Reviewerly/exHarmony huggingface dataset:
Interpretation of the ‘On Reducing the Size of the Student Body in Gymnasiums and Progymnasiums and Changing the Composition Thereof’ Report in the Kharkov Educational District

This paper features an analysis of a set of documents produced in the Kharkov Educational District to enable pedagogues to interpret correctly the 'On Reducing the Size of the Student Body in Gymnasiums and Progymnasiums and Changing the Composition Thereof' report, more commonly known as "The Kitchen Staff Children Circulaire".It is shown that the initial version of the document not only did not contain any specific measures to introduce the estate principle into education but also reflected the will of Alexander III, who regarded the introduction of tough estate restrictions for gymnasium students as inopportune and inconvenient.Yet the Trustee of the Kharkov Educational District, N.P. Vorontsov-Vel'yaminov, set a greater store by the mere wishes of the Minister of Public Education, I.D. Delyanov -the hope that administering control over the conditions in which gymnasium students were taught at home would make it possible to gradually free gymnasiums of children from the lower estates.N.P. Vorontsov-Vel'yaminov issued a special document intended to expound, and, in actual fact, to adjust, the ministerial circulaire and directed expressly that enrollment preference be given to gymnasium entrants of noble descent, while no children from the lower estates be admitted unless a special scholarship was available for them.Interestingly, in doing so N.P. Vorontsov-Vel'yaminov claimed that the new ministerial circulaire contained nothing particularly new and based the restrictions not on that document but on an 1870 legislation, which he interpreted in a biased manner.Thus, the estate restrictions introduced under Alexander III in Russian gymnasiums were engendered not so much by the 'On Reducing the Size of the Student Body in Gymnasiums and Progymnasiums and Changing the Composition Thereof'
Medial Turn in Education: Philosophy of Understanding Words and Images

The research presented in the article aimed to diagnose the profound changes that have taken place in education as a result of the expansion of screen technologies and the introduction of distance interaction in pedagogy. The live-sounding speech of a teacher is the true historical form of paideia as such and the technique of maieutics in particular. Any intermediary, even a writing process, let alone the screen, reduces the effect of pedagogical dialog and produces significant and radical changes in the understanding of meaning. The article deals with the problems of understanding in the pressing context of two turns of educational philosophy: linguistic and medial. The empirical background of these turns is represented, on the one hand, by the increasing extent of the visualized (“printed,” “screened”) word in educational procedures vs. the sounding word, and by the irreversible introduction of screen technologies, digitalization, and distance learning, on the other hand. The consideration is made through the prism of the comprehension-based approach focusing on the values and meanings of professional-pedagogical activity, namely, the systemic approach, the principles of language sign ambivalence, the method of stylistic decoding, the method of analysis of vocabulary definitions, and intertextuality.
The Implication of Artificial Intelligence and Machine Learning to Incorporate Intelligence in Architecture of Smart Cities

Worldwide rapid urbanisation has fuelled the growth of the importance of cities and enhanced economic activity. Increasing urban population creates the urgency to design the cities in the right fashion that will cater for the raising economical and social demands without compromising the environmental aspects. Inclusion of the efficient technologies and analysis of real-time data helps to improve the service of the cities and quality of life. A smart city collects the data through instrumentation and integrates data to derive intelligence. Utilisation of the data contributes to developing better public services, addressing the emerging issues before they turn to crisis and efficient uses of the resources. Automation in the optimisation of data assists to translate data from different devices in real-time. Effective use of Information and Communication Technologies (ICT), Analytics and Decision-making algorithm track data to predict the outcome and ensures the corrective action to meet the goals. Health care, traffic management, cyber security, energy management etc. are some bright examples of the application of AI and ML in smart cities.
Assessing soil mineralogy and weathering degree by a multi-range sensor synergistic approach: From parent rock to topsoil

The weathering degree (WD) of soils is usually based on the presence of weatherable primary minerals and the content of chemical elements along the soil profile, which traditionally demand expensive, time-consuming and environmentally unfriendly laboratory analyses (e.g. wet chemistry). To address this issue, proximal sensors often operating in only a single spectral range have been used in soil WD assessments. In this sense, we analyzed in detail the performance of proximal sensors operating at several spectral ranges to assess the soil mineralogy and WD. We selected three soil profiles developed from sedimentary rocks and analyzed samples from parent rock to topsoil. Samples from each horizon of the soil profiles and from the parent rock were collected and submitted to chemical, physical and mineralogical laboratory analyses. These samples were also analyzed using several sensors operating within different spectral ranges, i.e., gamma-ray, X-ray fluorescence (XRF), visible (VIS), infrared (IR) (near infrared, NIR, shortwave infrared, SWIR, and middle infrared, Mid-IR), and the AISA Fenix hyperspectral imaging systems (VIS-NIR-SWIR). Several WD indices were calculated using laboratory (reference) and multi-range sensor data for comparison. The spectral differences in the VIS-IR range were evaluated according to the soil mineralogy and WD. Gamma-ray and XRF sensors presented high correlation with the main soil elements in oxide minerals used in the WD quantification (R ranging from 0.70 to 0.99). We detected differences associated with the WD of each horizon of all profiles using data from VIS to Mid-IR ranges. The application of linear models to AISA images enabled the mapping and quantification of main elements along one soil profile (R2 ranging from 0.48 to 0.8) using a pixel-to-pixel approach, which facilitated the interpretation of the WD. Due to the specificity of each spectral region, the use of several sensors at different wavelengths provided a holistic overview of mineralogy and WD of the analyzed soil profiles, using a quick and non-destructive approach. This technique produces overlapping and/or complementary information of WD and mineralogy from all spectral ranges and can significantly assist traditional laboratory methods.
Diversity Preference-Aware Link Recommendation for Online Social Networks

Link recommendation, which recommends links to connect unlinked online social network users, is a fundamental social network analytics problem with ample business implications. Existing link recommendation methods tend to recommend similar friends to a user but overlook the user's diversity preference, although social psychology theories suggest the criticality of diversity preference to link recommendation performance. In recommender systems, a field related to link recommendation, a number of diversification methods have been proposed to improve the diversity of recommended items. Nevertheless, diversity preference is distinct from diversity studied by diversification methods. To address these research gaps, we define and operationalize the concept of diversity preference for link recommendation and propose a new link recommendation problem: the diversity preference-aware link recommendation problem. We then analyze key properties of the new link recommendation problem and develop a novel link recommendation method to solve the problem. Using two large-scale online social network data sets, we conduct extensive empirical evaluations to demonstrate the superior performance of our method over representative diversification methods adapted for link recommendation as well as state-of-the-art link recommendation methods.
Efficient Reinforcement Learning from Demonstration Using Local Ensemble and Reparameterization with Split and Merge of Expert Policies

The current work on reinforcement learning (RL) from demonstrations often assumes the demonstrations are sam-ples from an optimal policy, an unrealistic assumption in practice. When demonstrations are generated by sub-optimal policies or have sparse state-action pairs, policy learned from sub-optimal demonstrations may mislead an agent with incorrect or non-local action decisions. We propose a new method called Local Ensemble and Reparameterization with Split and Merge of expert policies (LEARN-SAM) to improve efficiency and make better use of the sub-optimal demonstrations. First, LEARN-SAM employs a new concept, the A-function, based on a discrepancy measure between the current state to demonstrated states to “localize” the weights of the expert policies during learning. Second, LEARN-SAM employs a split-and-merge (SAM) mechanism by separating the helpful parts in each expert demonstration and regrouping them into new expert policies to use the demonstrations selectively. Both the A-function and SAM mechanism help boost the learning speed. Theoretically, we prove the invariant property of reparameterized policy before and after the SAM mechanism, providing theoretical guarantees for the convergence of the employed policy gradient method. We demonstrate the superiority of the LEARN-SAM method and its robustness with varying demonstration quality and sparsity in six experiments on complex continuous control problems of low to high dimensions, compared to existing methods on RL from demonstration.
The Colony: An Evo Devo Art Performance on Social Life

Abstract The Colony is an opera-theater performance about the evolution of social life in ants and humans. As part of a genre called Evo Devo Art, The Colony takes inspiration from evolutionary and developmental biology (i.e., Evo Devo) in both its subject matter and creation process. The work features projected animations and video, spoken performance, and music for soprano, keyboard, and electronics, composed using algorithms modeled on biological processes. The Colony combines art and science with the two-fold purpose of inspiring appreciation for our diverse biological world and using the ant colony to reflect on the challenges of human connection.
Research on the application of computer vision in the police equipment : Auto-targeting through YOLOv5s as an example

The previous automatic targeting techniques are prone to deviation and low robustness. To address the problem that physiological factors are liable to misfire and miscalculation when police shoot in critical situations, an automatic target recognition assistance system based on a lightweight and efficient YOLOv5s network is proposed. The system first introduces a lightweight feature-enhanced representation module to detect and classify targets according to real-time images captured by RGB cameras, which reduces the false detection rate of the network. Then, the user selects a controller based on the system's labeling of the target Head and Cavity. Finally, the feature-enhanced representation module extracts higher resolution maps, which helps in tiny target detection. Experiments show that the system maintains the lightness and efficiency of the YOLOv5s network. It achieves an average detection accuracy of 83.3% for Head and Cavity, which meets the performance requirements of AI models in police application scenarios and provides a theoretical basis for future computer vision applications in police equipment development.
RVAE-LAMOL: Residual Variational Autoencoder to Enhance Lifelong Language Learning

Lifelong Language Learning (LLL) aims to train a neural network to learn a stream of NLP tasks while retaining knowledge from previous tasks. However, previous works which followed data-free constraint still suffer from catastrophic forgetting issue, where the model forgets what it just learned from previous tasks. In order to alleviate catastrophic forgetting, we propose the residual variational autoencoder (RVAE) to enhance LAMOL, a recent LLL model, by mapping different tasks into a limited unified semantic space. In this space, previous tasks are easy to be correct to their own distribution by pseudo samples. Furthermore, we propose an identity task to make the model is discriminative to recognize the sample belonging to which task. For training RVAE-LAMOL better, we propose a novel training scheme Alternate Lag Training. In the experiments, we test RVAE-LAMOL on permutations of three datasets from DecaNLP. The experimental results demonstrate that RVAE-LAMOL outperforms naïve LAMOL on all permutations and generates more meaningful pseudo-samples.
Face Attribute Recognition Based on Task Similarity and Attention Module

Face attributes related single-task recognition technology has become mature, however, the research on deep multi-task learning in the field of face attribute recognition is not enough. Aiming at the problem that single-task recognition ignores the correlation of different attributes and the subjectivity of deep multi-task model construction, a model construction method is proposed for recognition of multiple attributes of human face simultaneously. Firstly, CKA is introduced to calculate the similarity between different single-task models, and the shared parts and branch parts of the deep multi-task model are determined; Then, a deep multi-task learning model is constructed, realizing information sharing through parameter sharing in the shallow layer, and completing the identification of specific attributes by setting different task output layers in the deep layer, through attention module to better accomplish the recognition of specific attributes. Finally, the effectiveness of this method is verified on the public datasets Adience and UTKFace, and more optimal experimental results are obtained for gender and age recognition.
Tourism Smart Building Condition Monitoring Platform based on Cloud Storage Data Center

Tourism smart building condition monitoring platform based on cloud storage data center is implemented in the study. For remote image remote sensing analysis, in fact, the problem of under-segmentation and over-segmentation will lead to the inability of uniform shape description of the candidate cultivated land objects. Therefore, the difficult-to-identify objects are classified into categories, that is, the preliminarily extracted cultivated land and non-cultivated land are used as samples to classify the remaining objects to be judged. Hence, for the designed model, the data center with the cloud is integrated. The Web server realizes the interaction with the Web browser by invoking the CGI program, that is, the CGI program receives and processes the information sent by the Web browser to the Web server, and the novel information transmission algorithm is designed for the systematic implementation.
PGCE: A distributed storage causal consistency model based on partial geo-replication and cloud-edge collaboration architecture

The current causal consistency model has trouble facing the high synchronization overheads and response delays found in cloud storage systems. This paper proposes a causal consistency model for distributed storage based on partial geographical replication and Cloud-Edge collaboration structure (PGCE). The model is based on the distributed network architecture of Cloud-Edge collaboration, and the cloud dataset is divided into multiple subsets by a hash function to store these subsets in the edge nodes that are close to the user network to realize partial geo-replication. At the same time, the timestamp stabilization mechanism and metadata processing service are set up to implement data consistency between nodes on the premise of ensuring causality, reducing the overhead of metadata processing and data synchronization. The client interacts directly with the edge nodes, which reduces the response delay of interacts with the cloud DC. An evaluation of the PGCE compared with existing models shows that it has better performance in response latency and throughput.
PAC-Wrap

Anomaly detection is essential for preventing hazardous outcomes for safety-critical applications like autonomous driving. Given their safety-criticality, these applications benefit from provable bounds on various errors in anomaly detection. To achieve this goal in the semi-supervised setting, we propose to provide Probably Approximately Correct (PAC) guarantees on the false negative and false positive detection rates for anomaly detection algorithms. Our method (PAC-Wrap) can wrap around virtually any existing semi-supervised and unsupervised anomaly detection method, endowing it with rigorous guarantees. Our experiments with various anomaly detectors and datasets indicate that PAC-Wrap is broadly effective.
Study on Generation of Picture Barcodes based on Encoding and Decoding Techniques

The study aims to throw light on the role of barcodes in our everyday life. Starting from the product identification codes, barcodes have now proved their efficiency in every field. Many fields that require quick data transfer like text, picture, audio and video or identification have installed barcodes as their medium. The key factor of barcode is its adaptability to any field, which has eased the usage of automatic machines. The study has also experimented with the best modulation algorithms- pattern substituted and Bincode for picture embedded barcodes that involved replacing image patterns as per the proposed algorithms. Both achieved the highest message embedding capacity of 8kb and 16kb respectively, which is the best results achieved so far.
Logic-based machine comprehension for chatbots

We target here the interpretation of natural language by means of Equational First Order Logic (FOL). The communicative acts of the human agent are automatically converted into a First Order Logic theory. The models of the FOL theory are analysed by Mace4 model finder. To goal of the chatbot is to reduce the number of interpretation models. With a single interpretation model, the chatbot is sure about the human agent preferences. To reduce the number of models, the agent asks questions, while the corresponding answers are used to restrict the models. The order in which these questions are asked is extremely relevant to effectevely reduce the huge number of interpretation models of a FOL theory. We apply here an entropy-based method to improve the dialogues between a conversational agent and a human agen. The new method computes the entropy on the set of possible interpretation models. The experiments indicated that with entropy-based chatbots it is easier to estimate the budget of questions needed to elicit client preferences.
Through-Lifecycle Whole-Design Optimisation Using Software Deployment Toolchains

Abstract Through-Lifecycle Whole Design Design Optimisation is widely considered one of the future approaches to solving design problems featuring prominently in Model-Based Systems Engineering, Set-Based Design and Digital Twins. Yet, design optimisation remains siloed optimising for design subsets. In this paper, we review academic literature and a series of case studies to uncover the challenges in achieving Through-Lifecycle Whole Design Design Optimisation. This is followed by action research that has investigated the application of software deployment toolchains to overcome the challenges.
Hybrid Consensus Approach for Blockchain based Vehicular Communication

Vehicular Communication involves Vehicle-to-Vehicle communication (V2V) and Vehicle to Roadside Units (V2R) communication. The faster communication among these nodes the better is the communication. To ensure data security and reliability this paper proposed a Blockchain-based method to share data and access data in a Blockchain -based Vehicular Communication, a hybrid consensus method that consumes less energy and reduces e-waste and at the same time hard to hack the system which makes the proposed system safer. Block Manager(BM) is a block node that is installed at the roadside areas to monitor and control vehicular communication in an easy way. PoL and PBFT consensus has been integrated together to get a better result. This approach shows a better result as compared to the previous study and resist's many common attacks which are discussed in this paper in detail.
Barcolits: Barcodes using LED tags and optical camera communications

This work presents Barcolits, active LED tags that exploit the sequential acquisition of rolling-shutter cameras to materialize virtual barcodes in images. The generated barcodes have valuable properties. Compared to physically printed bar-codes, its size (in pixels) does not change with the distance to the camera. Also, its stripes are always vertically aligned in the image independently of the rotation and position of the camera. Moreover, as Barcolits are active devices, they can be scanned under low light conditions or from reflections in walls or mirrors. Furthermore, this approach reuses conventional barcode detection and decoding algorithms, allowing interoperability and backwards compatibility with current barcode scanners (image or photodiode-based). These benefits have been experimentally validated using three different barcode encoding schemes: Code 128, EAN-13 and ITF. In the experiment conducted, these barcodes are compared in terms of their minimum required size to ensure high reliability, their data density, their signal level, and their likelihood to produce flickering in the LED source. The experiment results show that Code 128 are ideal for high throughput data sharing, with the highest achievable data density of 4.92 bits per pixel and the lowest power consumption. It outperforms standard manchester codes by encoding 42% more bits using the same payload size. On the other hand, EAN -13 barcodes require the smallest size for a successful scan but produce noticeable flickering if a long stream of different barcodes is transmitted. Finally, ITF codes mitigate flickering at the cost of reducing their density. This extensive analysis provides a comparison tool to help choose the optimal barcode scheme for different applications and environments.
Anomaly Detection and Prediction of Base Station KPI

This paper uses the periodic time series analysis method to study the short-term prediction of time series business data in specific operation and maintenance scenarios, identify and correct abnormal values of historical data series of the performance index of the operator&#x0027;s base station KPI, and realize the short-term multi-step prediction with high accuracy. This study innovates the detection method of outliers using the Nonhomogeneous Poisson Process (NHPP) and multilayer perceptron (MLP), and completes the multi-step time series prediction utilizing three periodic prediction methods, AUTO-ARIMA, Prophet and Trendy and Seasonal Linear Model (TSLM), based on the correction of outliers. The competitive side of the National University Big Data Challenge acknowledged the forecast result, and it was awarded the fifth place of the first prize in the competition. In intelligent operation and maintenance settings, this prediction model can serve as a guide for index prediction.