Reviewerly/exHarmony huggingface dataset:
Supervised learning: no loss no cry

Supervised learning requires the specification of a loss function to minimise. While the theory of admissible losses from both a computational and statistical perspective is well-developed, these offer a panoply of different choices. In practice, this choice is typically made in an \emph{ad hoc} manner. In hopes of making this procedure more principled, the problem of \emph{learning the loss function} for a downstream task (e.g., classification) has garnered recent interest. However, works in this area have been generally empirical in nature. In this paper, we revisit the {\sc SLIsotron} algorithm of Kakade et al. (2011) through a novel lens, derive a generalisation based on Bregman divergences, and show how it provides a principled procedure for learning the loss. In detail, we cast {\sc SLIsotron} as learning a loss from a family of composite square losses. By interpreting this through the lens of \emph{proper losses}, we derive a generalisation of {\sc SLIsotron} based on Bregman divergences. The resulting {\sc BregmanTron} algorithm jointly learns the loss along with the classifier. It comes equipped with a simple guarantee of convergence for the loss it learns, and its set of possible outputs comes with a guarantee of agnostic approximability of Bayes rule. Experiments indicate that the {\sc BregmanTron} substantially outperforms the {\sc SLIsotron}, and that the loss it learns can be minimized by other algorithms for different tasks, thereby opening the interesting problem of \textit{loss transfer} between domains.
Variance Reduced Coordinate Descent with Acceleration: New Method With a Surprising Application to Finite-Sum Problems

We propose an accelerated version of stochastic variance reduced coordinate descent -- ASVRCD. As other variance reduced coordinate descent methods such as SEGA or SVRCD, our method can deal with problems that include a non-separable and non-smooth regularizer, while accessing a random block of partial derivatives in each iteration only. However, ASVRCD incorporates Nesterov's momentum, which offers favorable iteration complexity guarantees over both SEGA and SVRCD. As a by-product of our theory, we show that a variant of Allen-Zhu (2017) is a specific case of ASVRCD, recovering the optimal oracle complexity for the finite sum objective.
An Imitation Learning Approach for Cache Replacement

Program execution speed critically depends on increasing cache hits, as cache hits are orders of magnitude faster than misses. To increase cache hits, we focus on the problem of cache replacement: choosing which cache line to evict upon inserting a new line. This is challenging because it requires planning far ahead and currently there is no known practical solution. As a result, current replacement policies typically resort to heuristics designed for specific common access patterns, which fail on more diverse and complex access patterns. In contrast, we propose an imitation learning approach to automatically learn cache access patterns by leveraging Belady's, an oracle policy that computes the optimal eviction decision given the future cache accesses. While directly applying Belady's is infeasible since the future is unknown, we train a policy conditioned only on past accesses that accurately approximates Belady's even on diverse and complex access patterns, and call this approach Parrot. When evaluated on 13 of the most memory-intensive SPEC applications, Parrot increases cache miss rates by 20% over the current state of the art. In addition, on a large-scale web search benchmark, Parrot increases cache hit rates by 61% over a conventional LRU policy. We release a Gym environment to facilitate research in this area, as data is plentiful, and further advancements can have significant real-world impact.
Efficient Evaluation-Time Uncertainty Estimation by Improved Distillation

In this work we aim to obtain computationally-efficient uncertainty estimates with deep networks. For this, we propose a modified knowledge distillation procedure that achieves state-of-the-art uncertainty estimates both for in and out-of-distribution samples. Our contributions include a) demonstrating and adapting to distillation's regularization effect b) proposing a novel target teacher distribution c) a simple augmentation procedure to improve out-of-distribution uncertainty estimates d) shedding light on the distillation procedure through comprehensive set of experiments.
Developing a Taxonomic Framework of Security Methods for Security Management and Information Resource Management

People who are concerned with security (such as security professionals) are naturally interested in methods of achieving security. This paper proposes an approach to creating a taxonomic framework of security methods. The taxonomic framework is developed using facet analysis based on a tetra-facet model of security, which identifies four facets of security: subject/scope of security, object of protection, source of insecurity, and method of protection. The taxonomic framework of security methods is created by combining two of the facets: source of insecurity and method of protection. The taxonomic framework of security methods can be integrated with the taxonomic framework of security (which was developed in a previous study), and can be used for security management and the management of information resources related to security and security methods.
Effects of the Application of STEAM Education on Students’ Learning Attitude and Outcome - ‎The Case of Fujian Chuanzheng Communications College

Going through the major change of learning styles in the 21st century, traditional single-field education and productive labor oriented and targeted education could no longer satisfy the social needs. The education model of STEAM becomes the latest issue, with which people create brand-new professional fields through the integration of science, technology, engineering, arts, and mathematics to have the future students keep up with the advance. With experimental design, total 198 students of Fujian Chuanzheng Communications College are studied. A half of them are in the experimental class (99 students) for STEAM education and another half are the control class (99 students) for traditional education. The research results show significant effects of 1.STEAM education on learning attitude and 2.STEAM education on learning outcome, and 3.remarkably positive effects of learning attitude on learning outcome. According to the results, suggestions are proposed, expecting to help Fujian Chuanzheng Communications College exceed traditional apprenticeship education in the systematicness and effectiveness of teaching, the modernity of teaching content, and the strictness of assessment.
On the Construction of Multilingual Corpora for Clinical Text Mining.

The amount of digital data derived from healthcare processes have increased tremendously in the last years. This applies especially to unstructured data, which are often hard to analyze due to the lack of available tools to process and extract information. Natural language processing is often used in medicine, but the majority of tools used by researchers are developed primarily for the English language. For developing and testing natural language processing methods, it is important to have a suitable corpus, specific to the medical domain that covers the intended target language. To improve the potential of natural language processing research, we developed tools to derive language specific medical corpora from publicly available text sources. n order to extract medicine-specific unstructured text data, openly available pub-lications from biomedical journals were used in a four-step process: (1) medical journal databases were scraped to download the articles, (2) the articles were parsed and consolidated into a single repository, (3) the content of the repository was de-scribed, and (4) the text data and the codes were released. In total, 93 969 articles were retrieved, with a word count of 83 868 501 in three different languages (German, English, and Spanish) from two medical journal databases Our results show that unstructured text data extraction from openly available medical journal databases for the construction of unified corpora of medical text data can be achieved through web scraping techniques.
Readiness Exercises: Are Risk Assessment Methodologies Ready for the Cloud?

Cloud computing is a type of service that allows the use of computing resources from a distance, rather than a new technology. Various services exist on-demand, ranging from data storage and processing to software as a service, like email and developing platforms. Cloud computing enables ubiquitous, on-demand access over the net to a shared pool of configurable resources, like servers, applications, etc. that can be accessed, altered or even restored rapidly with minimal service provider interaction or management effort. Still, due to the vast growth of cloud computing, new security issues have been introduced. Key factors are the loss of control over any outsourced resources and cloud’s computing inherent security vulnerabilities. Managing these risks requires the adoption of an effective risk management method, capable of involving both the Cloud customer and the Cloud Service Provider. Risk assessment methods are common tools amongst IT security consultants for managing the risk of entire companies. Still, traditional risk management methodologies are having trouble managing cloud services. Extending our previous work, the purpose of this paper is to compare and examine whether popular risk management methods and tools (e.g. NIST SP800, EBIOS, MEHARI, OCTAVE, IT-Grundschutz, MAGERIT, CRAMM, HTRA, Risk-Safe Assessment, CORAS) are suitable for cloud computing environments. Specifically, based upon existing literature, this paper points out the essential characteristics that any risk assessment method addressed to cloud computing should incorporate, and suggests three new ones that are more appropriate based on their features.
Techniques for augmented-tangibles on mobile devices for early childhood learning

Integrating physical learning materials with mobile device applications may have benefits for early childhood learning. We present three techniques for creating a hybrid tangible-augmented reality (T-AR) enabling technology platform. This platform enables researchers to develop applications that use readily available physical learning materials, such as letters, numbers, symbols or shapes. The techniques are visual marker-based; computer-vision and machine-learning; and capacitive touches. We describe details of implementation and demonstrate these techniques through a use case of a reading tablet app that uses wooden/plastic letters for input and augmented output. Our comparative analysis revealed that the machine-learning technique most flexibly sensed different physical letter sets but had variable accuracy impacted by lighting and tracking lag at this time. Lastly, we demonstrate how this enabling technology can be generalized to a variety of early learning apps through a second use case with physical numbers.
Network Transaction Cost Advantage and Its Effects on Shopping Intention in Coastal Economic Zone

Tao, K.; Xiao, H., and Ye, J., 2020. Network transaction cost advantage and its effects on shopping intention in coastal economic zone. In: Yang, Y.; Mi, C.; Zhao, L., and Lam, S. (eds.), Global Topics and New Trends in Coastal Research: Port, Coastal and Ocean Engineering. Journal of Coastal Research, Special Issue No. 103, pp. 60–64. Coconut Creek (Florida), ISSN 0749-0208.A method of forecasting and diagnosing the causal relationship by combining PLS-SEM model and Bayesian network is proposed. This method not only exerts the ability of PLS-SEM model to be proved by theory, but also highlights the ability of Bayesian network to forecast and diagnosis and deal with nonlinear relationship, which is beneficial to the development of management decision. Furthermore, the method is applied to study the complex relationship between the cost of online shopping transaction and purchase intention. It clarifies the change of consumers' willingness to purchase when the transaction cost is in different states, and provides the basis for the enterprise to formulate effective transaction cost management decision.
Semi-Modular Inference: enhanced learning in multi-modular models by tempering the influence of components.

Bayesian statistical inference loses predictive optimality when generative models are misspecified. Working within an existing coherent loss-based generalisation of Bayesian inference, we show existing Modular/Cut-model inference is coherent, and write down a new family of Semi-Modular Inference (SMI) schemes, indexed by an influence parameter, with Bayesian inference and Cut-models as special cases. We give a meta-learning criterion and estimation procedure to choose the inference scheme. This returns Bayesian inference when there is no misspecification. The framework applies naturally to Multi-modular models. Cut-model inference allows directed information flow from well-specified modules to misspecified modules, but not vice versa. An existing alternative power posterior method gives tunable but undirected control of information flow, improving prediction in some settings. In contrast, SMI allows tunable and directed information flow between modules. We illustrate our methods on two standard test cases from the literature and a motivating archaeological data set.
Measures of distinguishability between stochastic processes

Quantifying how distinguishable two stochastic processes are is at the heart of many fields, such as machine learning and quantitative finance. While several measures have been proposed for this task, none have universal applicability and ease of use. In this article, we suggest a set of requirements for a well-behaved measure of process distinguishability. Moreover, we propose a family of measures, called divergence rates, that satisfy all of these requirements. Focusing on a particular member of this family---the coemission divergence rate---we show that it can be computed efficiently, behaves qualitatively similar to other commonly used measures in their regimes of applicability, and remains well behaved in scenarios where other measures break down.
Influence of Wind Speed on Solar PV Plant Power Production—Prediction Model Using Decision-Based Artificial Neural Network

An effort has been made to study the different factors influencing the output of a solar photovoltaic (PV) plant. Environmental factors play a significant role in planning the placement of PV modules. Various factors like irradiation, wind speed, atmospheric temperature and cloud coverage affect the power obtained from the plant. Establishing a relationship between the multiple factors and the output of the plant makes it easy to decide and plan the site of installation of the module and for further research purposes. The effect of wind speed on the power output of S. N. Mohanty solar plant located in Patapur, Cuttack (Odisha, IN) is studied. The research work has been carried out with data acquired every 15 min from January 2015 to December 2015 and keeping the irradiation constant. The monthly plots help establish a relationship between the wind speed and the power generated from the plant. The data is then used to create and train a neural network model which is used to predict the power output of the plant given the appropriate input data. This paper demonstrates the training of artificial neural network using environmental and PV module data (module temperature, wind speed and month).
Succinct Filters for Sets of Unknown Sizes

The membership problem asks to maintain a set S ⊆ [u], supporting insertions and membership queries, i.e., testing if a given element is in the set. A data structure that computes exact answers is called a dictionary. When a (small) false positive rate e is allowed, the data structure is called a filter.The space usages of the standard dictionaries or filters usually depend on the upper bound on the size of S, while the actual set can be much smaller.Pagh, Segev and Wieder [Pagh et al., 2013] were the first to study filters with varying space usage based on the current |S|. They showed in order to match the space with the current set size n = |S|, any filter data structure must use (1-o(1))n(log(1/e)+(1-O(e))log log n) bits, in contrast to the well-known lower bound of N log(1/e) bits, where N is an upper bound on |S|. They also presented a data structure with almost optimal space of (1+o(1))n(log(1/e)+O(log log n)) bits provided that n > u^0.001, with expected amortized constant insertion time and worst-case constant lookup time.In this work, we present a filter data structure with improvements in two aspects: - it has constant worst-case time for all insertions and lookups with high probability; - it uses space (1+o(1))n(log (1/e)+log log n) bits when n > u^0.001, achieving optimal leading constant for all e = o(1). We also present a dictionary that uses (1+o(1))nlog(u/n) bits of space, matching the optimal space in terms of the current size, and performs all operations in constant time with high probability.
Application and Practice of Artificial Intelligence in Maker Education and Teaching

&lt;p&gt;The article expatiates concept of artificial intelligence and a guest education connotation, proposed under the background of artificial intelligence, build a guest education curriculum system the main method, and explains in detail the course in the age of artificial intelligence development way, and artificial intelligence in a guest education concrete application in the process of teaching, the last on the analysis of the artificial intelligence education, on the basis of existing problems, points out the artificial intelligence education realize own path to the long-term development of the science.&lt;/p&gt;
Image Information Hiding Method Based on Image Compression and Deep Neural Network

Image steganography is a technique that hides secret information into the cover image to protect information security. The current image steganography is mainly to embed a smaller secret image in an area such as a texture of a larger-sized cover image, which will cause the size of the secret image to be much smaller than the cover image. Therefore, the problem of small steganographic capacity needs to be solved urgently. This paper proposes a steganography framework that combines image compression. In this framework, the Vector Quantized Variational AutoEncoder (VQ-VAE) is used to achieve the compression of the secret image. The compressed and reconstructed image is visually indistinguishable from the original image and facilitates more embedded data information later. Finally, the compressed image is transmitted to a SegNet deep neural network that contains a set of encoders and decoders to achieve image hiding and extraction. Experimental results show that the steganographic framework guarantees the quality of steganography while its relative steganographic capacity reaches 1. Besides, Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index (SSIM) values can reach 42 dB and 0.94, respectively.
POTENSI PEMANFAATAN ATAP GEDUNG UNTUK PLTS DI KANTOR DINAS PEKERJAAN UMUM, PERUMAHAN DAN ENERGI SUMBER DAYA MINERAL (PUP-ESDM) PROVINSI DAERAH ISTIMEWA YOGYAKARTA

Electricity consumption in Indonesia from year to year has increased. The sales of PLN 2018 electricity in 234,617.88 GWh covering industrial sector, household sector, commercial sector, and public sector. While electrical energy consumption in D. I. Yogyakarta in 2018 of 2,857.06 GWh covering household sector, industrial sector, business sector, social sector, government office building sector and public street lighting. In fulfilling the demand of electrical energy, Yogyakarta installed power plant consists of PLTMH, PLTBm, PLTS and PLTHybrid with an installed capacity of 4.84 MW so that electricity in Yogyakarta is supplied from the interconnection network of Java-Madura-Bali. Energy resources used by interconnection networks generally use fossil energy (coal). Therefore we need a substitute for future fossil fuels. Utilization of renewable energy is one of the solar energy optimizations that can be applied to urban areas. The building sector consumes up to 40% of total annual energy. One of the buildings that can apply the utilization of renewable energy is the office building PUP-ESDM D. I. Yogyakarta. This research aims to know the potential power generated from the PLTS roofing if it is built on the office building Public Works, housing and Energy Mineral resources (PUP-ESDM) D. I. Yogyakarta. Research is conducted by conducting energy simulations using the HelioScope software. Simulated results show the east side of Building 1, the east side of Building 2, the east side of Building 3, the west side of Building 2 and the north side of Building 2 is the optimal location of photovoltaics. The Total energy potential generated from these five roofs is 73,484.5 kWh/year and is able to supply the energy needs of the PUP-ESDM office by 74.42%.
QA-NIZK Arguments of Same Opening for Bilateral Commitments

Zero-knowledge proofs of satisfiability of linear equations over a group are often used as a building block of more complex protocols. In particular, in an asymmetric bilinear group we often have two commitments in different sides of the pairing, and we want to prove that they open to the same value. This problem was tackled by González, Hevia and Ràfols (ASIACRYPT 2015), who presented an aggregated proof, in the QA-NIZK setting, consisting of only four group elements. In this work, we present a more efficient proof, which is based on the same assumptions and consists of three group elements. We argue that our construction is optimal in terms of proof size.
FFT-Based Data Hiding on Audio in LWT-Domain Using Spread Spectrum Technique

Audio watermarking is a process to hide digital data without being seen or heard by the sense of sight or hearing. Watermaking is applied to insert the copyright on digital media, such as an image file, an audio file or a video file. In this paper, we propose watermarking procedure to embed spread spectrum watermark into frequency domain of adaptive selected subband from host audio. Lifting Wavelet Transform (LWT) is used to decompose the host audio into several subbands, and then Fast Fourier Transform (FFT) transforms selected several subbands with lowest energy. The watermark image is converted into one-dimensional signal, then it is modulated by imperceptible pseudo-noise (PN) code with controlled gain. Next, the frequency domain of audio is added by modulated and imperceptible watermark prior to transforming it to time domain by Inverse FFT (IFFT) obtaining watermarked subbands. Finally, the watermarked subbands are combined with other unused subbands by inverse LWT (ILWT) becoming the perfect version of watermarked audio. The result of this method has good robustness against most attacks from stirmark benchmark experiments, good imperceptibility with Signal to Noise Ratio (SNR) more than 30 dB and payload 172.66 bps.