To adopt neural networks in safety critical domains, knowing whether we can trust their predictions is crucial. Bayesian neural networks (BNNs) provide uncertainty estimates by averaging predictions with respect to the posterior weight distribution. Variational inference methods for BNNs approximate the intractable weight posterior with a tractable distribution, yet mostly rely on sampling from the variational distribution during training and inference. Recent sampling-free approaches offer an alternative, but incur a significant parameter overhead. We here propose a more efficient parameterization of the posterior approximation for sampling-free variational inference that relies on the distribution induced by multiplicative Gaussian activation noise. This allows us to combine parameter efficiency with the benefits of sampling-free variational inference. Our approach yields competitive results for standard regression problems and scales well to large-scale image classification tasks, e.g. ImageNet.
RobCap: A Mobile Motion Capture System Mounted on a Robotic Arm

Fixed Motion Capture systems ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Mocap</i> ) are well-established nowadays and are available in indoor and outdoor environments. Here, we introduce <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">RobCap</i> , a Moving Motion Capture System designed for acquiring accurate three-dimensional kinematics of human motion with large displacements. RobCap consists of an optoelectronic Mocap (Codamotion) mounted on a robotic arm, to track the human motion in the local moving frame. This paper presents this device and expresses the local kinematics of the markers in a global coordinate system. We have acquired a set of 560 measurements, by controlling the Robotic arm to follow the subject’s movement and to track the markers attached to his wrist. The performance of the RobCap is evaluated through comparison with state-of-the-art technologies. The 3D error is found to be <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${9.7}\pm {4.1}\;\textit {mm}$ </tex-math></inline-formula> and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${3.9}\pm {0.8}\;\textit {mm}$ </tex-math></inline-formula> , respectively without and with calibration. RobCap increases the nominal capture volume of the fixed Mocap from <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${75}\;{m}^{{3}}$ </tex-math></inline-formula> to <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${523}\;{m}^{{3}}$ </tex-math></inline-formula> and the operational volume from <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${105}\;{m}^{{3}}$ </tex-math></inline-formula> to <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${1436}\;{m}^{{3}}$ </tex-math></inline-formula> . Our findings show that the RobCap is accurate and precise enough to be used as a mobile Mocap system for large displacements.
Improved chaos‐RSA‐based hybrid cryptosystem for image encryption and authentication

Summary This article puts forward a fast chaos‐RSA‐based hybrid cryptosystem to secure and authenticate secret images. The SHA‐512 is used to generate a 512‐bit initial key. The RSA system is used to encrypt the initial secret key and signature generation for both the sender and image authentication. In fact, a powerful block‐cipher algorithm is developed to encrypt and decrypt images with a high level of security. At this stage, a strong PRNG based on four chaotic systems is propounded to generate high‐quality keys. Therefore, an improved architecture is suggested. It performs confusion and diffusion of images with low computational complexity. In the final step, the encrypted secret key, signature, and encrypted image are combined together in order to obtain an encrypted signed image. The block‐cipher algorithm is evaluated in‐depth for several ordinary and medical images with different types, content, and size. The obtained simulation results demonstrate that the system enables high‐level security. The entropy has achieved a value of 7.9998 which is the most important feature of randomness. A comparative study against numerous recent encryption algorithms demonstrates that the proposed algorithm provides good results.
Fuzzy matrix game: A fast approach using artificial hybrid neural-net logic-gate switching circuit

Introducing neuro-fuzzy concept in decision-making problems, makes a new way in artificial intelligence and expert systems. Sometimes, neural networks are used to optimize certain performances. In general, knowledge acquisition becomes difficult when problem's variables, constraints, environment, decision maker's attitude and complex behavior are encountered with. A sense of fuzziness prevails in these situations, sometimes numerically and sometimes linguistically. Neural networks (or neural nets) help to overcome this problem. Neural networks are explicitly and implicitly hyped to draw out fuzzy rules from numerical information and linguistic information. Logic-gate and switching circuit mobilize the fuzzy data in crisp environment and can be used in artificial neural network, also. Game theory has a tremendous scope in decision making; consequently, decision makers' hesitant characters play an important role in it. In this paper, a game situation is clarified under artificial neural network through logic-gate switching circuit in hesitant fuzzy environment with a suitable example, and this concept can be applied in future for real-life situations.
Virtual Laboratory ChemСollective: Features, Benefits and Prospects of Using in Chemistry Practical Classes in Higher Education Establishments

The article examines the application of the virtual laboratory ChemCollective in the educational process of pharmacy student studying. The ChemCollective Virtual Lab is an online chemistry lab simulation that allows students to conduct experimental research according instruction and procedures, but also to design, invent and carry out their own home experiments. The Virtual Lab helps students to link chemical computations with authentic laboratory chemistry. The autograded labs create unknowns and provide feedback on students answers. The lab allows students to select from hundreds of standard reagents (aqueous) and manipulate them in a manner resembling a real lab. &#x0D; It is established that such virtual experimental works are interesting and useful for students. It is shown that the use of electronic instructions and procedures in Google Form for virtual laboratory work allows to get grades automatically, to set the time. These are too important during online studying. In addition, instructions in Google Form have the ability to attach photos, videos for illustration of processes, as well as providing of English vocabulary with Ukrainian translation to avoid language barrier. It is established that the effectiveness of the learning process increases because virtual laboratory is a simulator for skills training and independent preparation for laboratory research. It is shown that only a combination of virtual work with investigations in a real laboratory can develop skills for work with real equipments and cause rapid development of research competencies. The direction of further research is to study the resources of Royal Society of Chemistry, in particular the application of a multi-stage virtual screen experiment in chemistry practical classes for foreign students of higher education establishments.
An efficient multi-objective optimization approach for sensor management via multi-Bernoulli filtering

Abstract Intelligent sensor management is generally required for efficient and accurate data processing when the multi-sensor system is used for multi-target tracking (MTT). However, this is theoretically and computationally challenging. To deal with this problem, we propose a novel sensor management approach based on efficient multi-objective optimization for MTT under the framework of partially observed Markov decision process. The multi-Bernoulli filter is used in conjunction with two objective functions. To simplify the multi-objective optimization problem, we use the Euclidean distance (ED) between the feasible and utopian solution vectors as a measure of the objectives and then sequentially select sensors from the candidates. For the selected sensors, we rank them according to the obtained ED measure and implement the iterated-corrector fusion scheme after the ranking. Numerical studies demonstrate the effectiveness and efficiency of our approach in multi-sensor MTT scenarios.
The Internet and K-12 Education: Capturing Digital Metrics During the COVID-19 Era

As districts nationwide transitioned online to deliver remote or hybrid instruction as a result of the COVID-19 pandemic, the Internet has become an increasingly important space for K-12 students, teachers, and many other stakeholders. This brief discusses the utility of Internet metrics to measure K-12 district websites and its usefulness for exploring the digital nature of K-12 schools. We close this brief by discussing the necessity to explore K-12 Internet metrics, as the Internet has become an important data source for many diverse stakeholders.
On conceptualising and defining DevOps: short comments to Hemon-Hildgen and Rowe

"On conceptualising and defining DevOps: short comments to Hemon-Hildgen and Rowe." European Journal of Information Systems, 31(5), pp. 575–576
Biomechanics of Volleyball Players’ Run-Up and Take-Off Link under Deep Learning

In volleyball, the correct approach and start (including the number of steps and stride speed) are a prerequisite for all technical movements to attack. It can not only improve the horizontal speed of the athlete, but also properly convert the total speed into vertical speed, so that the hitting point is improved and the ball speed is accelerated. To explore the biomechanical characteristics of lower limb movements in the run-up and take-off stage of volleyball spiking, this paper takes four male volleyball players from the Physical Education College of X University as the research objects to analyze the kinematics and dynamics of the run-up process and the take-off process. This paper uses the precise recognition method under the background of deep learning to accurately capture the movements of the research object. This paper discusses the effects of time, speed, distance, knee, and hip parameters (angle, joint muscle torque, and power) on the effect of spiking techniques. It is expected to provide reference for the diagnosis, guidance, and muscle strength training of this special technical movement. The research results show that the horizontal speed of No. 2 athlete is 3.62 m/s and the vertical speed is 2.71 m/s when he takes off. The landing time is 0.375 s and the lift-off time is 0.16 s. The torque and power of the knee joint changed greatly during the take-off process, and the change of the hip joint was small.
Fair Facial Attribute Classification via Causal Graph-Based Attribute Translation

Recent studies have raised concerns regarding racial and gender disparity in facial attribute classification performance. As these attributes are directly and indirectly correlated with the sensitive attribute in a complex manner, simple disparate treatment is ineffective in reducing performance disparity. This paper focuses on achieving counterfactual fairness for facial attribute classification. Each labeled input image is used to generate two synthetic replicas: one under factual assumptions about the sensitive attribute and one under counterfactual. The proposed causal graph-based attribute translation generates realistic counterfactual images that consider the complicated causal relationship among the attributes with an encoder-decoder framework. A causal graph represents complex relationships among the attributes and is used to sample factual and counterfactual facial attributes of the given face image. The encoder-decoder architecture translates the given facial image to have sampled factual or counterfactual attributes while preserving its identity. The attribute classifier is trained for fair prediction with counterfactual regularization between factual and corresponding counterfactual translated images. Extensive experimental results on the CelebA dataset demonstrate the effectiveness and interpretability of the proposed learning method for classifying multiple face attributes.
Use of Similarity of Triangular Fuzzy Numbers and a Derivation Calculation Formula in Assessment of Mortise-Tenon Joints Applied in the Joinery Category of Regional Taiwan Skills Competitions

This study is intended to assess the types of joints applied in the Joinery Category of the Regional Taiwan Skills Competitions (hereafter referred to as the Regional Competitions, including Northern Taiwan, Central Taiwan and Southern Taiwan). The authors use a self-developed derivation calculation formula (∑i=1nw×hi) to examine the differences in the types of joints applied. Then a regression equation is adopted to estimate the distribution of weight values of the joint types to calculate the assessed values of the joint types likely to be used in the Regional Competitions of the 51st Taiwan Skills Competition. Subsequently, the similarity of the fuzzy number is employed to understand the tendency in the development of joint types from the 36th Regional Competition to the 50th Regional Competition. The outcome shows that 19 types of joints were applied and the assessed value of 2.3 of open bevel mortise-tenon joints was the highest, followed by the 2 of bevel inclined mortise-tenon joints and the 0.73 of sloped haunch mortise-tenon joints. Meanwhile, the application of similarity of triangular fuzzy numbers to examine the 19 types of mortise-tenon joints indicates that the area of the affiliation map of open bevel mortise-tenon joints is the largest. Compared to the other 18 types of mortise-tenon joints, the assessed value of level of similarity is the lowest. In other words, the assessed values acquired by using the similarity of triangular fuzzy numbers and by using the derivation calculation formula have the same tendency. Hence, it is suggested that when trainers/instructors determine training strategies, they should adopt open bevel mortise-tenon joints, bevel inclined mortise-tenon joints and sloped haunch mortise-tenon joints to be the assessment standards. At the same time, they can also use the affiliation map of similarity of triangular fuzzy numbers to evaluate the differences between various types of mortise-tenon joints to understand the tendency of joint type development. Thus, the teaching quality of trainers/instructors and the learning results of contestants can both be upgraded. Systematic approaches also have to be consolidated to strengthen the ability to assess vocational education and training (VET) in order to improve the learning effects and technical capacity of Joinery category contestants. It is hoped that the derivation calculation formula can be applied to evaluate the training of Joinery category contestants in regional competitions to improve the quality of VET.
Channel attention and multi-scale graph neural networks for skeleton-based action recognition

3D skeleton data has been widely used in action recognition as the skeleton-based method has achieved good performance in complex dynamic environments. The rise of spatio-temporal graph convolutions has attracted much attention to use graph convolution to extract spatial and temporal features together in the field of skeleton-based action recognition. However, due to the huge difference in the focus of spatial and temporal features, it is difficult to improve the efficiency of extracting the spatiotemporal features. In this paper, we propose a channel attention and multi-scale neural network (CA-MSN) for skeleton-based action recognition with a series of spatio-temporal extraction modules. We exploit the relationship of body joints hierarchically through two modules, i.e., a spatial module which uses the residual GCN network with the channel attention block to extract the high-level spatial features, and a temporal module which uses the multi-scale TCN network to extract the temporal features at different scales. We perform extensive experiments on both the NTU-RGBD60 and NTU-RGBD120 datasets to verify the effectiveness of our network. The comparison results show that our method achieves the state-of-the-art performance with the competitive computing speed. In order to test the application effect of our CA-MSN model, we design a multi-task tandem network consisting of 2D pose estimation, 2D to 3D pose regression and skeleton action recognition model. The end-to-end (RGB video-to-action type) recognition effect is demonstrated. The code is available at https://github.com/Rh-Dang/CA-MSN-action-recognition.git.