Reviewerly/exHarmony huggingface dataset:
A combined informative and representative active learning approach for plankton taxa labeling

With an ever-increasing amount of image data, the manual labeling process has become the bottleneck in many machine learning applications. Plankton taxa labeling is especially a challenge due to its complex nature, and the manual labeling effort places a large burden on the domain experts. The Active Learning (AL) paradigm is a promising research direction adopted in the literature to minimize the manual labeling effort exerted by domain experts. Many approaches for AL have been proposed over the recent years to improve the labeling task by supporting the construction of large datasets suitable to train machine learning models while minimizing human involvement in the process. Our empirical study suggests that many modern active learning methods fail to incorporate both the samples that represent the statistical pattern of the data and the samples in which the machine learning model is not confident about. Inspired by these limitations, we propose an algorithm that combines these two types of sampling in order to capture the data distribution of the whole feature space, prevent redundant sampling from correlated uncertainty queries and finetune the inter-class decision boundary. Our experiments show that the proposed method outperforms each of the methods separately. Further, it also proves to be efficient on both the CIFAR dataset and the more complex Kaggle plankton dataset.
Difference Rewards Policy Gradients

Policy gradient methods have become one of the most popular classes of algorithms for multi-agent reinforcement learning. A key challenge, however, that is not addressed by many of these methods is multi-agent credit assignment: assessing an agent’s contribution to the overall performance, which is crucial for learning good policies. We propose a novel algorithm called Dr.Reinforce that explicitly tackles this by combining difference rewards with policy gradients to allow for learning decentralized policies when the reward function is known. By differencing the reward function directly, Dr.Reinforce avoids difficulties associated with learning the 푄-function as done by Counterfactual Multiagent Policy Gradients (COMA), a state-of-the-art difference rewards method. For applications where the reward function is unknown, we show the effectiveness of a version of Dr.Reinforce that learns a reward network that is used to estimate the difference rewards.
Statistical Modelling Of Global Solar Radiation On Horizontal Surface Using Monthly Means Daily Sunshine Hours And Some Climatic Variables For Zamfara State, Nigeria

In this study, four empirical models on the basis of linear Angstrom-Prescotte model for predicting monthly mean daily global solar radiation on horizontal surface were developed, correlating solar radiation to climatic parameters of sunshine hours, temperature and relative humidity.The models were used to predict Global solar radiation, an average solar radiation of 16.9690 and maximum and minimum of 19.2647 and 13.5222 C 0 were respectively predicted.For validation and the accuracy of the models, statistical coefficient of determination, R 2 , mean percentage error, (MPE), mean bias error, (MBE), and root mean square error (RMSE) were used.The result showed that, model correlating solar radiation with Sunshine hours, temperature and relative humidity gives the best fit for the study location with R 2 = 83.89%,that was;
`Less Than One'-Shot Learning: Learning N Classes From M < N Samples

Deep neural networks require large training sets but suffer from high computational cost and long training times. Training on much smaller training sets while maintaining nearly the same accuracy would be very beneficial. In the few-shot learning setting, a model must learn a new class given only a small number of samples from that class. One-shot learning is an extreme form of few-shot learning where the model must learn a new class from a single example. We propose the 'less than one'-shot learning task where models must learn N new classes given only M
Online Spatio-temporal 3D Convolutional Neural Network for Early Recognition of Handwritten Gestures

Inspired by recent spatio-temporal Convolutional Neural Networks in computer vision field, we propose OLT-C3D (Online Long-Term Convolutional 3D), a new architecture based on a 3D Convolutional Neural Network (3D CNN) to address the complex task of early recognition of 2D handwritten gestures in real time. The input signal of the gesture is translated into an image sequence along time with the trajectory history. The image sequence is passed into our 3D CNN OLT-C3D which gives a prediction at each new frame. OLT-C3D is coupled with an integrated temporal reject system to postpone the decision in time if more information is needed. Moreover our system is end-to-end trainable, OLT-C3D and the temporal reject system are jointly trained to optimize the earliness of the decision. Our approach achieves superior performances on two complementary and freely available datasets: ILGDB and MTGSetB.
Causative Factor of Library Usage Among Digital Native

Research on library use has been carried out with the results that the level of library use by digital natives is still relatively low. The purpose of this study is to determine the factors that cause digital native users to use or not use a library. The method of this study use a descriptive qualitative research methods, and total of 341 digital natives in 25 cities in East Java were given open questions to express their experiences when using a library, resulting in 991 answers (respondents answered more than 1 opinion about the library). The data analysis technique was carried out in 3 stages namely open coding, axial coding, and selective coding. The result of this study found something new that the main factor of library use or not use is service quality (68.9 %) not from external factor, where users prioritise comfort while in the library, even though the internet as a library rival and as a source of information offers the speed and ease of access to information. Because of that innovation is done by improving the factors that cause users to use or not use the library.
Causal Influence Detection for Improving Efficiency in Reinforcement Learning

Many reinforcement learning (RL) environments consist of independent entities that interact sparsely. In such environments, RL agents have only limited influence over other entities in any particular situation. Our idea in this work is that learning can be efficiently guided by knowing when and what the agent can influence with its actions. To achieve this, we introduce a measure of situation-dependent causal influence based on conditional mutual information and show that it can reliably detect states of influence. We then propose several ways to integrate this measure into RL algorithms to improve exploration and off-policy learning. All modified algorithms show strong increases in data efficiency on robotic manipulation tasks.
Analisis Optimasi Algoritma Klasifikasi Naive Bayes menggunakan Genetic Algorithm dan Bagging

The increasing demand for credit applications to banks has motivated the banking world to switch to more sophisticated techniques for analyzing the level of credit risk. One technique for analyzing the level of credit risk is the data mining approach. Data mining provides a technique for finding meaningful information from large amounts of data by way of classification. However, bank marketing data is a type of imbalance data so that if the classification is done the results are less than optimal. The classification algorithm that can be used for imbalance data types can use naïve Bayes. Naïve Bayes performs well in terms of classification. However, optimization is needed in order to obtain more optimal classification results. Optimization techniques in handling imbalance data have been developed with several approaches. Bagging and Genetic Algorithms can be used to overcome imbalance data. This study aims to compare the accuracy level of the naïve Bayes algorithm after optimization using the bagging and genetic algorithm. The results showed that the combination of bagging and a genetic algorithm could improve the performance of Naive Bayes by 4.57%.
A Comprehensive Review on Medical Image Steganography Based on LSB Technique and Potential Challenges

The rapid development of telemedicine services and the requirements for exchanging medical information between physicians, consultants, and health institutions have made the protection of patients’ information an important priority for any future e-health system. The protection of medical information, including the cover (i.e. medical image), has a specificity that slightly differs from the requirements for protecting other information. It is necessary to preserve the cover greatly due to its importance on the reception side as medical staff use this information to provide a diagnosis to save a patient's life. If the cover is tampered with, this leads to failure in achieving the goal of telemedicine. Therefore, this work provides an investigation of information security techniques in medical imaging, focusing on security goals. Encrypting a message before hiding them gives an extra layer of security, and thus, will provide an excellent solution to protect the sensitive information of patients during the sharing of medical information. Medical image steganography is a special case of image steganography, while Digital Imaging and Communications in Medicine (DICOM) is the backbone of all medical imaging divisions, whereby it is most broadly used to store and transmit medical images. The main objective of this study is to provide a general idea of what Least Significant Bit-based (LSB) steganography techniques have achieved in medical images.
The making of a cyber crash: A conceptual model for systemic risk in the financial sector

In October 2017, the European Systemic Risk Board (ESRB) set up a group whose objective was to examine cyber security vulnerabilities within the financial sector, and their potential impact on financial stability and the real economy. In its first year, the European Systemic Cyber Group (ESCG) sought to develop a shared understanding of Common Individual Vulnerabilities (CIVs) across ESRB members, and to identify the unique characteristics of cyber risk that could contribute to a systemic event. Building on this work, this paper describes a conceptual model for systemic cyber risk, and aims to:•provide a structured approach that can be used to describe cyber incidents, from genesis through to a potential systemic event;•demonstrate the link between the crystallisation of cyber risk in a firm-specific context(portraying microprudential concerns), and the possible ramifications for the financial system (applying a macroprudential focus);•identify system-wide vulnerabilities and the unique characteristics of cyber incidents which can act as amplifiers, thereby propagating shocks through the financial system;•support the use of historical or theoretical scenario-based analysis to demonstrate the viability of the model;•suggest system-wide interventions that could act as systemic mitigants.Although the model is geared towards disruption arising from cyber incidents, it can also be used for any source of operational disruption (although some elements of the model may be less relevant).
Self Texture Transfer Networks for Low Bitrate Image Compression

Lossy image compression causes a loss of texture, especially at low bitrate. To mitigate this problem, we propose a novel image compression method that utilizes a reference-based image super-resolution model. We use two image compression models and a self texture transfer model. The image compression models encode and decode a whole input image and selected reference patches. The reference patches are small but compressed with high quality. The self texture transfer model transfers the texture of reference patches into similar regions in the compressed image. The experimental results show that our method can reconstruct accurate texture by transferring the texture of reference patches.
Applying of Fuzzy Nonlinear Regression to Investigate the Effect of Information and Communication Technology (ICT) on Income Distribution

The main focus of this paper is to extend fuzzy regression and its application. Therefore, we used nonlinear modeling to investigate the impact of information and communication technology (ICT) on the Gini coefficient. Hence, we calculated the three borders upside, medium, and downside of the Gini coefficient. We found no evidence of reduced income inequality when decreasing the use of ICT goods. However, with the increased use of ICTs, income inequality decreases over time. Also, the results showed that the effect of ICT commodities on the Gini coefficient was the inverted U situation. Understanding this impact is very important from the perspective of planning and building infrastructure for poverty reduction.
A Modified Symmetric Key Fully Homomorphic Encryption Scheme Based on Read-Muller Code

Homomorphic encryption became popular and powerful cryptographic primitive for various cloud computing applications. In the recent decades several developments has been made. Few schemes based on coding theory have been proposed but none of them support unlimited operations with security. We propose a modified Reed-Muller Code based symmetric key fully homomorphic encryption to improve its security by using message expansion technique. Message expansion with prepended random fixed length string provides one-to-many mapping between message and codeword, thus one-to many mapping between plaintext and ciphertext. The proposed scheme supports both (MOD 2) additive and multiplication operations unlimitedly. We make an effort to prove the security of the scheme under indistinguishability under chosen-plaintext attack (IND-CPA) through a game-based security proof. The security proof gives a mathematical analysis and its complexity of hardness. Also, it presents security analysis against all the known attacks with respect to the message expansion and homomorphic operations.
Pairwise Oblivious Transfer

We consider oblivious transfer between one client and two non-colluding servers that store independent contents. The client requests one file from each server such that (i) the servers do not learn the file selection of the client, and (ii) the client does not learn information about the non-selected files. We show under the honest-but-curious assumption that oblivious transfer with non-zero rates can be achieved with a multiuser protocol between the client and the servers. This contrasts with the case where the client engages in independent protocols with each of the two servers, for which it is known that oblivious transfer with information-theoretic security is impossible in the absence of additional resources. Furthermore, we derive a capacity result for the proposed setting.
A New Image Hashing based on Discrete Trinion Fourier Transform

In this paper, a new robust image hashing scheme is proposed base on discrete trinion Fourier transform(DTFT). The key features of the present scheme rely on (i) The DTFT provides an effective way to jointly deal with the three channels of color images. (ii) The final hash is generated according to the amplitudes of these DTFT coefficients. (iii) Scrambling is applied at the last stage of hash generation to enhance the system security. The experiment results show that the proposed method offers good robust to most image content preserving operations and have good discrimination performance.
Learning in a designed world

See RECORDING&#x0D; Human beings have an incredible talent for learning and for converting the insights they make into technologies. Some of these technologies (hammers, knives, bicycles etc.) transform our bodily capacities; they change the way we interact with the world when we repair an object or move between places. Other technologies (numbers, writing systems, texts, calculators) transform our capacities to think, remember, solve problems and communicate with fellow human beings. These symbolic technologies, as the evolutionary psychologist Merlin Donald calls them, play a decisive role for our capacity to learn, to preserve information and, more generally, to think at the individual as well as collective level. And these technologies are restless, they change continuously. In a designed world, our intellectual capacities are dependent on our abilities to productively utilize such external resources in what we do. What we know is no longer exclusively beneath the skin or between our ears.
Stable, Fast and Accurate: Kernelized Attention with Relative Positional  Encoding

The attention module, which is a crucial component in Transformer, cannot scale efficiently to long sequences due to its quadratic complexity. Many works focus on approximating the dot-then-exponentiate softmax function in the original attention, leading to sub-quadratic or even linear-complexity Transformer architectures. However, we show that these methods cannot be applied to more powerful attention modules that go beyond the dot-then-exponentiate style, e.g., Transformers with relative positional encoding (RPE). Since in many state-of-the-art models, relative positional encoding is used as default, designing efficient Transformers that can incorporate RPE is appealing. In this paper, we propose a novel way to accelerate attention calculation for Transformers with RPE on top of the kernelized attention. Based upon the observation that relative positional encoding forms a Toeplitz matrix, we mathematically show that kernelized attention with RPE can be calculated efficiently using Fast Fourier Transform (FFT). With FFT, our method achieves $\mathcal{O}(n\log n)$ time complexity. Interestingly, we further demonstrate that properly using relative positional encoding can mitigate the training instability problem of vanilla kernelized attention. On a wide range of tasks, we empirically show that our models can be trained from scratch without any optimization issues. The learned model performs better than many efficient Transformer variants and is faster than standard Transformer in the long-sequence regime.
Proving Quantum Programs Correct.

As quantum computing progresses steadily from theory into practice, programmers will face a common problem: How can they be sure that their code does what they intend it to do? This paper presents encouraging results in the application of mechanized proof to the domain of quantum programming in the context of the SQIR development. It verifies the correctness of a range of a quantum algorithms including Grover’s algorithm and quantum phase estimation, a key component of Shor’s algorithm. In doing so, it aims to highlight both the successes and challenges of formal verification in the quantum context and motivate the theorem proving community to target quantum computing as an application domain.
The Development of E-Module for Batik Local Content in Pekalongan ElementarySchool

The teaching materials are to help the teachers in conducting learning activitiesin the classroom. The materials are written and unwritten. This study purposedto analyze the teaching material using e-module as local content Batik, develope-module local content Batik that is practically and effectiveness in improvingthe local content learning for elementary school in Pekalongan, analyze thevalidity test of Batik’s e-module is to measure the learning of local content forelementary school in Pekalongan, the effectiveness of Batik’s e-module canenhance the learning of local content for elementary school in Pekalongan. Thisstudy uses research and development models (R&D). The development ofteaching material in this study is Batik’s e-module. The teaching materialprovides Figures and videos about Batik, so it will be easily for students inunderstanding the material. The result of the feasibility material by the expertshave obtained the percentage 88.6%. Then, the result of media validation is97.14%. Based on the results validation show that the developing e-module is inthe excellent categories, so it has been feasible to be applied in the learningprocess. The effectiveness tests are conducted in three requirements, there areanalyzing the minimum completeness test, individual classical completenesstest, and improved learning outcomes. The results of three requirements test,show that Batik’s e-module could improve the students learning outcomessignificantly compared to the learning by using the published book. This studyis supported in education, to develop Batik’s e-module. The product becomes analternative media to assist the students learn Batik by using technology. It canalso use as a source of reading in the learning of local content.
Towards Measuring Bias in Image Classification

Convolutional Neural Networks (CNN) have become de fact state-of-the-art for the main computer vision tasks. However, due to the complex underlying structure their decisions are hard to understand which limits their use in some context of the industrial world. A common and hard to detect challenge in machine learning (ML) tasks is data bias. In this work, we present a systematic approach to uncover data bias by means of attribution maps. For this purpose, first an artificial dataset with a known bias is created and used to train intentionally biased CNNs. The networks' decisions are then inspected using attribution maps. Finally, meaningful metrics are used to measure the attribution maps' representativeness with respect to the known bias. The proposed study shows that some attribution map techniques highlight the presence of bias in the data better than others and metrics can support the identification of bias.
How Far Can Copyright Be Stretched? Framing the Debate on Whether New and Different Forms of Creativity Can Be Protected

This paper expands on whether copyright protection may be available for certain new and non-conventional works as diverse as graffiti, sports movements, dj-sets, culinary presentations, jokes, magic tricks, works created by artificial intelligence and engineered DNA. The potential expansion of copyright in a knowledge-based society is a relevant and topical subject at the moment also in light of the current scholarly and policy debates on the modernisation of copyright rules in many countries, including the EU and US. The issue of whether copyright can protect certain new and non-traditional products of human ingenuity is here addressed by carrying out a specific-work-related analysis of core tenets of copyright laws, including copyrightable subject matter, originality, fixation and authorship requirements, functionality exception, morality, and public policy provisions.