Reviewerly/exHarmony huggingface dataset:
Adaptive Moment Estimation To Minimize Square Error In Backpropagation Algorithm

Back - propagation Neural Network has weaknesses such as errors of gradient descent training slowly of error function, training time is too long and is easy to fall into local optimum. Back - propagation algorithm is one of the artificial neural network training algorithm that has weaknesses such as the convergence of long, over-fitting and easy to get stuck in local optima. Back - propagation is used to minimize errors in each iteration. This paper investigates and evaluates the performance of Adaptive Moment Estimation (ADAM) to minimize the squared error in back - propagation gradient descent algorithm. Adaptive Estimation moment can speed up the training and achieve the level of acceleration to get linear. ADAM can adapt to changes in the system, and can optimize many parameters with a low calculation. The results of the study indicate that the performance of adaptive moment estimation can minimize the squared error in the output of neural networks.
Factorizing Declarative and Procedural Knowledge in Structured, Dynamical Environments

Modeling a structured, dynamic environment like a video game requires keeping track of the objects and their states (declarative knowledge) as well as predicting how objects behave (procedural knowledge). Black-box models with a monolithic hidden state often fail to apply procedural knowledge consistently and uniformly, i.e., they lack systematicity. For example, in a video game, correct prediction of one enemy's trajectory does not ensure correct prediction of another's. We address this issue via an architecture that factorizes declarative and procedural knowledge and that imposes modularity within each form of knowledge. The architecture consists of active modules called object files that maintain the state of a single object and invoke passive external knowledge sources called schemata that prescribe state updates. To use a video game as an illustration, two enemies of the same type will share schemata but will have separate object files to encode their distinct state (e.g., health, position). We propose to use attention to determine which object files to update, the selection of schemata, and the propagation of information between object files. The resulting architecture is a drop-in replacement conforming to the same input-output interface as normal recurrent networks (e.g., LSTM, GRU) yet achieves substantially better generalization on environments that have multiple object tokens of the same type, including a challenging intuitive physics benchmark.
THE RELATION BETWEEN CENTRAL BANK INDEPENDENCE AND INFLATION RATE IN EGYPT: AN EMPIRICAL ANALYSIS DURING 1998-2019

This paper examines the relation between granting central banks more independence represented in both its manifested legal charter as well as actual practices adopted and inflation in Egypt during the period from 1998-2019. To do so, this paper first looks at the evolution of different central bank independence (CBI) measures and adopts the most comprehensive relevant one. Consequently, it runs a regression model adopting an autoregressive distributed lag (ARDL) model, regressing the dependent variable inflation on its determinants of legal CBI, actual CBI, M2 and openness, to capture such relation in both the short and long term time dimensions in the case of Egypt. The paper further provides an interpretation of computed outcomes, where it presents robust evidence of an inverse relation between inflation and CBI in Egypt in the period under study in its legal and actual form and in both the short-run and the long-run. Keywords: Central bank independence, Central Bank of Egypt, Inflation, Autoregressive distributed lag, unit root testJEL Classifications: E31, E42, E58, E370DOI: https://doi.org/10.32479/ijefi.10941
Near-Optimal Linear Regression under Distribution Shift

Transfer learning is an essential technique when sufficient data comes from the source domain, while no or scarce labeled data is from the target domain. We develop estimators that achieve minimax linear risk for linear regression problems under the distribution shift. Our algorithms cover different kinds of settings with covariate shift or model shift. We also consider when data are generating from either linear or general nonlinear models. We show that affine minimax rules are within an absolute constant of the minimax risk even among nonlinear rules, for a variety of source/target distributions.
Backlit image enhancement based on foreground extraction

To deal with the problem of low foreground brightness in the backlit image, a novel backlit image enhancement method is proposed in this paper. First, K nearest neighbors (KNN) matting is used on the original images. By dividing the lower-brightness part into the foreground and marking the contour as the unknown area, the trimap of the image is defined, and the opacity of each pixel is calculated in the marked foreground. Second, logarithmic transformation method is used to enhance the extracted foreground image, and the brightness can be improved to different degrees by setting the base number. Finally, the original image is combined with the enhanced foreground part to obtain the synthesized image, which is more natural and the phenomenon of insufficient or excessive enhancement is avoided. Experimental results show that the influence of backlight conditions on image can be eliminated by the proposed approach, and the image quality is improved significantly and is superior to the conventional and the cutting-edge algorithms.
Putting Question-Answering Systems into Practice: Transfer Learning for Efficient Domain Customization

Traditional information retrieval (such as that offered by web search engines) impedes users with information overload from extensive result pages and the need to manually locate the desired information therein. Conversely, question-answering systems change how humans interact with information systems: users can now ask specific questions and obtain a tailored answer—both conveniently in natural language. Despite obvious benefits, their use is often limited to an academic context, largely because of expensive domain customizations, which means that the performance in domain-specific applications often fails to meet expectations. This article proposes cost-efficient remedies: (i) we leverage metadata through a filtering mechanism, which increases the precision of document retrieval, and (ii) we develop a novel fuse-and-oversample approach for transfer learning to improve the performance of answer extraction. Here, knowledge is inductively transferred from related, yet different, tasks to the domain-specific application, while accounting for potential differences in the sample sizes across both tasks. The resulting performance is demonstrated with actual use cases from a finance company and the film industry, where fewer than 400 question-answer pairs had to be annotated to yield significant performance gains. As a direct implication to management, this presents a promising path to better leveraging of knowledge stored in information systems.
The Statistical Properties of Dimension Calculations Using Small Data Sets:

The statistical properties of estimates of pointwise dimension and their errors have been investigated for some maps and for some random variables. Substantial bias in the estimates is detected and modelled as a function of the sample size and the embedding dimension. The usual methods for calculating error bars are shown to underestimate the actual error bars by factors of ten and more. Procedures to improve the estimation of dimension in the cases studied are discussed, as are methods to improve the ability to distinguish noise from an attractor when using small data sets. Some idea of small is given when the attractors are similar to the ones studied.
Image Resolution Enhancement Using Convolutional Autoencoders

Resolution is an important characteristic to determine the nature and features of the image. Enhancing the resolution strengthens the features hidden within the image, and make the image sharper and more informative. The image quality is improved when noise is removed/suppressed from it. The proposed model provides a technique to enhance the resolution of different types of images, obtained from imaging devices, using a convolutional autoencoder. A convolutional neural network (CNN) architecture is developed by adding different layers to the neural network. An autoencoder capable of encoding and decoding the structure of the images is proposed to enhance their resolution. The model tends to learn the lower-dimensional features of unclear images and provide a high resolution to them by predicting and enhancing their dimensions. The model is trained on low-resolution images and the corresponding high-resolution images, and a convolutional auto-encoder is implemented to denoise the image to introduce high-resolution in the blurred or corrupted images. The model overcomes the limitations of the existing denoising filter techniques and provides a higher level of image quality enhancement.
News Recommendation Based on Content Fusion of User Behavior

To address the problem that content-based recommendation algorithm lacks diversified user interests, a news recommendation algorithm based on content fusion user behavior is proposed. Firstly, the content-based algorithm is used to improve the computation of user interest weights by considering the effect of time on user interest and to discover users' own preferences. Secondly, considering the time utility and the correlation between the popularity of the news and users' interests, finding users' nearest neighbors using content- based and improved behavior-based mixed similarity to discover their potential preferences. Finally, the user's own preferences are fused with potential preferences to generate a recommended list. The experimental result shows that compared to the traditional recommendation algorithm, the improved algorithm has a maximum increase of 9. 80% in recommendation accuracy, a maximum increase of 10. 12% in recall, and a maximum increase of 6. 23% in diversity. The experimental result shows that the improved recommendation algorithm effectively improves the quality of recommendation, enrichs diversity, and has good recommendation effect.
Skill Transfer via Partially Amortized Hierarchical Planning

To quickly solve new tasks in complex environments, intelligent agents need to build up reusable knowledge. For example, a learned world model captures knowledge about the environment that applies to new tasks. Similarly, skills capture general behaviors that can apply to new tasks. In this paper, we investigate how these two approaches can be integrated into a single reinforcement learning agent. Specifically, we leverage the idea of partial amortization for fast adaptation at test time. For this, actions are produced by a policy that is learned over time while the skills it conditions on are chosen using online planning. We demonstrate the benefits of our design decisions across a suite of challenging locomotion tasks and demonstrate improved sample efficiency in single tasks as well as in transfer from one task to another, as compared to competitive baselines. Videos are available at: https://sites.google.com/view/partial-amortization/home
Boolean Functions and Permanents of Sylvester Hadamard Matrices

One of the fastest known general techniques for computing permanents is Ryser’s formula. On this note, we show that this formula over Sylvester Hadamard matrices of order 2m, Hm, can be carried out by enumerating m-variable Boolean functions with an arbitrary Walsh spectrum. As a consequence, the quotient per(Hm)/22m might be a measure of the “density” of m-variable Boolean functions with high nonlinearity.
Legal Risks of Adversarial Machine Learning Research

Adversarial machine learning is the systematic study of how motivated adversaries can compromise the confidentiality, integrity, and availability of machine learning (ML) systems through targeted or blanket attacks. The problem of attacking ML systems is so prevalent that CERT, the federally funded research and development center tasked with studying attacks, issued a broad vulnerability note on how most ML classifiers are vulnerable to adversarial manipulation. Google, IBM, Facebook, and Microsoft have committed to investing in securing machine learning systems. The US and EU are likewise putting security and safety of AI systems as a top priority.Now, research on adversarial machine learning is booming but it is not without risks. Studying or testing the security of any operational system may violate the Computer Fraud and Abuse Act (CFAA), the primary United States federal statute that creates liability for hacking. The CFAA’s broad scope, rigid requirements, and heavy penalties, critics argue, has a chilling effect on security research. Adversarial ML security research is likely no different. However, prior work on adversarial ML research and the CFAA is sparse and narrowly focused. In this article, we help address this gap in the literature. For legal practitioners, we describe the complex and confusing legal landscape of applying the CFAA to adversarial ML. For adversarial ML researchers, we describe the potential risks of conducting adversarial ML research. We also conclude with an analysis predicting how the US Supreme Court may resolve some present inconsistencies in the CFAA’s application in Van Buren v. United States, an appeal expected to be decided in 2021. We argue that the court is likely to adopt a narrow construction of the CFAA, and that this will actually lead to better adversarial ML security outcomes in the long term.
Should Accountants Care About Blockchain?

The use of blockchain technology has increased tremendously over the last decade. Blockchain continues to evolve and new features and capabilities are developed continuously. Although the use of the technology started in cryptocurrency (specifically, Bitcoin) it has expanded to other areas that can benefit from a shared, secure, ledger. This paper investigates the potential impact of blockchain technology on the accounting profession. The paper analyzes data security and privacy considerations, technology, adoption and implementation considerations, and some considerations that relate specifically to accounting and auditing. We find that the unique needs of an accounting information system may not be a good match for blockchain as it currently exists. While we explain that blockchain may deliver many benefits, particularly in the areas of data reliability and the financial statement audit, we identify several factors which raise significant questions about whether blockchain will ever be significantly integrated in the accounting function. Chief among those concerns is the scalability of the technology at an acceptable cost. While significant investment has and will be made for further development of blockchain business applications, it is our assessment that proponents of blockchain integration in accounting have not yet made the economic case for it. We also have significant concerns about whether blockchain technology can adequately address risks associated with data security and privacy. Addressing all these issues will be a minimum requirement for gaining widespread acceptance by firms and their accountants.
Cluster-based Task Scheduling Using K-Means Clustering for Load Balancing in Cloud Datacenters

Load balancing is a process of distributing incoming tasks to available resources in cloud datacenters, where a resource exists in terms of a virtual machine (VM). Proper load balancing results in minimizing the computation time and improving the resource utilization rate. Various scheduling algorithms are applied to achieve load balancing in cloud datacenters. Due to the heterogeneous nature of resources in the cloud, greedy approaches are used to schedule the tasks to the VMs. This paper suggests a cluster-based task scheduling framework (CBTS) using K-Means clustering by considering task length and VM capacity. Here, the tasks are clustered based on their length, and the VMs are grouped based on their processing capacity. After clustering, the individual task in each cluster is scheduled to appropriate VM in the VM groups.The proposed system performs dynamic load balancing with an aim in minimizing the makespan and execution time. The experimental results reveal that the proposed method produces better results in terms of execution time, makespan, and deviation in workload than the conventional Min-Min algorithm and the recently developed heuristic algorithms such as Online Potential Finish Time (OPFT), Dynamic Cloud Task Scheduling (DCTS), and Grouped Task Scheduling (GTS).
Higher education in Ecuador in times of COVID 19 within the framework of the TPACK model

Covid-19 has disrupted educational systems, so the objective of the research was to describe the current situation of teachers in Ecuador who carry out tele-education under the lens of the TPACK model (Technological Pedagogical Knowledge of Content) in the context of the confinement due to the pandemic, the study included a specific analysis of technological knowledge (TK) and pedagogical technology (TPK) explained by the model First, a survey was carried out to know the teacher's perception about the domain of the types of knowledge proposed by TPACK, then a comparative analysis was carried out between the teacher perceptions obtained in the TK and TPK knowledge;and the results of evaluative tests that measured these types of knowledge The important findings were: The teacher has the necessary TK knowledge for the development of their work, but there is a deficiency in the TPK knowledge
Blockchain technologies for commodity value chains: The solution for more sustainability?

With the rising public awareness of poor social and environmental production conditions in many global value chains (GVCs), the pressure for more transparency and traceability is growing. Applications of distributed ledgers (DL) technologies such as blockchains are seen as a key solution in this context. These technologies enable the collection, recording and sharing of the information about physical transactions and related metadata in a tamperresistant way, without the control of a central actor. This briefing paper presents the basic concepts behind the DL and blockchain technologies and discusses the opportunities and limits of these applications in the context of GVCs. The challenges are due more to power asymmetries in the value chains than to technical issues. Thus, most DL applications could only be tools to bring existing sustainable conditions in GVCs to the fore as long as chain governance and the lack of legal frameworks remain as the main obstacles to extending sustainability in GVCs.
Artificially Intelligent Law

This paper explores a series of thought experiments that postulate the existence of “artificially intelligent law.” An artificially-intelligent legal system is defined as one with three functional capacities: 1. The system has the capacity to generate legal norms. 2. The system has the capacity to apply the legal norms that it generates. 3. The system has the capacity to use deep learning to modify the legal norms that it generates. The paper then considers the question whether such a system would be desirable as a matter of legitimacy and justice. The core idea of the paper is that the key to the evaluation of artificially intelligent law is to focus on the functional capacities of the system in comparison to comparable human systems, such as regulatory agencies.
Maximum Categorical Cross Entropy (MCCE): A noise-robust alternative loss function to mitigate racial bias in Convolutional Neural Networks (CNNs) by reducing overfitting

Categorical Cross Entropy (CCE) is the most commonly used loss function in deep neural networks such as Convolutional Neural Networks (CNNs) for multi-class classification problems. In spite of the fact that CCE is highly susceptible to noise; CNN models trained without accounting for the unique noise characteristics of the input data, or noise introduced during model training, invariably suffer from overfitting affecting model generalizability. The lack of generalizability becomes especially apparent in the context of ethnicity/racial image classification problems encountered in the domain of computer vision. One such problem is the unintended discriminatory racial bias that CNN models trained using CCE fail to adequately address. In other words, CNN models trained using CCE offer a skewed representation of classification performance favoring lighter skin tones. In this paper, we propose and empirically validate a novel noise-robust extension to the existing CCE loss function called Maximum Categorical Cross-Entropy (MCCE), which utilizes CCE loss and a novel reconstruction loss, calculated using the Maximum Entropy (ME) measures of the convolutional kernel weights and input training dataset. We compare the use of MCCE with CCE-trained models on two benchmarking datasets, colorFERET and UTKFace, using a Residual Network (ResNet) CNN architecture. MCCE-trained models reduce overfitting by 5.85% and 4.3% on colorFERET and UTKFace datasets respectively. In cross-validation testing, MCCE-trained models outperform CCE-trained models by 8.8% and 25.16% on the colorFERET and UTKFace datasets respectively. MCCE addresses and mitigates the persistent problem of inadvertent racial bias for facial recognition problems in the domain of computer vision.
A Mass-Based Approach for Local Outlier Detection

This paper proposes a new outlier detection approach that measures the degree of outlierness for each instance in a given dataset. The proposed model utilizes a mass-based dissimilarity measure to address the weaknesses of neighbor-based outlier models while detecting local outliers in the dataset within a variety of data point densities. In particular, it first applies a hierarchical partitioning technique to generate a set of tree-like nested structure partitions for the input dataset, and then a mass-based dissimilarity measure is defined to quantify the dissimilarity between two data instances given the generated hierarchical partition structure. After that, for each data instance, a context set is obtained by gathering the neighbors around it with the k lowest mass dissimilarities, and based on those context sets, a mass-based local outlier score model is introduced to compute the outlierness for each individual instance. The proposed approach fundamentally changes the perspective of the outlier model by using the mass-based measurement instead of the distance-based functions used in most neighbor-based methods. A comprehensive experiment conducted on both synthetic and real-world datasets demonstrates that the proposed approach is not only competitive with the existing state-of-the-art outlier detection models but is also an efficient and effective alternative for local outlier detection methods.
New results for the Mondrian art problem

The Mondrian problem consists of dissecting a square of side length n∈N into non-congruent rectangles with natural length sides such that the difference d(n) between the largest and the smallest areas of the rectangles partitioning the square is minimum. In this paper, we compute some bounds on d(n) in terms of the number of rectangles of the square partition. These bounds provide us optimal partitions for some values of n∈N. We provide a sequence of square partitions such that d(n)∕n2 tends to zero for n large enough. For the case of ‘perfect’ partitions, that is, with d(n)=0, we show that, for any fixed powers s1,…,sm, a square with side length n=p1s1⋯pmsm, can have a perfect Mondrian partition only if p1 satisfies a given lower bound. Moreover, if n(x) is the number of side lengths x (with n≤x) of squares not having a perfect partition, we prove that its ‘density’ n(x)x is asymptotic to (log(log(x)))22logx, which improves previous results.