Reviewerly/exHarmony huggingface dataset:
HUMANITARIAN KNOWLEDGE AT THE ENTREPRENEURIAL UNIVERSITY: STRATEGIES OF «JUSTIFICATION»

Humanitarian knowledge and “liberal education” have been a problem for the modern innovative/entrepreneurial university as a commercial firm for the last fifty years: “impact” as an imperative of the university’s public discourse in liberal democracies and university policy apparently contradicts the statements about the “self-worth” or “uselessness” of humanitarian knowledge. The purpose of the article is to consider the main directions of the “justification” of the “humanitarian component” of the university, the ways of its politicization (the term U. Beck): appeals to values (culture), to the public good, civic education, to the methodological function of philosophy and the humanities, undertaken in this regard. The absence of grounds for the above-mentioned methods of politicization to matter is demonstrated: both in connection with their own inconsistency of such values, and as an argument for the prevailing public discourse of the university. It is concluded that the prevailing liberal interpretation of humanitarian knowledge necessarily provides for its transformation into a product whose price is determined by the market. The actual “problem situation” is not related to the “nature of humanitarian knowledge”, but to the “struggle for status”, that is, it is a question of power and prestige in connection with their redistribution in an entrepreneurial university as a commercial company.
Research on the Course Teaching Reform of Advanced Application of Databases

Advanced application of databases is a comprehensive new curriculum involving computer science, mathematics, informatics and management. Students are required to master more database-related knowledge. It cultivates students' database programming ability. However, as far as the current arrangement of advanced application courses for the database in undergraduate colleges and universities is concerned, the practical content of some schools is still relatively backward. This paper mainly aims at the problems existing in the practical teaching of the advanced application course of the database, and puts forward specific suggestions for improvement and corresponding measures.
Ode to Heraclitus

Other| May 01 2022 Ode to Heraclitus Leemon McHenry Leemon McHenry Search for other works by this author on: This Site Google Process Studies (2022) 51 (1): 5. https://doi.org/10.5406/21543682.51.1.01 Cite Icon Cite Share Icon Share Twitter Permissions Search Site Citation Leemon McHenry; Ode to Heraclitus. Process Studies 1 January 2022; 51 (1): 5. doi: https://doi.org/10.5406/21543682.51.1.01 Download citation file: Zotero Reference Manager EasyBib Bookends Mendeley Papers EndNote RefWorks BibTex toolbar search Search Dropdown Menu toolbar search search input Search input auto suggest filter your search All Scholarly Publishing CollectiveUniversity of Illinois PressProcess Studies Search Advanced Search You do not currently have access to this content.
MODERN TECHNIQUES OF DIGITAL VISUAL DIDACTIC CONTENT OF MATHEMATICAL, INFORMATIC AND NATURAL EDUCATIONAL BRANCHES IN ELEMENTARY SCHOOL

The article clarifies the concept and features of the visual thinking development of primary school students. The effectiveness of visualization of didactic content in the primary school educational space is theoretically substantiated. Modern techniques of digital visualization of educational material in mathematics, computer science, and natural sciences are characterized. They are the following: scribing, infographics, intelligence cards, comics, crosswords, memes, book trailers, word clouds, etc. The basic principles of creation and presentation of didactic content in the format of digital visualization are singled out, in particular, principles of system quantization and cognitive visualization.
Sparsity and Compressed Sensing

Data-driven discovery is revolutionizing how we model, predict, and control complex systems. Now with Python and MATLAB®, this textbook trains mathematical scientists and engineers for the next generation of scientific discovery by offering a broad overview of the growing intersection of data-driven methods, machine learning, applied optimization, and classical fields of engineering mathematics and mathematical physics. With a focus on integrating dynamical systems modeling and control with modern methods in applied machine learning, this text includes methods that were chosen for their relevance, simplicity, and generality. Topics range from introductory to research-level material, making it accessible to advanced undergraduate and beginning graduate students from the engineering and physical sciences. The second edition features new chapters on reinforcement learning and physics-informed machine learning, significant new sections throughout, and chapter exercises. Online supplementary material – including lecture videos per section, homeworks, data, and code in MATLAB®, Python, Julia, and R – available on databookuw.com.
Multi-robot Cooperation Learning Based on Univariate Search Technique and Deep Deterministic Policy Gradient

Directly applying the method of single-agent re-inforcement learning(SARL) to multi-agent tasks is difficult, because it is often hard to achieve good cooperation results. In this paper, a novel training framework(called Uni-DDPG) based on Univariate Search Technique and Deep Deterministic Policy Gradient(DDPG) is proposed which can learn the cooperation between two agents. Specifically, the policy of one agent is fixed, and DDPG is used to learn the policy of the other agent, then the learned policy is fixed and the previously fixed policy is optimized with DDPG, so on and so on. In addition, in order to improve the robustness and efficiency of learning, Win-or-Learn-Fast method is adopted to update parameters. Finally, a two-robot cooperation task is built and the proposed method is utilized to learn the optimal policies. Results show that the two robots can learn cooperation with the proposed method which outperforms independent DDPG largely.
An Adaptive δ-GLMB Filter Under Noise Statistics Mismatch

Aiming at the problem of multi-target tracking under noise statistics mismatch, an adaptive δ-GLMB filter based on variational Bayesian (VB) approach is proposed. The joint distribution of predicted state and corresponding predicted error covariance matrix, and the joint distribution of measurement noise mean vector and covariance matrix are modeled as the Normal-inverse Wishart (NIW) distributions, in which the latent variables are described as the Gamma distributions. In this paper, the single-target filtering density is expressed as the mixture of Normal inverse Wishart inverse Wishart Gamma Gamma (NNIWNIWGG), and an NNIWNIWGG mixture implementation of δ-generalized labeled multi-Bernoulli (δ-GLMB) filter for linear Gaussian is given. According to the minimization of Kullback-Leibler divergence, the approximate solution of predicted likelihood is obtained. Simulation results show that the proposed adaptive δ-GLMB filter has high tracking accuracy in the case of noises statistics mismatch.
Coordinately assisted distillation of quantum coherence in multipartite system

We investigate the issue of assisted coherence distillation in the asymptotic limit (considering infinite copies of the resource states), by coordinately performing the identical local operations on the auxiliary systems of each copy. When we further restrict the coordinate operations to projective measurements, the distillation process is branched into many sub-processes. Finally, a simple formula is given that the assisted distillable coherence should be the maximal average coherence of the residual states. The formula makes the experimental research of assisted coherence distillation possible and convenient, especially for the case that the system and its auxiliary are in mixed states. By using the formula,\ we for the first time study the assisted coherence distillation in multipartite systems. Monogamy-like inequalities are given to constrain the distribution of the assisted distillable coherence in the subsystems. Taking three-qubit system for example, we experimentally prepare two types of tripartite correlated states, i.e., the $W$-type and GHZ-type states in a linear optical setup, and experimentally explore the assisted coherence distillation. Theoretical and experimental results agree well to verify the distribution inequalities given by us. Three measures of multipartite quantum correlation are also considered. The close relationship between the assisted coherence distillation and the genuine multipartite correlation is revealed.
Image enhancement on low-light and dark images for object detection using Artificial Intelligence for field practitioners

In recent times, there is a lot of demand for Artificial Intelligence solutions based on computer vision in various fields. Many solutions like object detection, fault detection, environment description, and scene prediction are helping to solve many real-life problems. But these solutions are dependent on vision-based computations. Generally, all these computations are designed in such a way that environment in each frame is visible and computation performed with data captured from the frame is in visible condition. But in the case of dark condition, the photons count that a camera capture decreases drastically, and the environment may not be visible. In this scenario, the system will fail to compute all the tasks that are dependent on visibility of environment. With the increase in Artificial Intelligence solutions using vision data, it is important to process low-light/dark images and draw intelligence from them. The information and subsequent intelligence available during low-light scenarios can be extracted wisely by our proposed deep learning architecture. The algorithm will process the raw data taken from the camera sensor and provides you the enhanced JPEG images. These enhanced images will be used to train the object detection using TensorFlow lite to detect the objects in the frames. The entire solution will be ported into the mobile devices for capturing the raw data to enhance images and detect the objects on the enhanced images. The proposed chapter will also explain how this solution will be used in the field assistance where user can be able to see the objects in the scene clearly with the enhanced images and detect the objects for machine repair and maintenance of various tasks.
Propositional Logic Syntax

An introduction to Propositional Logic, focusing on the syntax. The unique readability theorem. Implementation of a “Formula” Python object and parsing formulas.
Blockchain data privacy protection modeling based on CP-ABE algorithm

Abstract With the proposal of the concept of ubiquitous power Internet of Things and the further promotion of blockchain strategy, modern information technologies such as the Internet of Things and cloud computing have been widely used in the power system. While the power grid is becoming more intelligent, it is also accompanied by the emergence of various security threats, and the power grid will face greater challenges and pressures. In this paper, aiming at the current situation of information security faced by the power grid blockchain under the cloud Internet of Things, a multi-authorization center ciphertext policy attribute encryption access control method is constructed for the power grid blockchain. It adds the function of visitor attribute update revocation, aggregates multi-dimensional data into ciphertext, and uses CP-ABE encryption to implement fine-grained access control at the dimension level. Through experimental analysis, in terms of security and functionality, this method can resist physical collision attacks and manage access rights flexibly. In terms of encryption and decryption efficiency, the encryption and decryption calculation speed of the proposed method is 6.2–15.6% higher than that of the traditional method, and the time consumption efficiency is more than 5% higher than that of other methods. It proves that this model has more advantages for open network environments such as power grid blockchain with high confidentiality requirements and large scale of users. It is of great significance to study the data security and privacy protection methods of the power grid blockchain.
Binary decomposition-based image cipher algorithm with flexible method for key construction

&lt;span&gt;Image security is still one of the important fields in multimedia processing because it’s used in our daily lives. Watermarking, steganography, and ciphering are three directions to keep an image secret. Encryption defined as the process of changing information (which called plaintext) into an unreadable secret format (which called cipher-text). A new ciphering algorithm based on binary decomposition and binary codes conversion is proposed in this paper. The key is constructed in a flexible way based on the size of a secret image using some logical operations to increase the security levels. Three test images in different sizes were used to evaluate the performance of the proposed algorithm. The results of the visual scene and statistical factors proved that the suggested method was ciphering the image with high security. The proposed work was validated to confirm its effectiveness. As conclusions, the uses of decomposition and simple binary operations have given high-level image security. Also, key construction is an important step to face several types of attackers.&lt;/span&gt;
Enhanced Security Mechanism for Cryptographic File Systems Using Trusted Computing

Trusted Computing Platform (TCP) using Trusted Platform Module (TPM) cryptographic microcontroller system was introduced by the Trusted Computing Group (TCG). It can produce, store and manage the keys used for cryptographic purpose on the hardware itself. A number of companies, including HP, IBM, Apple, Dell, etc. now release their systems with an inbuilt TPM component. This paper elucidates the method of using TPM chip for efficient management of cryptographic keys in Enterprise-class Cryptographic File System (ECFS) through Linux operating systems. It provides an extra layer of superior quality security when it comes to security based on hardware devices. The proposed security architecture is deployed using TPM tools version 1.3.8 packages and TrouSerS, a Public Key Infrastructure (PKI) API with version number 0.3.9. A comparison analysis in terms of performance using TPM based ECFS with the state-of-art ECFS is presented in this paper. The results of the proposed scheme outperforms the existing systems.
A Method to Identify Graphs Containing a Specific Sub-graph

Graph structure represents a variety of adjacencies. A subgraph is a portion of a graph, and a particular subgraph can obtain the characteristics of an entire graph. In this paper, we propose a method to infer a graph that contain a target subgraph. A graph convolutional network (GCN) was used to turn the graphs into embedding vectors, which represent tendency of their structure. In the loss function used for training, we used Simpson coefficient to measure the overlapping of graphs. The degree of inclusion was measured as cosine similarity of the obtained vectors. To evaluate the method, we used card images of competitive Karuta. The adjacency of letters on the card was represented as a graph, and lack of recognized letters under the realistic environment such as under sunlight or shadow gives a subgraph of the graph for the card. We used our model to identify card with the subgraph, and found our method achieved over 90% of accuracy in this setting.
Wireless Standard Identification via Mel Frequency Cepstrum

Wireless standard identification is a process of determining the standard category or perceiving the system properties from the received wireless signal, which is applied to the cognitive radio or the spectrum monitoring. In this letter, we propose an identification approach that adopts the Mel frequency cepstral coefficients and the convolutional neural network to improve identification performance, especially for the signals with the identical carrier frequency and the same bandwidth or the similar time-frequency distributions. Its procedure is summarized as follows: First, the frequency and the bandwidth of the received signal are estimated using a modified thresholding method. Then, we devise a general warping function for the Mel filter bank to extract its coefficients. Finally, the coefficients are adopted as the specific features and the received signal can be identified using a convolutional neural network. Numerical results indicate that the proposed identifier outperforms the existing ones in terms of the identification accuracy.
Towards Open Science: The Precariat as a Subject of Scientific Creativity

In this reply to the article by I.T. Kasavin “Creativity as a social phenomenon” the authors discuss the possibilities of the scientific precariat as a free creative class, which having entered the scientific community, will give it a new creative potential. The authors express some doubts that such a merger will preserve precariat's special creative spirit. The article draws attention to the diversity in understanding the nature, goals and values of creativity. The specificity of understanding creativity in the scientific community is due to its rational and methodological guidelines, while the precariat is part of postmodern society, and its creative potential stems from a break with traditional rationalistic methodology. However, as long as peer review remains in the hands of the exclusively institutionalized scientific community, there is a high probability that the creativity of the precariat will be marginalized, and becoming part of the scientific community, the precariat will be forced to accept its epistemic values and lose the creative potential that is expected from them. The authors refer to P. Feyerabend’s theory of creativity, in which he rejects the subject-centered model of scientific creativity. According to Feyerabend, creativity is intersubjective in nature and grows organically from the sociocultural environment of the subject. The scientist creates following a cultural instinct, but the subject does not disappear from the creative process, since the cultural instinct is refracted through his individual style. If creativity is a reflection of the spirit of times, the precariat is the best way to embody it. In conclusion, a way for overcoming the social is outlined and, in a broad sense, the methodological problematic nature of the unification of the scientific community and the precariat, which the authors see in the general democratization and pluralism of society.
Adding Feature Images to Blog Posts

Feature images are commonly used for blog posts, including on this blog. We can use our screenshots or photos or stock photos (ideally license free or with an open license) from sites like Flickr, Unsplash, or Pexels. More recently, we can also use artificial intelligence tools such as DALL·E 2 that generate images from a description in natural language.
Trajectory Control of Multirotor Unmanned Aerial Vehicles via GPS Spoofing

Unmanned aerial vehicles (UAVs) defense is important to low altitude security. When countering multirotor unmanned aerial vehicles (MUAVs), it is required to consider the problem of trajectory control to avoid rushing into defense zones or crushing building obstacles, especially in the urban environment. GPS spoofing is a potential method to conduct the control but target studies on MUAVs are lacking, of which threats are daily on the increase. Therefore, a linearized model of MUAVs is established, and this linearized model is applied to the overall system of UAV-spoofer given by a series of linear dynamics. A discussion of the trajectory control problem at the view of deceived UAV is demonstrated, which regards a spoofer as an inverse process of UAV. A simulation is conducted to validate the established MUAV-spoofer model’s ability to realize trajectory control via GPS spoofing and the results showed that this model could achieve the goal to make MUAVs track a polygonal-line or curve trajectory as the spoofer expected.
Survey of Surveillance of Suspicious Behavior from CCTV Video Recording

Many of crimes that are executed through online-mode are un-identified. Currently, most of crimes are explicitly executed in front of surveillance cameras which are implicitly recorded through CCTV cameras. The surveillance systems recording failed to catch those culprits from recorded videos spontaneously. No doubt, the strategies were developed where the culprit’s photos are captured and criminal department start hunting for those culprits for days, months or years. Unfortunately, in most of cases, searching for culprits is prolonged for short span of time and later the hunting task may be stopped or closed, due to many reasons such as delay in searching process or permission restrictions or insufficient proofs or change of detective officers. Innumerable works exists in the market that monitors the suspicious behavior analysis of persons, but fails to catch them on the spot at the offence location. To resolve this critical issue, we need to make use of these recorded stored videos for catching the culprits on the spot at run-time by implementing an alerting strategy via an automated alarming system at run-time during the crime scenario itself. This survey emphasizes on various video surveillance systems that were developed earlier for catching the culprits. Many of the state-of-art systems lack the ability to surveillance such suspicious videos at run-time. Many of the existing surveillance systems fail to generate an alert or alarms during the execution of crime which are video recorded implicitly.
Grammatical Errors in Using Tenses Made by ESP Students

Sometimes the students of ESP or Non-English majoring error in using tenses. The low level of students' understanding of tense is one of the factors that greatly influences errors in making sentences. The objective of this research was to describe the students’ errors and its causes in using tenses. In this research, the writers used a qualitative method in a form of descriptive analysis (percentage) that included observation, collecting the students’ test, analyzing, and interpreting the data. For collecting the data, the researcher used test as the instrument. This research was conducted to 35 ESP students of Pharmaceutical Study Program of Institute of Health Science of Siti Khadijah Palembang. The result of the research showed that the percentage of the error made by 35 students as the samples in using tenses was 75%, consisted of the error of selection was 30%, the error of omission was 24% and error of ordering 21%. It means that the students of Pharmaceutical Study Program of Institute of Health Science of Siti Khadijah Palembang made error in using tense, because more than 70% of the students made error. The probable sources of the students’ error were interlanguage error and context in learning error, where 52% errors were caused by interlanguage.