Reviewerly/exHarmony huggingface dataset:
A Differential Entropy Estimator for Training Neural Networks

Mutual Information (MI) has been widely used as a loss regularizer for training neural networks. This has been particularly effective when learn disentangled or compressed representations of high dimensional data. However, differential entropy (DE), another fundamental measure of information, has not found widespread use in neural network training. Although DE offers a potentially wider range of applications than MI, off-the-shelf DE estimators are either non differentiable, computationally intractable or fail to adapt to changes in the underlying distribution. These drawbacks prevent them from being used as regularizers in neural networks training. To address shortcomings in previously proposed estimators for DE, here we introduce KNIFE, a fully parameterized, differentiable kernel-based estimator of DE. The flexibility of our approach also allows us to construct KNIFE-based estimators for conditional (on either discrete or continuous variables) DE, as well as MI. We empirically validate our method on high-dimensional synthetic data and further apply it to guide the training of neural networks for real-world tasks. Our experiments on a large variety of tasks, including visual domain adaptation, textual fair classification, and textual fine-tuning demonstrate the effectiveness of KNIFE-based estimation. Code can be found at https://github.com/g-pichler/knife.
STEAM Skills Developed in Basic General Education Through the Creation of the Digital Robot Through the mBlock Platform

STEAM is an interdisciplinary methodology that seeks to generate holistic and inclusive knowledge, its purpose is to develop scientific and technological skills in students, which is why this research aimed to implement a project based on STEAM (Science, Technology, Engineering, Art and Mathematics) for the development of a digital robot, which tells the Ecuadorian legend of the Indian of Cantuña, with the purpose of promoting a pedagogical experience incorporating emerging technologies such as Educational Robotics. The research had a quantitative approach, with an exploratory descriptive scope, with an inductive-deductive method. For data collection, the survey technique was shown and as instruments a rubric to evaluate STEAM skills and a questionnaire that was applied in two moments: at the beginning the Pre-test and once the project was completed the Post-test, for the The construction of the digital robot was applied each step described in the methodology of the STEAM Instructional Framework. The results obtained show a significant increase in the development of skills, verifying the change of the indicators from Regular with 51,19% to High with 59,06%. Concluding that the interdisciplinarity promoted by the STEAM methodology helps students consolidate learning and develop autonomy.
Replay Buffer with Local Forgetting for Adapting to Local Environment Changes in Deep Model-Based Reinforcement Learning

One of the key behavioral characteristics used in neuroscience to determine whether the subject of study -- be it a rodent or a human -- exhibits model-based learning is effective adaptation to local changes in the environment, a particular form of adaptivity that is the focus of this work. In reinforcement learning, however, recent work has shown that modern deep model-based reinforcement-learning (MBRL) methods adapt poorly to local environment changes. An explanation for this mismatch is that MBRL methods are typically designed with sample-efficiency on a single task in mind and the requirements for effective adaptation are substantially higher, both in terms of the learned world model and the planning routine. One particularly challenging requirement is that the learned world model has to be sufficiently accurate throughout relevant parts of the state-space. This is challenging for deep-learning-based world models due to catastrophic forgetting. And while a replay buffer can mitigate the effects of catastrophic forgetting, the traditional first-in-first-out replay buffer precludes effective adaptation due to maintaining stale data. In this work, we show that a conceptually simple variation of this traditional replay buffer is able to overcome this limitation. By removing only samples from the buffer from the local neighbourhood of the newly observed samples, deep world models can be built that maintain their accuracy across the state-space, while also being able to effectively adapt to local changes in the reward function. We demonstrate this by applying our replay-buffer variation to a deep version of the classical Dyna method, as well as to recent methods such as PlaNet and DreamerV2, demonstrating that deep model-based methods can adapt effectively as well to local changes in the environment.
Entity Management Using Wikidata for Cultural Heritage Information

Entity management in a Linked Open Data (LOD) environment is a process of associating a unique, persistent, and dereferenceable Uniform Resource Identifier (URI) with a single entity. It allows data from various sources to be reused and connected to the Web. It can help improve data quality and enable more efficient workflows. This article describes a semi-automated entity management project conducted by the “Wikidata: WikiProject Chinese Culture and Heritage Group,” explores the challenges and opportunities in describing Chinese women poets and historical places in Wikidata, the largest crowdsourcing LOD platform in the world, and discusses lessons learned and future opportunities.
On Deep Holes of Projective Reed-Solomon Codes over Finite Fields with Even Characteristic

Projective Reed-Solomon code is an important class of maximal distance separable codes in reliable communication and deep holes play important roles in its decoding. In this paper, we obtain two classes of deep holes of projective Reed-Solomon codes over finite fields with even characteristic. That is, let [see formula in PDF] be finite field with even characteristic, [see formula in PDF], and let [see formula in PDF] be the Lagrange interpolation polynomial of the first [see formula in PDF] components of the received vector [see formula in PDF]. Suppose that the [see formula in PDF]-th component of [see formula in PDF] is 0, and [see formula in PDF],[see formula in PDF] where [see formula in PDF], and [see formula in PDF] is a polynomial over [see formula in PDF] with degree no more than [see formula in PDF]. Then the received vector [see formula in PDF] is a deep hole of projective Reed-Solomon codes [see formula in PDF]. In fact, our result partially solved an open problem on deep holes of projective Reed-Solomon codes proposed by Wan in 2020.
Cyber insurance is no longer optional

Computer Fraud & SecurityVol. 2023, No. 3 The SandboxCyber insurance is no longer optionalMark WarrenMark WarrenOsiriumSearch for more papers by this authorMark WarrenPublished Online:17 Mar 2023https://doi.org/10.12968/S1361-3723(23)70015-8AboutSectionsView articleView Full TextView EPUB ToolsAdd to favoritesDownload CitationsTrack CitationsPermissions ShareShare onFacebookTwitterLinked InEmail View article References 1. ‘Optus notifies customers of cyber attack compromising customer information’. Optus, 22 Sep 2022. Accessed Mar 2023. www.optus.com.au/about/media-centre/media-releases/2022/09/optus-notifies-customers-of-cyber attack. Google Scholar2. ‘An incident impacting some accounts and private information on Twitter’. Twitter Privacy Centre, 5 Aug 2022. Accessed Mar 2023. https://privacy.twitter.com/en/blog/2022/an-issue-affecting-some-anonymous-accounts. Google Scholar3. ‘Cyber security – Worldwide’. Statista. Accessed Mar 2023. www.statista.com/outlook/tmo/cyber security/worldwide. Google Scholar4. ‘GDP (current US$)’. Word Bank. Accessed Mar 2023. https://data.worldbank.org/indicator/Ny.Gdp.Mktp.Cd?most_recent_value_desc=true. Google Scholar5. ‘Cost of a data breach report 2022’. IBM Security. Accessed Mar 2023. www.ibm.com/downloads/cas/3R8N1DZJ. Google Scholar FiguresReferencesRelatedDetails 1 March 2023Volume 2023Issue 3ISSN (print): 1361-3723ISSN (online): 1873-7056 Metrics History Published online 17 March 2023 Published in print 1 March 2023 Information© MA Business Ltd.
Voting Schemes in DAO Governance

This paper aims to provide a comprehensive overview of the different voting schemes used in DAO governance. We will examine the various features of these schemes and compare their differences. We propose a new hypothetical voting mechanism specifically designed for decentralized and permissionless DAO governance. This new scheme, which incorporates incentive designs, is intended to be more efficient than existing schemes and can be easily adapted to a permissioned scenario. Through this examination and proposal, we hope to contribute to the ongoing discourse on how to govern decentralized autonomous organizations effectively.
Query extraction based on encrypted features for CBIR from cloud

Due to the increasing use of mobile devices, content-based image retrieval is gaining massive popularity as mobile devices run on batteries. It cannot perform heavy image processing computation, so mobile users can extract just image features and offload them to the cloud. The cloud will perform content-based image searches and return a search result. COREL10K images dataset was selected, and VGG16, RESNET, DENSENET, and MOBILE NET algorithms were applied. Finally, the method with the best performance is preferred. Also, PAHE (Packed Additive Homomorphic Encryption) algorithm is selected to encrypt the feature vectors, and image similarity calculations are done with Secure Multi-party Computation (SMC). The following parameters are used for each algorithm to calculate accuracy, precision, recall, Jaccard index, error rates, and F1-score. The algorithms are then compared and selected the best algorithm. The best model obtained is VGG16, with the highest accuracy. The procedure can help Mobile users perform content-based image searches by extracting features and then uploading them to the cloud.
A fast method for obtaining convex combination coefficients

This technical communiqué presents a novel approach for fast computation of convex combination coefficients based on ray shooting. Such a problem arises in the real-time implementation of many set-based control techniques, which require a computationally efficient solution to cope with systems of high dimension or fast dynamics. The proposed method poses no constraints on the polytope structure and requires only a list of vertices and boundary hyperplanes. In test cases involving polytopes of various dimensions and numbers of vertices, the proposed method provided up to seven-fold reductions in the 95% percentile of computation time, compared with a state-of-the-art technique.
Developing Web Services for the Production of Mixed Reality Games Based on RFID Sensors

The Internet of Things (IoT) is a technology that enabled the construction of accessible systems that allow the connection of several physical and virtual objects. This work presents the development of a collection of web services capable of assisting the production of mixed reality games based on RFID (Radio Frequency Identification) sensors. To this end, hardware devices, REST and IoT communication services are presented, as well as the production of educational games based on these proposed services for validation purposes.
Dynamic Scanning Desensitization of Sensitive Data Based on Low Code Modeling Language Technology

With the advent of the network era, it is particularly important to protect users' sensitive data and realize information security. Data desensitization technology can not only expand the use range of original data, but also increase information sharing objects on the premise of ensuring data security. Low code can provide convenient and efficient customized data desensitization services for network users, and prevent the disclosure of users' private information. Therefore, this paper studies the method of dynamic scanning desensitization of sensitive data based on low code modeling language technology, and uses low code technology to build a data desensitization system. This paper first gives a detailed overview of the principle and route of data desensitization and the reversibility of desensitization technology, then analyzes and designs the non-functional requirements of the system, the dynamic desensitization module and the interpretation and compilation module respectively, and finally analyzes the desensitization scheme and tests the system. The research results show that the data desensitization time of the system is short, which meets the user's needs and has good performance.
Collaborative Governance Method of Power Application Data Based on Blockchain Technology

With the explosive growth of power application data, enterprises mainly carry out collaborative management of data quality through a manual review mechanism, which leads to a long time for standard terms to apply for review in the process of governance. In view of the problem that the audit time is too long, this study introduces the block chain technology into the governance process, and designs a new collaborative governance method for the power application data. Firstly, data collection mechanism and data consensus mechanism are combined to realize the collection and storage of power application data. The data cleaning mechanism is established to remove invalid data. Then, based on the block chain technology, a data quality collaboration model is built to standardize the data. Finally, the secure sharing of the power application data is realized through smart contracts. The experimental results show that when the number of spare words is 6×103, the time of application and approval of standard words is only 0. 83 min, which effectively improves the time efficiency of data collaborative governance.
Feature Extraction Based Face Mask Detection

Face masks are becoming an essential part of every person's life to protect against COVID-19, air pollution, etc. In public venues like airports, hospitals, malls, and many other locations, face masks are now required. This research introduces a unique method for face mask identification that combines deep learning and machine learning. Thepade Sorted 10-ary Block Trucation Code with RGB colour plane, Thepade Sorted Block Truncation Code with LUV colour plane, Local Binary Pattern method, and Gray Level Co-occurrence Matrics are used for feature extraction. Deep learning techniques are used to train these retrieved characteristics. Accuracy, precision, and f1-score are the performance measures used to evaluate performance.
HPFilter: An R Implementation of the One- and Two-Sided Hodrick-Prescott Filter

The Hodrick-Prescott filter is a technique commonly used to smooth macroeconomic data such as the GDP. It consists in separating short-term, cyclical movements in the data from the long-term trend. Although the HP filter has received numerous critiques, recent studies show that alternative methods do not necessarily perform better. Furthermore, the Basel committee, an international body responsible for setting international guidelines for prudential regulation, has recommended the usage of the one-sided HP filter for calculations pertaining to the analysis of financial cycles. Although software programs for both the one-sided and two-sided versions have started to appear for commonly used programming languages, to this date a one-sided HP filter implementation for R was not available. This paper, and the accompanying package “hpfilter” close this gap by providing a Kalman filter-based implementation of the one-sided HP filter, as well as a two-sided filter using sparse matrices to improve the efficiency of calculations when using large datasets. Making the two versions of the HP filter available in R aims to provide a more comprehensive tool set, natively, to economists and Central Bankers who use filtering techniques in their workflows.
Development of A Sprite-Based Architecture for Creating 2D Games in Reconfigurable Environments Using Fpga Devices

The understanding of concepts worked on FPGAs combined with the creation of games in hardware platforms has helped in the understanding and assimilation of the necessary techniques and approaches to build digital game systems. This work presents the development and validation of a Sprite-Based Architecture that aims to develop 2D games in reconfigurable environments based on FPGA devices. To this end, a set of functions and other resources implemented in C language were develop, that aims to help the creation of games through the developed architecture.
Group Signatures with Equality Test on Signers

We propose a new variant of group signature scheme which has the function of equality test on signers (GSET). When two signers who belong to different groups issue group signatures, our scheme enables a new entity “equality tester” to verify whether or not the signers of them are the same while maintaining anonymity. We introduce the syntax, security definitions and a generic construction of our GSET. In addition to the functions of an ordinary group signature scheme by Bellare, Shi and Zhang (at CT-RSA 2005), the equality-test function of GSET utilizes a “SemiOpen” algorithm with an equality key, which keeps the anonymity by working on hash values of identity data.
Security and Risk Assessment of Academic Information System By Using NIST Framework (A Case Study Approach)

Security audit and risk management can be done using standard framework. NIST is one of the framework which usually used to identify the security and risks on information system. The assessment is done to the security and risk in academic information system at Univeristas Sangga Buana (USB) Bandung that it is expected to minimize the risks occurs to the academic information system. The result of security audit on academic information system with NIST SP 800–26 framework shows the security on the system has the whole score as big as 72.43%. the score is obtained from the calculation of 3 categories tested namely Management Control, Operational Control, and Technical Control. Based on the data, the security of information system in USB belongs to level 3, Implemented Procedures and Controls. Risk management is done using some stages on NIST SP 800–30, with the help of Acunetix Scanning Application. Based on the result of scanning using Acunetix, academic information system at USB has 600 pieces of vulnerabilities. These vulnerabilities divided into 4 levels of vulnerability, namely 4 pieces of high level of vulnerabilities, 40 pieces of medium level of vulnerabilities, 13 pieces of low level of vulnerabilities, and 543 pieces of informational vulnerabilities
Towards the Success of DevOps Environments in Software Organizations: A Conceptual Model Approach

This research assesses the aspects of software organizations’ DevOps environments and identifies the factors contributing to these environments’ success. DevOps is a recent concept, and many organizations are moving from old-style software development methods to agile approaches such as DevOps. However, there is no comprehensive information on what factors impact the success of the DevOps environment once organizations adopt it. This research focused on addressing this gap through a systematic literature review. The systematic review consisted of 33 articles from five selected search systems and databases from 2015 to 2021. Based on the included articles, 15 factors were identified and grouped into four categories: Collaborative Culture, Organizational Aspects, Tooling and Technology, and Continuous Practices. In addition, this research proposes a DevOps environment success factors model to potentially contribute to DevOps research and practice. Recommendations are made for additional research on the effectiveness of the proposed model and its success factors.
Contributory role of innovative capabilities in Malaysian small and medium enterprises performance

Despite a growing body of literature focusing on business performance enhanced by innovation capabilities, empirical research examining its impact in Malaysia remains scarce. Adopting the view that monitoring and analysing business performance through innovation is necessary for strategic planning and business survival. This study surveyed 124 businesses in peninsular Malaysia via two-stage e-mail and telephone calls, supplemented by survey questionnaires. The findings indicate that SMEs should prioritise strategic planning, marketing innovation, and learning capability to improve business performance, particularly in Malaysia's manufacturing and export sectors. While this study makes no claim to be exhaustive, it demonstrates that understanding of the relationship between innovation capabilities and business performance is incomplete and that this area of research warrants further examination. Nevertheless, the discovery contributes to the body of knowledge by demonstrating how innovative strategic planning, marketing, and learning capabilities can enhance successful business performance.
Dataset: Uncovering Trends in Digital Forensics and Criminal Law Research: A Decade of Insights

This data article explores the landscape of digital forensics and criminal law research from 2012 to 2022 by analyzing scholarly works available on Lens.org. By applying various filters such as release year, document type, and field of study, we identified 23 relevant journal articles. The analysis revealed insights into the most active institutions, authors, and publishers in the field, as well as the most prolific countries/regions. These findings offer a valuable resource for researchers, policymakers, and practitioners interested in digital forensics and criminal law.&#x0D; Highlights:&#x0D; &#x0D; A decade-long analysis of digital forensics and criminal law research from 2012 to 2022.&#x0D; Identification of the most active institutions, authors, publishers, and countries/regions in the field.&#x0D; Valuable insights for researchers and practitioners interested in digital forensics and criminal law.&#x0D; &#x0D; &#x0D;
Dataset: Exploring the Landscape of Cyber Harassment: A Data-driven Approach to Understanding the Legal Framework

This research focuses on the search for articles and literature reviews on cyber harassment. The search was conducted using Lens.org, a reliable indexing agency, by using keywords such as Cyber law and Cyber Harassment. Three keywords were selected and entered into the 'edit search' feature, and the search was filtered by selecting the law category in the subject matter and choosing journal articles as the document type. The data was exported in both CSV and BibTeX formats, and a screenshot was taken of the available data graphs. The goal of this research is to collect data on cyber harassment and its legal implications. The method involved searching scholarly works using specific keywords and filtering the results based on subject matter and document type. The results obtained are valuable as they provide a comprehensive understanding of the current state of cyber harassment and its legal implications.&#x0D; Highlights:&#x0D; &#x0D; Filtering through specific categories and document types is an effective way to obtain relevant literature on cyber harassment.&#x0D; The ten articles found provide a diverse range of topics related to cyber harassment, from the impact of technology on online sexual harassment to the legal response to cyberbullying.&#x0D; These articles offer valuable insights for future research and can be used as references for scholars studying cyber harassment.&#x0D; &#x0D; &#x0D; &#x0D;