Reviewerly/exHarmony huggingface dataset:
Proposal of Time-based evaluation for Universal Sensor Evaluation Index in Self-generation of Reward

Designing a reward function for Reinforcement Learning is tedious such that a new reward function needs to be uniquely designed for each environment. Self-Generation of Reward (SGR) solves this by making the agent creates its own reward from the changes in the surrounding, rather than being dependent to the reward produced by the environment. SGR achieved this by perceiving the changes using sensors, similar to how living things perceive the changes in environment in term of stimulus. The input from sensors are evaluated using Universal Sensor Evaluation Index (USEI), before converting it into rewards. Current USEI uses only strength and predictability evaluation, making the evaluation for danger detection inaccurate for certain environment. To create a more accurate evaluation, we proposed that time-based evaluation needs to be included in USEI. The performance for both previous and proposed evaluation index are tested using maze-like environment and Q-learning. Performances for both evaluation index are then compared against one another.
True Random Number Generator Based on Wake-Up Ring Oscillator Utilizing Post-Processing Optimization to Generate Random Bit Sequence

Generating a series of secure and random keys for encryption and decryption process is a challenge in cryptography applications. The True Random Number Generator (TRNG) is one of the methods for producing a random key sequence. Its randomness depends on the entropy source, harvester method, and post-processing. In this research, we propose a TRNG design that utilizes a wake-up ring oscillator as an entropy source and the von Neumann corrector as post-processing. The wake-up ring oscillator is a modification of the conventional ring oscillator, while Von Neumann functions to prevent bias in the resulting number sequence. This design is implemented on the Zedboard Zynq-7000. The output randomness test of the proposed design shows that a number sequence passes all tests of the Five Basic Test with an alpha of 0.05. Furthermore, using the NIST 800-22 test indicates the resulting bits sequence has a P-value>0.01 with a proportion value more significant than the minimum pass rate for all tests. The implementation of the TRNG proposed scheme on the ZedBoard Zynq-7000 presents the percentage of resource utilization is 0.06% register, 0.14% LUT slice, and DSP = 1.5%.
Optical Manipulation of Qubits by Deep Reinforcement Learning

Quantum compiling and qubit manipulations can be efficiently solved by using deep reinforcement learning algorithms. The advantages range from lower computational time to real-time programming We review examples such as STIRAP and single qubits operation
A NOTE ON <i>k</i>-GALOIS LCD CODES OVER THE RING

Abstract We study the k -Galois linear complementary dual (LCD) codes over the finite chain ring $R=\mathbb {F}_q+u\mathbb {F}_q$ with $u^2=0$ , where $q=p^e$ and p is a prime number. We give a sufficient condition on the generator matrix for the existence of k -Galois LCD codes over R . Finally, we show that a linear code over R (for $k=0, q&gt; 3$ ) is equivalent to a Euclidean LCD code, and a linear code over R (for $0&lt;k&lt;e$ , $(p^{e-k}+1)\mid (p^e-1)$ and ${(p^e-1)}/{(p^{e-k}+1)}&gt;1$ ) is equivalent to a k -Galois LCD code.
Reverse Checking of Quantum Algorithm Execution

Verification of quantum computation is critical because undesirable interference and noise are major technical hurdles in quantum computing. We propose an approach for reverse checking of computation results that takes advantage of quantum teleportation and reversibility of unitary quantum gates. The main idea is to preserve the quantum state after the computation ends and before the result is measured, so that quantum teleportation can be performed to “save” the quantum state before the readout. After that, the computation is reversely performed in the reverse order of gate operations. The end result should match the original input. Any discrepancy would be proof of errors during computation or verification. The advantage of this approach is that the reverse computation circuit can be automatically generated and performed and that the error rate obtained reflects what happened during the actual computation. In addition, this approach leads to a potential way to reduce error rates in the future by discarding results from individual shots of the execution with detected errors.
PR-RRT*: Motion Planning of 6-DOF Robotic Arm Based on Improved RRT Algorithm

Based on RRT* algorithm, an improved sampling-based motion planning algorithm is proposed in this paper. Firstly, the kinematics analysis of the Schunk lwa3 manipulator is performed, and the D-H model is built. Then the forward kinematics solution of the manipulator is obtained, and the working space of the manipulator is solved based on the Monte Carlo method. Finally, based on the classic RRT motion planning algorithm and the rewiring method in RRT*, the probabilistic-root rapidly-exploring random tree motion planning algorithm is designed and verified by experiments.
Interaction Data Analysis for Personalized Recommendation System

As the volume of information exploded, the recommendation system emerged as an effective way to address information overload. In particular, personalized recommendation systems based on user-item interaction data (e.g. click rate and purchase history) are used in most recommendation systems such as e-commerce, video streaming service, and social media. These interaction data have a significant impact on the computing resources as well as the performance of the algorithm. However, in most personalized recommendation system studies, the computing resource problem of interaction data has received relatively less scrutiny. In this paper, we show in detail the computing resource problems caused by interaction data and define them as the long-tail phenomenon. Also, to analyze the long-tail phenomenon, a social phenomenon, we propose the analytical method applying graph theory.
Concurrent Associative Memories With Synaptic Delays

This article presents concurrent associative memories with synaptic delays useful for processing sequences of real vectors. Associative memories with synaptic delays were introduced by the authors for symbolic sequential inputs and demonstrated several advantages over other sequential memories. They were easy to organize and train. It was demonstrated that they were more robust than long short-term memories in recognition of damaged sequences. The associative memories can be applied in combination with deep neural networks to solve such symbol grounding problems, such as speech recognition, and support sequential memories triggered by sensory inputs. Several practical considerations for developed memories were discussed and illustrated. A continuous speech database was used to compare the developed method with LSTM memories. Tests demonstrated that the developed approach is more robust in recognition of speech sequences, particularly when the test sequences are damaged.
Modern technology for evaluating measures to optimize and improve the working of teacher and staff

Abstract The study was conducted using a natural science approach to assessing the working conditions of teachers and staff. Applied methods: system analysis, mathematical modeling, logical generalization of statistical data of a sociological survey. The introduction describes changes in the regulatory framework of the Ministry of labor of Russia. The requirements for the working conditions of the teaching staff during the period of self-isolation COVID-19 are described. Modern technology for evaluating measures to optimize and improve the working conditions of teachers and staff offers new calculation coefficients of criteria and indicators for evaluating the effectiveness of measures to optimize and improve working conditions. The effectiveness of measures is proposed to be evaluated by four groups of indicators: physical, social, economic, and reputational (reputation). For the resulting analysis of the effectiveness of measures, it is proposed to apply a "Generalized criterion". The generalized efficiency criterion is proposed to be calculated as a generalized Harrington desirability function. It is proposed to use regression methods, expert evaluation methods, and Bayesian methods to predict the effectiveness of the assessment. The simulation is performed using the Monte Carlo method, using Markov chains. The identified modern trends in achieving the University’s target indicators and indicators are described. Technologies that reduce time, labor, financial, and material costs are described. Overall satisfaction with the working conditions of teachers and staff was revealed. Modeling and forecasting were performed. New trends in achieving the University’s target indicators and indicators are identified and described. Ways to automate processes are suggested. A new motivational approach is described, which forms a new General vector of the University’s orientation. The practical significance of the research results is proved. In conclusion, the results of a sociological survey of teachers on satisfaction with working conditions are promised. The directions of development of the University that increase satisfaction with the working conditions of teachers and staff are identified.
Research on online cloud storage technology

Cloud computing is a popular concept nowadays is rapidly plays an important role in all walks of life to seize. As a typical application of SaaS, online cloud storage provides cloud data storage and handling, the major Internet companies are facing the public with their cloud network disk, the user can be your own file upload to the cloud, and then will be able to access these files from a variety of devices and locations. Cloud network disk in the technology still need to consider quite a lot of problems, such as user authentication, physical data storage, space compression. At the same time, users are increasingly concerned about the privacy issues, data reliability issues and how to profit is also a need to consider the object. Through understanding to personal online cloud storage implementation may be met, by reading the relevant information and documents, and combined with the knowledge learned in daily study, from a certain extent, put forward the corresponding solutions.
Group Abnormal Behavior Detection Based on Fuzzy Clustering

For group abnormal behavior analysis, previous methods are easy to be affected by the normal fluctuation characteristics of target or cause “missing” alarm. To overcome these problems, this paper proposes a group abnormal behavior detection method based on fuzzy clustering strategy, to improve the accuracy of abnormal trip behavior measurement and further mine the abnormal targets in the group. Experimental results demonstrate the efficiency of the proposed method on real-world datasets.
Analysis and Verification on the Equivalence Between Jerk-Level and Acceleration-Level Schemes Applied to Manipulators Controlling

Two different-level schemes are researched in this article for achieving the kinematic control of redundant manipulators, one of which is exploited at the acceleration level, and the other is at the jerk level. Firstly, they are both reconstructed as a standard quadratic programming problem with different parameter definitions and addressed by a gradient neural network (GNN) method. Secondly, from the perspective of the GNN algorithm, a theoretical interpretation of the intrinsic equivalence between the acceleration-level scheme and jerk-level scheme is performed. Further, simulations on the manipulator synthesized by the two schemes aided with the GNN method tracking two different trajectories are conducted. Finally, comparisons of relevant joint data (i.e., joint angles, joint velocities, and joint accelerations) are presented to substantiate the equivalence between the two schemes, and simulative experiments are carried out at the same time.
Decoupling graph convolutional networks for large-scale supervised classification

We present a new approach to large-scale supervised heterogeneous graph classification. We decouple a large heterogeneous graph into smaller homogeneous ones. In this paper, we show that our model provides results close to the state-of-the-art model while greatly simplifying calculations and makes it possible to process complex heterogeneous graphs on a much larger scale.
Contextual and Multi-Scale Feature Fusion Network for Traffic Sign Detection

The traffic sign detection, as an important part of the automatic driving system, requires high accuracy. In this paper, we proposed an end-to-end deep learning network, named the Contextual and Multi-Scale Feature Fusion Network, for traffic sign detection. The model consists of two sub-networks: the Weighted Multi-scale Feature Learning network (W-net) and the Contextual-Attention Learning network (C-net). The W-net extracts multi-scale features and calculates the weights of each feature map layer to detect traffic signs under different scales. The C-net learns the contextual attention mask of interference items and concatenates it with the multi-scale feature, which reduce the detection false efficiently. Compared with several state-of-the-art traffic sign detection methods, our proposed model outperforms others on extensive quantitative and qualitative experiments.
v1e: A Kernel for Domain-specific Textual Variability Modelling Languages

v1e is a language kernel for textual variability modelling built on top of the language-development system DjDSL. As a language kernel, v1e provides a minimal but extensible set of abstractions to implement language families for textual variability modelling. v1e provides for a small and versatile abstract syntax to encode variability models using multiplicity constraints and canonical semantics. v1e offers built-in analysis support via BDDs, such as configuration validation. A derived language becomes realised as a collection of extensions dependent on the language kernel. We showcase the design and implementation of a v1e-based implementation of TVL. We conclude the paper by pointing out current limitations (e.g., representing attributed variability models) and future directions (e.g., analysis support beyond BDD).
Large-Scale Agile Development Patterns for Enterprise and Solution Architects

Over the past decades, the emergence of agile development approaches has transformed the way software is developed. Even though systems are getting more and more complex, companies have to develop and release software faster and at the same time increase software quality. Due to the proven success of agile approaches, companies also try to make use of these benefits in large-scale software development projects with over 50 people and over 6 agile teams. However, this represents a risk and is often associated with challenges such as managing silos, complex functional dependencies between systems, and establishing an agile way of working for multiple teams. Especially enterprise and solution architects face a large number of problems in large-scale software development projects. Regardless of their importance for large-scale agile endeavors, there is a lack of research on their typical concerns and best practices. Based on mixed-methods research design, we provide an overview of typical concerns of enterprise and solution architects in large-scale agile development and present one principle: (1) Simplest Working Architecture, three patterns: (2) Lunch Talks, (3) Solution Space, (4) Principle-Based Intentional Architecture, and one anti-pattern: (5) Don't bw a PowerPoint Architect for addressing them.
Local Analytical System for Early Epidemic Detection

An Epidemic is a big threat to humanity. To reduce its catastrophic effect, many clinical practices and AI-based models are introduced to detect the onset of future Epidemic. An Analytical System can be useful for the prediction of an epidemic by collecting Quality data, modelling them and visualizing in different dimensions. This study deals with designing a Local Analytical System for early Epidemic detection in which the data related to human regular needs and responses are stored in the in-cube format. Analytical rules are used to produced faster pre-computed and pre-summarized inputs of the warehouse. Some desired inputs are selected from many local Warehouses which are then consolidated to form an incremental next higher-level data using the Layered Architecture style. This system can find the most commonly deviated data from the most frequently occurred patterns in the data submitted from the participating warehouses. The above-summarized patterns are mined using an FP-Growth algorithm to predict a new pattern. The patterns are ranked and inspected with their correlations for a possible unknown Epidemic.
Machine Learning Approach for Sentiment Analysis in Crime Information Retrieval

Crime information is usually announced periodically. To get the information in real-time, the information should be retrieved through the information retrieval system automatically. The system should choose some appropriate keywords for retrieving the crime data. Eight keywords have been chosen which represented the most viral topic. The keywords in this study were analyzed regarding their sentiment from the hashtags in twitter posts. The Machine Learning algorithms were utilised such as Multinomal Naive Bayes, Random Forest Classifier, Linear SVM, and Nearestneighborhood (kNN) finding a better classifier. Sentiments, both positive and negative, are usually have been used by the website content designer to attract the reader. Most keywords showed negative sentiment which showed the negative reaction from the people. The sentiment analysis in Bahasa Indonesia also useful for understanding the people's view on the types of crime as well as for keyword selection in the crime information retrieval system. As the result, near-repeat crime effect as a condition where criminal activity tends to repeat in the near place and time, can be predicted.
Content of career guidance work in “Advertising and Public Relations” Programme at Derzhavin TSU

We considered modern problems of career guidance at the university. Competition between universities for applicants (both quantitative and qualitative) makes it necessary to review the content of career guidance work. Universities have to become a center for career guidance with schoolchildren along with other interested persons (parents, employers, school). As a basis, we considered a level model of career guidance work (career guidance at the level of a university, institute/faculty, department, training/educational program directions), focused attention on the level of the educational program. We described the experience of career guidance work in “Advertising and Public Relations” Programme at Derzhavin Tambov State University. Students of schools and colleges are offered the project "School of the Young Advertiser", offering lectures and master classes from teachers and practitioners aimed at popularizing the professional areas of the advertiser and public relations specialist. As part of the regional project “Professions in Detail”, professional tests “Advertiser – Profession of the 21st Century” and “Modern PR Player” are offered for students in ninth grades.
Image Descriptors for Weakly Annotated Histopathological Breast Cancer Data

Cancerous Tissue Recognition (CTR) methodologies are continuously integrating advancements at the forefront of machine learning and computer vision, providing a variety of inference schemes for histopathological data. Histopathological data, in most cases, come in the form of high-resolution images, and thus methodologies operating at the patch level are more computationally attractive. Such methodologies capitalize on pixel level annotations (tissue delineations) from expert pathologists, which are then used to derive labels at the patch level. In this work, we envision a digital connected health system that augments the capabilities of the clinicians by providing powerful feature descriptors that may describe malignant regions.We start with a patch level descriptor, termed Covariance-Kernel Descriptor (CKD), capable of compactly describing tissue architectures associated with carcinomas. To leverage the recognition capability of the CKDs to larger slide regions, we resort to a multiple instance learning framework. In that direction, we derive the Weakly Annotated Image Descriptor (WAID) as the parameters of classifier decision boundaries in a Multiple Instance Learning framework. The WAID is computed on bags of patches corresponding to larger image regions for which binary labels (malignant vs. benign) are provided, thus obviating the necessity for tissue delineations.The CKD was seen to outperform all the considered descriptors, reaching classification accuracy (ACC) of 92.83%. and area under the curve (AUC) of 0.98. The CKD captures higher order correlations between features and was shown to achieve superior performance against a large collection of computer vision features on a private breast cancer dataset. The WAID outperform all other descriptors on the Breast Cancer Histopathological database (BreakHis) where correctly classified malignant (CCM) instances reached 91.27 and 92.00% at the patient and image level, respectively, without resorting to a deep learning scheme achieves state-of-the-art performance.Our proposed derivation of the CKD and WAID can help medical experts accomplish their work accurately and faster than the current state-of-the-art.
Attention-based contextual interaction asymmetric network for RGB-D saliency prediction

Saliency prediction on RGB-D images is an underexplored and challenging task in computer vision. We propose a channel-wise attention and contextual interaction asymmetric network for RGB-D saliency prediction. In the proposed network, a common feature extractor provides cross-modal complementarity between the RGB image and corresponding depth map. In addition, we introduce a four-stream feature-interaction module that fully leverages multiscale and cross-modal features for extracting contextual information. Moreover, we propose a channel-wise attention module to highlight the feature representation of salient regions. Finally, we refine coarse maps through a corresponding refinement block. Experimental results show that the proposed network achieves a performance comparable with state-of-the-art saliency prediction methods on two representative datasets.
Smart Farming Precision Agriculture Project Success based on Information Technology Capability

The imperative of any area to involve technology in anything creates loopholes. In terms of implementing Smart Farming Precision Agriculture, several problems arise, especially in rural areas, one of which is the availability of information technology infrastructure. The solution to these problems requires input for policymakers in the success or failure of implementing Smart Farming Precision Agriculture which is based on information technology capabilities. The aim of this research is to understand the relationship between Information Technology capability and Information Systems Project Success models in terms of applying Smart Farming Precision Agriculture and to integrate Information Technology capability and Information Systems Project Success models in the context of implementing Smart Farming Precision Agriculture. The proposed model is developed by adopting Chen's Information Technology capability model and McLean and DeLone's Information Systems Project Success model. The research result is a new model with 21 influential research hypotheses and aims to assess the success of the application of Smart Farming Precision Agriculture as a decision-making material in a project. Coherent submission of the definitions of variables, indicators, and questions from each measurement item is shown in this paper.