Reviewerly/exHarmony huggingface dataset:
A Study on De-Identification Methods to Create a Basis for Safety Report Text Mining Analysis

In order to identify and analyze potential aviation safety hazards, analysis of aviation safety report data must be preceded. Therefore, in consideration of the provisions of the Aviation Safety Act and the recommendations of ICAO Doc 9859 SMM Edition 4th, personal information in the reporting data and sensitive information of the reporter, etc. It identifies the scope of de-identification targets and suggests a method for applying de-identification processing technology to personal and sensitive information including unstructured text data.
New Adaptive Perceptual Quantization Method for HDR Video Coding

High dynamic range (HDR) videos need to be perceptually quantized into 10bit integer for compression, this perceptually quantization (PQ) process may lead to distortion and coding efficiency decreasing. To solve this problem, this paper proposes a new adaptive PQ (APQ) method. Firstly, the contrast distortion caused by PQ is analyzed, and the regions prone to contrast distortion are extracted. Then, by exploring the distribution characteristics in each channel, the pixel probability densities in different luminance ranges are counted and the distribution of the quantized 10bit HDR video is optimized by combining with visual perception. Furthermore, the regions prone to contrast distortion is optimized to protect the contrast information. Finally, the mapping for original HDR video signal is obtained by using the optimization strategy of 10-bit quantized data combined with inverse PQ. The experimental results show that the proposed APQ method can decrease the BD-rate in an average of 8.73 with HDR-VDP-2 as quality metric, which proves that the proposed APQ method can effectively reduce the distortion and improve coding performance.
Integrated Optics for Quantum Computer

Many of the proof-of-principle experiments in quantum optics have been carried out using table-top free-space optics. While they provide good testbeds for optical quantum optics experiments, to build actual devices and quantum computers, we have to move to the integrated optics where reproducibility and stability are more reliable. In this chapter, we review the developments of the integrated optics for optical quantum computer with the main focus on their applications in continuous-variable optical quantum computation.
Volunteer Drone: Search and Rescue of the Industrial Building Collapsed Worker

Drones are effective in search and rescue (SAR) operations due to their commercial availability and the way in which they access hard to reach areas. Specifically, a drone is compact enough to discover victims and gain access to accidents inside of a collapsed buildings. However, there are limitations when it comes to structure and control of the drone during such SAR operations. In practical applications, the condition of the disaster area is unpredictable. For academic purposes, this study assumes that the victim is pressed against a thick wall of cement in a low-light area, which is not reachable by GPS. The study proposes an overall architecture for a victim detection process, which aims to detect the victim in the shortest amount of time and with maximum accuracy. The drone model used is the DJI Mavic air 2 drone, with a Bluetooth Low Energy (BLE) detection application, a Beacon chipset, a Bluetooth enabled mobile phone, and the Hokuyo Lidar for detection and mapping. The study will implement a data mining approach and the results/analysis will show that using this approach can significantly improve the rescuers' efforts to find and save victims of accidents.
Design and Research of Braille Machine Control System Supported by Human-Computer Interaction

In order to meet the learning needs of the visually impaired patients, based on the tactile psychological and physiological characteristics of the visually impaired patients, this paper designs a braille machine control system. By analyzing the structural and functional principles of the braille machine, the overall flow chart of the control system of the braille machine was established, and the software program of the control system was compiled in combination with the needs of human-computer interaction and the particularity of the operating group, and the control system was used to carry out the braille test. The results showed that: Using the braille text written by the control system, its dot diameter, dot height, dot pitch and square distance are in line with national standards. And the variance of each parameter obtained by calculation is smaller than that of the national braille standard, which verifies the feasibility of the designed braille machine control system.
Signature system based on RSA algorithm and its algorithm optimization

With the rapid development of Internet technology, our life is increasingly inseparable from the Internet. The information exchange platform provided by the Internet makes our work and life simple, fast and efficient.At the same time, various network information attack methods are also threatening the security of network information transmission.In the process of data transmission through the network, we need to solve two problems. The first is to ensure that the data will not be leaked after being attacked. The second is that the data receiver can authenticate the data sender through certain methods.A digital signature encrypts and decrypts data by carrying a key. Generally, the key can be divided into a public key and a private key. The public key is used for data encryption and signature verification, and the private key is used for data decryption and signature.In view of the technical characteristics of digital signature, this paper designs a SET of RSA digital signature system based on C/C++ development platform after consulting relevant literature at home and abroad. Compared with the traditional RSA algorithm, this design has made some optimization in the selection of large prime numbers. For the application of digital signature system in various small systems,This is bound to have certain reference significance.
A familiar thought: Machines that replace us?

In this issue of Neuron, Tyulmankov et al., 2022 propose a model for familiarity detection whose parameters-including those guiding plasticity-are fully machine-tuned.
Pliable lasso for the support vector machine

In this article, we study the support vector machine with interaction effects. The pliable lasso penalty, which allows for estimating the main effects of the covariates X and the interaction effects between the covariates and a set modifiers Z is implemented to handle the interaction effect. Interaction variables are included in a hierarchical manner by first considering whether their corresponding main effect variables have been included in the model to avoid over-fitting. The loss function employed is the squared hinge loss, with the pliable lasso penalty and then, the block-wise coordinate descent approach is employed. The results from the simulation and real data show the effectiveness of the pliable lasso in building support vector machine models in situations where interaction effects are involved.
Methodological peculiarities of organizing work in the profession-oriented pedagogical class

The subject of this research is the arrangement of educational process in in the profession-oriented pedagogical class. The goal lies in explication of methodological peculiarities of working with high school students for professional self-determination and formation of their readiness to study at the pedagogical university. Leaning on the principles of multidimensional and nonlinear arrangement of educational process, humanistic orientation, unity of collective and individual forms of organizing the activity, as well as focus on the development of socially successful personality, the author outlines the methodological peculiarities of arrangement of educational environment of high school students in the &amp;ldquo;School &amp;ndash; University&amp;rdquo; system. The project paradigm is implemented from the perspective of personality-oriented and subject-activity approaches in the four main modules of variable part of the curriculum of the profession-oriented pedagogical class: 1) &amp;ldquo;In the world of pedagogical profession&amp;rdquo;; 2) &amp;ldquo;The learned person: practical psychology of cognition&amp;rdquo;; 3) &amp;ldquo;I find myself in pedagogical profession; 4) &amp;ldquo;Learn who you truly are&amp;rdquo;. Readiness of high school students for project activity implies the abilities to search for data and establish correlations, create a project, analyze the problem field of research, find alternative solutions, and assess the acquired results. Project and game methods meet the goals of professional self-determination of high school students. The article presents the experience of functioning of the profession-oriented pedagogical class as the joint project of the Department of Pedagogy and Psychology of Bashkir State Pedagogical University named after M. Akmulla and School No. 113 named after Hero of the Soviet Union Ivan Ignatievich Rybalko (Ufa). Methodological recommendations on organizing the educational process in the profession-oriented pedagogical class can be applied in the senior middle school.
Seismic Soundoff: The hidden stories data tell

In this episode, Joe Dellinger discusses his 2022 Distinguished Instructor Short Course, “Forensic data processing.” Dellinger brings his experience, expertise, wisdom, and humor to this essential conversation on data that will be valuable for every geophysicist. Hear the full episode at https://seg.org/podcast/post/13652 .
Controlling an effector with eye movements: The effect of entangled sensory and motor responsibilities

Restoring arm and hand function has been indicated by individuals with tetraplegia as one of the most important factors for regaining independence. The overall goal of our research is to develop assistive technologies that allow individuals with tetraplegia to control functional reaching movements. This study served as an initial step toward our overall goal by assessing the feasibility of using eye movements to control the motion of an effector in an experimental environment. We aimed to understand how additional motor requirements placed on the eyes affected eye-hand coordination during functional reaching. We were particularly interested in how eye fixation error was affected when the sensory and motor functions of the eyes were entangled due to the additional motor responsibility. We recorded participants' eye and hand movements while they reached for targets on a monitor. We presented a cursor at the participant's point of gaze position which can be thought of as being similar to the control of an assistive robot arm. To measure eye fixation error, we used an offline filter to extract eye fixations from the raw eye movement data. We compared the fixations to the locations of the targets presented on the monitor. The results show that not only are humans able to use eye movements to direct the cursor to a desired location (1.04 ± 0.15 cm), but they can do so with error similar to that of the hand (0.84 ± 0.05 cm). In other words, despite the additional motor responsibility placed on the eyes during direct eye-movement control of an effector, the ability to coordinate functional reaching movements was unaffected. The outcomes of this study support the efficacy of using the eyes as a direct command input for controlling movement.
Online Algorithms for Polynomial Regression on Physical Reservoir Computers with Noisy Measurements

Reservoir computing is a neuromorphic computing paradigm that employs nonlinear dynamical systems for signal processing. Hardware realization of reservoir computers (RCs), physical RCs, has demonstrated promising ability for applications such as high-speed speech processing. However, physical RCs may be impacted by intrinsic noise in the hardware. In this work, we propose a modified least-mean squares and a modified recursive least squares adaptive algorithms for online estimation using RCs with noisy state measurements. We establish their convergence and demonstrate their efficacy on numerical examples.
TTV Regularized LRTA Technique for the Estimation of Haze Model Parameters in Video Dehazing

Nowadays, intelligent transport systems have a major role in providing a safe and secure traffic society for passengers, pedestrians, and vehicles. However, some bad weather conditions such as haze or fog may affect the visual clarity of video footage captured by the camera. This will cause a malfunction in further video processing algorithms performed by such automated systems. This article proposes an efficient technique for estimating the atmospheric light and the transmission map in the haze model entirely in tensor domain for video dehazing. In this work, the atmospheric light is appraised using the Mie scattering principle of visible light and the temporal coherency among the frames is achieved by means of tensor algebra. Furthermore, the transmission map is computed using Low Rank Tensor Approximation (LRTA) based on Weighted Tensor Nuclear Norm (WTNN) minimization and Tensor Total Variation (TTV) regularization. WTNN minimization is used to smooth the coarse transmission map, and TTV regularization is employed to maintain spatio-temporal continuity by preserving the details of salient structures and edges. The novelty of the proposed model is confined in the efficient formulation of a unified optimization model for the estimation of transmission map and atmospheric light in the tensor domain with fine-tuned regularization terms, which is not reported till now in the direction of video dehazing. Extensive experiments show that the proposed method outperforms state-of-the-art methods in video dehazing.
Power Management and Tracking System Using Dual Axis Solar Panel-DASP

Global inhabitant’s number is rapidly growing, and thus we are worried about the electricity. Non-renewable energy sources like fossil fuels are soon being depleted and in future days they will no longer be existing. Another possible energy source which is renewable and available in huge quantity is needed. Solar radiation fits both the ideas. In today’s generation energy crisis is a huge problem. Now the only option left to meet the energy requirements is to make use of abundantly available renewable resources like wind, solar energy, etc. Most cost effective energy is solar energy because it will not only meet our energy demands but also provides cheap and clean energy. Hence, there are many technologies coming up to focus on gathering solar energy. One of such technologies is using a Double Axis Sun tracing system which traces the sunrays in both X and Y axis and gives rise to maximum power outcome in an efficient way.
An Effective Machine Learning Approach for Clustering Categorical Data with High Dimensions

Many modern real world databases include redundant quantities of categorical data that contribute in data processing and efficient decision-making with their advances in database technology. However, for the reasons that they are identical to measurements the clustering algorithms are only devised for numerical results. An immense amount of work is being performed on the clustering of categorical data using a specifically defined similarity measure over categorical data. Thereby, the dynamic issue with real-world domain, which does not clearly take the predictive form, is the inner function. The function is based on both unseen and transonic perspective. This then offers a detailed and inventive collaboration with categorical results. The paper describes a stratified, immune-based approach with a new similarity metric, in order to reduce distance function, for clustering CAIS categorical data. For successful exploration of clusters over categorical results, CAIS adopts an immunology focused approach. It also selects subsistent nomadic characteristics as a representative entity and organize them into clusters that quantify affinity. To minimize database throughput, CAIS is segmented into several attributes. The analytical findings show that the proposed solution yields greater mining performance on different categorical datasets and outperforms EM on categorical datasets.
Hospital Assistant Robotic Vehicle (HARVi)

With the advancements of robotic technologies, the medical environments are adopting more and more aspects of automation to enhance the services in hospitals. In pandemic situations such as COVID 19, direct contact with patients may result in the spreading of disease. In this paper we are going to discuss the design and development of an automatic guided vehicle for hospital applications, which can be controlled remotely. Here we are going to specify the design and development of an automatic guided vehicle for hospital applications, which can be controlled remotely. HARVi is a line follower robot, powered by a battery that can be charged from solar energy.
Monocular Multi-Pose Pedestrian Ranging Algorithm Based on Key Point Detection

A monocular multi-pose pedestrian ranging algorithm is proposed for the problem that when existing ranging algorithms perform pedestrian ranging from monocular images, the multi-scale variation caused by the different height, body size, posture and angle of pedestrians usually produces huge deviations in the detection results. Firstly, the key point information of the person in the monocular image is acquired using the human key point extraction algorithm openpose, the key point coordinate information of the shoulder is filtered out, the shoulder width is pre-set, the information is fused using C++ API, then the depth distance between the pedestrian and the camera is measured using the improved pinhole imaging ranging model, and finally the data output is processed using the mean filter. Through experimental validation, the algorithm is finally verified on the collected data set to have a very high accuracy within 12m, with an error within 0.2m, and a high improvement in robustness for multi-attitude and multi-angle pedestrian ranging.
Satellite Attitude Estimation based on an Improved Strong Tracking Particle Filter

Particle filter has been an efficient tool in many areas, how to improve the sampling efficiency of particle filter and the capability of an algorithm to track an abrupt change state is the key to the research and application of particle filter. This paper combines the strong tracking filter with the particle filter, and proposes the improved strong tracking particle filtering (ISTPF) algorithm. The algorithm uses strong tracking Kalman filter to generate a correction term and uses it to correct the sample particles during particle filtering, which greatly improves sampling efficiency and resolves the problem of particle degeneracy and low sampling efficiency in the traditional strong tracking particle filtering algorithms. Finally, the ISTPF algorithm and the other two strong tracking filters STUKF and STEPF are applied to the satellite attitude estimation which is described by multiplicative error quaternion under the same conditions, and the simulation results demonstrate that ISTPF has the highest precision, which indicating that the ISTPF is feasible and superior.
THE ROLE OF TECHNOLOGY ON BUSINESS STRATEGY PERFORMANCE IN BUILDING CORE COMPETENCE IN SMALL MEDIUM ENTERPRISES MOGADISHU - SOMALIA.

&#x0D; &#x0D; &#x0D; The goal of this research was to look at the effect of technology on business strategy performance in Mogadishu, Somalia. The study focused on the impact of communication technologies on business strategy performance in Mogadishu, Somalia, the impact of digital marketing technologies on business strategy performance in Mogadishu, Somalia, and the significance of business information management technologies on business strategy performance in Mogadishu, Somalia. This study was conducted among a pre-selected group of local small and medium-sized enterprises (SME). The intended audience consisted of business employees and general managers. Staff and general managers were chosen based on their knowledge and expertise in the roles of technology in the attainment of business goals in their respective regions. The study's sample size was 92 respondents from the target population. This is due to the nature of the data to be created, which involves the employment of numerous methodologies to gain a better knowledge of the study issue under consideration. According to Amin, the method is also well-known for enhancing validity and reliability while decreasing biases (2012). Sloven's algorithm was used to estimate the sample size of genuine respondents. To collect data, self-administered questionnaires were used, and these were sent to employees and general managers for replies. This study found that communication technologies have a significant impact on the success of business strategies at small and medium-sized enterprises (SMEs) in Mogadishu, Somalia, although digital marketing and business information management technologies had a minor impact. Business strategy and technology are tightly connected. A business strategy cannot be implemented if a firm lacks technology.&#x0D; Even firms that have successfully linked business and technology strategies fail to recognize the l ink between effective intellectual capital strategy and both business and technology strategies, so&#x0D; the researcher advised companies to focus on technology as a core competence and continue to b enefit from its various strategies such as communication strategies and digital marketing strategie s.&#x0D; &#x0D; &#x0D;
Multi-Granularity Feature Utilization Network for Cross-Modality Visible-Infrared Person Re-Identification

Cross-modality Visible-Infrared Person Re-identification aims to recognize images with the same identity between visible and infrared modalities. It not only includes the troubles of variations between cross-cameras, but also suffers from the huge differences between two modalities. Some existing methods deal with global features, while ignoring the fine-grained information. To solve this problem, our paper designs a multi-granularity feature utilization network (MFUN), which pays attention to both fine-grained and coarse-grained information. Firstly, for the sake of learning local features, we simultaneously use hard-mining triplet loss and heterogeneous center loss to better promote intra-class closeness and inter-class differences at coarse and fine granularity levels. Then, aiming at solving the problem of lacking modality-shared information, our method uses a multi-modality feature aggregation module for global features to narrow the modality gap. Extensive experimental results on RegDB and SYSU-MM01 datasets fully prove that our proposed MFUN has superiority over the state-of-the-art solutions.