Reviewerly/exHarmony huggingface dataset:
Ramifications of Approximate Posterior Inference for Bayesian Deep Learning in Adversarial and Out-of-Distribution Settings

Deep neural networks have been successful in diverse discriminative classification tasks, although, they are poorly calibrated often assigning high probability to misclassified predictions. Potential consequences could lead to trustworthiness and accountability of the models when deployed in real applications, where predictions are evaluated based on their confidence scores. Existing solutions suggest the benefits attained by combining deep neural networks and Bayesian inference to quantify uncertainty over the models’ predictions for ambiguous data points. In this work we propose to validate and test the efficacy of likelihood based models in the task of out of distribution detection (OoD). Across different datasets and metrics we show that Bayesian deep learning models indeed outperform conventional neural networks but in the event of minimal overlap between in/out distribution classes, even the best models exhibit a reduction in AUC scores in detecting OoD data. We hypothesise that the sensitivity of neural networks to unseen inputs could be a multi-factor phenomenon arising from the different architectural design choices often amplified by the curse of dimensionality. Preliminary investigations indicate the potential inherent role of bias due to choices of initialisation, architecture or activation functions. Furthermore, we perform an analysis on the effect of adversarial noise resistance methods regarding in and out-of-distribution performance when combined with Bayesian deep learners.
Some Fundamental Principles of Living Systems' Functioning and Their Impact on Human Psychological Systems

This theoretical article provides a brief description of the model of living systems’ functioning by defining them as self-reproducing information or as self-reproduction of resource flows patterns. It reviews the living systems growth limitation between their development cycles by the Fibonacci sequence. Besides, there are presented systems resource base criteria, necessary for accumulating the resources and their investment. The article also considers the conditions for the formation of various systems strategies. Then we reviewed the principles of elemental analysis of information by a person as a living system according to the considered model. The study also shows the possibility of forming priorities in analyzing information for 16 combinations as maximum. At that, it remains crucial to divide a human’s information analysis between the two hemispheres of the brain. The described combinations of priorities in a person’s information analysis are compared with the existing differential personality models, such as the big five personality traits, the Myers–Briggs type indicator, temperaments model and Honey and Mumford Learning styles.
Coherence and Probability in Legal Evidence

The authors investigate to what extent an evaluation of legal evidence in terms of coherence (suggested by Thagard, Amaya, Van Koppen and others) is reconcilable with a probabilistic (Bayesian) approach to legal evidence. The article is written by one author (Dahlman) with a background in the bayesian approach to legal evidence, and one author (Mackor) with a background in scenario theory. The authors find common ground but partly diverge in their conclusions. Their findings give support to the claim (reductionism) that coherence can be translated into probability without loss. Dahlman therefore concludes that the probabilistic vocabulary is superior to the coherence vocabulary, since it is more precise. Mackor is more agnostic in her conclusions about reductionism. In Mackor’s view, the findings of their joint investigation do not imply that the probabilistic approach is superior to the coherentist approach.
Next Big Challenges in Core AI Technology

The field of AI is rich in scientific and technical challenges. Progress needs to be made in machine learning paradigms to make them more efficient and less data intensive. Bridges between data-based and model-based AI are needed in order to benefit from the best of both approaches. Many real-life situations cannot yet be addressed by current robots, demanding progress in perception, scene interpretation or group coordination. This chapter addresses some of the major scientific and technological challenges in core AI technology.
Perspectivas de enseñanza en docentes que integran una red de matemáticas: percepciones sobre la integración de TIC y las formas de enseñar/ Teaching perspectives in teachers who integrate a mathematics network: perceptions about the integration of ICT and the ways of teaching

ResumenEl estudio pretendio identificar las perspectivas de ensenanza, las percepciones en la forma de ensenar y las intenciones de integracion de las TIC. La investigacion se contextualizo dentro del enfoque cuantitativo no experimental transaccional, de caracter descriptivo; asi como la tecnica cualitativa basada en los grupos de discusion, en un ambiente permisivo, no directivo. La muestra estuvo conformada por 28 docentes que forman parte de la Red de experienciasmatematicas de Norte de Santander-Colombia. Los instrumentos empleados fueron el inventario acerca de las perspectivas de ensenanza (Teaching Perspectives Inventory –TPI-), una pregunta para medir el nivel de uso de las TIC y los registros de la reactividad de los docentes, a traves de una discusion grupal en un foro virtual, donde se hallo que la perspectiva de ensenanza dominante es la de acompanamiento. Se concluyo que la integracion de las TIC esta influenciada por las creencias y determina en la practica la forma de ensenar las matematicas.AbstractThe study aimed to identify teaching perspectives, perceptions in the way of teaching, and integration intentions of ICT. The research was contextualized within the descriptive non-experimental transactional quantitative approach; as well as the qualitative technique based on the discussion groups, in a permissive, non-directive environment. The sample consisted of 28 teachers who are part of the Red de experiencias matematicas de Norte de Santander-Colombia. The instruments used were the Teaching Perspectives Inventory (TPI), a question to measure the level of use of ICT and the records of teachers' reactivity through a group discussion in a virtual forum, where it was found that the dominant teaching perspective is that of accompaniment. It was concluded that the integration of ICT is influenced by beliefs and determines in practice theway of teaching mathematics.
Implementasi Teori Ron Weber Mengenai User Interface dan Input Website Penerimaan Mahasiswa Baru (Studi Kasus Universitas X)

This study analyzes the new student acceptance website at University X related to input control and user interface. The theory used is Ron Weber's theory with his book that explains input control and effective and efficient for user interface. This research was conducted because prospective students who often have difficulty when registering online. The data collection method used a questionnaire against University X 2019 students. In addition to the questionnaire, the website for admission of new students was analyzed also using Ron Weber's standard theory of access rights for prospective students and admins. The results showed that, for data input validation, the results did not reach the Ron Weber theory standard target because there were still many shortcomings based on the answer determination score and the percentage of suitability. For the user interface, the new student acceptance website has reached the target even though there are still some shortcomings that are not in accordance with Ron Weber's theoretical standards.
Binary Maps for Image Separation in Iterative Neuronal Network Applications

Generating a series of images is an important task invarious fields of scientific research, e. g. Computational fluiddynamics (CFD). In the past years, solutions based on deepneural networks gained importance. In these tasks, it’s oftennecessary to declare regions of interest in the image. Further-more, the NN should only perform on these regions and therest should be ignored. With this paper, we propose an inno-vative and easy method for implementing this behavior in thefield of CFD.
LibGuides: Free Resources for Home Use: Work

Free resources to keep yourself happy and busy while staying at home during the COVID-19 epidemic.
CHALLENGES AND WAY OUT OF CYBER SECURITY ISSUES IN NIGERIA

Cyber-space is a platform where you can process, receive and send information. It includes phone set, computer system, network, information stored on the network, network devices, all this constitute what is being called Cyber space. This cyber space is not secure, and the process to make it secure is known as Cyber Security. Cyber-crime refers to the series of organized crime attacking both cyber space and cyber security. The Internet is one of the fastest-growing areas of technical infrastructure development. Google, Wikipedia and Bing to mention a few, give detailed answers to millions of questions every day. Cyberspace is a world that contains just about anything one is searching for. With the advent of these advancements in information accessibility and the advantages and applications of the internet comes an exponentially growing disadvantage- Cyber Crime. Cyber security has risen to become a national concern as threats concerning it now need to be taken more seriously. This paper attempts to provide an overview of Cybercrime and Cyber-security. It defines the concept of cybercrime, identify reasons for cyber-crime and its eradication. It looks at those involved and the reasons for their involvement. Methods of stepping up cyber security and the recommendations that would help in checking the increasing rate of cyber-crimes were highlighted. The paper also attempts to name some bottleneck of cybercrime and present practical and logical solutions to these threats.
Neighbor Class Consistency on Unsupervised Domain Adaptation

Unsupervised domain adaptation (UDA) is to make predictions for unlabeled data in a target domain with labeled data from source domain available. Recent advances exploit entropy minimization and self-training to align the feature of two domains. However, as decision boundary is largely biased towards source data, class-wise pseudo labels generated by target predictions are usually very noisy, and trusting those noisy supervisions might potentially deteriorate the intrinsic target discriminative feature. Motivated by agglomerative clustering which assumes that features in the near neighborhood should be clustered together, we observe that target features from source pre-trained model are highly intrinsic discriminative and have a high probability of sharing the same label with their neighbors. Based on those observations, we propose a simple but effective method to impose Neighbor Class Consistency on target features to preserve and further strengthen the intrinsic discriminative nature of target data while regularizing the unified classifier less biased towards source data. We also introduce an entropy-based weighting scheme to help our framework more robust to the potential noisy neighbor supervision. We conduct ablation studies and extensive experiments on three UDA image classification benchmarks. Our method outperforms all existing UDA state-of-the-art.
A Distributed Neural Network Architecture for Robust Non-Linear Spatio-Temporal Prediction.

We introduce a distributed spatio-temporal artificial neural network architecture (DISTANA). It encodes mesh nodes using recurrent, neural prediction kernels (PKs), while neural transition kernels (TKs) transfer information between neighboring PKs, together modeling and predicting spatio-temporal time series dynamics. As a consequence, DISTANA assumes that generally applicable causes, which may be locally modified, generate the observed data. DISTANA learns in a parallel, spatially distributed manner, scales to large problem spaces, is capable of approximating complex dynamics, and is particularly robust to overfitting when compared to other competitive ANN models. Moreover, it is applicable to heterogeneously structured meshes.
PENGARUH KUALITAS PEMBELAJARAN DAN INTENSITAS MEMBACA TERHADAP HASIL BELAJAR MATA PELAJARAN AL-QURâ€™AN HADITS SISWA MI KECAMATAN DONOROJO KABUPATEN JEPARA

Abstract This research is aimed to find out how big the influence of learning quality toward Qurâ€™an Hadits learning output, to find out how big the influence of reading intensity toward Qurâ€™an Hadits learning output, and to find out how big the influence of learning quality and reading intensity toward Qurâ€™an Hadits learning output of MI students in Donorojo Sub-district Jepara Regency. This research is quantitative research with the kind of field research. Data obtained by interview, documentation, and questionnaire. Data analyzed by using quantitative analysis. The obtained data then analyzed using three analysis techniques, which are preliminary analysis, hypothesis analysis, and advanced analysis. Based on the result of research, it is known that: 1) Learning quality affects the Qurâ€™an Hadits learning output. Shown by the correlation coefficient of 0.57 while the simultaneous contribution is 0.325 and the remaining 0.675 is determined by other variables. (2) Reading intensity affects the learning outcomes of the Al-Qur'an Hadith. Shown by the correlation coefficient is 0.582 while the contribution simultaneously is 0.339 and the remaining 0.661 is determined by other variables. (3) Learning quality and reading intensity affect the learning outcomes of the Qur'an. The correlation coefficient is shown to be 0.662 while the simultaneous contribution is 0.438 or the remaining 0.562 is determined by other variables. Keywords: Reading Intensity; Learning Quality; Learning Outcomes
To Learn Effective Features: Understanding the Task-Specific Adaptation of MAML

Meta learning, an effective way for learning unseen tasks with few samples, is an important research area in machine learning. Model Agnostic Meta-Learning~(MAML)~(\cite{finn2017model}) is one of the most well-known gradient-based meta learning algorithms, that learns the meta-initialization through the inner and outer optimization loop. The inner loop is to perform fast adaptation in several gradient update steps with the support datapoints, while the outer loop to generalize the updated model to the query datapoints. Recently, it has been argued that instead of rapid learning and adaptation, the learned meta-initialization through MAML has already absorbed the high-quality features prior, where the task-specific head at training facilitates the feature learning. In this work, we investigate the impact of the task-specific adaptation of MAML and discuss the general formula for other gradient-based and metric-based meta-learning approaches. From our analysis, we further devise the Random Decision Planes~(RDP) algorithm to find a suitable linear classifier without any gradient descent step and the Meta Contrastive Learning~(MCL) algorithm to exploit the inter-samples relationship instead of the expensive inner-loop adaptation. We conduct sufficient experiments on various datasets to explore our proposed algorithms.
Research on Construction and Classification of Civil Aviation Cabin Corpus Based on Linguistics

For a long time, flight accidents caused by errors in land and air communications have not been uncommon. In order to reduce errors in land and air calls, the International Civil Aviation Organization and national regulatory agencies continue to improve the language standards for Chinese language communication in land and air calls. However, incidents of misunderstanding between the two parties on the ground and air communication occur from time to time. Among them, the problem of the ground and air communication caused by the pilot's command repeat error is the most common. Therefore, strengthening the semantic automation consistency measurement of the ground-air Chinese communication command has great and urgent practical significance for the pilot to correctly execute the command issued by the controller. First, use land-air call recordings to construct a repetitive land-air call corpus, and use word vectors to represent the words in the land-air call instructions; then use two RNN or LSTM-RNN models to describe the air traffic control instructions in each group of land-air calls. Repeat the instructions with the pilot and generate the semantic vectors of the two sentences; third, use the cosine similarity to measure the similarity of the two semantic vectors; finally, use common classifiers to determine the semantic consistency of the two parties in the land-air Chinese communication. The paper compares the algorithm models of two word vector generation methods, one-hot and word2vec in Chinese communication. Experimental results show that it is feasible to use RNN and LSTM-RNN models to describe the semantic vector of land-air call instructions and use classification methods to determine the semantic consistency of land-air calls, and the LSTM-RNN model is more suitable for describing land-air calls than the RNN model The semantics of the call Chinese language command has achieved a higher accuracy rate in the experiment.
Pose Measurement for Unmanned Aerial Vehicle Based on Rigid Skeleton

Pose measurement is a necessary technology for UAV navigation. Accurate pose measurement is the most important guarantee for a UAV stable flight. UAV pose measurement methods mostly use image matching with aircraft models or 2D points corresponding with 3D points. These methods will lead to pose measurement errors due to inaccurate contour and key feature point extraction. In order to solve these problems, a pose measurement method based on the structural characteristics of aircraft rigid skeleton is proposed in this paper. The depth information is introduced to guide and label the 2D feature points to eliminate the feature mismatch and segment the region. The space points obtained from the marked feature points fit the space linear equation of the rigid skeleton, and the UAV attitude is calculated by combining with the geometric model. This method does not need cooperative identification of the aircraft model, and can stably measure the position and attitude of short-range UAV in various environments. The effectiveness and reliability of the proposed method are verified by experiments on a visual simulation platform. The method proposed can prevent aircraft collision and ensure the safety of UAV navigation in autonomous refueling or formation flight.
Text Mining An Introduction, Technology, Techniques and Applications

Abstract This work presents a review of text mining areas, as long as the most familiar techniques and technologies are used., Including the proposals depending on the applications. There exits the various methods and devices/technologies to mine the contents and frequent data for future enhancement and decision-making purposes. The correct usage of right techniques helps for the speed enhancement. It reduces the time and efforts to extract useful information. Some of the most frequented text mining applications are cited. We discuss the existing approaches and then we conclude them as they are different and very supportive in nature. In the sense of that they can able to retrieve different information.
Digital Branch: Tween Explorations: Minute to Win It!

Join us as we compete in silly challenges together. The biggest challenge may be keeping a straight face!
Enchanted Environments as an identity rhizome; Story-making and its relation the the creators’ self

This paper re-appropriates the Deleuze and Guattari concept of the rhizome to reframe what the enchanted environments tell us about their creators’ identities and lives. The paper draws from several case-studies and research projects I have ran during the years, to review how the creation of enchanted environments (both story and design) results in non-linear often non-conscious reverberations of the author’s lives.The paper will include a book, the ‘backstage’ (2007), that was part of a wider research project exploring the connection between fairy-tale ‘fictions’ and ‘factual’ stories. The paper will analyse how the Backstage book traces my own personal experiences in graphic and journal form, providing a conscious reflection on how these had influenced my research choices. Amongst others, the case studies will include a project with Professor Jack Zipes titled Fw: One More Image - One More Line (2018-2019). This was about co-writing a story by just continuing each other’s story-lines and images. I would like to present the book, the paintings and objects that resulted from it and reflect on how the process of communicating and co-creating this enchanted environment was as a meaningful social function that allowed me to constantly reflect and reframe my everyday life. I will also signpost a few other story and performance based projects that I had ran in museums and schools the last 10 years and were about the retelling and remaking of stories as a way to ‘re-play’ one’s identity. The paper will reflect how by creating enchanted environments has transformed everyday spaces into enchanted spaces of telling the self.
Et SoMe-horrorshow: Et studie af billedmakroens multimodalitet og metaforicitet på Det Konservative Folkepartis officielle Instagramprofil

Over the past decade, the meme has been a defining feature of internet culture, and the image macro, a sub-genre of memes, has become a staple on most social media platforms. The image macro can now also be found in political communication, and the format is used by political parties in campaigning prior to elections. This paper examines the use of image macros on the official Instagram account of the Danish Conservative People’s party up to the 2019 election. I approach the analysis with a linguistic perspective, focusing on the multimodality, metaphoricity and humor of the image macro format. I hypothesize that these three perspectives bring to light interesting affordances, and that multimodality and metaphoricity are codependent features in the genre of image macros. This paper firstly concludes that the image macro format possesses certain affordances through its multimodal and metaphoric nature that make it ideal for political communication on social media platforms. Secondly, I propose different ways the image macro as an interesting multimodal format should be investigated further from a linguistic perspective.
Design and Application of Micro-Class Based on Hybrid Teaching Method of Theory and Practice

The education department proposed an action plan for education informatization 2.0 in 2018, and pointed out that the hybrid teaching model is an important trend in education activities for now and in the future. For vocational education activities, the integration of theory and practice can break through the disconnection between the previous teaching theory and practice, and improve the quality of professional teaching. Based on this, the following article takes the integrated physics-practice teaching in vocational education as the research object, and discusses the application of micro-class design, the application methods in the application-oriented undergraduate colleges, and the physics-practice-integrated teaching process. It aims at providing teaching methods and approaches for students’ actual needs to further improve students’ independent learning ability.