Reviewerly/exHarmony huggingface dataset:
Adversarial Transformer Language Models for Contextual Commonsense Inference

Contextualized or discourse aware commonsense inference is the task of generating coherent commonsense assertions (i.e., facts) from a given story, and a particular sentence from that story. Some problems with the task are: lack of controllability for topics of the inferred facts; lack of commonsense knowledge during training; and, possibly, hallucinated or false facts. In this work, we utilize a transformer model for this task and develop techniques to address the aforementioned problems in the task. We control the inference by introducing a new technique we call "hinting". Hinting is a kind of language model prompting, that utilizes both hard prompts (specific words) and soft prompts (virtual learnable templates). This serves as a control signal to advise the language model "what to talk about". Next, we establish a methodology for performing joint inference with multiple commonsense knowledge bases. Joint inference of commonsense requires care, because it is imprecise and the level of generality is more flexible. You want to be sure that the results "still make sense" for the context. To this end, we align the textual version of assertions from three knowledge graphs (ConceptNet, ATOMIC2020, and GLUCOSE) with a story and a target sentence. This combination allows us to train a single model to perform joint inference with multiple knowledge graphs. We show experimental results for the three knowledge graphs on joint inference. Our final contribution is exploring a GAN architecture that generates the contextualized commonsense assertions and scores them as to their plausibility through a discriminator. The result is an integrated system for contextual commonsense inference in stories, that can controllably generate plausible commonsense assertions, and takes advantage of joint inference between multiple commonsense knowledge bases.
Refined $F_5$ Algorithms for Ideals of Minors of Square Matrices

We consider the problem of computing a grevlex Gr\"obner basis for the set $F_r(M)$ of minors of size $r$ of an $n\times n$ matrix $M$ of generic linear forms over a field of characteristic zero or large enough. Such sets are not regular sequences; in fact, the ideal $\langle F_r(M) \rangle$ cannot be generated by a regular sequence. As such, when using the general-purpose algorithm $F_5$ to find the sought Gr\"obner basis, some computing time is wasted on reductions to zero. We use known results about the first syzygy module of $F_r(M)$ to refine the $F_5$ algorithm in order to detect more reductions to zero. In practice, our approach avoids a significant number of reductions to zero. In particular, in the case $r=n-2$, we prove that our new algorithm avoids all reductions to zero, and we provide a corresponding complexity analysis which improves upon the previously known estimates.
Matching Correlated Inhomogeneous Random Graphs using the $k$-core Estimator

We consider the task of estimating the latent vertex correspondence between two edge-correlated random graphs with generic, inhomogeneous structure. We study the so-called \emph{$k$-core estimator}, which outputs a vertex correspondence that induces a large, common subgraph of both graphs which has minimum degree at least $k$. We derive sufficient conditions under which the $k$-core estimator exactly or partially recovers the latent vertex correspondence. Finally, we specialize our general framework to derive new results on exact and partial recovery in correlated stochastic block models, correlated Chung-Lu graphs, and correlated random geometric graphs.
A Mathematical Model of Package Management Systems -- from General Event Structures to Antimatroids

This paper brings mathematical tools to bear on the study of package dependencies in software systems. We introduce structures known as Dependency Structures with Choice (DSC) that provide a mathematical account of such dependencies, inspired by the definition of general event structures in the study of concurrency. We equip DSCs with a particular notion of morphism and show that the category of DSCs is isomorphic to the category of antimatroids. We study the exactness properties of these equivalent categories, and show that they are finitely complete, have finite coproducts but not all coequalizers. Further, we construct a functor from a category of DSCs equipped with a certain subclass of morphisms to the opposite of the category of finite distributive lattices, making use of a simple finite characterization of the Bruns-Lakser completion, and finally, we introduce a formal account of versions of packages and introduce a mathematical account of package version-bound policies.
Q-Match: Self-Supervised Learning by Matching Distributions Induced by a Queue

In semi-supervised learning, student-teacher distribution matching has been successful in improving performance of models using unlabeled data in conjunction with few labeled samples. In this paper, we aim to replicate that success in the self-supervised setup where we do not have access to any labeled data during pre-training. We introduce our algorithm, Q-Match, and show it is possible to induce the student-teacher distributions without any knowledge of downstream classes by using a queue of embeddings of samples from the unlabeled dataset. We focus our study on tabular datasets and show that Q-Match outperforms previous self-supervised learning techniques when measuring downstream classification performance. Furthermore, we show that our method is sample efficient--in terms of both the labels required for downstream training and the amount of unlabeled data required for pre-training--and scales well to the sizes of both the labeled and unlabeled data.
Finite Model Theory and Proof Complexity revisited: Distinguishing graphs in Choiceless Polynomial Time and the Extended Polynomial Calculus

This paper extends prior work on the connections between logics from finite model theory and propositional/algebraic proof systems. We show that if all non-isomorphic graphs in a given graph class can be distinguished in the logic Choiceless Polynomial Time with counting (CPT), then they can also be distinguished in the bounded-degree extended polynomial calculus (EPC), and the refutations have roughly the same size as the resource consumption of the CPT-sentence. This allows to transfer lower bounds for EPC to CPT and thus constitutes a new potential approach towards better understanding the limits of CPT. A super-polynomial EPC lower bound for a PTIME-instance of the graph isomorphism problem would separate CPT from PTIME and thus solve a major open question in finite model theory. Further, using our result, we provide a model theoretic proof for the separation of bounded-degree polynomial calculus and bounded-degree extended polynomial calculus.
The Use of Web 2.0 Applications in Chemistry Teaching: Acids, Bases And Salts Unit

By engaging the focus of today's students, who are immersed in technology from birth, the employment of Web 2.0 tools in the educational process enriches learning environments. Because of this, the purpose of this study is to ascertain the impact of course materials created using Web 2.0 applications on the academic achievement and attitudes of 10th-grade chemistry students in the unit on "Acids, Bases, and Salts." One of the quantitative research techniques used in this study is a quasi-experimental design with a pre-and post-test control group. For this reason, students in the experimental group who used course materials developed by Web 2.0 applications and the control group who used the traditional teaching technique had their academic achievement scores and attitudes in the chemistry course compared. 48 students in the 10th grade from a public high school in the Yüreğir district of the province of Adana participated in the study, and the application procedure lasted for a total of 9 weeks. The academic achievement test post-test scores of the experimental and control groups showed a statistically significant difference in favor of the experimental group, according to the analysis's findings, whereas there was no statistically significant difference between the chemistry attitude scores. These findings revealed that students' achievement in chemistry was raised by activities created using Web 2.0 tools.
DEEP LEARNING APPROACHES FOR ANOMALY DETECTION IN INTERNET OF THINGS SYSTEMS

The utilization of Internet of Things (IoT) technology has become widespread across various industries and government services.Regrettably, IoT devices and networks are known for their susceptibility to security attacks that threaten data integrity and service reliability.The diversity of data collected from different IoT devices, combined with disruptions within the IoT system, make the identification of anomalous behavior and compromised nodes more challenging compared to traditional Information Technology (IT) networks.To address this issue, there is an urgent need for a reliable and efficient method for anomaly detection to prevent malicious data from being used in IoT-based decision support systems.In this study, we present a deep learning-based solution for anomaly detection in IoT systems.The proposed deep learning model is capable of learning and capturing features that are robust against unstable conditions and can provide useful information even in the presence of heterogeneity.The deep learning model is designed based on a denoising autoencoder and aims to obtain features that are resistant to the diverse environment of IoT.Empirical results, based on real-world IoT datasets, demonstrate the efficacy of the proposed framework in enhancing the accuracy of detecting malicious data compared to the state-of-the-art anomaly detection models in IoT systems.
Self-supervised phase unwrapping in fringe projection profilometry

Fast-speed and high-accuracy three-dimensional (3D) shape measurement has been the goal all along in fringe projection profilometry (FPP). The dual-frequency phase shifting (DF-PS) is one of the prominent technologies to achieve this goal. However, the period number of the high-frequency pattern of existing DF-PS approaches is usually limited by the amplified phase errors, setting a limit to measurement accuracy. Deep-learning-based phase unwrapping methods for single-camera FPP usually require labeled data for training. In this letter, a novel self-supervised phase unwrapping method for single-camera FPP systems is proposed. The trained network can retrieve the absolute fringe order from one phase map of 64-period and overperform DF-PS approaches in terms of depth accuracy. Experiments on real scenes demonstrate that the proposed method can unwrap the phase map of motion blur, isolated objects, low reflectivity, and phase discontinuity.
TEXT ENCODER USING NEURAL NETWORK

One of the active and difficult study areas in the world of pattern recognition and image processing has been handwriting recognition.It is an effort to use neural networks to recognise handwritten English alphabets.The neural network is trained using several character data sets. Classification and recognition are performed using the trained network.Character recognition (CR) has undergone substantial research over the past 50 years and has advanced to the point where it can develop technology-driven applications.Designing an expert system for "HCR(English) using Neural Network" is the primary goal of this research. utilizing an Artificial Neural Network technique, is capable of accurately identifying a certain character of type format.Given a specific initial state (data input), neural computers are taught (not programmed) to either classify the input data into one of the many classes or to cause the original data to grow in such a way that a specific desirable attribute is optimized. Keywords: Character recognition, Neural Network, Trained dataset, Image processing.
GESTICULATION BASED MEDIA PLAYER CONTROLLER USING DEEP LEARNING

In early, the world goes with interaction with complex systems that ensure a response. Gesticulation recognition systems receives attention in past and previous years because of their ability to interact with human and computer. Between the human and systems communication creates a complicated understanding about the signals. Our aim of this paper proposed the easy way to perform fast efficient of media player using gesticulation .In this concept without hardware we can control the media using the movement of hands (Gesture).In major role of deep learning model Keras detecting the motion of hands. This video breaks down this sometimes complicated concept into easy-to-understand parts. You’ll learn about 3 concepts: local receptive fields, shared weights and biases, and activation and pool. Keywords —Tensor flow, pyautogui ,keras ,streamlit, python open cv
Deep Learning for the Recognition of Human Speech

Abstract—Humans primarily communicate through speech, and language is their main means of doing so. Emotion is crucial to social connection. Knowing how to recognise the emotions in a speech is important and challenging given that we are dealing with human-machine interaction. Depending on the mood a person is going through, they will express themselves differently. When someone expresses their emotions, each one has a distinctive energy, pitch, and tone fluctuation that is categorised according to the situation. The identification of spoken emotions is thus a future goal for computer vision. The objective of our project is to develop intelligent speechthat utilises a convolutional neural network to recognise emotions. which uses a variety of modules for emotion recognition and classifier. Keywords— Human speech, Emotion, recognition of speech, GMM, Mel-Frequency-Cepstrum Coefficient (MFCC), linear prediction Cepstral coefficients (LPCC) , (LSTM)
Customer Classification Using Naive Bayes Classifier With Genetic Algorithm Feature Selection

There is a tendency to decrease the number of speedy customers in the operational area of ​​North Sumatra due to customer dissatisfaction. Termination of employment is carried out by the customer against PT. Telekomunikasi Indonesia, Tbk in North Sumatra. There is no management of customer data classification so that classification information based on certain product purchases cannot be known. Naïve Bayes is a classification algorithm that is easy to use but has weaknesses which result in poor performance, therefore feature selection is needed, the genetic algorithm is an algorithm that is able to select attributes in research, will be selected based on the highest weight so that the accuracy of the prediction results is more optimal. The steps taken in the measurement model using the Naive Bayes Classifier (NBC) approach and the model using the GA-NBC approach obtained accurate results from cross validation measurements, Confusion Matrix, ROC curves for the classification of existing and speedy telephone subscribers. The stages of the Naive Bayes process are: data collection, data preprocessing, processing of the Naive Bayes Classifier algorithm. Then the results are validated and evaluated using the Text Mining Algorithm, and calculating the parameters based on the genetic algorithm. The accuracy produced by the Naive Bayes Classifier model is 85.08%. The accuracy produced by the Naive Bayes Classifier model with the selection of Genetic Algorithm features increased to 89.31%.
COLOR DETECTION USING OPENCV

The program that displays the user's desired print color is called color recognition technology. It compares the color codes that the program already provides with the image whose colors we want to know. Let's begin by asking, We are aware of a disability that is very common in humans called "color blindness." Though it is very common in humans, most people can live with it because, as long as you don't think too much about the colors, it won't affect your overall performance. The future is self- driving vehicles that require color recognition to make road travel simple. It is true that no one has seen the future, that currently there is no cure for color blindness. How ever is always small). The program compares the R, G, and B values of colors to those in the image. After that, the name of the color is shown to the user in a way that is easy for them to see. This system can help normal people, especially men, understand colors, so it could be very useful. It could also be used to connect this system to goggles or specs and helmets for motorcycle drivers. These things may not seem like they would be of any use right now to most people, but those who suffer from it probably find Because it will help them recognize the color (even if they won't see it, they'll still be able to recognize it). Therefore, it is straightforward to assert that the implementation of this program will benefit a large number of individuals. Asa result, the program is designed with very low sensitivity and is equipped with a large number of colors to provide many comparisons and a very small chance of getting a wrong color or a missing color. This program makes use of OpenCV. Keywords: Color Blindness, RGB Values, OpenCV.
IAG Membership Information

Geostandards and Geoanalytical ResearchVolume 47, Issue 1 p. 211-211 Membership Information IAG Membership Information First published: 14 February 2023 https://doi.org/10.1111/ggr.12432AboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onEmailFacebookTwitterLinkedInRedditWechat No abstract is available for this article. Volume47, Issue1March 2023Pages 211-211 RelatedInformation
Application of the flipped classroom model to stimulate university students’ learning with online education

This work sets out the results of a teaching innovation project applying the flipped classroom model in the Knowledge of Mathematics module on a bachelor’s degree in primary education over two consecutive years at a university that exclusively uses online teaching. This model is used with the aim of stimulating the students’ working methods and improve their intrinsic motivation. The research evaluates the impact of the flipped classroom on academic performance in the subject and collects information on student satisfaction with the development of the experiment. This is a pioneering study as it analyses learning outcomes with mathematical content in university-level classes delivered online synchronously on a permanent basis and not owing to exceptional circumstances such as the Covid-19 pandemic.To do this, specific videos were developed, which students had to watch, with various practical exercises embedded in them to be done before classes. The marks from these exercises along with the final grades for the course are considered to assess the learning outcomes throughout the semester. The results obtained were clearly positive both in performance and in terms of students’ motivation and awareness of their own learning process, as well as the contribution of this proposal to improving their teaching–learning process, essentially through greater student involvement in and commitment to the subject.
A service design thinking approach: What are the barriers and opportunities of using Augmented Reality for primary science education?

It has previously been argued that Augmented Reality (AR) has the potential to provide relevant digital information to support pupils learning in real-time through engaging formats. However, the use of AR in the classroom remains uncommon for mainstream adoption. This study uses a service design methodology to focus on a more holistic approach toward AR as an educational service by investigating the challenges and conditions of integrating AR learning experiences into a primary curriculum. The research paper aims to provide an overview of how primary science (Key stage 2) in the UK is currently being delivered and where both barriers and opportunities occur for adoption to integrate more meaningful AR experiences that add real value to primary science education. An exploration stage (phase one) includes a questionnaire, contextual interviews, classroom observations, and focus groups. A thematic analysis was conducted on gathered data, identifying emerging patterns around teachers’ needs. The discussion section empathises with teachers’ needs and unearths key factors surrounding the implications of using augmented reality for primary science education.
The End of Art - The Subjective Objectification of DIKWP Philosophy

We proposed a modelling of art expression space including objective content and cognition in the form of data, information, knowledge, wisdom composition driven by purpose/intent/goal analysis (DIKWP modeling and analysis). Thereafter in the unified DIKWP expression space and interaction space, we analyze the motivation and creation of art both in conceptual language expression level and in the semantic level beneath the conceptual thinking crossing subjective category, objective category and the bidirectional transformation between subjective-objective transformation. We identified through reasoning that the completion of the existing creation patterns in terms of covering the main creation patterns in terms of the location and interplay between subjective-objective representing mind, process, and physical world based on the DIKWP reality and DIKWP anesthetic conformation and composition. We also foresee the art creation in the background of the future human evolution towards human-machine/AI integration.
LANDMARK DETECTION USING DEEP LEARNING

In many computer vision applications, finding landmarks is a crucial challenge. In this thesis, we suggest a unique convolutional neural network-based method for landmark detection (CNNs). On the PASCAL VOC and COCO datasets, our solution surpasses cutting-edge techniques. The landmark detection method we suggest using CNN is built on a three-layer deep network. The input image's features are extracted by the feature extraction layer, which is the first layer. The input image is categorised into one of the pre-established landmark categories in the second layer, which is a classification layer. A localization layer, which is the third layer, locates the landmarks in the input picture. Keywords—CNN,tensorflow_hub,streamlit, geolocation,landmark detection, classification
The Impact of E-Human Resources Management on the Organizational Success of Telecommunications Companies Operating in Jordan

This study's objective was to evaluate the impact of electronic human resource management with its dimensions (e-recruitment, e-training, e-performance evaluation, and e-compensation) on organizational success with its combined dimensions (organizational readiness, survival, and growth) in Jordanian telecom companies. The study employed a descriptive-analytical approach, and the study population included all employees of Jordanian telecommunications companies Orange, Umniah, and Zain. From the 433 upper and middle administrative levels, a proportional stratified sample of 2019 employees was drawn. Significant findings include the existence of high levels in all dimensions of electronic human resources management, a high level of organizational success among Jordanian telecommunications firms, and a statistically significant effect of all dimensions of electronic human resources management on organizational success. In order to achieve a high level of organizational success, boost efficiency and effectiveness, and gain a competitive advantage, the study recommends that telecom companies operating in Jordan continue to enhance the dimensions of electronic human resource management by assisting them in understanding its significance. Keywords: Electronic human resource management, organizational success, Telecom companies operating in Jordan